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Academic literature on the topic 'Explicit Approximated Model Predictive Control'

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Published: 11 March 2023

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Journal articles on the topic "Explicit Approximated Model Predictive Control"

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Pregelj, Boštjan, and Samo Gerkšič. "Hybrid explicit model predictive control of a nonlinear process approximated with a piecewise affine model." Journal of Process Control 20, no. 7 (August 2010): 832–39. http://dx.doi.org/10.1016/j.jprocont.2010.05.002.

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Grancharova, Alexandra, and Tor A. Johansen. "Reduced Dimension Approach to Approximate Explicit Model Predictive Control." IFAC Proceedings Volumes 36, no. 18 (September 2003): 531–36. http://dx.doi.org/10.1016/s1474-6670(17)34723-7.

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Bussell, E. H., and N. J. Cunniffe. "Applying optimal control theory to a spatial simulation model of sudden oak death: ongoing surveillance protects tanoak while conserving biodiversity." Journal of The Royal Society Interface 17, no. 165 (April 2020): 20190671. http://dx.doi.org/10.1098/rsif.2019.0671.

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Sudden oak death has devastated tree populations across California. However, management might still slow disease spread at local scales. We demonstrate how to unambiguously characterize effective, local management strategies using a detailed, spatially explicit simulation model of spread in a single forest stand. This pre-existing, parameterized simulation is approximated here by a carefully calibrated, non-spatial model, explicitly constructed to be sufficiently simple to allow optimal control theory (OCT) to be applied. By lifting management strategies from the approximate model to the detailed simulation, effective time-dependent controls can be identified. These protect tanoak—a culturally and ecologically important species—while conserving forest biodiversity within a limited budget. We also consider model predictive control, in which both the approximating model and optimal control are repeatedly updated as the epidemic progresses. This allows management which is robust to both parameter uncertainty and systematic differences between simulation and approximate models. Including the costs of disease surveillance then introduces an optimal intensity of surveillance. Our study demonstrates that successful control of sudden oak death is likely to rely on adaptive strategies updated via ongoing surveillance. More broadly, it illustrates how OCT can inform effective real-world management, even when underpinning disease spread models are highly complex.
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Johansen, T. A., and A. Grancharova. "Approximate explicit constrained linear model predictive control via orthogonal search tree." IEEE Transactions on Automatic Control 48, no. 5 (May 2003): 810–15. http://dx.doi.org/10.1109/tac.2003.811259.

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Johansen, Tor A., and Alexandra Grancharova. "APPROXIMATE EXPLICIT MODEL PREDICTIVE CONTROL IMPLEMENTED VIA ORTHOGONAL SEARCH TREE PARTITIONING." IFAC Proceedings Volumes 35, no. 1 (2002): 195–200. http://dx.doi.org/10.3182/20020721-6-es-1901.00601.

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Sun, Weiquan, Min Li, and Kexin Wang. "Approximate explicit model predictive control using high-level canonical piecewise-affine functions." International Journal of Automation and Control 6, no. 1 (2012): 66. http://dx.doi.org/10.1504/ijaac.2012.045441.

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Bakaráč, Peter, and Michal Kvasnica. "Approximate explicit robust model predictive control of a CSTR with fast reactions." Chemical Papers 73, no. 3 (November 9, 2018): 611–18. http://dx.doi.org/10.1007/s11696-018-0630-4.

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Charitopoulos, Vassilis M., Lazaros G. Papageorgiou, and Vivek Dua. "Multi Set-Point Explicit Model Predictive Control for Nonlinear Process Systems." Processes 9, no. 7 (July 2, 2021): 1156. http://dx.doi.org/10.3390/pr9071156.

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In this article, we introduce a novel framework for the design of multi set-point nonlinear explicit controllers for process systems engineering problems where the set-points are treated as uncertain parameters simultaneously with the initial state of the dynamical system at each sampling instance. To this end, an algorithm for a special class of multi-parametric nonlinear programming problems with uncertain parameters on the right-hand side of the constraints and the cost coefficients of the objective function is presented. The algorithm is based on computed algebra methods for symbolic manipulation that enable an analytical solution of the optimality conditions of the underlying multi-parametric nonlinear program. A notable property of the presented algorithm is the computation of exact, in general nonconvex, critical regions that results in potentially great computational savings through a reduction in the number of convex approximate critical regions.
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Kiš, Karol, and Martin Klaučo. "Neural network based explicit MPC for chemical reactor control." Acta Chimica Slovaca 12, no. 2 (October 1, 2019): 218–23. http://dx.doi.org/10.2478/acs-2019-0030.

