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Dissertations / Theses on the topic 'MPC'
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Lima, Marcelo Lopes de. "Distributed satisficing MPC." reponame:Repositório Institucional da UFSC, 2014. https://repositorio.ufsc.br/xmlui/handle/123456789/122752.
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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2013.Made available in DSpace on 2014-08-06T17:19:47Z (GMT). No. of bitstreams: 1 325382.pdf: 1772540 bytes, checksum: 52b8f812eaccad89bad0282c1c7c5db4 (MD5)
Abstract : To obtain a Pareto-optimal solution, the classical cooperative MPC implementsa categorical altruism imposed by a fixed global cost sharedby all the local controllers. Instead, this thesis implements a situationalaltruism where a global cost, neither imposed nor fixed, emerges fromconvex local costs and local specifications. The satisficing controllersemploy a distributed algorithm to find a solution that lies in a convexregion that is satisfactory and sufficient for all controllers (satisficing= satisfy + suffice), while optimizing in the direction of the analyticcenter of such a region. The system is modeled as being a network oflinear subsystems, coupled by their inputs, and the algorithm uses adistributed interior-point method to avoid fixed points when the constraintsare also coupled. The optimal solution of the satisficing MPC,besides Pareto-optimal, gives more importance to the controllers witha worst performance at the moment. Situational altruism permits amore balanced division of resources, avoiding the exploitation of onecontroller by the others. The satisficing MPC is shown to be stabilizingeven if suboptimal, provided that it is satisficing. To this end,stabilizing constraints are added to the basic formulation.
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Kück, Corvin. "MPC Design For Autonomous Drifting." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215873.
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The goal of this thesis is to evaluate the performance of different controllers to keep a remotecontrolledvehicle in a sustained drift. A bicycle model and an empirical tyre model are used formodelling the vehicle. The parameters for the used Fiala tyre model are experimentally identifiedand the simulation results of the modelled vehicle are compared to measured experimental data. Itfollows a stability analysis of the modelled system. The system is then linearized around one ofthe drift equilibria to allow controller design. A state feedback controller is designed to stabilizethe system, the controller gains are optimized using a Linear Quadratic Regulator (LQR) design,subsequently a Model Predictive Controller (MPC) is designed. Finally, the performance of the 3controllers is evaluated for a simulation with a disturbance acting on the system.Målet med denna studie är att undersöka prestandan för olika reglerstrukturer när en radiostyrd bildriftar. En cykelmodell och en empirisk däcksmodell används för att modellera bilen.Parametrarna som användes för Fiala däcksmodellen är framtagna genom experiment ochsimuleringsresultatet av den modellerade bilen jämförs med verklig data. En stabilitetsanalys ärockså gjord för det modellerade systemet. Systemet är sedan linjäriserat runt ett jämviktsläge fördrifting för att kunna skapa en regulator. En tillståndsregulator med återkoppling används för attstabilisera systemet. Förstärkningskonstanterna för regulatorn optimeras med linjärkvadratiskreglering och sedan designas en modell prediktiv kontroller. Slutligen utvärderas prestandan,genom simulering, hos de tre regulatorerna när en störning finns i systemet.
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Torabi, Zahra. "Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
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In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference
trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated
reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances.
The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local
terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based
distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average
performance result for the real closed-loop system.
To this end, we present our outcomes on local and global performance, illustrated by a numerical example.
4
Jimoh, Mohammed Tajudeen. "A vision for MPC performance maintenance." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4739/.
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Model predictive control (MPC) is an advanced control that has found widespread use in industries, particularly in process industries like oil refining and petrochemicals. Although the basic premise behind MPC is easy to comprehend, its inner workings are still generally viewed or regarded as too advanced for actual plant operator understanding. This lack of understanding is exposed when MPC performance deteriorates sometime after commissioning, as is often the case in some commercially operated process plants. Currently operators might have difficulty over reasoning about MPC performance degradation and formulating steps to investigate the cause. A tool is described that aims to make MPC more transparent to the operators. Commonly reported causes of MPC performance degradation are discussed and ways in which the operator can recognise them when they occur are outlined. Issues that are addressed include: making the set of controlled variables to be used for a given set of manipulated variables simpler and clearer; ways to recognise when a MPC controller is performing poorly and to identify the source of performance deterioration. An aim is to determine under what instances the operator can return the MPC performance to previous levels or request for specialist support or simply switch the MPC off. A goal is to avoid the kind of often reported situation where the operator gets worried that the controller is deteriorating and ends up taking knee jerk actions that cause further problems in MPC. At the top of the maintenance tool hierarchy is the trends comparison group, where MPC reference graphical performance trends are compared with actual graphical performance trends counterpart. If any abnormality is observed in these trends, the operator is encouraged to choose an option from a list of preliminary diagnostic questions contained in a group below trends comparison group, which best describes the observed abnormality. Each abnormality is associated with a list of suspected causes. When a suspected cause is chosen from the associated list, the operator is led to the symptoms investigation window, which contains scripts detailing steps for systematic examination of each symptom, with a view to either rejecting or confirming the suspicion. Assisted in the investigation are four background information windows: the virtual plant without MPC window, the virtual plant with MPC window, the transfer function matrix window and steady state gain, relative gain array (RGA) and relative weight array (RWA) window. The windows contain information and guidance that the operator might refer to from time to time during symptom investigation. Development of the maintenance tool is still at the design stage. The key components described have been research implementing MPC on three nonlinear process models, a continuous stirred tank reactor (CSTR), an evaporator process and a fluid catalytic cracking unit (FCCU). Case studies representing different MPC degradation scenarios are simulated, followed by a systematic procedure for diagnosing, isolating and recovering from such degradation, based on assumed operator’s perspective and expert’s technical reasoning. The knowledge gained from the case studies is used to develop an outline of a vision for a data-driven model predictive maintenance tool to help the operator make sensible judgements about performance degradation, the form and direction of diagnosis and fault isolation, and possibly, the recovery procedure.
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Klaesson, Filip, and John Friberg. "Autonomous Overtaking Using Reachability Analysisand MPC." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230162.
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The era of autonomous cars is on the rise. Asdrivers lose control of the steering wheel, it is crucial that thecars themselves can guarantee safety for all traffic participants.This study aims to design a complete control system that cansafely perform an overtaking maneuver. To guarantee safety ofthe maneuver, reachability calculations will be carried out andanalyzed. The overtaking will be planned by using the modelpredictive control, MPC, framework. To complete the controlsystem a modified proportional controller will be designed totrack the planned path. The control system is implemented inMATLAB and the entire overtaking maneuver is simulated. Theresults show that the designed control framework successfullyperforms the overtaking on a straight two-lane highway in asafe manner.Autonoma bilar är på frammarsch. När förare inte längre har kontroll över ratten är det avgörande att bilarna själva kan garantera säkerheten för alla trafikanter. Denna studie syftar till att utforma ett komplett styrsystem som kan utföra en säker omkörning. Omkörningen planeras med hjälp av ramverket för modell-prediktiv reglering. För att garantera säkerhet används nåbarhetsanalys. Slutligen utformas en modifierad proportionell regulator för att följa den planerade omkörningsvägen. Styrsystemet har implementerats i MATLAB och hela omkörningen har simulerats. Resultaten visar att det konstruerade styrsystemet utför omkörningen på en rak motorväg med två filer på ett säkert och framgångsrikt sätt.
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Andersson, Amanda, and Elin Näsholm. "Fast Real-Time MPC for Fighter Aircraft." Thesis, Linköpings universitet, Reglerteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148580.
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The main topic of this thesis is model predictive control (MPC) of an unstable fighter aircraft. When flying it is important to be able to reach, but not exceed the aircraft limitations and to consider the physical boundaries on the control signals. MPC is a method for controlling a system while considering constraints on states and control signals by formulating it as an optimization problem. The drawback with MPC is the computational time needed and because of that, it is primarily developed for systems with a slowly varying dynamics. Two different methods are chosen to speed up the process by making simplifications, approximations and exploiting the structure of the problem. The first method is an explicit method, performing most of the calculations offline. By solving the optimization problem for a number of data sets and thereafter training a neural network, it can be treated as a simpler function solved online. The second method is called fast MPC, in this case the entire optimization is done online. It uses Cholesky decomposition, backward-forward substitution and warm start to decrease the complexity and calculation time of the program. Both methods perform reference tracking by solving an underdetermined system by minimizing the weighted norm of the control signals. Integral control is also implemented by using a Kalman filter to observe constant disturbances. An implementation was made in MATLAB for a discrete time linear model and in ARES, a simulation tool used at Saab Aeronautics, with a more accurate nonlinear model. The result is a neural network function computed in tenth of a millisecond, a time independent of the size of the prediction horizon. The size of the fast MPC problem is however directly affected by the horizon and the computational time will never be as small, but it can be reduced to a couple of milliseconds at the cost of optimality.
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Bin, Elisa. "MPC-based Visual Servo Control for UAVs." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284503.
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Vision information is essential for planning and control of autonomous systems.Vision-based control systems leverage rich visual input for motion planningand manipulation tasks. This thesis studies the problem of Image-BasedVisual Servo (IBVS) control for quadrotor UAVs. Despite the effectiveness ofvision-based systems, the control of quadrotors with IBVS presents the nontrivialchallenge of matching the 6 DoF control output obtained by the IBVSwith the 4DoF of the quadrotor. The novelty of this work lies in addressing theunder-actuation problem of quadrotors using linear Model Predictive Control(MPC). MPC is a well-known optimization control technique that leverages amodel of the system to predict its future behaviour as a function of the inputsignal. We extensively evaluate the performance of the designed solution onboth simulated environment and real-world experiments.Visuell information är grundläggande för planering och kontroll av autonomasystem. Visionsbaserade kontrollsystem drar nytta av rik visuell inmatningför rörelseplanerings- och manipuleringsuppgifter. Den här avhandlingenstuderar problemet med Image-Based Visual Servo (IBVS) -kontroll förquadrotor UAVs. Trots effektiviteten hos visionsbaserade system utgör kontrollenav quadrotorer med IBVS den icke-triviala utmaningen att matcha 6DoF-kontrollutgång som erhållits av IBVS med 4DoF från quadrotorn. Nyheteni detta arbete ligger i en ny formulering av underaktiveringsproblemetför quadrotorer med linjär Model Predictive Control (MPC). MPC är en välkändoptimeringskontrollteknik som utnyttjar en modell av systemet för attförutsäga dess framtida beteende som en funktion av insignalen. Vi utvärderaromfattande prestandan för den designade lösningen i både simulerad miljö ochverkliga experiment.
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Maguire, Emma. "Monitoring of Lubricant Degradation with RULER and MPC." Thesis, Linköping University, Department of Physics, Chemistry and Biology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57846.
