Dissertations / Theses on the topic 'Supervisory model predictive control'
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Huang, Yang, and S3110949@student rmit edu au. "Model Predictive Control of Magnetic Bearing System." RMIT University. Electrical and Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080430.152045.
Full textBoussemart, Yves 1980. "Predictive models of procedural human supervisory control behavior." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/79543.
Full textPage 150 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 138-149).
Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision making process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected. By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the methodological and practical limitations of the proposed approach notably in terms of input data requirements and model complexity. This thesis thus provides theoretical and practical contributions by exploring the validity of using statistical models of operators as the basis for detecting and predicting anomalous conditions.
by Yves Boussemart.
Ph.D.
Sadr, Faramarz. "Supervisory model predictive control of building integrated renewable and low carbon energy systems." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9518.
Full textAbraham, Etimbuk. "Adaptive supervisory control scheme for voltage controlled demand response in power systems." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-supervisory-control-scheme-for-voltage-controlled-demand-response-in-power-systems(3e64537d-52c7-4eb5-87f2-b73fe920b9cb).html.
Full textBacic, Marko. "Model predictive control." Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400060.
Full textHanger, Martin Bøgseth. "Model Predictive Control Allocation." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13308.
Full textSriniwas, Ganti Ravi. "Nonlinear model predictive control." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10267.
Full textQi, Kent Zhihua. "Dual-model predictive control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq21621.pdf.
Full textCouchman, Paul. "Stochastic model predictive control." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442384.
Full textWu, Xingjian. "Stochastic model predictive control." Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497157.
Full textGormandy, Brent Anthony. "Fuzzy model predictive control." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248858.
Full textBuerger, Johannes Albert. "Fast model predictive control." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:6e296415-f02c-4bc2-b171-3bee80fc081a.
Full textNg, Desmond Han Tien. "Stochastic model predictive control." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:b56df5ea-10ee-428f-aeb9-1479ce9a7b5f.
Full textSchaich, Rainer Manuel. "Robust model predictive control." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:94e75a62-a801-47e1-8cb8-668e8309d477.
Full textRosdal, David. "Missilstyrning med Model Predictive Control." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2748.
Full textThis thesis has been conducted at Saab Bofors Dynamics AB. The purpose was to investigate if a non-linear missile model could be stabilized when the optimal control signal is computed considering constraints on the control input. This is particularly interesting because the missile is controlled with rudders that have physical bounds. This strategy is called Model Predictive Control. Simulations are conducted to compare this strategy with others; firstly simulations with step responses and secondly simulations when the missile is supposed to hit a moving target. The latter is performed to show that the missile can be stabilized in its whole area of operation. The simulations show that the controller indeed can stabilize the missile for the given scenarios. However, this control strategy does not show any obvious improvements in comparison with alternative ones.
Truong, Quan, and trunongluongquan@yahoo com au. "Continuous-time Model Predictive Control." RMIT University. Electrical and Computer Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090813.163701.
Full textTownsend, Shane Martin Joseph. "Non-linear model predictive control." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301061.
Full textHeise, Sharon Ann. "Multivariable constrained Model Predictive Control." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361703.
Full textRichards, Arthur George 1977. "Robust constrained model predictive control." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/28914.
Full textIncludes bibliographical references (p. 203-209).
(cont.) multiple Uninhabited Aerial Vehicles (UAVs) demonstrate that the new DMPC algorithm offers significant computational improvement compared to its centralized counterpart. The controllers developed in this thesis are demonstrated throughout in simulated examples related to vehicle control. Also, some of the controllers have been implemented on vehicle testbeds to verify their operation. The tools developed in this thesis improve the applicability of MPC to problems involving uncertainty and high complexity, for example, the control of a team of cooperating UAVs.
This thesis extends Model Predictive Control (MPC) for constrained linear systems subject to uncertainty, including persistent disturbances, estimation error and the effects of delay. Previous work has shown that feasibility and constraint satisfaction can be guaranteed by tightening the constraints in a suitable, monotonic sequence. This thesis extends that work in several ways, including more flexible constraint tightening, applied within the prediction horizon, and more general terminal constraints, applied to ensure feasible evolution beyond the horizon. These modifications reduce the conservatism associated with the constraint tightening approach. Modifications to account for estimation error, enabling output feedback control, are presented, and we show that the effects of time delay can be handled in a similar manner. A further extension combines robust MPC with a novel uncertainty estimation algorithm, providing an adaptive MPC that adjusts the optimization constraints to suit the level of uncertainty detected. This adaptive control replaces the need for accurate a priori knowledge of uncertainty bounds. An approximate algorithm is developed for the prediction of the closed-loop performance using the new robust MPC formulation, enabling rapid trade studies on the effect of controller parameters. The constraint tightening concept is applied to develop a novel algorithm for Decentralized MPC (DMPC) for teams of cooperating subsystems with coupled constraints. The centralized MPC optimization is divided into smaller subproblems, each solving for the future actions of a single subsystem. Each subproblem is solved only once per time step, without iteration, and is guaranteed to be feasible. Simulation examples involving
by Arthur George Richards.