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Abstract In this paper, implementation of deep neural networks applied in process control is presented. In our approach, training of the neural network is based on model predictive control, which is popular for its ability to be tuned by the weighting matrices and for it respecting the system constraints. A neural network that can approximate the MPC behavior by mimicking the control input trajectory while the constraints on states and control input remain unimpaired by the weighting matrices is introduced. This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor where a multi-component chemical reaction takes place.
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Ławryńczuk, Maciej, Piotr M. Marusak, Patryk Chaber, and Dawid Seredyński. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods." Energies 15, no. 7 (March 28, 2022): 2483. http://dx.doi.org/10.3390/en15072483.

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In nonlinear Model Predictive Control (MPC) algorithms, the number of cost-function evaluations and the resulting calculation time depend on the initial solution to the nonlinear optimisation task. Since calculations must be performed fast on-line, the objective is to minimise these indicators. This work discusses twelve initialisation strategies for nonlinear MPC. In general, three categories of strategies are discussed: (a) five simple strategies, including constant and random guesses as well as the one based on the previous optimal solution, (b) three strategies that utilise a neural approximator and an inverse nonlinear static model of the process and (c) four hybrid original methods developed by the authors in which an auxiliary quadratic optimisation task is solved or an explicit MPC controller is used; in both approaches, linear or successively linearised on-line models can be used. Efficiency of all methods is thoroughly discussed for a neutralisation reactor benchmark process and some of them are evaluated for a robot manipulator, which is a multivariable process. Two strategies are found to be the fastest and most robust to model imperfections and disturbances acting on the process: the hybrid strategy with an auxiliary explicit MPC controller based on a successively linearised model and the method which uses the optimal solution obtained at the previous sampling instant. Concerning the hybrid strategies, since a simplified model is used in the auxiliary controller, they perform much better than the approximation-based ones with complex neural networks. It is because the auxiliary controller has a negative feedback mechanism that allows it to compensate model errors and disturbances efficiently. Thus, when the auxiliary MPC controller based on a successively linearised model is available, it may be successfully and efficiently used for the initialisation of nonlinear MPC, whereas quite sophisticated methods based on a neural approximator are very disappointing.
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Dissertations / Theses on the topic "Explicit Approximated Model Predictive Control"

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Filippo, Marco. "Stabilizing nonlinear model predictive control in presence of disturbances and off - line approximations of the control law." Doctoral thesis, Università degli studi di Trieste, 2011. http://hdl.handle.net/10077/4519.