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Traditional oil analysis methods - e.g. acidity and viscosity measurements - have been used to monitor lubricant conditions. These methods can detect when the useful life of a lubricant is over but fall short when trying to gain insight on how long a lubricant in current use could last. This makes it difficult to make proactive decisions and estimate oil drain periods. Lubricants do not start to degrade until the antioxidants, which prevent from oxidation, have depleted to a certain level where they no longer can protect the base oil from degradation. During the degradation process insoluble contaminants form that can lead to sludge and varnish.
Four engine oils were oxidized using oxygen pressurized vessels and four hydraulic oils were oxidized with turbine oil stability test (TOST). At different stages of oxidation, sample aliquots were withdrawn and analysed. A blend of engine oil and biodiesel was also tested as well as a mixture of hydraulic oil and water. Samples of engine oils were also tested from a rig test running at SCANIA’s facilities in Södertälje, Sweden. The samples were evaluated with Remaining Useful Life Evaluation Routine (RULER) and Membrane Patch Colorimetry (MPC). RULER is a voltammetric method that measures the antioxidant level in a lubricant sample and MPC measure the insoluble contaminants by spectrophotometric analysis. Results from these analyses were compared to conventional methods such as acid number, viscosity, and Fourier Transform Infrared spectroscopy (FTIR).
Results from the MPC-analyses showed that this method is dependent on the type of the lubricant tested. RULER performed well for all tested lubricants. It was shown that this analyse method can predict when the lubricant is going to start to degrade due to oxidation. Tests showed that the oxidation of the lubricant starts when there are 20-25% of the antioxidants remaining.
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Meum, Patrick. "Optimal Reservoir control using nonlinear MPC and ECLIPSE." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9610.
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Recent years advances within well deployment and instrumentation technology offers huge potentials for increased oil recovery from reservoir production. Wells can now be equipped with controllable valves at reservoir depth, which may possibly alter the production profitability of the field completely, if the devices are used in an intelligent manner. This thesis investigates this potential by using model predictive control to maximize reservoir production performance and total oil production. The report describes an algorithm for nonlinear model predictive control, using a single shooting, multistep, quasi-Newton method, and implements it on an existing industrial MPC platform - Statoil's in-house MPC tool SEPTIC. The method is an iterative method, solving a series of quadratic problems analogous to sequential quadratic programming, to find the optimal control settings. An interface between SEPTIC and a commercial reservoir simulator, ECLIPSE, is developed for process modelling and predictions. ECLIPSE provides highly realistic and detailed reservoir behaviour and is used by SEPTIC to obtain numerical gradients for optimization. The method is applied to two reservoir examples, Case 1 and Case 2, and develops optimal control strategies for each of these. The two examples have conceptually different model structures. Case 1 is a simple introduction model. Case 2 is a benchmark model previously used in Yeten, Durlofsky and Aziz (2002) and models a North Sea type channelized reservoir. It is described by a set of different realizations, to capture a notion of model uncertainty. The report addresses each of the available realizations and shows how the value of an optimal production strategy can vary for equally probable realizations. Improvements in reservoir production performance using the model predictive control method are shown for all cases, compared to basic controlled references cases. For the benchmark example improvements range up to as much as 68% increase in one realization, and 30% on average for all realizations. This is an increase from the results previously published for the benchmark, with a 3% average. However, the level of improvement shows significant variation, and is only marginal for example Case 1. A thorough field analysis should therefore be performed before deciding to take the extra cost of well equipment and optimal control.
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Fleming, James. "Robust and stochastic MPC of uncertain-parameter systems." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:c19ff07c-0756-45f6-977b-9d54a5214310.
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Constraint handling is difficult in model predictive control (MPC) of linear differential inclusions (LDIs) and linear parameter varying (LPV) systems. The designer is faced with a choice of using conservative bounds that may give poor performance, or accurate ones that require heavy online computation. This thesis presents a framework to achieve a more flexible trade-off between these two extremes by using a state tube, a sequence of parametrised polyhedra that is guaranteed to contain the future state. To define controllers using a tube, one must ensure that the polyhedra are a sub-set of the region defined by constraints. Necessary and sufficient conditions for these subset relations follow from duality theory, and it is possible to apply these conditions to constrain predicted system states and inputs with only a little conservatism. This leads to a general method of MPC design for uncertain-parameter systems. The resulting controllers have strong theoretical properties, can be implemented using standard algorithms and outperform existing techniques. Crucially, the online optimisation used in the controller is a convex problem with a number of constraints and variables that increases only linearly with the length of the prediction horizon. This holds true for both LDI and LPV systems. For the latter it is possible to optimise over a class of gain-scheduled control policies to improve performance, with a similar linear increase in problem size. The framework extends to stochastic LDIs with chance constraints, for which there are efficient suboptimal methods using online sampling. Sample approximations of chance constraint-admissible sets are generally not positively invariant, which motivates the novel concept of âsample-admissible' sets with this property to ensure recursive feasibility when using sampling methods. The thesis concludes by introducing a simple, convex alternative to chance-constrained MPC that applies a robust bound to the time average of constraint violations in closed-loop.
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Barsk, Karl-Johan. "Model Predictive Control of a Tricopter." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79066.
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In this master thesis, a real-time control system that stabilizes the rotational rates of a tri-copter, has been studied. The tricopter is a rotorcraft with three rotors. The tricopter has been modelled and identified, using system identification algorithms. The model has been used in a Kalman filter to estimate the state of the system and for design ofa model based controller. The control approach used in this thesis is a model predictive controller, which is a multi-variable controller that uses a quadratic optimization problem to compute the optimal con-trol signal. The problem is solved subject to a linear model of the system and the physicallimitations of the system. Two different types of algorithms that solves the MPC problem have been studied. These are explicit MPC and the fast gradient method. Explicit MPC is a pre-computed solution to the problem, while the fast gradient method is an online solution. The algorithms have been simulated with the Kalman filter and were implemented on themicrocontroller of the tricopter.
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Bonne, François. "Modélisation et contrôle des grands réfrigérateurs cryogéniques." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT094/document.
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Ce manuscrit de thèse s'intéresse à la modélisation et au contrôle des réfrigérateurs cryogéniques. Le cas particulier des réfrigérateurs soumis à de fortes variations de charges thermiques est étudié. Un modèle de chaque objet pouvant se trouver dans un réfrigérateur est proposé. La méthodologie d'assemblage pour obtenir le modèle des sous-systèmes qui composent le réfrigérateur est présenté, accompagnée de la méthode permettant d'obtenir une approximation linéaire des modèles des sous-systèmes. Grâce aux modèles développées, des lois de commande avancées sont synthétisées. Un contrôleur linéaire quadratique pour les stations de compression à deux ou trois niveaux de pression est proposé, ainsi qu'un contrôleur prédictif sous contrainte pour la boite froide. La particularité de ces stratégies de contrôle est qu'elles sont compatibles avec un automate programmable industriel (API) , doté d'une capacité de calcul et de stockage de donnée réduite. La capacité de prédiction en boucle ouverte du modèle développé est validé au regard de données expérimentales et les stratégies de contrôle sont validés en simulation et expérimentalement sur la station d'essais 400W@1.8K du SBT et sur la station de compression du LHC, au CERNThis manuscript is concern with both the modeling and the derivation of control schemes for large cryogenic refrigerators. The particular case of those which are submitted to highly variable pulsed heat load is studied. A model of each objet that normally compose a large cryorefrigerator is proposed. The methodology to gather objects model into the model of a subsystem is presented. The manuscript also shows how to obtain a linear equivalent model of the subsystem. Based on the derived models, advances control scheme are proposed. Precisely, a linear quadratic controller for warm compression station working with both two and three pressures state is derived, and a predictive constrained one for the cold-box is obtained. The particularity of those control schemes is that they fit the computing and data storage capabilities of Programmable Logic Controllers (PLC) with are well used in industry. The open loop model prediction capability is assessed using experimental data. Developed control schemes are validated in simulation and experimentally on the 400W@1.8K SBT's cryogenic test facility and on the CERN's LHC warm compression station
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Ng, Desmond Han Tien. "Stochastic model predictive control." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:b56df5ea-10ee-428f-aeb9-1479ce9a7b5f.
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The work in this thesis focuses on the development of a Stochastic Model Predictive Control (SMPC) algorithm for linear systems with additive and multiplicative stochastic uncertainty subjected to linear input/state constraints. Constraints can be in the form of hard constraints, which must be satisfied at all times, or soft constraints, which can be violated up to a pre-defined limit on the frequency of violation or the expected number of violations in a given period. When constraints are included in the SMPC algorithm, the difficulty arising from stochastic model parameters manifests itself in the online optimization in two ways. Namely, the difficulty lies in predicting the probability distribution of future states and imposing constraints on closed loop responses through constraints on predictions. This problem is overcome through the introduction of layered tubes around a centre trajectory. These tubes are optimized online in order to produce a systematic and less conservative approach of handling constraints. The layered tubes centered around a nominal trajectory achieve soft constraint satisfaction through the imposition of constraints on the probabilities of one-step-ahead transition of the predicted state between the layered tubes and constraints on the probability of one-step-ahead constraint violations. An application in the field of Sustainable Development policy is used as an example. With some adaptation, the algorithm is extended the case where the uncertainty is not identically and independently distributed. Also, by including linearization errors, it is extended to non-linear systems with additive uncertainty.
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Sjögren, Sofia, and Nina Wollinger. "Slutfasstyrning av robot : en jämförelse mellan LQ och MPC." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10539.
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Arbetet har utförts på Saab Bofors Dynamics i Karlskoga och dess syfte var att undersöka om det är möjligt att använda modellbaserad prediktionsreglering, MPC, vid slutfasstyrning av en viss typ av robot. Som referensram används linjärkvadratisk reglering, LQ, eftersom denna reglermetod har undersökts tidigare och visat sig fungera bra vid slutfasstyrning, dock för en annan typ av robot. Anledningen till att man vill undersöka om det är möjligt att använda MPC är att styrlagen enkelt tar hand om begränsningar på systemet på ett direkt och intuitivt sätt.
Styrlagarnas uppgift är att styra en robot i dess slutfas då det finns krav och önskemål på roboten som bör vara uppfyllda. Till exempel finns det begränsningar på styrsignalen samt önskemål om att träff ska ske i en viss träffpunkt och även med en viss träffvinkel. För att utvärdera resultaten undersöks och jämförs de två styrlagarnas prestanda och robusthet.
För att kunna utvärdera styrlagarnas egenskaper och jämföra dem implementeras de båda i en befintlig detaljerad simuleringsmiljö, som har utvecklats på Saab Bofors Dynamics i Karlskoga.