Ph.D.
Sha'Aban, Yusuf. "Regulatory level model predictive control." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/regulatory-level-model-predictive-control(1cca6fc1-8473-4191-8edd-06ddb0884040).html.
Full textBell, Geoffrey Laurence. "Robust model predictive control design." Thesis, Imperial College London, 2000. http://hdl.handle.net/10044/1/7450.
Full textTowhidkhah, Farzad. "Model predictive impedance control, a model for joint movement control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq24019.pdf.
Full textBarsk, 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.
Full textMegías, Jiménez David. "Robustness aspects of Model Predictive Control." Doctoral thesis, Universitat Autònoma de Barcelona, 2000. http://hdl.handle.net/10803/32173.
Full textEl Control Predictiu Basat en Models (Model, Model-based o Receding-horizon Predictive Control; MPC o RHPC) és una estratègia de control madura i de gran èxit, que ha assolit l'acceptació de les comunitats acadèmica i industrial. La base d'aquest tipus de lleis de control, la capacitat de les quals per treballar amb dinàmiques complexes s'ha documentat en la literatura, és realitzar prediccions del sistema a controlar mitjançant un model. A partir de les prediccions, es calcula un perfil de controls per tal de minimitzar un funció de cost definida en termes de les prediccions i dels controls futurs. Després de les primeres formulacions es van detectar las carències dels controladors predictius per satisfer determinades propietats essencials, com garantir l'estabilitat del sistema nominal en llaç tancat. A més, era ben conegut que les discrepàncies existents entre el model i el procés, denominades incertesa del sistema, podien afectar severament el rendiment. Calia, per tant, abordar el problema de la robustesa. En aquesta tesi es revisa i s'investiguen els problemes de l'estabilitat nominal i la robustesa. En particular, la satisfacció de les especificacions de restriccions en presència de diverses fonts d'incertesa és un objectiu principal dels mètodes desenvolupats al llarg d'aquesta recerca. En primer lloc, s'ha fet una revisió dels controladors que asseguren estabilitat nominal, com el CRHPC i el GPC∞, i s'han suggerit controladors equivalents en norma 1. A continuació, s'ha estudiat la robustesa d'aquestes estratègies en absència de restriccions i s'ha conclòs que l'aproximació d'horitzons infinits condueix, habitualment, a millors resultats pel que fa al rendiment i a la robustesa per a valors típics dels paràmetres de sintonia. Seguidament s'ha tractat el problema de la robustesa en presència de restriccions i s'han formulat controladors min-max, tant en norma 1 com en norma 2, basats en el concepte d'incertesa global. Per a aquests mètodes, s'ha proposat un algorisme d'actualització de les bandes que permet modificar les fites de la incertesa en línia. Tot i que ambdues formulacions proporcionen resultats semblants, que superen els mètodes clàssics de robustesa quan s'especifiquen restriccions, els controladors en norma 1 són més eficients des del punt de vista del temps de còmput, atès que el problema d'optimització es pot resoldre fent servir programació lineal. Finalment, s'han proposat nous controladors basats en un últim avanç de l'aproximació min-max que incorpora la noció que la realimentació és present en la implementació d'horitzó mòbil dels controladors predictius. Aquestes tècniques, anomenades feedback min-max MPC, permeten de superar alguns dels desavantatges de la formulació min-max estàndard.