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2009/2010
One of the more recent and promising approaches to control is the Receding Horizon one. Due to its intrinsic characteristics, this methodology, also know as Model Predictive Control, allows to easily face disturbances and model uncertainties: indeed at each sampling instant the control action is recalculated on the basis of the reached state (closed loop). More in detail, the procedure consists in the minimization of an adequate cost function with respect to a control input sequence; then the first element of the optimal sequence is applied. The whole procedure is then continuously reiterated. In this thesis, we will focus in particular on robust control of constrained systems. This is motivated by the fact that, in practice, every real system is subjected to uncertainties, disturbances and constraints, in particular on state and input (for instance, plants can work without being damaged only in a limited set of configurations and, on the other side, control actions must be compatible with actuators' physical limits). With regard to the first aspect, maintaining the closed loop stability even in presence of disturbances or model mismatches can result in an essential strategy: moreover it can be exploited in order to design an approximate stabilizing controller, as it will be shown. The control input values are obtained recurring to a Nearest Neighbour technique or, in more favourable cases, to a Neural Network based approach to the exact RH law, which can be then calculated off line: this implies a strong improvement related to the applicability of MPC policy in particular in terms of on line computational burden. The proposed scheme is capable to guarantee stability even for systems that are not stabilizable by means of a continuous feedback control law. Another interesting framework in which the study of the influence of uncertainties on stability can lead to significant contributions is the networked MPC one. In this case, due to the absence of physical interconnections between the controller and the systems to be controlled, stability can be obtained only taking into account of the presence of disturbances, delays and data losses: indeed this kind of uncertainties are anything but infrequent in a communication network. The analysis carried out in this thesis regards interconnected systems and leads to two distinct procedures, respectively stabilizing the linear systems with TCP protocol and nonlinear systems with non-acknowledged protocol. The core of both the schemes resides in the online solution of an adequate reduced horizon optimal control problem.
Una delle strategie di controllo emerse più recentemente, più promettenti e di conseguenza più studiate negli ultimi anni è quella basata sull'approccio Receding Horizon. Grazie alle caratteristiche che contraddistinguono questa tecnica, cui si fa spesso riferimento anche col nome di Model Predictive Control, risulta piuttosto agevole trattare eventuali disturbi e incertezze di modellazione; tale metodo prevede infatti il calcolo di un nuovo ingresso di controllo per ciascun istante di campionamento, in seguito alla minimizzazione ad ogni passo di un'opportuna funzione di costo rispetto ad una sequenza di possibili futuri ingressi, inizializzata sulla base del valore dello stato del sistema all'istante considerato. Il controllo è dato dal primo elemento di tale sequenza ottima; tutto questo viene continuamente ripetuto, il che comporta un aggiornamento costante del segnale di controllo. Gli inconvenienti di questa tecnica risiedono nelle elevate risorse computazionali e nei tempi di calcolo richiesti, così da ridurne drasticamente l'applicabilità specie nel caso di sistemi con elevata dinamica. In questa tesi ci si concentrerà sulle caratteristiche di robustezza del controllore: l'importanza di quest'analisi risiede nel fatto che ogni sistema reale è soggetto a incertezze e disturbi di varia origine cui bisogna far fronte durante le normali condizioni di funzionamento. Inoltre, la capacità di gestire errori di modellazione, come si vedrà, può essere sfruttata per ottenere un notevole incremento delle prestazioni nella stima del valore da fornire in ingresso all'impianto: si tratta di ripartire l'errore complessivo in modo da garantirsi dei margini che consentano di lavorare con un'approssimazione della legge di controllo, come specificato più avanti. In tutto il lavoro si considereranno sistemi vincolati: l'interesse per questa caratteristica dipende dal fatto che nella pratica vanno sempre tenuti in considerazione eventuali vincoli su stato e ingressi: basti pensare al fatto che ogni impianto è progettato per lavorare solo all'interno un determinato insieme di configurazioni, determinato ad esempio da vincoli fisici su attuatori, sensori e così via: non riporre sufficiente attenzione in tali restrizioni può risultare nel danneggiamento del sistema di controllo o dell'impianto stesso. Le caratteristiche di stabilità di un sistema controllato mediante MPC dipendono in modo determinante dalla scelta dei parametri e degli attributi della funzione di costo da minimizzare; nel seguito, con riferimento al caso dei sistemi non lineari, saranno forniti suggerimenti e strumenti utili in tal senso, al fine di ottenere la stabilità anche in presenza di disturbi (che si assumeranno opportunamente limitati). Successivamente tale robustezza verrà sfruttata per la progettazione di controllori stabilizzanti approssimati: si dimostrerà infatti che, una volta progettato adeguatamente il sistema di controllo “esatto” basato su approccio RH e conseguentemente calcolati off-line i valori ottimi degli ingressi su una griglia opportunamente costruita sul dominio dello stato, il ricorso a una conveniente approssimazione di tali valori non compromette le proprietà di stabilità del sistema complessivo, che continua per di più a mantenere una certa robustezza. Da notare che ciò vale anche per sistemi non stabilizzabili mediante legge di controllo feedback continua: la funzione approssimante può essere ottenuta in questo caso con tecniche di tipo Nearest Neighbour; qualora invece la legge di controllo sia sufficientemente regolare si potrà far ricorso ad approssimatori smooth, quali ad esempio le reti neurali. Tutto ciò comporta un notevole miglioramento delle prestazioni del controllore RH sia dal punto di vista del tempo di calcolo richiesto che (nel secondo caso) della memoria necessaria ad immagazzinare i parametri del controllore, risultando nell'applicabilità dell'approccio basato su MPC anche al caso di sistemi con elevata dinamica. Un altro ambito in cui lo studio dell'influenza delle incertezze e dei disturbi sulla stabilità richiede una notevole attenzione è quello dei sistemi networked; anche in questo caso il ricorso all'MPC può portare a ottimi risultati di stabilità robusta, a patto di individuare un' opportuna struttura per il sistema complessivo ed effettuare scelte adeguate per il problema di ottimizzazione. In particolare, si considererà il caso di trasmissione di dati tra un controllore centralizzato e le varie parti dell'impianto in assenza di collegamento fisico diretto. Lo studio della stabilità dovrà allora tenere in considerazione la presenza di perdite di pacchetti o ritardi di trasmissione, condizioni tutt'altro che infrequenti per le reti. Saranno quindi proposte due distinte procedure, che si dimostreranno essere in grado di garantire robustezza a sistemi rispettivamente lineari comunicanti con protocolli di tipo TCP e non lineari in presenza di protocolli UDP. Questo secondo caso è senz'altro il più complesso ma allo stesso tempo il più concreto tra i due. Il nucleo del controllo è ancora basato su una tecnica MPC, ma stavolta il controllore è chiamato a risolvere il problema di ottimizzazione su un orizzonte “ridotto”, che consente la gestione dei ritardi e di eventuali perdite di pacchetto su determinati canali. La lunghezza dell'orizzonte dipenderà dalla presenza o meno dei segnali di ricezione del pacchetto (acknowledgement).
XXIII Ciclo
1977
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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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Felipe, Dominguez Luis Felipe Dominguez. "Advances in multiparametric nonlinear programming & explicit model predictive control." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536023.