De prestanda och robusthetstester som har utförts uppvisar små skillnader på de två styrlagarna och slutsatsen blir därmed att det är möjligt att använda modellbaserad prediktionsreglering vid slutfasstyrning av en viss typ av robot eftersom det sedan tidigare är känt att linjärkvadratisk reglering är en bra styrlag att använda. För att se vilken av de två styrlagarna som är bäst vid slutfasstyrning av en viss typ av robot behöver det göras vissa ändringar och mer detaljerade undersökningar utföras.
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Hein, Sabine. "MPC/LQG-Based Optimal Control of Nonlinear Parabolic PDEs." Doctoral thesis, Universitätsbibliothek Chemnitz, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-201000134.
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The topic of this thesis is the theoretical and numerical research of optimal control problems for uncertain nonlinear systems, described by semilinear parabolic differential equations with additive noise, where the state is not completely available.
Based on a paper by Kazufumi Ito and Karl Kunisch, which was published in 2006 with the title "Receding Horizon Control with Incomplete Observations", we analyze a Model Predictive Control (MPC) approach where the resulting linear problems on small intervals are solved with a Linear Quadratic Gaussian (LQG) design. Further we define a performance index for the MPC/LQG approach, find estimates for it and present bounds for the solutions of the underlying Riccati equations.
Another large part of the thesis is devoted to extensive numerical studies for an 1+1- and 3+1-dimensional problem to show the robustness of the MPC/LQG strategy.
The last part is a generalization of the MPC/LQG approach to infinite-dimensional problems.
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Asar, Isik. "Model Predictive Control (mpc) Performance For Controlling Reaction Systems." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605001/index.pdf.
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In this study, the performance of the Model Predictive Controller (MPC) algorithm is investigated in two different reaction systems. The first case is a saponification reaction system where ethyl acetate reacts with sodium hydroxide to produce sodium acetate and ethanol in a CSTR. In the reactor, temperature and sodium acetate concentration are controlled by manipulating the flow rates of ethyl acetate and cooling water. The model of the reactor is developed considering first principal models. The experiments are done to obtain steady state data from the reaction system and these are compared with the model outputs to find the unknown parameters of the model. Then, the developed model is used for designing SISO and MIMO-MPC considering Singular Value Decomposition (SVD) technique for coupling.
The second case is the reaction system used for the production of boric acid by the reaction of colemanite and sulfuric acid in four CSTR&rsquos connected in series. In the reactor, the boric acid concentration in the fourth reactor is controlled by manipulating the sulfuric acid flow rate fed to the reactor. The transfer functions of the process and disturbance (colemanite flow rate) are obtained experimentally by giving step changes to the manipulated variable and to the disturbance. A model-based and constrained SISO-MPC is designed utilizing linear step response coefficients. The designed controllers are tested for performance in set point tracking, disturbance rejection and robustness issues for the two case studies. It is found that, they are satisfactory except in robustness issues for disturbance rejection in boric acid system.
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Overvåg, Thomas Ferstad. "Centrifugal Compressor Load Sharing with the use of MPC." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-20687.
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The work presented in this thesis examines the possibilities of having compressors running in an optimal manner which can result in energy savings. This research looks at how a compressor operates and the problems which can occur when connecting several machines, both in series and parallel. It is mainly focused on using Model Predictive Control (MPC), as a setup for controlling each compressor to a fixed operating point on the characteristic with relation to mass flow and pressure. Constraints are set on the controller outputs to help minimize the area of operation. Several types of efficiency are investigated in detail and this has helped to create a continuous efficiency island definition. These continuous curves are assumed to be equal to the characteristic plotted from tests and measurements in the lab. The continuous efficiency definition enables the possibility of calculating an operating point for each compressor. This is done using a Quadric Programming setup, which is calculated explicitly beforehand. Together with this and the implementation of MPC, the result is a load sharing scheme used here on two compressors connected in parallel. The control inputs to the plant being the impeller rotational speeds and the outflow from the throttle valve, while the measurements are in the form of plenum pressure and mass flow from each machine.
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Correa, Córdova Max Leo. "High performance implementation of MPC schemes for fast systems." Master's thesis, Pontificia Universidad Católica del Perú, 2016. http://tesis.pucp.edu.pe/repositorio/handle/123456789/7011.
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In recent years, the number of applications of model predictive control (MPC) is rapidly
increasing due to the better control performance that it provides in comparison to
traditional control methods. However, the main limitation of MPC is the computational
e ort required for the online solution of an optimization problem. This shortcoming
restricts the use of MPC for real-time control of dynamic systems with high sampling
rates. This thesis aims to overcome this limitation by implementing high-performance
MPC solvers for real-time control of fast systems. Hence, one of the objectives of this
work is to take the advantage of the particular mathematical structures that MPC
schemes exhibit and use parallel computing to improve the computational e ciency.
Firstly, this thesis focuses on implementing e cient parallel solvers for linear MPC
(LMPC) problems, which are described by block-structured quadratic programming
(QP) problems. Speci cally, three parallel solvers are implemented: a primal-dual
interior-point method with Schur-complement decomposition, a quasi-Newton method
for solving the dual problem, and the operator splitting method based on the alternating
direction method of multipliers (ADMM). The implementation of all these solvers is
based on C++. The software package Eigen is used to implement the linear algebra
operations. The Open Message Passing Interface (Open MPI) library is used for the
communication between processors. Four case-studies are presented to demonstrate the
potential of the implementation. Hence, the implemented solvers have shown high
performance for tackling large-scale LMPC problems by providing the solutions in
computation times below milliseconds.
Secondly, the thesis addresses the solution of nonlinear MPC (NMPC) problems, which
are described by general optimal control problems (OCPs). More precisely,
implementations are done for the combined multiple-shooting and collocation (CMSC)
method using a parallelization scheme. The CMSC method transforms the OCP into a
nonlinear optimization problem (NLP) and de nes a set of underlying sub-problems for
computing the sensitivities and discretized state values within the NLP solver. These
underlying sub-problems are decoupled on the variables and thus, are solved in parallel.
For the implementation, the software package IPOPT is used to solve the resulting NLP
problems. The parallel solution of the sub-problems is performed based on MPI and
Eigen. The computational performance of the parallel CMSC solver is tested using case
studies for both OCPs and NMPC showing very promising results.
Finally, applications to autonomous navigation for the SUMMIT robot are presented.
Specially, reference tracking and obstacle avoidance problems are addressed using an
NMPC approach. Both simulation and experimental results are presented and compared
to a previous work on the SUMMIT, showing a much better computational e ciency
and control performance.Tesis
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Gallieri, Marco. "ℓasso-MPC - predictive control with ℓ₁-regularised least squares." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708356.
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Paula, Neander Alessandro da Silva. "MPC adaptativo - multimodelos para controle de sistemas não-lineares." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3137/tde-14052009-000836/.
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Durante a operação de um controlador MPC, a planta pode ir para outro ponto de operação principalmente pela decisão operacional ou pela presença de perturbações medidas/não-medidas. Assim, o modelo do controlador deve ser adaptado para a nova condição de operação favorecendo o controle sob as novas condições. Desta forma, as condições ótimas de controle podem ser alcançadas com a maior quantidade de modelos identificados e com um controlador adaptativo que seja capaz de selecionar o melhor modelo. Neste trabalho é apresentada uma metodologia de controle adaptativo com identificação on-line do melhor modelo o qual pertence a um conjunto previamente levantado. A metodologia proposta considera um controlador em duas camadas e a excitação do processo através de um sinal GBN na camada de otimização com o controlador em malha fechada. Está sendo considerada a validação deste controlador adaptativo através da comparação dos resultados com duas diferentes técnicas Controlador MMPC e Identificação ARX, para a comprovação dos bons resultados desta metodologia.During the operation of a MPC, the plant can change the operation point mainly due to management decision or due to the presence of measured or unmeasured disturbances. Thus, the model of the controller must be adapted to improve the control in the new operation conditions. In such a way, a better control policy can be achieved if a large number of models are identified at the possible operation points and it is available an adaptive controller that is capable of selecting the best model. In this work is presented a methodology of adaptive control with on-line identification of the most adequate model which belongs to a set of models previously obtained. The proposed methodology considers a two-layer controller and process excitation by a GBN signal in the LP optimization layer with the controller in closed loop mode. It is also presented the adaptive controller validation by comparing the proposed approach with two different techniques - MMPC and ARX Identification, to confirm the good results with this new methodology to the adaptive controller.
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PINHEIRO, Tarcísio Carlos Farias. "Controle MPC multivariável com restrições usando funções de Laguerre." Universidade Federal do Pará, 2018. http://repositorio.ufpa.br/jspui/handle/2011/10067.
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FAPESPA - Fundação Amazônia de Amparo a Estudos e Pesquisas
Neste trabalho apresenta-se o projeto de um controlador preditivo multivariável baseado em modelo, com restrições, usando as Funções de Laguerre, tendo o intuito de demonstrar os benefícios e facilidades da aplicação deste tipo de controlador em sistemas MIMO (MultipleInput and Multiple-Output) com restrições. O controlador proposto apresenta a vantagem de diminuir a carga computacional utilizada para resolver o problema de otimização envolvido no projeto, isto porque utiliza uma rede de filtros de funções ortonormais de Laguerre para obter a trajetória futura do sinal de controle dentro de um horizonte de predição, além de melhorar o compromisso entre a viabilidade do sinal de controle e o desempenho de malha fechada do sistema para os casos com restrições, no qual as funções de Laguerre são utilizadas em conjunto com a Programação Quadrática de Hildreth para encontrar a solução ótima do sinal de controle com restrições. Este controlador apresenta grandes vantagens se comparado com o controle preditivo baseado em modelo em sua abordagem clássica, em que os operadores de avanço de tempo são utilizados para predizer a trajetória futura do sinal de controle, o que leva à soluções, em alguns casos, pouco satisfatórias, e a uma alta carga computacional para casos onde o sinal de controle requer um longo horizonte de predição e uma alto desempenho em malha fechada. Este trabalho também relata testes experimentais com um manipulador robótico configurado como um sistema MIMO com três entradas e três saídas e testes simulados com a coluna de destilação binária de Wood e Berry que é um sistema MIMO com duas entradas e duas saídas, contendo atrasos de transporte. Os testes têm como objetivo comparar os resultados do controlador apresentado com o controlador que usa a abordagem tradicional e com isso demonstrar as vantagens do método usando as funções de Laguerre e sua eficiência para sistemas MIMO.
