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Lu, Yaohui. "Scheduling quasi-min-max model predictve control". Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/11692.
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Hosseinkhan-Boucher, Rémy. "On Learning-Based Control of Dynamical Systems". Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG029.
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Les impératifs environnementaux suscitent un regain d’intérêt pour la recherche sur le contrôle de l’écoulement des fluides afin de réduire la consommation d’énergie et les émissions dans diverses applications telles que l’aéronautique et l’automobile. Les stratégies de contrôle des fluides peuvent optimiser le système en temps réel, en tirant parti des mesures des capteurs et des modèles physiques. Ces stratégies visent à manipuler le comportement d’un système pour atteindre un état souhaité (stabilité, performance, consommation d’énergie). Dans le même temps, le développement d’approches de contrôle pilotées par les données dans des domaines concurrents tels que les jeux et la robotique a ouvert de nouvelles perspectives pour le contrôle des fluides. Cependant, l’intégration du contrôle basé sur l’apprentissage en dynamique des fluides présente de nombreux défis, notamment en ce qui concerne la robustesse de la stratégie de contrôle, l’efficacité de l’échantillon de l’algorithme d’apprentissage, et la présence de retards de toute nature dans le système. Ainsi, cette thèse vise à étudier et à développer des stratégies de contrôle basées sur l’apprentissage en tenant compte de ces défis, dans lesquels deux classes principales de stratégies de contrôle basées sur les données sont considérées : l’apprentissage par renforcement (RL) et la commande prédictive basée sur l’apprentissage (LB-MPC). De multiples contributions sont apportées dans ce contexte. Tout d’abord, un développement étendu sur la connexion entre les domaines du contrôle stochastique (temps continu) et du processus de décision de Markov (temps discret) est fourni pour unifier les deux approches. Deuxièmement, des preuves empiriques sur les propriétés de régularisation de l’algorithme d’apprentissage par renforcement par maximum d’entropie sont présentées à travers des concepts d’apprentissage statistique pour mieux comprendre la caractéristique de robustesse de l’approche par maximum d’entropie. Troisièmement, la notion d’abstraction temporelle est utilisée pour améliorer l’efficacité de l’échantillonnage d’un algorithme de commande prédictive par modèle basé sur l’apprentissage et piloté par une règle d’échantillonnage de la théorie de l’information. Enfin, les modèles différentiels neuronaux sont introduits à travers le concept d’équations différentielles neuronales à retard pour modéliser des systèmes à temps continu avec des retards pour des applications en commande prédictive. Les différentes études sont développées à l’aide de simulations numériques appliquées à des systèmes minimalistes issus des théories des systèmes dynamiques et du contrôle afin d’illustrer les résultats théoriques. Les expériences de la dernière partie sont également menées sur des simulations d’écoulement de fluides en 2DEnvironmental needs are driving renewed research interest in fluid flow control to reduce energy consumption and emissions in various applications such as aeronautics and automotive industries. Flow control strategies can optimise the system in real time, taking advantage of sensor measurements and physical models. These strategies aim at manipulating the behaviour of a system to reach a desired state (textit{e.g.}, stability, performance, energy consumption). Meanwhile, the development of data-driven control approaches in concurrent areas such as games and robotics has opened new perspectives for flow control. However, the integration of learning-based control in fluid dynamics comes with multiple challenges, including the robustness of the control strategy, the sample efficiency of the learning algorithm, and the presence of delays of any nature in the system. Thus, this thesis aims to study and develop learning-based control strategies with respect to these challenges where two main classes of data-driven control strategies are considered: Reinforcement Learning (RL) and Learning-based Model Predictive Control (LB-MPC). Multiple contributions are made in this context. First, an extended development on the connection between the fields of (continuous-time) Stochastic Control and (discrete-time) Markov Decision Process is provided to bridge the gap between the two approaches. Second, empirical evidence on the regularisation properties of the Maximum Entropy Reinforcement Learning algorithm is presented through statistical learning concepts to further understand the robustness feature of the Maximum Entropy approach. Third, the notion of temporal abstraction is used to improve the sample efficiency of a Learning-based Model Predictive Control algorithm driven by an Information Theoretic sampling rule. Lastly, neural differential models are introduced through the concept of Neural Delay Differential Equations to model continuous-time systems with delays for Model Predictive Control applications. The different studies are developed with numerical simulations applied on minimalistic systems from Dynamical Systems and Control theories to illustrate the theoretical results. The training experiments of the last part are also conducted on 2D fluid flow simulations
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Bacic, Marko. "Model predictive control". Thesis, University of Oxford, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400060.
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Borgesen, Jørgen Frenken. "Efficient optimization for Model Predictive Control in reservoir models". Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9959.