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Lambert, Romain. "Approximation methodologies for explicit model predictive control of complex systems." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/13943.

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This thesis concerns the development of complexity reduction methodologies for the application of multi-parametric/explicit model predictive (mp-MPC) control to complex high fidelity models. The main advantage of mp-MPC is the offline relocation of the optimization task and the associated computational expense through the use of multi-parametric programming. This allows for the application of MPC to fast sampling systems or systems for which it is not possible to perform online optimization due to cycle time requirements. The application of mp-MPC to complex nonlinear systems is of critical importance and is the subject of the thesis. The first part is concerned with the adaptation and development of model order reduction (MOR) techniques for application in combination to mp-MPC algorithms. This first part includes the mp-MPC oriented use of existing MOR techniques as well as the development of new ones. The use of MOR for multi-parametric moving horizon estimation is also investigated. The second part of the thesis introduces a framework for the ‘equation free’ surrogate-model based design of explicit controllers as a possible alternative to multi-parametric based methods. The methodology relies upon the use of advanced data-classification approaches and surrogate modelling techniques, and is illustrated with different numerical examples.
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Rivotti, Pedro. "Multi-parametric programming and explicit model predictive control of hybrid systems." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24432.

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This thesis is concerned with different topics in multi-parametric programming and explicit model predictive control, with particular emphasis on hybrid systems. The main goal is to extend the applicability of these concepts to a wider range of problems of practical interest, and to propose algorithmic solutions to challenging problems such as constrained dynamic programming of hybrid linear systems and nonlinear explicit model predictive control. The concepts of multi-parametric programming and explicit model predictive control are presented in detail, and it is shown how the solution to explicit model predictive control may be efficiently computed using a combination of multi-parametric programming and dynamic programming. A novel algorithm for constrained dynamic programming of mixed-integer linear problems is proposed and illustrated with a numerical example that arises in the context of inventory scheduling. Based on the developments on constrained dynamic programming of mixed-integer linear problems, an algorithm for explicit model predictive control of hybrid systems with linear cost function is presented. This method is further extended to the design of robust explicit controllers for hybrid linear systems for the case when uncertainty is present in the model. The final part of the thesis is concerned with developments in nonlinear explicit model predictive control. By using suitable model reduction techniques, the model captures the essential nonlinear dynamics of the system, while the achieved reduction in dimensionality allows the use of nonlinear multi-parametric programming methods.
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Krieger, Alexandra. "Modelling, optimisation and explicit model predictive control of anaesthesia drug delivery systems." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23908.