El Control Predictivo Basado en Modelos (Model, Model-based o Receding-horizon Predictive Control; MPC o RHPC) es una estrategia de control madura y de gran éxito, que ha conseguido la aceptación de las comunidades académica e industrial. La base de este tipo de leyes de control, cuya capacidad para manejar dinámicas complejas se ha documentado en la literatura, es realizar predicciones del sistema a controlar por medio de un modelo. A partir de las predicciones, se calcula un perfil de controles para minimizar una función de coste definida en términos de las predicciones y de los controles futuros. Tras las primeras formulaciones se detectaron las carencias de los controladores predictivos para satisfacer determinadas propiedades esenciales, como garantizar la estabilidad del sistema nominal en lazo cerrado. Además, era bien sabido que las discrepancias existentes entre el modelo y el proceso, denominadas incertidumbre del sistema, podían afectar severamente al rendimiento. El problema de la robustez debía, por tanto, ser abordado. En esta tesis se revisan e investigan los problemas de estabilidad nominal y robustez. En particular, la satisfacción de las especificaciones de restricciones en presencia de varias fuentes de incertidumbre es un objetivo principal de los métodos desarrollados a lo largo de esta investigación. En primer lugar, se han revisado los controladores que aseguran estabilidad nominal, como el CRHPC y el GPC∞ y se han propuesto controladores equivalentes en norma 1. A continuación se ha estudiado la robustez de estas estrategias en ausencia de restricciones y se ha concluido que la aproximación de horizontes infinitos conduce, habitualmente, a mejores resultados en lo referente al rendimiento y a la robustez para valores típicos de los parámetros de sintonía. Seguidamente, se ha tratado el problema de la robustez en presencia de restricciones, y se han formulado controladores min-max, tanto en norma 1como en norma 2, basados en el concepto de incertidumbre global. Para estos métodos, se ha sugerido un algoritmo de actualización de las bandas que permite modificar las cotas de la incertidumbre en línea. Aunque ambas formulaciones proporcionan resultados similares, que superan al enfoque clásico de la robustez cuando se especifican restricciones, los controladores en norma 1 son más eficientes desde el punto de vista de tiempo de cómputo, puesto que el problema de optimización se puede resolver usando programación lineal. Finalmente, se han propuesto otros controladores basados en un último avance de la aproximación min-max que incorpora la noción de que la realimentación está presente en la implementación de horizonte móvil de los controladores predictivos. Estas técnicas, denominadas feedback min-max MPC, permiten superar algunas de las desventajas de la formulación min-max estándar.
Ringset, Ruben Køste. "Efficient optimization in Model Predictive Control." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9098.
Full textCheng, Qifeng. "Robust & stochastic model predictive control." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:89da4934-9de7-4142-958e-513065189518.
Full textHartley, Edward Nicholas. "Model predictive control for spacecraft rendezvous." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609090.
Full textLarsen, Oscar. "Autonomous Overtaking Using Model Predictive Control." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293819.
Full textUnder de senaste åren har forskare världen över försökt utveckla fullt autonoma fordon. Ett av problemen som behöver lösas är hur man navigerar i en dynamisk värld med ständigt förändrande variabler. Detta projekt startades för att titta närmare på en aspekt av att planera en rutt; att köra om ett mänskligt styrt fordon. Model Predictive Control (MPC) har historiskt sett blivit använt i system med långsammare dynamik, men med framsteg inom datorers beräkningskraft kan det nu användas i system med snabbare dynamik. I detta projekt simulerades självkörande fordon, styrda av MPC, i Python. Fordonsmodellen som används var kinematic bicycle model. Begränsningar sattes på det omkörande fordonet så att de två fordonen inte kolliderar. Resultaten visar att en omkörning, som håller avstånd till det andra fordonet samt följer trafikregler, är möjligt i vissa scenarion.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
Asadi, Fatemeh. "Self-organized distributed model predictive control." Thesis, University of Bristol, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.720820.
Full textKhosravi, Sara. "Constrained model predictive control of hypnosis." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56230.
Full textApplied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
Curinga, Florian. "Autonomous racing using model predictive control." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-222801.