This work presents a constrained multivariable model predictive controller using Laguerre Functions. This controller uses a set of orthonormal Laguerre networks for representation of the control trajectory within a control horizon. In order to demonstrate the advantages of applying this type of controller in MIMO (Multiple-Input and Multiple-Output) systems, the Laguerre Functions Functions are used to decrease the computational load used to calculate the optimal control. In addition, It improves the compromise between control signal viability and closed-loop performance of the system. The Laguerre Functions are also used in conjunction with Hildreth’s Quadratic Programming to find the optimal solution for the case where the control signal is constrained. The proposed controller presents advantages when compared to the classical model predictive control approach, where forward shift operators are used to predict the future trajectory of the control signal, leading to unsatisfactory solutions and a high computational load for cases where the control signal demands a long prediction horizon and a high closed-loop performance.It is also reported the practical testes with a robotic manipulator configured as a MIMO system with three inputs and three outputs and tests simulated with the Wood and Berry binary distillation column which is a MIMO system with two inputs and two outputs, also containing transport time delays. The tests aim to compare the controller results presented with the traditional predictive control approach and thereby demonstrate the advantages of the method using the Laguerre functions and their efficiency for MIMO systems.
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Cowie, Lauren. "The synthesis and self-assembly of MPC block copolymers." Thesis, Durham University, 2013. http://etheses.dur.ac.uk/7341/.
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Biocompatible and biodegradable poly(lactide)-2-methacryloyloxyethyl phosphorylcholine (PLA-PMPC) amphiphilic block copolymers were synthesized by a combination of Ring Opening Polymerization (ROP) and Reversible Addition-Fragmentation Chain Transfer (RAFT) polymerization techniques. The PLA-macroRAFT agent was synthesized by the derivatization of PLA-OH with RAFT agent 4-cyano-4-(phenylcarbonothioylthio)pentanoic acid (CPADB) achieving high levels of functionalization and narrow weight distributions (PDI range of 1.02-1.17). PLA-PMPC with varied MPC block lengths were synthesized yielding polymers with a narrow polydispersity PDI = 1.16-1.21. Triblock copolymers PMPC-PLA-PMPC with varying hydrophilic weight ratios were synthesized following an analogous method, the polymerizations were shown to be controlled with PDI’s of 1.24 and 1.36. PLA-PMPC block copolymers with varied compositions were self-assembled using several techniques to target different morphologies. Nanostructures were characterised by DLS and TEM. Block copolymers with a larger PLA block length were shown to generate smaller aggregates i.e. micelles. The morphologies observed for the various block copolymers were consistent amongst different preparative techniques. Vesicle structures were reproducible by the self-assembly of PMPC50-PLA51-PMPC50, however, by preparing nanoparticles by direct dissolution micelles formed. The block copolymers were shown to encapsulate a hydrophobic dye in aqueous media thereby demonstrating its potential drug delivery applications.
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Friberg, John. "Tuning an MPC-based Motion Planner using Imitation Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300908.
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Model Predictive Control, MPC, is one of the most commonly used controllers today. They are well understood with a vastly researched theoretical background. One of the core strengths of MPC is that it is possible to provide safety guarantees for complex systems through the constraints enforced in the optimization problem. This makes them useful for autonomous vehicle trajectory planning. However, as system complexity goes up, so does the number of states and thus also the number of weight parameters. Tuning these parameters is a difficult, tiresome and time-consuming task that could be automated from human driving data. The aim of this thesis is to tune the cost function of an MPC-based motion planner for in- lane driving from human demonstrations. The motion planner used is a non-linear path-following MPC designed with a cost function of 6 features commonly used for autonomous in-lane driving. The cost function weights are learned from real human data through the framework of Maximum Margin Planning, which is an inverse reinforcement learning algorithm. The proposed framework is evaluated and it is shown that it can successfully learn the underlying behavior of drivers in selected data sets. However, it is also shown that the non-convexity of the motion planner becomes problematic for other data sets. Therefore, a simpler convex MPC motion planner is designed, for which the proposed framework is shown to successfully tune weight parameters based on expert driving data.Modell-prediktiv reglering, MPC, är en av de mest använda styrmetoderna idag. Metoden är välförstådd och baserade på en mycket gedigen teoretisk bakgrund. En av fördelarna med MPC är att det är möjligt att garantera säkerheten för komplexa system genom att införa begränsningar i optimeringsproblemet. Detta gör MPC användbart för rörelseplanering av autonoma fordon. Inom rörelseplanering är systemkomplexiteten hög, vilket betyder att det finns ett stort antal tillstånd och därmed också ett stort antal vikt-parametrar. Att ställa in dessa parametrar är en svår, tröttsam och tidskrävande uppgift som kan automatiseras från mänsklig ködata. Syftet med denna avhandling är att från mänskliga demonstrationer, ställa in kostnadsfunktionen för en MPC-baserad rörelseplanerare för körning i körfält. Den använda rörelseplaneraren är en icke-linjär vägföljande MPC utformad med en kostnadsfunktion på 6 funktioner som vanligtvis används för autonom körning i körfält. Kostnadsfunktionens vikter ställs in från riktig mänsklig data genom ramverket för Maximum Margin Planning, vilket är en metod utvecklad inom området Inverse Reinforcement Learning. Det föreslagna ramverket utvärderas och det visas att det kan lära sig det underliggande beteendet i utvalda dataset. Det visas dock också att rörelseplanerarens icke-konvexitet blir problematisk för andra data-set. På grund av detta utvecklades en enklare konvex MPC-rörelse-planerare, för vilken det föreslagna ramverket för inlärning framgångsrikt ställer in viktparametrarna från expertdata.
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Lilja, Joakim. "Combined Attitude and Orbital MPC for Thruster Based Spacecrafts." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211552.
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A spacecraft needs to simultaneously provide orbital and attitude controlbut these are in general treated as separate systems. Normally the attitudecontrol is conducted via reaction wheels but can in scenarios with high manoeuvrabilitydemands be handed over to pure thruster control. In specificcases the reaction wheels are removed from the spacecraft to save mass. Ifboth the orbital and attitude control is regulated with thrusters, there is apotential to save fuel in a combined control strategy. Model predictive controlhas been shown to be a viable method for orbital control with a fuel minimisingobjective. This thesis investigates a combined orbital and attitude model predictivecontrol strategy. Three test cases are simulated with a specific thrusterconfiguration; maintaining a passive orbit relative to a target, large-angle reorientationand repositioning, and rendezvous. Preliminary results show thatincluding the coupled dynamics lowers the overall fuel consumption while satisfyingrequirements on position and attitude in scenarios where the timescaleof the orbital and attitude control is similar.En rymdfarkost behöver simultant reglera både omloppsbana och attityd,men vanligtvis hanteras detta av två separata system. Normalt styrs attitydregleringenav reaktionshjul men kan i scenarion med höga krav på manövrerbarhetistället skötas av styrraketer. I speciella fall tas reaktionshjulen bort för attspara på massa. Om både omloppsbana samt attityd regleras via styrraketerkan det finnas en potential att spara bränsle genom en kombinerad reglerstrategi.Modellprediktiv reglering (MPC) har visats vara en kapabel metodför reglering av omloppsbana med ett bränsleminimerande mål. Denna uppsatsundersöker en kombinerad MPC för attityd och omloppsbana. Tre testfallär simulerade med en specifik konfiguration av styrraketer; bibehålla en passivbana relativt ett mål, stor omorientering och ompositionering samt rendezvous.Preliminära resultat visar att om hänsyn tas till den kopplade dynamiken, kanbränsleförbrukningen minskas i scenarion där tidsskalan för regleringen av attitydoch omloppsbana är liknande.
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Berglund, Erik. "LQR and MPC control of a simulated data center." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214564.
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One of the largest contributions to a data center’s power usage is its cooling system. To decrease the energy usage of the cooling system, an automatic control scheme that adapts the capacity of the cooling units is needed. In this master thesis, a Simulink model of a data center is developed, along with several LQR and one MPC controller. The controllers control the outlet temperature and volumetric airflow of two CRAH units in the simulated data center. Simulations are performed in which the controllers are judged based on their estimated energy usage and how often the server temperatures in the data center exceed 35°C. Based on the experimental results, recommendations are made regarding what kinds of controllers to investigate in ABB’s further research.Ett av de största bidragen till ett datacenters energiförbrukning kommer från kylsystemet. För att minska kylsystemets energianvändning krävs ett automatiskt reglersystem som anpassar hur stor andel av kylningsenheternas kapacitet som utnyttjas. I detta examensarbete utvecklas en Simulink-modell av ett datacenter, samt flera LQR-regulatorer och en MPCregulator. Regulatorerna kontrollerar utblåsningstemperaturen och luftflödet hos två CRAHenheter i det simulerade datacentret. Simuleringar utförs, där regulatorerna bedöms efter uppskattad energianvändning och efter hur ofta servertemperaturerna övergår 35 ° C. Baserat på experimentella resultat ges rekommendationer angående vilken typ av regulatorer som bör undersökas närmare i ABBs fortsatta forskning.
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Simon, Daniel. "Fighter Aircraft Maneuver Limiting Using MPC : Theory and Application." Doctoral thesis, Linköpings universitet, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139945.
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Flight control design for modern fighter aircraft is a challenging task. Aircraft are dynamical systems, which naturally contain a variety of constraints and nonlinearities such as, e.g., maximum permissible load factor, angle of attack and control surface deflections. Taking these limitations into account in the design of control systems is becoming increasingly important as the performance and complexity of the aircraft is constantly increasing. The aeronautical industry has traditionally applied feedforward, anti-windup or similar techniques and different ad hoc engineering solutions to handle constraints on the aircraft. However these approaches often rely on engineering experience and insight rather than a theoretical foundation, and can often require a tremendous amount of time to tune. In this thesis we investigate model predictive control as an alternative design tool to handle the constraints that arises in the flight control design. We derive a simple reference tracking MPC algorithm for linear systems that build on the dual mode formulation with guaranteed stability and low complexity suitable for implementation in real time safety critical systems. To reduce the computational burden of nonlinear model predictive control we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefit of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence. An alternative to deriving MPC algorithms with guaranteed stability properties is to analyze the closed loop stability, post design. Here we focus on deriving a tool based on Mixed Integer Linear Programming for analysis of the closed loop stability and robust stability of linear systems controlled with MPC controllers. To test the performance of model predictive control for a real world example we design and implement a standard MPC controller in the development simulator for the JAS 39 Gripen aircraft at Saab Aeronautics. This part of the thesis focuses on practical and tuning aspects of designing MPC controllers for fighter aircraft. Finally we have compared the MPC design with an alternative approach to maneuver limiting using a command governor.
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Nery, Júnior Gesner Andrade. "Sintonia ótima de controladores MPC considerando incertezas de modelagem." Universidade Federal da Bahia. Escola Politécnica, 2015. http://repositorio.ufba.br/ri/handle/ri/19395.