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The contributions of this thesis are organised in two parts. Part I presents a mathematical model for drug distribution and drug effect of volatile anaesthesia. Part II presents model predictive control strategies for depth of anaesthesia control based on the derived model. Closed-loop model predictive control strategies for anaesthesia are aiming to improve patient's safety and to fine-tune drug delivery, routinely performed by the anaesthetist. The framework presented in this thesis highlights the advantages of extensive modelling and model analysis, which are contributing to a detailed understanding of the system, when aiming for the optimal control of such system. As part of the presented framework, the model uncertainty originated from patient-variability is analysed and the designed control strategy is tested against the identified uncertainty. An individualised physiologically based model of drug distribution and uptake, pharmacokinetics, and drug effect, pharmacodynamics, of volatile anaesthesia is presented, where the pharmacokinetic model is adjusted to the weight, height, gender and age of the patient. The pharmacodynamic model links the hypnotic depth measured by the Bispectral index (BIS), to the arterial concentration by an artificial effect site compartment and the Hill equation. The individualised pharmacokinetic and pharmacodynamic variables and parameters are analysed with respect to their influence on the measurable outputs, the end-tidal concentration and the BIS. The validation of the model, performed with clinical data for isoflurane and desflurane based anaesthesia, shows a good prediction of the drug uptake, while the pharmacodynamic parameters are individually estimated for each patient. The derived control design consists of a linear multi-parametric model predictive controller and a state estimator. The non-measurable tissue and blood concentrations are estimated based on the end-tidal concentration of the volatile anaesthetic. The designed controller adapts to the individual patient's dynamics based on measured data. In an alternative approach, the individual patient's sensitivity is estimated on-line by solving a least squares parameter estimation problem.
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Hellström, Erik. "Explicit use of road topography for model predictive cruise control in heavy trucks." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2843.

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New and exciting possibilities in vehicle control are revealed by the consideration of topography through the combination GPS and three dimensional road maps. This thesis explores how information about future road slopes can be utilized in a heavy truck with the aim at reducing the fuel consumption over a route without increasing the total travel time.

A model predictive control (MPC) scheme is used to control the longitudinal behavior of the vehicle, which entails determining accelerator and brake levels and also which gear to engage. The optimization is accomplished through discrete dynamic programming. A cost function is used to define the optimization criterion. Through the function parameters the user is enabled to decide how fuel use, negative deviations from the reference velocity, velocity changes, gear shifts and brake use are weighed.

Computer simulations with a load of 40 metric tons shows that the fuel consumption can be reduced with 2.5% with a negligible change in travel time, going from Link¨oping to J¨onk¨oping and back. The road slopes are calculated by differentiation of authentic altitude measurements along this route. The complexity of the algorithm when achieving these results allows the simulations to run two to four times faster than real time on a standard PC, depending on the desired update frequency of the control signals.

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Bolin, Tobias. "Nonlinear Approximative Explicit Model Predictive Control Through Neural Networks : Characterizing Architectures and Training Behavior." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264994.