Full textAutonoma fordon förväntas få en betydande inverkan på världen och därigenom elimineraden mänskliga faktorn på en av de farligaste platserna: vägar. Faktum är att dödsfall ären av de största källorna till mänsklig dödlighet och många länder i världen. Det förväntasatt autonoma fordon kommer att bidra dramatiskt för att uppnå det. Dessutom använderman kontroller för att optimera både beteende och kommunikationshastighet.För att minimera vägskador är ett tillvägagångssätt att utforma styrenheter som skullehantera bilen vid sina gränser för hantering, genom att integrera komplex dynamik, såsomvidhäftningsförlust, är det möjligt att förhindra att bilen lämnar vägen. En praktisk inställningför att utvärdera denna typ av kontroller är ett racing sammanhang: En styrenhetstyr en bil för att slutföra ett spår så snabbt som möjligt utan att lämna vägen och genomatt bränna bilen till dess gränser för hantering.I denna avhandling designar vi en kontroller för ett autonomt fordon med målet attdriva det från A till B så fort som möjligt. Detta är den främsta motivationen i racingapplikationer.Kontrollern ska styra bilen med målet att minimera racingtiden.Denna styrenhet utformades inom ramen för Model Predictive Controller (MPC), där vianvände begreppet vägjusterad modell. I motsats till standard mpc tekniker använder viobjektivfunktionen för att maximera framstegen längs referensvägen genom att integreraen linjär modell av fordonsprogressionen längs mittlinjen. Kombinerat med linjär fordonsmodelloch begränsningar, ett optimeringsproblem som ger fordonet framtida styr- ochgasvärden att applicera formuleras och lösas med linjär programmering i ett onlinemönsterunder loppet. Vi visar effektiviteten hos vår controller i simulering, där den designade regulatornuppvisar typiska racerförare beteenden och strategier när du styr ett fordon längsett visst spår. Vi konfronterar oss slutligen med liknande kontrollanter från litteraturenoch härleder dess styrka och svagheter jämfört med dem.
Nejati, Fard Razieh. "Finite Control Set Model Predictive Control in Power Converters." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elkraftteknikk, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23084.
Full textAtić, Nedz̆ad. "Model predictive control design for load frequency control problem." Morgantown, W. Va. : [West Virginia University Libraries], 2003. http://etd.wvu.edu/templates/showETD.cfm?recnum=3192.
Full textTitle from document title page. Document formatted into pages; contains vii, 68 p. : ill. Includes abstract. Includes bibliographical references (p. 66-68).
Wang, Jiaying. "Model Predictive Control of Power Electronics Converter." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elkraftteknikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18835.
Full textKristoffersson, Ida. "Model Predictive Control of a Turbocharged Engine." Thesis, KTH, Reglerteknik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107508.
Full textGabrielsson, Fredrik. "Model Predictive Control of Skeboå Water system." Thesis, KTH, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98868.
Full textLundh, Joachim. "Model Predictive Control for Active Magnetic Bearings." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81325.
Full textDet här examensarbetet diskuterar möjligheten att positionsreglera en rotor som leviteras på aktiva magnetlager. Reglerstrategin som används är modellbaserad prediktionsreglering vilket är en online-metod där ett optimeringsproblem löses i varje sampel. Detta gör att regulatorn blir mycket beräkningskrävande. Samplingstiden för systemet är mycket kort för att fånga dynamiken hos rotorn. Det betyder att regulatorn inte ges mycket tid att lösa optimeringsproblemet. Olika metoder för att lösa QP-problem betraktas för att se om det är möjligt att köra regulatorn i realtid. Dessutom diskuteras hur valet av prediktionshorisont, reglerhorisont och straff på sluttillståndet påverkar regleringen. Simuleringar som visar karakteristiken av dessa val har utförts.
Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.
Full textMa, Yudong. "Model Predictive Control for Energy Efficient Buildings." Thesis, University of California, Berkeley, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3593911.
Full textThe building sector consumes about 40% of energy used in the United States and is responsible for nearly 40% of greenhouse gas emissions. Energy reduction in this sector by means of cost-effective and scalable approaches will have an enormous economic, social, and environmental impact. Achieving substantial energy reduction in buildings may require to rethink the entire processes of design, construction, and operation of buildings. This thesis focuses on advanced control system design for energy efficient commercial buildings.
Commercial buildings are plants that process air in order to provide comfort for their occupants. The components used are similar to those employed in the process industry: chillers, boilers, heat exchangers, pumps, and fans. The control design complexity resides in adapting to time-varying user loads as well as occupant requirements, and quickly responding to weather changes. Today this is easily achievable by over sizing the building components and using simple control strategies. Building controls design becomes challenging when predictions of weather, occupancy, renewable energy availability, and energy price are used for feedback control. Green buildings are expected to maintain occupants comfort while minimizing energy consumption, being robust to intermittency in the renewable energy generation and responsive to signals from the smart grid. Achieving all these features in a systematic and cost-effective way is challenging. The challenge is even greater when conventional systems are replaced by innovative heating and cooling systems that use active storage of thermal energy with critical operational constraints.