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Os métodos heurísticos de sintonia de controladores preditivos se mostram muito específicos, tanto em relação ao tipo de controlador, como ao tipo de sistema a ser controlado e, algumas vezes, não refletem com o desempenho ou a robustez desejados. Além disso, esses métodos não consideram a incerteza das medidas ou dos modelos, sendo que frequentemente a sintonia é feita na base da experiência do engenheiro, ou ainda, na falta desta, por tentativa e erro. Por outro lado, os métodos de sintonia ótima possuem a vantagem de serem bastante flexíveis, podendo ser utilizados para uma ampla gama de tipos de controladores e sistemas, e as sintonias resultantes da aplicação de tais métodos atendem o desempenho demandado pelo usuário, de acordo com o critério de desempenho ou função-objetivo previamente escolhidos, desde que sejam factíveis. O objetivo deste trabalho é desenvolver uma metodologia para a sintonia ótima de controladores preditivos multivariáveis, considerando a incerteza de modelagem nos parâmetros do modelo da planta. Uma vez que a formulação do problema de sintonia ótima resulta em uma programação mista-inteira não-linear, um algoritmo de otimização meta-heurístico, baseado na técnica de otimização por enxame de partículas, é utilizado para solucionar o problema proposto. Como forma de alcançar um controle também robusto às incertezas de modelagem, o método inclui na sua formulação a identificação do cenário do pior caso de controle, determinado no domínio da incerteza dos parâmetros do modelo, baseado no Índice de Resilência de Morari e no Número Condicional. Estudos de caso típicos da indústria de processos são realizados e as funções de densidade de probabilidade das funções-objetivo são analisadas, evidenciando o bom desempenho e robustez das sintonias propostas
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Diaz, Dorado Alberto. "Efficient Convex Quadratic Optimization Solver for Embedded MPC Applications." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240421.
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Model predictive control (MPC) is an advanced control technique thatrequires solving an optimization problem at each sampling instant. Severalemerging applications require the use of short sampling times tocope with the fast dynamics of the underlying process. In many cases,these applications also need to be implemented on embedded hardwarewith limited resources. As a result, the use of model predictivecontrollers in these application domains remains challenging.This work deals with the implementation of an interior point algorithmfor use in embedded MPC applications. We propose a modularsoftware design that allows for high solver customization, while stillproducing compact and fast code. Our interior point method includesan efficient implementation of a novel approach to constraint softening,which has only been tested in high-level languages before. We showthat a well conceived low-level implementation of integrated constraintsoftening adds no significant overhead to the solution time, and hence,constitutes an attractive alternative in embedded MPC solvers.Modell prediktiv reglering (MPC) är en avancerad regler-teknik som involverar att lösa ett optimeringsproblem vid varje sampeltillfälle. Flera nya tillämpningar kräver användning av korta samplingstider för att klara av den snabba dynamiken av den underliggande processen. I många fall implementeras dessa tekniker på inbyggd hårdvara med begränsade resurser, där det som följd är utmanande att utnyttja MPC. Det här arbetet berör implementering av en inrepunktsmetod för att lösa optimeringsproblem i inbyggda MPC-tillämpningar. Vi föreslår en modulär design som gör det möjligt att skräddarsy lösaren i detalj, men ändå samtidigt producera kompakt och snabb kod. Vår inrepunktsme- tod inkluderar en effektiv implementering av ett nytt tillvägagångssätt för att lätta på optimeringsvillkor. Denna method har tidigare endast implementerats i högnivåspråk. Vi visar att denna integrerade metod för att lätta på optimeringsvillkor inte medför någon signifikant ök- ning av lösningstiden och därmed är en attraktiv teknik för inbyggda MPC-lösare.
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Sörliden, Pär. "3D Visualization of MPC-based Algorithms for Autonomous Vehicles." Thesis, Linköpings universitet, Fordonssystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157380.
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The area of autonomous vehicles is an interesting research topic, which is popular in both research and industry worldwide. Linköping university is no exception and some of their research is based on using Model Predictive Control (MPC) for autonomous vehicles. They are using MPC to plan a path and control the autonomous vehicles. Additionally, they are using different methods (for example deep learning or likelihood) to calculate collision probabilities for the obstacles. These are very complex algorithms, and it is not always easy to see how they work. Therefore, it is interesting to study if a visualization tool, where the algorithms are presented in a three-dimensional way, can be useful in understanding them, and if it can be useful in the development of the algorithms. This project has consisted of implementing such a visualization tool, and evaluating it. This has been done by implementing a visualization using a 3D library, and then evaluating it both analytically and empirically. The evaluation showed positive results, where the proposed tool is shown to be helpful when developing algorithms for autonomous vehicles, but also showing that some aspects of the algorithm still would need more research on how they could be implemented. This concerns the neural networks, which was shown to be difficult to visualize, especially given the available data. It was found that more information about the internal variables in the network would be needed to make a better visualization of them.
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Morinelly, Sanchez Juan Eduardo. "Adaptive Model Predictive Control with Generalized Orthonormal Basis Functions." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1091.
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An adaptive model predictive control (MPC) method using models derived from orthonormal basis functions is presented. The defining predictor dynamics are obtained from state-space realizations of finite truncations of generalized orthonormal basis functions (GOBF). A structured approach to define multivariable system models with customizable, open-loop stable linear dynamics is presented in Chapter 2. Properties of these model objects that are relevant to the adaptation component of the overall scheme, are also discussed. In Chapter 3, non-adaptive model predictive control policies are presented with the definition of extended state representations through filter operations that enable output feedback. An infinite horizon set-point tracking policy which always exists under the adopted modeling framework is presented. This policy and its associated cost are included as the terminal stage elements for a more general constrained case. The analysis of robust stability guarantees for the non-adaptive constrained formulation is presented, under the assumption of small prediction errors. In Chapter 4, adaptation is introduced and the certainty equivalence constrained MPC policy is formulated under the same framework of its non-adaptive counterpart. Information constraints that induce the excitation of the signals relevant to the adaptation process are formulated in Chapter 5. The constraint generation leverages the GOBF model structure by enforcing a sufficient richness condition directly on the state elements relevant to the control task. This is accomplished by the definition of a selection procedure that takes into account the characteristics of the most current parameter estimate distribution. Throughout the manuscript, illustrative simulation examples are provided with respect to minimal plant models. Concluding remarks and general descriptions for potential future work are outlined in Chapter 6.
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Javalera, Rincón Valeria. "Distributed large scale systems : a multi-agent RL-MPC architecture." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/393922.
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This thesis describes a methodology to deal with the interaction between MPC controllers in a distributed MPC architecture. This approach combines ideas from Distributed Artificial Intelligence (DAI) and Reinforcement Learning (RL) in order to provide a controller interaction based on cooperative agents and learning techniques. The aim of this methodology is to provide a general structure to perform optimal control in networked distributed environments, where multiple dependencies between subsystems are found. Those dependencies or connections often correspond to control variables. In that case, the distributed control has to be consistent in both subsystems. One of the main new concepts of this architecture is the negotiator agent. Negotiator agents interact with MPC agents to determine the optimal value of the shared control variables in a cooperative way using learning techniques (RL). The optimal value of those shared control variables has to accomplish a common goal, probably different from the specific goal of each agent sharing the variable. Two cases of study, in which the proposed architecture is applied and tested are considered, a small water distribution network and the Barcelona water network. The results suggest this approach is a promising strategy when centralized control is not a reasonable choice.Esta tesis describe una metodología para hacer frente a la interacción entre controladores MPC en una arquitectura MPC distribuida. Este enfoque combina las ideas de Inteligencia Artificial Distribuida (DIA) y aprendizaje por refuerzo (RL) con el fin de proporcionar una interacción entre controladores basado en agentes de cooperativos y técnicas de aprendizaje. El objetivo de esta metodología es proporcionar una estructura general para llevar a cabo un control óptimo en entornos de redes distribuidas, donde se encuentran varias dependencias entre subsistemas. Esas dependencias o conexiones corresponden a menudo a variables de control. En ese caso, el control distribuido tiene que ser coherente en ambos subsistemas. Uno de los principales conceptos novedosos de esta arquitectura es el agente negociador. Los agentes negociadores actúan junto con agentes MPC para determinar el valor óptimo de las variables de control compartidas de forma cooperativa utilizando técnicas de aprendizaje (RL). El valor óptimo de esas variables compartidas debe lograr un objetivo común, probablemente diferente de los objetivos específicos de cada agente que está compartiendo la variable. Se consideran dos casos de estudio, en el que la arquitectura propuesta se ha aplicado y probado, una pequeña red de distribución de agua y la red de agua de Barcelona. Los resultados sugieren que este enfoque es una estrategia prometedora cuando el control centralizado no es una opción razonable.
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Wan, Jian. "Computationally reliable approaches of contractive MPC for discrete-time systems." Doctoral thesis, Universitat de Girona, 2007. http://hdl.handle.net/10803/7740.
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La tesis pretende explorar acercamientos computacionalmente confiables y eficientes de contractivo MPC para sistemas de tiempo discreto. Dos tipos de contractivo MPC han sido estudiados: MPC con coacción contractiva obligatoria y MPC con una secuencia contractiva de conjuntos controlables. Las técnicas basadas en optimización convexa y análisis de intervalos son aplicadas para tratar MPC contractivo lineal y no lineal, respectivamente. El análisis de intervalos clásicos es ampliado a zonotopes en la geometría para diseñar un conjunto invariante de control terminal para el modo dual de MPC. También es ampliado a intervalos modales para tener en cuenta la modalidad al calcula de conjuntos controlables robustos con una interpretación semántica clara. Los instrumentos de optimización convexa y análisis de intervalos han sido combinados para mejorar la eficacia de contractive MPC para varias clases de sistemas de tiempo discreto inciertos no lineales limitados. Finalmente, los dos tipos dirigidos de contractivo MPC han sido aplicados para controlar un Torneo de Fútbol de Copa Mundial de Micro Robot (MiroSot) y un Tanque-Reactor de Mezcla Continua (CSTR), respectivamente.The thesis aims to explore computationally reliable and efficient approaches of contractive MPC for discrete-time systems. Two types of contractive MPC have been studied: MPC with compulsory contractive constraint and MPC with a contractive sequence of controllable sets. Techniques based on convex optimization and interval analysis are applied to deal with linear and nonlinear contractive MPC, respectively. Classical interval analysis is extended to zonotopes in geometry for designing a terminal control invariant set in the dual-mode approach of MPC. It is also extended to modal intervals in modality for computing robust controllable sets with a clear semantic interpretation. The tools of convex optimization and interval analysis have been combined further to improve the efficiency of contractive MPC for various kinds of constrained nonlinear uncertain discrete-time systems. Finally, the addressed two types of contractive MPC have been applied to control a Micro Robot World Cup Soccer Tournament (MiroSot) robot and a Continuous Stirred-Tank Reactor (CSTR), respectively.
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Selvatici, Luca. "Distributed cooperative MPC for aerial robots: a ROS 2 implementation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Find full textAbstract:
The significant decrease in manufacturing costs of hardware components for quadrotors has greatly encouraged research into the design of flight control algorithm for quadrotors, which has seen great growth in recent years. One of the key aspects of the research is the communication between the quadrotors. Nowadays it is considered essential that the quadrotors can communicate with each other. This feature allows numerous advantages: it is possible to generate a network capable of collaborating to solve complex tasks that single quadrotors would not be able to perform, or complete them in a shorter time.