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Model predictive control (MPC) is a paradigm within automatic control notable for its ability to handle constraints. This ability come at the cost of high computational demand, which until recently has limited use of MPC to slow systems. Recent advances have however enabled MPC to be used in embedded applications, where its ability to handle constraints can be leveraged to reduce wear, increase efficiency and improve overall performance in everything from cars to wind turbines. MPC controllers can be made even faster by precomputing the resulting policy and storing it in a lookup table. A method known as explicit MPC. An alternative way of leveraging precomputation is to train a neural network to approximate the policy. This is an attractive proposal both due to neural networks ability to imitate policies for nonlinear systems, and results that indicate that neural networks can efficiently represent explicit MPC policies. Limited work has been done in this area. How the networks are setup and trained therefore tends to reflect recent trends in other application areas rather than being based on what is known to work well for approximating MPC policies. This thesis attempts to alleviate this situation by evaluating how some common neural network architectures and training methods performs when used for this purpose. The evaluations are carried out through a literature study and by training several networks with different architectures to replicate the policy of a nonlinear MPC controller tasked with stabilizing an inverted pendulum. The results suggest that ReLU activation functions give better performance than hyperbolic tangent and SELU functions; and that dropout and batch normalization degrades the ability to approximate policies; and that depth significantly increases the performance. However, the neural network controllers do occasionally exhibit problematic behaviors, such as steady state errors and oscillating control signals close to constraints.
Modell-prediktiv reglering (MPC, efter engelskans Model Predictive Control) är ett paradigm inom reglertekniken som på ett effektivt sätt kan hantera begränsningar i systemet som ska regleras. Den här egenskapen kommer på bekostnad av att MPC kräver mycket datorkraft. Tidigare har  användning av den här typen av kontroller därför varit begränsad till långsamma system. På senare tid har framsteg inom hård- och mjukvara dock möjliggjort användning av MPC på inbyggda system. Där kan dess förmåga att hantera begränsningar användas för att minska slitage, öka effektivitet och förbättra prestanda inom allt från bilar till vindkraftverk. Ett sätt att minska beräkningsbördan ytterligare är att beräkna MPC-policyn i förväg och spara den i en tabell. Det här tillvägagångssättet kallas explicit MPC. Ett alternativt tillvägagångssätt är att träna ett neuralt nätverk till att approximera policyn. Potentiellt har det här fördelarna att ett neuralt nätverk inte är begränsat till att efterlikna policys för system med linjär dynamik, och att det finns resultat som pekar på att neurala nätverk är väl lämpade för att lagra policys för explicit MPC. En begränsad mängd arbete har gjorts inom det här området. Hur nätverken designas och tränas tenderar därför att reflektera trender inom andra applikationsområden för neurala nätverk istället för att baseras på vad som fungerar för att implementera MPC. Det här examensarbetet försöker avhjälpa det här problemet. Dels genom en litteraturstudie och dels genom att undersöka hur olika arkitekturer för neurala nätverk beter sig när de tränas för att efterlikna en ickelinjär MPC-kontroller som ska stabilisera en inverterad pendel. Resultaten tyder på att nätverk med ReLU-aktivering ger bättre prestanda än motsvarande nätverk som använder SELU eller tangens hyperbolicus som aktiveringsfunktion. Resultaten visar också att batch noralization och dropout försämmrar nätverkens förmåga att lära sig policyn och att prestandan blir bättre om antalet lager i nätverket ökar. De neurala nätverken uppvisar dock i vissa fall kvalitativa problem, så som statiska fel och oscillerande kontrollsignaler nära begränsningar.
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Volker, Anna. "Explicit/multi-parametric moving horizon estimation and model : predictive control & application to small unmanned aerial vehicles." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538787.

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Voelker, Anna. "Explicit/multi-parametric moving horizon estimation and model predictive control & their application to small unmanned aerial vehicles." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/7030.

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Moving horizon estimation (MHE) is a class of estimation methods in which the system state and disturbance estimates are obtained by solving a constrained optimization problem. The main advantage of MHE is that information about the system can be explicitly considered in the form of constraints and hence improve the estimates. In stochastic systems the estimation error will inevitably be non-zero and the controller needs to explicitly account for it to prevent constraint violations. In order for the controller to be robustified against the estimation error, bounds on the error need to be known. These bounds can be calculated if the dynamics that govern the estimation error are known. This work presents those dynamics for the unconstrained and the constrained case of the moving horizon estimator with a linear time-invariant model, and also discusses how the bounds on the estimation error can be obtained with set-theoretical methods. Those bounds are then used for robust output-feedback model predictive control (MPC). The MHE and the MPC are derived explicitly through multi-parametric programming. The complete framework is demonstrated using simultaneous MHE and tubebased MPC. The possibility of solving MPC explicitly is very appealing for flight control of small unmanned aerial vehicles (UAVs) because the behaviour of the controller is known in advance and can be guaranteed. Flight control is a challenging task that involves a multi-layer control structure where each decision influences the other layers and the overall performance. This work investigates the requirements on the different layers and their cross-effects. A linear model of the UAV is derived such that it captures the wind which is the most challenging disturbance for UAV flight. Particular focus is placed on the design of a model predictive controller as the autopilot and on in-flight wind estimation.
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Books on the topic "Explicit Approximated Model Predictive Control"

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Grancharova, Alexandra, and Tor Arne Johansen. Explicit Nonlinear Model Predictive Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0.

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Grancharova, Alexandra. Explicit Nonlinear Model Predictive Control: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Explicit Nonlinear Model Predictive Control Theory And Applications. Springer, 2012.

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Book chapters on the topic "Explicit Approximated Model Predictive Control"

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Grancharova, Alexandra, and Tor Arne Johansen. "Explicit NMPC via Approximate mp-NLP." In Explicit Nonlinear Model Predictive Control, 87–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_4.

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Grancharova, Alexandra, and Tor A. Johansen. "Explicit Approximate Model Predictive Control of Constrained Nonlinear Systems with Quantized Input." In Nonlinear Model Predictive Control, 371–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_30.