Model predictive control (MPC) is the only control methodology that can systematically take into account future predictions during the control design stage while satisfying the system operating constraints. This thesis focuses on the design and implementation of MPC for building cooling and heating systems. The objective is to develop a control methodology that can 1) reduce building energy consumption while maintaining indoor thermal comfort by using predictive knowledge of occupancy loads and weather information, (2) easily and systematically take into account the presence of storage devices, demand response signals from the grid, and occupants feedback, (3) be implemented on existing inexpensive and distributed building control platform in real-time, and (4) handle model uncertainties and prediction errors both at the design and implementation stage.
The thesis is organized into six chapters. Chapter 1 motivates our research and reviews existing control approaches for building cooling and heating systems.
Chapter 2 presents our approach to developing low-complexity control oriented models learned from historical data. Details on models for building components and spaces thermal response are provided. The thesis focuses on the dynamics of both the energy conversion and storage as well as energy distribution by means of heating ventilation and air conditioning (HVAC) systems.
In Chapter 3, deterministic model predictive control problems are formulated for the energy conversion systems and energy distribution systems to minimize the energy consumption while maintaining comfort requirement and operational constraints. Experimental and simulative results demonstrate the effectiveness of the MPC scheme, and reveal significant energy reduction without compromising indoor comfort requirement.
As the size and complexity of buildings grow, the MPC problem quickly becomes computationally intractable to be solved in a centralized fashion. This limitation is addressed in Chapter 4. We propose a distributed algorithm to decompose the MPC problem into a set of small problems using dual decomposition and fast gradient projection. Simulation results show good performance and computational tractability of the resulting scheme.
The MPC formulation in Chapter 3 and 4 assumes prefect knowledge of system model, load disturbance, and weather. However, the predictions in practice are different from actual realizations. In order to take into account the prediction uncertainties at control design stage, stochastic MPC (SMPC) is introduced in Chapter 5 to minimize expected costs and satisfy constraints with a given probability. In particular, the proposed novel SMPC method applies feedback linearization to handle system nonlinearity, propagates the state statistics of linear systems subject to finite-support (non Gaussian) disturbances, and solves the resulting optimization problem by using large-scale nonlinear optimization solvers.
Overloop, Peter-Jules van. "Model predictive control on open water systems /." Amsterdam : IOS Press, 2006. http://opac.nebis.ch/cgi-bin/showAbstract.pl?u20=9781586036386.
Full textDave, Kedar Himanshu. "Inferential model predictive control using statistical tools." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2585.
Full textThesis research directed by: Chemical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
Fannemel, Åsmund Våge. "Dynamic Positioning by Nonlinear Model Predictive Control." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8921.
Full textThis thesis discusses the theoretical aspects of the unscented Kalman filter (UKF) and nonlinear model predictive control (NMPC) and try to evaluate their practical value in a dynamic positioning (DP) system. A nonlinear horizontal vessel model is used as the basis for performing state, disturbance, and parameter estimation, and attempts at controling the vessel using NMPC are made. It is shown that the extended Kalman filter (EKF), which is much used in various navigation applications including DP, is outperformed both theoretically and practically in simulations by the UKF. Much of which is due to the UKF's improved approximation of the estimated system's true stochastic properties. An attempt to estimate the current from the hydrodynamical damping forces have been applied and shown to be working when the vessel is not subjected to other slowly-varying drift forces. It is implemented a dual estimation approach to try to estimate hydrodynamic damping, which is a very real problem for actual vessels and DP systems. A theoretical evaluation of NMPC is performed and it is concluded that NMPC schemes could fulfill a need in vessel control and DP. Its combination of model based control, optimization approach to achieving performance and predictive properties are indeed useful also for DP. It is found that NMPC could be a step towards a unified control approach combining low and high speed reference tracking, station-keeping and several other control operations which today are handled by separate control approaches. NMPC provides the control designer with an exceptional amount of freedom when quantifying the performance, that it is impossible not to find some use for NMPC.
Marjanovic, O. "On constrained infinite horizon model predictive control." Thesis, University of Manchester, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495935.
Full textLöfberg, Johan. "Minimax Approaches to Robust Model Predictive Control." Doctoral thesis, Linköpings universitet, Reglerteknik, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-98168.
Full textFriedbaum, Jesse Robert. "Model Predictive Linear Control with Successive Linearization." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7063.
Full textBereza-Jarocinski, Robert. "Distributed Model Predictive Control for Rendezvous Problem." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254424.