The objective of this thesis is the design of a distributed algorithm to control the navigation of a set of quadrotors flying through the same navigation space. A surveillance task has been chosen as a case study, where quadrotors are in charge of arranging themselves in order to protect a target from intruders. Each quadrotor needs to complete both a specific task assigned to it (prevent a certain intruders from reaching the target) and a task in common with the other quadrotors (make sure that the center of the drones coincides with the target and the quadrotors do not collide). With this goal in mind, the project starts with the design of the quadrotor model, controller and trajectories from scratch. Then a Distributed Model Predictive Control algorithm is designed ad hoc to control the navigation of quadrotors. One of the challenges in the creation of this algorithm is the adaptation of the control algorithm to the simultaneous use of Model Predictive Control (MPC) and Online Distributed Gradient Tracking (O-DGT). Indeed, the speed required for the optimization calculations led us to reformulate the MPC in order to make the calculations faster and thus satisfy the limits imposed by the chosen time-step. The proposed model is tested with numerical examples, analyzing a series of cases that allowed us to test different combinations of the developed algorithms.
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Axehill, Daniel, and Johan Sjöberg. "Adaptive Cruise Control for Heavy Vehicles : Hybrid Control and MPC." Thesis, Linköping University, Department of Electrical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1604.
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An Adaptive Cruise Controller (ACC) is an extension of an ordinary cruise controller. In addition to maintaining a desired set velocity, an ACC can also maintain a desired time gap to the vehicle ahead. For this end, both the engine andthe brakes are controlled.
The purpose with this thesis has been to develop control strategies for an ACC used in heavy vehicles. The focus of the work has been the methods used for switching between the use of engine and brake. Two different methods have been studied, a hybrid controller and an MPC-controller.
For the hybrid controller, the main contribution has been to use the influence of the surroundings on the acceleration of the truck. This consists of several parts such as wind drag, road slope and rolling resistance. The estimated influence of the surroundings is used as a switch point between the use of engine and brakes. Ideally, these switch points give bumpless actuator switches.
The interest in the MPC-controller as an alternative solution was to achieve automatic actuator switching, thus with no explicitly defined switch points. The MPC-controller is based on a model of the system including bounds on the control signals. Using this knowledge, the MPC-controller will choose the correct actuator for the current driving situation.
Results from simulations show that both methods solve the actuator switch problem. The advantages with the hybrid controller are that it is implementable in a truck with the hardware used today and that it is relatively simple to parameterise. A drawback is that explicit switch points between the uses of the different actuators have to be included. The advantages with the MPC-controller are that no explicit switch points have to be introduced and that constraints and time delays on signals in the system can be handled in a simple way. Among the drawbacks, it can be mentioned that the variant of MPC, used in this thesis, is too complex to implement in the control system currently used in trucks. One further important drawback is that MPC demands a mathematical model of the system.
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Palma, Vryan Gil [Verfasser], and Lars [Akademischer Betreuer] Grüne. "Robust Updated MPC Schemes / Vryan Gil Palma. Betreuer: Lars Grüne." Bayreuth : Universität Bayreuth, 2015. http://d-nb.info/1071890077/34.
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Annergren, Mariette. "Optimal Input Signal Design and MPC of Nonlinear Dynamical Systems." Thesis, KTH, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105134.
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The main topics of this master’s project are control theory, system identification and convex optimization. The objective is to develop, implement and test methods for optimal input signal design and for control of a nonlinear dynamical system using MPC. The thesis begins with a theoretical part, in which some known results in these fields are summarized. In the applied part of the thesis, methods are developed and exemplified in MATLAB. Optimal input signal design is performed on specific FIR, ARX and DCmotor systems which are all controlled by an MPC. The implementation works very well for the FIR and ARX systems. The estimates of the true parameters fulfill the pre-specified requirements when using the optimal input signal constructed. An unexpected behavior is obtained of the estimates for the DC-motor system. Some additional approximations which were made in the design of the optimal input signal are thought to be the cause. Although, the source of the odd behavior were never confirmed. To be able to have a user-friendly environment for optimal input signal design, further work is necessary to overcome numerical problems in the implementation. A general method of implementing MPC of nonlinear dynamical systems is constructed. It is problematic to use MPC to control a nonlinear system. The reason for this is that a nonlinear system in general corresponds to a non-convex optimization problem in the MPC algorithm. Our method is based on making the problem convex through a linearization of the nonlinear system dynamics. The method is tested on simulations of a reaction wheel pendulum and a two link robot arm. It works very well and the systems fulfill the control objectives. Each optimization problem takes about 0.3-1 second to solve when using cvx. This is in some situations too slow to be able to control a system in reality. Further work is recommended on implementing our method with another solver so that it can be tested on an actual system which requires new updates every milli- or microsecond.Design av optimal insignal och MPC för ickelinjära dynamiska system Detta examensarbete berör områden som reglerteknik, systemidentifiering och konvex optimering. Syftet är att utveckla metoder för design av en optimal insignal och för att använda MPC på ickelinjära dynamiska system. Rapporten börjar med en teoretisk del som sammanfattar kända resultat inom dessa områden. Därefter följer beskrivningar av de metoder som har utvecklats och de exemplifieras genom simulering i MATLAB. En metod för att designa en optimal insignal för specifika FIR-, ARX och DC-system har utvecklats. Systemen styrs med hjälp av en MPC regulator. Metoden fungerar utmärkt för FIR- och ARX-system. Ett oväntat resultat erhålls för fallet med DC-motorn. Vi tror att det beror på de approximationer som har gjorts särskilt för detta system men det har inte kunnat bekräftas. För att skapa en användarvänlig miljö för design av en optimal insignal givet ett system och en regulator så krävs ytterligare arbete. Det bör fokusera på att eliminera de numeriska problem som uppstår när metoden implementeras i MATLAB. En allmän metod för att använda MPC på ickelinjära dynamiska system har implementerats. Ett ickelinjärt system ger generellt upphov till ett ickekonvext optimeringsproblem i MPC algoritmen. Det är därför problematiskt att använda MPC för att reglera ickelinjära system. Vår metod bygger på att göra problemet konvext genom att linjärisera det icke linjära systemet. Metoden har testats på simuleringar av en pendel och en två-länkad robotarm. Den presterar väldigt bra och systemen uppnår önskat beteende. Optimeringsproblemet tar cirka 0,3-1 sekund att lösa när vi använder cvx. Detta är i vissa fall för långsamt för att kunna reglera ett verkligt system. Ytterligare arbete där metoden implementeras med en annan lösare rekommenderas. Detta skulle möjliggöra att man kan testa den på ett verkligt system som kräver uppdateringar varje milli- eller mikrosekund.
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Poma, Aliaga Luis Felipe. "Reliable autonomous vehicle control - a chance constrained stochastic MPC approach." Master's thesis, Pontificia Universidad Católica del Perú, 2017. http://tesis.pucp.edu.pe/repositorio/handle/123456789/8834.
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In recent years, there is a growing interest in the development of systems capable of performing
tasks with a high level of autonomy without human supervision. This kind of systems are known as
autonomous systems and have been studied in many industrial applications such as automotive,
aerospace and industries. Autonomous vehicle have gained a lot of interest in recent years and have
been considered as a viable solution to minimize the number of road accidents. Due to the
complexity of dynamic calculation and the physical restrictions in autonomous vehicle, for example,
deterministic model predictive control is an attractive control technique to solve the problem of
path planning and obstacle avoidance. However, an autonomous vehicle should be capable of driving
adaptively facing deterministic and stochastic events on the road. Therefore, control design for
the safe, reliable and autonomous driving should consider vehicle model uncertainty as well
uncertain external influences. The stochastic model predictive control scheme provides the
most convenient scheme for the control of autonomous vehicles on moving horizons, where chance
constraints are to be used to guarantee the reliable fulfillment of trajectory constraints and
safety against static and random obstacles. To solve this kind of problems is known as chance
constrained model predictive control. Thus, requires the solution of a chance constrained
optimization on moving horizon. According to the literature, the major challenge for solving chance
constrained optimization is to calculate the value of probability. As a result, approximation
methods have been proposed for solving this task.
In the present thesis, the chance constrained optimization for the autonomous vehicle is solved
through approximation method, where the probability constraint is approximated by using a smooth
parametric function. This methodology presents two approaches that allow the solution of chance
constrained optimization problems in inner approximation and outer approximation. The aim of this
approximation methods is to reformulate the chance constrained optimizations problems as a sequence
of nonlinear programs. Finally, three case studies of autonomous vehicle for tracking and obstacle
avoidance are presented in this work, in which three levels probability of reliability are
considered
for the optimal solution.Tesis
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Al-Naumani, Yahya. "MPC for upstream oil & gas fields : a practical view." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17979/.
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This work aims to improve corporate functional departments' confidence in adopting modern control approaches in new scenarios and thus presents control structure solutions based on model predictive control (MPC) for two control problems facing existing upstream oil and gas production plants. These are the disturbance growth in the series connected process and the control system dependency on operators. The suggested control solution integrates MPC as a master controller for the existing classical control of each subsystem, with a focus on those with high interaction phenomena. The proposed approach simply and inexpensively encompass MPC features such as predictions, optimizations, coordination and constraint handling as well as PID features like simplicity and ease of troubleshoot. In addition, the proposed control concept utilises the process safeguarding information and enhances the plant-wide optimal performance. The suggested control solution supports the role of control room operators, which is shown to reduce the growth in the impact of process disturbances. Compared with some alternative control structures (centralised MPC, decentralised MPC, distributed MPC (DMPC), and hierarchical DMPC) this proposal is simple, inexpensive to implement, and critically, builds on the local team operational experience and maintenance skills. Three process models were developed that representing the common gas treatment processes in upstream oil and gas plants, gas sweetening, gas dehydration and hydrocarbon dewopointing. The models were utilised to examine different control structures and proposals. These models are not only of benefit to studies on upstream oil and gas processes, but also to Large Scale Systems (LSS) in general. The models were used to analyse the disturbance impacts on a series connected processes, therefore to provide answers about how process malfunctions and different disturbances affect the processing operations. The proposed control system is designed on a cascade strategy and thus provides a flexible system control almost like a decentralised structure in dealing with disturbances and unit failures, and at the same time improves the closed loop performance and the plant-wide optimal operation. The control system contain MPC's that are designed to regulate the critical loops only while the rest of the uncritical loops will continue to function in a decentralised fashion under PID control algorithm. This minimises any design and set up costs, reduces demand on the communication network and simplifies any associated real time optimisations. The improved local control reduced the need for control room operator interactions with their associated weaknesses. The proposed control structure communicate with the process safeguarding system to enable prompt response to disturbances caused by unit failures, and shares critical information with adjacent MPC's, which indeed works as a feed-forward, to reduce the impact of process disturbances and enhance optimality. The control system design is simple, inexpensive to implement and significantly reduces the frequency of plant shut downs and saves on operating costs by properly controlling the disturbance growth in the process.