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The purpose of this thesis was to study the use of adjoint methods for gradient calculations in Model Predictive Control (MPC) applications. The goal was to find and test efficient optimization methods to use in MPC on oil reservoir models. Handling output constraints in the optimization problem has been studied closer since they deteriorate the efficiency of the MPC applications greatly. Adjoint- and finite difference approaches for gradient calculations was tested on reservoir models to determine there efficiency on this particular type of problem. Techniques for reducing the number of output constraints was also utilized to decrease the computation time further. The results of this study shows us that adjoint methods can decrease the computation time for reservoir simulations greatly. Combining the adjoint methods with techniques that reduces the number of output constraints can reduce the computation time even more. Adjoint methods require some more work in the modeling process, but the simulation time can be greatly reduced. The principal conclusion is that more specialized optimization algorithms can reduce the simulation time for reservoir models.
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Hanger, 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.
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This thesis developes a control allocation method based on the Model Predictive Control algorithm, to be used on a missile in flight. The resulting Model Predictive Control Allocation (MPCA) method is able to account for actuator constraints and dynamics, setting it aside from most classical methods. A new effector configuration containing two groups of actuators with different dynamic authorities is also proposed. Using this configuration, the MPCA method is compared to the classical methods Linear Programming and Redistributed Pseudoinverse in various flight scenarios, highlighting performance differences aswell as emphasizing applications of the MPCA method. It is found to be superior to the two classical methods in terms of tracking performance and total cost. Nevertheless, some restrictions and weaknesses are revealed, but countermeasures to these are proposed. The newly developed convex optmization solver CVXGEN is utilized successfully in the method evaluation. Providing solve times in milliseconds even for large problems, CVXGEN makes real-time implementations of the MPCA method feasible.
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Qi, 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.
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Sriniwas, Ganti Ravi. "Nonlinear model predictive control". Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10267.
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Couchman, Paul. "Stochastic model predictive control". Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442384.
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Wu, Xingjian. "Stochastic model predictive control". Thesis, University of Oxford, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497157.
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Gormandy, Brent Anthony. "Fuzzy model predictive control". Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248858.
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Buerger, Johannes Albert. "Fast model predictive control". Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:6e296415-f02c-4bc2-b171-3bee80fc081a.
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This thesis develops efficient optimization methods for Model Predictive Control (MPC) to enable its application to constrained systems with fast and uncertain dynamics. The key contribution is an active set method which exploits the parametric nature of the sequential optimization problem and is obtained from a dynamic programming formulation of the MPC problem. This method is first applied to the nominal linear MPC problem and is successively extended to linear systems with additive uncertainty and input constraints or state/input constraints. The thesis discusses both offline (projection-based) and online (active set) methods for the solution of controllability problems for linear systems with additive uncertainty. The active set method uses first-order necessary conditions for optimality to construct parametric programming regions for a particular given active set locally along a line of search in the space of feasible initial conditions. Along this line of search the homotopy of optimal solutions is exploited: a known solution at some given plant state is continuously deformed into the solution at the actual measured current plant state by performing the required active set changes whenever a boundary of a parametric programming region is crossed during the line search operation. The sequence of solutions for the finite horizon optimal control problem is therefore obtained locally for the given plant state. This method overcomes the main limitation of parametric programming methods that have been applied in the MPC context which usually require the offline precomputation of all possible regions. In contrast to this the proposed approach is an online method with very low computational demands which efficiently exploits the parametric nature of the solution and returns exact local DP solutions. The final chapter of this thesis discusses an application of robust tube-based MPC to the nonlinear MPC problem based on successive linearization.
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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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Schaich, Rainer Manuel. "Robust model predictive control". Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:94e75a62-a801-47e1-8cb8-668e8309d477.
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This thesis deals with the topic of min-max formulations of robust model predictive control problems. The sets involved in guaranteeing robust feasibility of the min-max program in the presence of state constraints are of particular interest, and expanding the applicability of well understood solvers of linearly constrained quadratic min-max programs is the main focus. To this end, a generalisation for the set of uncertainty is considered: instead of fixed bounds on the uncertainty, state- and input-dependent bounds are used. To deal with state- and input dependent constraint sets a framework for a particular class of set-valued maps is utilised, namely parametrically convex set-valued maps. Relevant properties and operations are developed to accommodate parametrically convex set-valued maps in the context of robust model predictive control. A quintessential part of this work is the study of fundamental properties of piecewise polyhedral set-valued maps which are parametrically convex, we show that one particular property is that their combinatorial structure is constant. The study of polytopic maps with a rigid combinatorial structure allows the use of an optimisation based approach of robustifying constrained control problems with probabilistic constraints. Auxiliary polytopic constraint sets, used to replace probabilistic constraints by deterministic ones, can be optimised to minimise the conservatism introduced while guaranteeing constraint satisfaction of the original probabilistic constraint. We furthermore study the behaviour of the maximal robust positive invariant set for the case of scaled uncertainty and show that this set is continuously polytopic up to a critical scaling factor, which we can approximate a-priori with an arbitrary degree of accuracy. Relevant theoretical statements are developed, discussed and illustrated with examples.