Full textDen här masteruppsatsen undersöker de potentiella för- och nackdelarna medatt använda distribuerad reglering för att landa en autonom drönare på en autonombåt. De förväntade fördelarna inkluderar bättre användning av beräkningsresursersamt ökad robusthet mot fördröjningar i kommunikation mellanfordonen. Här betyder distribuerad reglering att separata datorer beräknar delarav systemets styrsignal. Detta skiljer sig från en redan existerande centraliseradlösning där drönaren själv beräknar alla styrsignaler. Två nya algoritmerföreslås, en som använder sig utav distribuerad modell-prediktiv reglering(DMPC) och en som använder sig av en kombination av DMPC och linjärtillstånds-återkoppling. De följande egenskaperna av algoritmerna testas:vilken den längsta möjliga prediktionshorisonten med tillräckligt snabb iterationstidvar, hur lång tid det tar att lösa optimeringsproblem för varje algoritmoch hur snabbt och säkert varje algoritm kunde landa drönaren. Slutligen så visadedet sig att i vissa scenarion så har DMPC-algoritmen förbättrad robusthetmot kommunikationsproblem.
Andre, do Nascimento Allan. "Robust Model Predictive Control for Marine Vessels." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-247883.
Full textDetta examensarbete studerar design och implementering av en robustmodellprediktiv regulator (MPC) för marina fartyg. En tub-baserad MPCär designad baserad på linjärisering av systemdynamiken runt en målpunkt,vilket garanterar local insignal-till-tillstånds stabilitet av det linjäriserade systemet.Metoden är sedan applicerad på tre olika uppgifter: dynamisk positionering,för vilken vi även kan garantera rekursiv lösbarhet för den nominellaregulatorn; riktningsstyrning; och banfötljning med en siktlinje-algoritm. Numeriskasimuleringsstudier bekräftar metodens effektivitet.
Breger, Louis Scott 1979. "Model predictive control for formation flying spacecraft." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17758.
Full textIncludes bibliographical references (p. 105-114).
Formation flying is an enabling technology for many future space missions. This thesis addresses some of the key dynamics and control issues expected in future missions by pursuing two areas of advancement: extensions of relative linear dynamics models and assessment and mitigation of sensor noise effects on control systems. Relative dynamics models play an important role in finding drift-free initial conditions for spacecraft formations and for designing feedback controllers. This thesis presents extensions to the equations of relative motion expressed in both Cartesian reference frames and Keplerian orbital elements, including new initialization techniques for widely spaced passive apertures with very general formation configurations. Also, a new linear time-varying form of the equations of relative motion is developed from Gauss' Variational Equations, and the linearizing assumptions for these equations are shown to be consistent with typical formation flying scenarios. The second area considers the impact of sensor noise, predicted by several researchers to have a significant effect on the fuel-use for formation flying control. This thesis analyzes the impact of carrier-phase differential GPS sensor noise using a new analytical method for predicting the effects of disturbances on a model predictive control formulation. Previous work used an "open-loop" planning approach to achieve robustness in the presence of sensor noise, but was limited to short planning horizons. This thesis employs a "closed-loop" approach which accounts for future replanning, enabling longer planning horizons and more general terminal constraints. This MPC formulation guarantees the robustness
(cont.) of the planning system to both process and sensing noise with fuel costs that are shown to be comparable to the previous approach.
by Louis Scott Breger.
S.M.
Bengtsson, Ivar. "Autonomous Overtaking with Learning Model Predictive Control." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276691.
Full textVi går igenom ny forskning inom trajectory planning för autonom omkörning för att förstå de utmaningar som finns. Därefter föreslås ramverket Learning Model Predictive Control (LMPC) som en lämplig metod för att iterativt förbättra en omkörning vid varje utförande. Vi tar upp utvidgningar av LMPC-ramverket för att göra det applicerbart på omkörningsproblem. Dessutom presenterar vi också två alternativa modelleringar i syfte att minska optimeringsproblemens komplexitet. Alla tre angreppssätt har byggts från grunden i Python3 och simulerats i utvärderingssyfte. Optimeringsproblem har modellerats och lösts med programvaran Gurobi 9.0s python-API gurobipy. Resultaten visar att LMPC kan tillämpas framgångsrikt på omkörningsproblem, med förbättrat utförande vid varje iteration. Den första alternativa modelleringen minskar inte beräkningstiden vilket var dess syfte. Det gör däremot den andra alternativa modelleringen som dock fungerar sämre i andra avseenden.
Balbis, Luisella. "Nonlinear model predictive control for industrial applications." Thesis, University of Strathclyde, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501892.
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