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Ludovico, Davide. "Robust dual adaptive MPC for output tracking of linear systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.
Find full textAbstract:
In this work, we present a novel robust dual adaptive model predictive control scheme for linear discrete-time systems with parametric uncertainty in the output and affected by a state-disturbance and a measurement noise. The proposed MPC framework promotes learning of the unknown parameters and in the meantime tracks a desired target output despite the presence of a state disturbance. This is possible through a suitable restriction of the state, input and output constraints that covers the whole possible range of the state disturbance. We prove that the proposed controller is practically stable and we ensure robust constraint satisfaction for state, input and output, also in the presence of parametric uncertainty and bounded noises. We corroborate the theoretical result through a numerical example simulated on Matlab.
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Laurí, Pla David. "MPC: Relevant Identification and Control in the Latent Variable Space." Doctoral thesis, Universitat Politècnica de València, 2012. http://hdl.handle.net/10251/15178.
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Control predictivo basado en modelos (MPC) es una metodología de control ampliamente utilizada en la industria por su habilidad para controlar procesos multivariable con restricciones en sus entradas y sus salidas. Se distinguen dos fases en la implementación de MPC: identificación y control. El propósito de esta tesis es doble: realizar contribuciones en la identificación para MPC y proponer una nueva metodología de control MPC.
La respuesta en bucle cerrado de una implementación de MPC depende, en gran medida, de la capacidad de predicción del modelo; luego la identificación del modelo es un punto crucial en MPC y la parte que a menudo exige la mayor parte del tiempo del proyecto. El primer objetivo que cubre la tesis es la identificación para MPC. Puesto que un modelo es una aproximación del comportamiento de un proceso, dicha aproximación se puede hacer teniendo en cuenta el fin que se le va a dar al modelo. En MPC, el modelo se utiliza para realizar predicciones dentro de una ventana futura, luego la identificación para MPC (MRI) tiene en cuenta dicho uso del modelo y considera los errores de predicción dentro de dicha ventana para el ajuste de los parámetros del modelo. En esta tesis, se cubren tres temas dentro de MRI. Primero se define MRI y las distintas formas de abordarlo. Luego se compara en términos de MRI el ajuste de un modelo con múltiples entradas y múltiples salidas con el ajuste de varios modelos con múltiples entradas y una salida concluyendo que el ajuste de un único modelo con múltiples entradas y múltiples salidas proporciona mejores resultados en términos de MRI para horizontes de predicción lo suficientemente grandes. Por último, se propone el algoritmo PLS-PH para implementar MRI con modelos paramétricos en el caso de correlación en los datos de identificación. PLS-PH es un método de optimización numérica por búsqueda lineal basado en PLS (mínimos cuadrados parciales). Se muestra en un ejemplo como PLS-PH es capaz de proporcionar mejores modelos que
las técnicas convencionales de MRI en modelos paramétricos en el caso de
correlación en los datos de identi ficación.
Una vez obtenido el modelo se puede formular el controlador predictivo.
En esta tesis se propone LV-MPC, un controlador predictivo para procesos
continuos que implementa la optimización en el espacio de las componentes
principales.Laurí Pla, D. (2012). MPC: Relevant Identification and Control in the Latent Variable Space [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/15178
Palancia
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Palma, Vryan Gil S. [Verfasser], and Lars [Akademischer Betreuer] Grüne. "Robust Updated MPC Schemes / Vryan Gil Palma. Betreuer: Lars Grüne." Bayreuth : Universität Bayreuth, 2015. http://d-nb.info/1071890077/34.
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Bärlund, Alexander. "Nonlinear MPC for Motion Control and Thruster Allocation of Ships." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158493.
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Critical automated maneuvers for ships typically require a redundant set of thrusters. The motion control system hierarchy is commonly separated into several layers using a high-level motion controller and a thruster allocation (TA) algorithm. This allows for a modular design of the software where the high-level controller can be designed without comprehensive information on the thrusters, while detailed issues such as input saturation and rate limits are handled by the TA. However, for a certain set of thruster configurations this decoupling may result in poor control performance due to the limited knowledge in the high-level controller about the physical limitations of the ship and the behavior of the TA. This thesis investigates different approaches of improving the control performance, using nonlinear Model Predictive Control (MPC) as a foundation for the developed motion controllers due to its optimized solution and capability of satisfying constraints. First, a decoupled system is implemented and results are provided for two simple motion tasks showing problems related to the decoupling. Thereafter, two different approaches are taken to remedy the observed drawbacks. A nonlinear MPC controller is developed combining the motion controller and thruster allocation resulting in a more robust control system. Then, in order to keep the control system modularized, an investigation of possible ways to augment the decoupled system so as to achieve similar performance as the combined system is carried out. One proposed solution is a nonlinear MPC controller with time-varying constraints accounting for the current limitations of the thruster system. However, this did not always improve the control performance since the behavior of the TA still is unknown to the MPC controller.
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Sehlin, Olov. "Genetic algorithm tuning of artificial pancreas MPC with individualized models." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76083.
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Diabetes is a growing chronic disease and a worldwide problem. Without any available cure in sight for the public other methods needs to be applied to increase the life quality of diabetic patients. Artificial Pancreas (AP), a concept of having a closed loop system to control the glucose level on Type 1 Diabetes (T1D) patients has been introduced and is under development. In this thesis, Model Predictive Control (MPC) has been re implemented from scratch in MATLAB/SIMULINK with associated Kalman filter and prediction function. It was implemented in the latest version of the UVA/Padova Simulator which is a tool approved by FDA for simulating diabetes treatment in order to speed up the AP development. Different MPC cost functions where tested together with integral action on a simplified system using a linear approximation of a population model. It was implemented and tuned with a new simulation tuning method using Genetic Algorithm (GA). It showed that the quadratic cost function without integral action was the best with respect to performance and time efficiency. 3 hours was the best prediction horizon and was used for the individualized tuning using the University of Virginia (UVA)/Padova simulator. For the individualized MPC, models identified by the University of Padova were used. These simulations showed that an individualized model could be used for improved T1D treatment compared to an average population model even though the results were mixed. Almost all of the patients got improved treatment with the closed treatment and non hypoglycemic event occurred. The identification of better models is a great challenge for the future development of the AP MPC due to the excitation problems.
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Hällerstam, Jonsson Linnea. "Fuel optimizing cruise controller with driveability." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209680.
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This thesis work is based on a dynamic programming solution of a fuel optimizing cruise controller that was developed at Scania CV AB last year. Known data of the road ahead, mainly the slope, is used to continuously calculate the optimal torque and gear choices of a given moving vehicle for a certain horizon. The optimization calculations are based on fuel consumption and the vehicle's arrival time to the final destination. This report has been focused on achieving better "driveability" of the cruise controller while still maintaining the good fuel saving qualities that is already there. Simulation is used to evaluate the cruise controller on roads where the wanted data is known. The result is smaller speed variations on at road segments, which will improve a driver's impression of the cruise controller. The great fuel benefits of using roll-techniques in hilly areas is maintained from the previous implementation. The key to the optimal balance between these two behaviors is found using a method that limits the torque usage of the truck to a certain speed interval and then finds exception areas where the torque usage should be unlimited.Detta examensarbete är baserad på en dynamisk programmeringslösning av en bränsleoptimerande farthållare som utvecklades på Scania CV AB förra året. Känd data om den framförvarande vägen, så som lutningen, används för att beräkna optimalt drivmoment och växelval för ett givet fordon för en viss horizont. Optimeringsberäkningarna baseras på bränsleförbrukning och fordonets ankomsttid till målet. Denna rapport focuserar på att uppnå bättre "körbarhet" för farthållaren och samtidigt behålla de goda bränslebesparande egenskaper som farthållaren redan har. Simulering nyttjas för att analysera farthållaren på vägar där önskad data är känd. Resultatet är mindre hastighetsvariationer på platta vägar, vilket bör förbättra förarens uppfattning av farthållaren. De stora fördelar som kommer med användning av rull-tekniker på kuperade vägsträckor bevaras från den tidigare implementeringen. Nyckeln till optimal balans mellan dessa två körbeteenden är en metod som går ut på att begränsa fordonets momentanvändning till ett visst hastighetsinterval och sedan hitta undantagsområden där momentanvändning borde vara obegränsad.
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Greer, William Bryce. "Advanced Linear Model Predictive Control For Helicopter Shipboard Maneuvers." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/95031.
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This dissertation focuses on implementing and analyzing advanced methods of model predictive control to control helicopters into stable flight near a ship and perform a soft touchdown from that state. A shrinking horizon model predictive control method is presented which can target specific states at specific times and take into account several important factors during landing. This controller is then used in simulation to perform a touchdown maneuver on a ship for a helicopter by targeting a landed state at a specific time. Increasing levels of fidelity are considered in the simulations. Computational power required reduces the closer the helicopter starts to the landing pad. An infinite horizon model predictive controller which allows simultaneous cost on state tracking, control energy, and control rates and allows tracking of an arbitrary equilibrium to infinity is then presented. It is applied in simulation to control a helicopter initially in a random flight condition far from a ship to slowly transition to stable flight near the ship, holding an arbitrary rough position relative to the ship indefinitely at the end. Three different target positions are simulated. This infinite horizon control method can be used to prepare for landing procedures that desire starting with the helicopter in some specific position in close proximity to the landing pad, such as the finite horizon method of landing control described previously which should start with the helicopter close to the ship to reduce computation power required. A method of constructing a landing envelope is then presented and used to construct a landing envelope for the finite horizon landing controller. A pre-existing method of combining linear controllers to account for nonlinearity is then slightly modified and used on implementations of the finite horizon landing controller to make a control that takes into account some of the nonlinearity of the problem. This control is tested in simulation.Doctor of Philosophy
This dissertation proposes and, using simulation, analyzes control algorithms and their use on helicopter shipboard operations. Various benefits and advances for controls in this area are suggested, tested, and discussed. The control methods presented and implemented, while not limited to these use cases, are particularly well suited for them. One control algorithm is used for controlling flight near the landing point on a ship and performing a soft touchdown on the ship. The algorithm is tested in simulation. Another algorithm is used to control a helicopter initially in flight far away from the ship to slowly transition to stable flight near the ship, holding a rough position relative to the ship indefinitely at the end. This control could be used to set up the helicopter for later use of the touchdown control. This control is also tested in simulation. A method of quantifying what conditions the touchdown controller has a relatively good chance of successfully landing in is then suggested. The range of conditions for which successful touchdown has a relatively high chance of being achieved along with an analysis of that likelihood is called the landing envelope. Using the landing envelope construction method with numerous simulations, a landing envelope for the touchdown controller is obtained. The touchdown controller assumes that the helicopter’s dynamics are linear. Helicopter dynamics (like most dynamics of real systems) are nonlinear. However, under conditions near the point that dynamics are linearized about, a linear approximation is sufficiently accurate. To improve on the above landing algorithm, a method of combining multiple specific implementations of the touchdown controller to help account for nonlinearity to improve the approximation of the dynamics that the controller assumes is then suggested and performed in simulation.