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Jones, Colin N., and Manfred Morari. "High-Speed Model Predictive Control: An Approximate Explicit Approach." In Three Decades of Progress in Control Sciences, 233–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11278-2_16.

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Maeder, Urban, Raphael Cagienard, and Manfred Morari. "Explicit Model Predictive Control." In Lecture Notes in Control and Information Sciences, 237–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-37010-9_8.

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Bemporad, Alberto. "Explicit Model Predictive Control." In Encyclopedia of Systems and Control, 405–11. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_10.

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Bemporad, PhDAlberto. "Explicit Model Predictive Control." In Encyclopedia of Systems and Control, 1–9. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5102-9_10-1.

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Bemporad, Alberto. "Explicit Model Predictive Control." In Encyclopedia of Systems and Control, 1–7. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-5102-9_10-2.

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Bemporad, Alberto. "Explicit Model Predictive Control." In Encyclopedia of Systems and Control, 744–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_10.

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Kouvaritakis, Basil, and Mark Cannon. "Explicit Use of Probability Distributions in SMPC." In Model Predictive Control, 303–41. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24853-0_8.

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Grancharova, Alexandra, and Tor Arne Johansen. "Nonlinear Model Predictive Control." In Explicit Nonlinear Model Predictive Control, 39–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28780-0_2.

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Conference papers on the topic "Explicit Approximated Model Predictive Control"

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Nam, Nguyen Hoai, Sorin Olaru, and Morten Hovd. "Patchy approximate explicit model predictive control." In 2010 International Conference on Control, Automation and Systems (ICCAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccas.2010.5670223.

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Chen, Y., S. Li, and N. Li. "Complexity reduced explicit model predictive control by solving approximated mp-QP program." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244434.

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Binder, Matthias, Georgios Darivianakis, Annika Eichler, and John Lygeros. "Approximate Explicit Model Predictive Controller using Gaussian Processes." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029942.

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Zaninit, Francesco, Colin N. Jones, David Atienza, and Giovanni De Micheli. "Multicore thermal management using approximate explicit model predictive control." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5537891.

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Grancharova, Alexandra, and Tor A. Johansen. "Explicit Approximate Approach to Feedback Min-Max Model Predictive Control of Constrained Nonlinear Systems." In Proceedings of the 45th IEEE Conference on Decision and Control. IEEE, 2006. http://dx.doi.org/10.1109/cdc.2006.377772.

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Huang, Mike, Ken Butts, and Ilya Kolmanovsky. "A Low Complexity Gain Scheduling Strategy for Explicit Model Predictive Control of a Diesel Air Path." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9754.

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This paper describes a gain scheduling strategy that can be used in conjunction with explicit Model “Predictive Control (MPC). Traditionally, explicit MPC is not reconfigurable to online model changes. To handle off-nominal plant conditions, a common practice is to design multiple explicit MPC’s which are each valid locally around their respective operating points. This inevitably requires large amounts of memory to store the explicit MPC’s and implementation of switching logic and observers. The gain scheduling strategy presented in this paper bypasses the need to store multiple explicit MPC’s. This is done by multiplying the control signal obtained from the nominal explicit MPC by a gain scheduling matrix such that the plant at off-nominal operating conditions is approximately matched to the nominal plant. This is further accomplished in a manner such that the original control constraints are satisfied. The gain scheduling strategy is demonstrated in simulations on a nonlinear diesel air path model over the New European Drive Cycle (NEDC).
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Sowman, J., D. S. Laila, and S. Longo. "Real-time approximate explicit nonlinear model predictive control for the swing-up of a reaction wheel pendulum." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402891.

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Knyazev, Andrew, Peizhen Zhu, and Stefano Di Cairano. "Explicit model predictive control accuracy analysis." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402565.

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Wang, Kai, Yuning Jiang, Juraj Oravec, Mario E. Villanueva, and Boris Houska. "Parallel Explicit Tube Model Predictive Control." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029177.

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Falugi, P., S. Olaru, and D. Dumur. "Explicit robust multi-model predictive control." In Automation (MED 2008). IEEE, 2008. http://dx.doi.org/10.1109/med.2008.4602102.

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Reports on the topic "Explicit Approximated Model Predictive Control"

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An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0741.

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Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).
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