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Ekström, Mats. "Slutfasstyrning av missil med explicit prediktionsreglering." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2750.
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Arbetet har utförts på Saab Bofors Dynamics AB i Linköping och dess syfte är att undersöka möjligheten att applicera teorin för prediktionsreglering, Model Predictive Control (MPC), på guidance systemet i en missil av typen Medium Range Air-to-Air Missiles (MRAAM). Även implementering via Explicit MPC har undersökts.
I tidigare studier har det visat sig att den moderna slutfasstyrningsalgoritmen Linear Quadratic Augmented Proportional Navigation (LQAPN), som återkopplar missilens acceleration och rotation, uppvisar en bättre prestanda än de mer klassiska styrlagarna. Det främsta intresset med denna studie är därför att undersöka hur tillvida en styrlag baserad på MPC kan mäata sig med dessa resultat. Fördelen med att använda MPC är framförallt att man kan ta hänsyn till styrsignalbegränsningar på ett direkt och intuitivt sätt.
En nackdel med MPC är beräkningstiden. På senare år har dock forskning bedrivits för att ta fram en variant av MPC som beräknar styrsignalen explicit som en affin funktion av det aktuella tillståndet. Denna metod kallas Explicit MPC och har betraktats som en separat metod i detta arbete.
Styrlagen baserad på MPC kallas i detta arbete för Model Predictive Control Augmented Proportional Navigation (MPCAPN) och utmärker sig framförallt i två fall. Dels då så kallade händelsestyrda simuleringar studeras, då den uppvisar ett klart bättre resultat än vad som erhålls med en styrlag baserad på Linear Quadratic Augmented Proportional Navigation (LQAPN). Även vid beräkningar av skjutzoner blir resultaten ibland bättre. Framförallt förbättras den inre skjutgränsen för flygscenariet då målet utför en så kallad ”tunnelroll”.
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Kestner, Brian. "Model predictive control (MPC) algorithm for tip-jet reaction drive systems." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31802.
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Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2010.Committee Chair: Mavris, Dimitri; Committee Member: German, Brian; Committee Member: Healy, Tim; Committee Member: Rosson, Randy; Committee Member: Tai, Jimmy. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Carlsson, Rickard. "A practical approach to detection of plant model mismatch for MPC." Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56581.
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The number of MPC installations in industry is growing as a reaction to demands of increased efficiency. An MPC controller uses an internal plant model to run real-time predictive optimization of future inputs. If a discrepancy between the internal plant model and the plant exists, control performance will be affected. As time from commissioning increases the model accuracy tends to deteriorate. This is natural as the plant changes over time. It is important to detect these changes and re-identify the plant model to maintain control performance over time. A method for identifying Model Plant Mismatch for MPC applications is developed. Focus has been on developing a method that is simple to implement but still robust. The method is able to run in parallel with the process in real time. The efficiency of the method is demonstrated via representative simulation examples.An extension to detection of nonlinear mismatch is also considered, which is important since linear plant models often are used within a small operating range. Since most processes are nonlinear this discrepancy is inevitable and should be detected.
Ökade krav på effektivitet gör att industrin söker efter mer avancerad processtyrning. MPC har växt fram som en kandidat. En MPC regulator änvänder en modell av systemet för att samtidigt som systemet körs utföra en optimering av framtida styrsignaler. Om modellen innehåller felaktigheter kan reglerprestandan påverkas. En modell försämras normalt då tiden från idrifttagning växer eftersom systemet förändras med tiden. Det är av största vikt att upptäcka dessa förändringar och sedan uppdatera modellen för att reglerprestandan inte ska påverkas. Avsikten är att utveckla en metod för att upptäcka modellfel med fokus på att den ska vara enkel att implementera. Det ska även vara möjligt att använda metoden parallellt med en process. För att utvärdera metoden så körs den på ett antal representativa simuleringsexempel. Det har även varit en avsikt att utveckla en metod för detektion av ickelinjära modellfel. Motivet till det är att linjära modeller används för att beskriva ickelinjära processer och då är modellfel naturliga.
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Sanchez-Rey, Roberto. "EKF-based parameter estimation for MPC applied to lateral vehicle dynamics." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217296.
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Research about model-based techniques (MPC) applications for automotive drivingis nowadays experiencing an increasing interest thanks to improvements on hardwarecapabilities. Those allow for computationally heavy techniques as non-linear MPC is.MPC-based techniques rely on models of the controlled plant for predicting future state-space vectors. Such models are either not accurate enough for the purpose or affected byuncertainties, hence potentially causing a lack of robustness. By updating the predictionsmodel in run-time, learning-based MPC techniques are theoretically suitable to increasemodel/plant resemblance hence system performance.This thesis is focused in the study of application of a learning technique when theuncertainty of the model is of type parametric, i.e., the structure and the order of themodel is known, but some relevant parameters are uncertain or varying. Such a learn-ing technique is based on a recursion of the well-known extended Kalman filter, thatmakes use of the measured (noiseless) states for estimating the unknown parameters ofthe model.The study is performed on a Matlab simulation environment where the lateral dy-namics of a ground vehicle are controlled. For the purpose, different trajectories areused as references for a simulated vehicle that is intended to keep a minimum lateraldeviation from them. Two different models are tested: kinematic and dynamic. For thefirst case, one parameter is estimated:the distance between the rear axis and the center ofgravity of the vehicle. The dynamic model has two uncertain parameters, correspondingto cornering stiffness coefficients of both front and rear tyres. Those are depending onroad-vehicle friction coefficient, hence could resemble an unknown road condition case.Simulation results proved that at the cost of an increased computational time, it ispossible to improve the lateral dynamics as consequence of a better estimation value forthe uncertain parameter.Intresset för forskning på tillämpningar av modellbaserade tekniker för autonomkörning är på uppgång tack vare ökad hårdvarukapacitet. Den ökade kapaciteten gördet möjligt att använda beräkningsmässigt krävande tekniker såsom olinjär MPC. MPC-baserade tekniker bygger på att en modell av systemet används för att beräkna framtidatillståndsvektorer. Sådana modeller kan emellertid vara alltför inexakta för den tänktatillämpningen eller vara under påverkan av osäkerheter, vilket kan leda till brist på ro-busthet. Genom att uppdatera modellen under körning, kan modellen förbättras ochdärmed även systemets perstanda. Ur ett teoretiskt persektiv är inlärnigsbaserade MPC-tekniker väl lämpade för detta ändamål.I detta arbete ligger fokus på att studera tillämpningar av en inlärningsteknik närosäkerheten i modellen är parametrisk, d.v.s. när modellens struktur är känd men detfinns osäkerhet i parametrarnas värden. En sådan inlärningsteknik bygger på rekursionav det välkända utökade Kalmanfiltret, i vilket uppmätta tillstånd (fria från mätbrus)används till att uppskatta de okända parametrarna i modellen.Studien genomfördes i en simuleringsmiljö i Matlab, i vilken den laterala dynamikenhos fordonet kan kontrolleras. För detta ändamål, användes flera olika referensbanor tillvilka det simulerade fordonet skulle hålla så liten lateral avvikelse från som möjligt. Idet första fallet uppskattas endast en parameter, nämligen avståndet mellan den bakreaxeln och fordonets tyngdpunkt. I den dynamiska modellen uppskattas däremot tvåparametrar; styvhetskoefficienterna för både bakre och främre däck. Dessa beror frik-tionskoefficient mellan väg och fordon och kan därför ses som ett fall i vilket underlagetär okänt.Simuleringsresultat visar på att det är möjligt att förbättra den laterala dynamiken,som följd av bättre uppskattning av den okända parameterns värde, men att detta skertill priset av ökad beräkningstid.
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Chan, Wai Kuen. "Configurations in manufacturing planning and control (MPC) systems : manufacturing environment perspective." Thesis, Loughborough University, 2001. https://dspace.lboro.ac.uk/2134/7531.
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The quest for a viable manufacturing planning and control (MPQ system that supports organizational strategy is a crucial issue in operations management. Previous studies on MPC discipline have paid little attention on the basic performance differences that associated with strategic and organization environmental issues. This thesis addresses these issues by exploring the configurations in MPC systems in a macro perspective that integrates several areas, namely: organizational environment, competitive strategy, manufacturing strategy, supply chains, NIPC system and organizational performance. This thesis attempts to shift the conventional research focus of NTC processes and mechanisms to enviromnent-strategy-system-performance (E-S-S-P) paradigm. In this respect, the configurational research in WC systems requires the study of a wider body of knowledge (Chapters 2- 5) including: (1) a detailed assessmenot f the current state-of-the art of MPC practices; (2) the review of the relations between strategies and MPC systems; (3) a study of organization environmental variables and their influences; and (4) an identification of methodological issues relating to configuration research. Thirty hypothesized relationships are proposed (Chapter 6) and tested (Chapters 8 and 9). The research methodology has been concentrated in three distinctive areas. The first area is in the design of instruments (Chapter 7) for the measurement of manufacturing environments, competitive strategy, manufacturing strategy, and MPC systems in several manufacturing industries. Five databases are gathered to test the hypotheses, i.e. trade census and industrial production statistics, published business data, published survey data (for content analysis), data from field visits, and questionnaire survey data. The second area is the construction of a 3-dimensional organization environment (Chapter 7). Besides, a reference model is proposed that takes into account of the theory of autopoiesis and enacted environment, based on several field visits (Chapter 8). The third area is the study of correlations between the organizational environment, competitive strategy, manufacturing strategy, and NIPC systems (Chapter 9). The quantitative analyses are used such as Pearson correlation, linear regression, and causal modeling. There are seven main contributions of this thesis: (1) It is the first study of the configurations in NTC systems that will have significant theoretical implications for the development of NTC practices. (2) It develops the network relationships of E-S-S-P research paradigm. (3) It broadens the knowledge in operations management by exploring the hypothesized relations between organizational environment, strategies, supply chains and MPC systems. (4) It shows the adoption of new thinking, i. e. the theory of autopoiesis, in the configuration study. (5) It develops a reference MPC model that adds to the body of knowledge in this discipline. (6) It constructs the task environment classification framework for the study related to manufacturing environment in Hong Kong. (7) It proposes path modelling analysis to explore the causal relationships of WC system and other organizational variables, which is rarely applied in this field.