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Truong, Quan y 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.
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Model Predictive Control (MPC) refers to a class of algorithms that optimize the future behavior of the plant subject to operational constraints [46]. The merits of the class algorithms include its ability to handle imposed hard constraints on the system and perform on-line optimization. This thesis investigates design and implementation of continuous time model predictive control using Laguerre polynomials and extends the design ap- proaches proposed in [43] to include intermittent predictive control, as well as to include the case of the nonlinear predictive control. In the Intermittent Predictive Control, the Laguerre functions are used to describe the control trajectories between two sample points to save the com- putational time and make the implementation feasible in the situation of the fast sampling of a dynamic system. In the nonlinear predictive control, the Laguerre polynomials are used to describe the trajectories of the nonlinear control signals so that the reced- ing horizon control principle are applied in the design with respect to the nonlinear system constraints. In addition, the thesis reviews several Quadratic Programming methods and compares their performances in the implementation of the predictive control. The thesis also presents simulation results of predictive control of the autonomous underwater vehicle and the water tank.
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Rosdal, 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.
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This 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.
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Bell, Geoffrey Laurence. "Robust model predictive control design". Thesis, Imperial College London, 2000. http://hdl.handle.net/10044/1/7450.
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Townsend, 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.
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Heise, Sharon Ann. "Multivariable constrained Model Predictive Control". Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361703.
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Richards, Arthur George 1977. "Robust constrained model predictive control". Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/28914.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.Includes 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.
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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.
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The need to save energy, cut costs, and increase profit margin in process manufactureincreases continually. There is also a global drive to reduce energy use and cut down co2 emission and combat climate change. These in turn have led to more stringent requirements on process control performance. Hence, the requirements for modern systems are often not achievable using classical control techniques. Therefore, advanced control strategies are often required to ensure optimal process performance. Despite these challenges, PID has continued to be the dominant industrial control scheme. However, for systems with complex dynamics and/or high performance requirements, PID control may not be sufficient. Therefore, a significant number of industrial control loops are not performing optimally and more advanced control than PID may be required in order to achieve optimal performance. MPC is one of the advanced control schemes that has had a significant impact in the industry. Despite the benefits associated with the implementation of MPC, the technology has remained a niche application in process manufacture. This thesis seeks to address these issues by developing ways that could lead to widespread application of MPC. In the first part of this thesis, a study was carried out to understand the characteristics of processes that would benefit from the application of MPC at the regulatory control level even in the single-input single-output (SISO) case. This is a departure from the common practice in which MPC is applied at the supervisory control layer delivering set points to PID controllers at the regulatory control layer. Both numerical simulation and industrial studies were used to show and quantify benefits of MPC for SISO applications at the regulatory control layer. Some issues that have led to the limited application of MPC include the cost and human efforts associated with modelling and controller design. And to achieve high process performance, accurate models are required. To address this issue, in the second part of this thesis, a novel technique for designing MPC from routine plant data – routine data MPC (RMPC) is proposed. The proposed technique was successfully implemented on process models. This technique would reduce the high human cost associated with MPC deployment, which could make it a widespread rather than niche application in the process manufacturing industry.
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Towhidkhah, 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.
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Tarragona, Roig Joan. "Smart control techniques for thermal energy storage systems". Doctoral thesis, Universitat de Lleida, 2021. http://hdl.handle.net/10803/671420.
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Augmentar l’ús d’energia provinent de fonts renovables és important en la lluita contra el canvi climàtic. No obstant, la seva implantació planteja reptes importants deguts a la manca de continuïtat en la seva generació i al desajust que existeix amb els perfils de consum. La present tesi doctoral s’emmarca en dues propostes per incrementar el rendiment dels panells fotovoltaics en l’àmbit dels sistemes de calefacció per edificis. Per una banda, el sistema integra un tanc d'emmagatzematge d'energia tèrmica, que permet emmagatzemar l'energia generada pels panells durant el dia, a fi de poder-la consumir a les hores amb més demanda. D'altra banda, el sistema també compta amb una estratègia de control predictiu, que permet pronosticar les condicions meteorològiques i les demandes de calefacció futures, per tal d'ajustar el funcionament de tot el conjunt d'elements, considerant aquesta informació. El sistema proposat ha demostrat ser efectiu en diferents tipus de clima i habitatges.Aumentar el uso de energía procedente de fuentes renovables es importante en la lucha contra el cambio climático. No obstante, su implantación plantea retos importantes debidos a la falta de continuidad en su generación y al desajuste que existe con los perfiles de consumo. La presente tesis doctoral se enmarca en dos propuestas para incrementar el rendimiento de los paneles fotovoltaicos en el ámbito de los sistemas de calefacción para edificios. Por un lado, el sistema integra un tanque de almacenaje de energía térmica, que permite almacenar la energía generada por los paneles durante el día, a fin de poderla consumir a las horas con más demanda. Por otro lado, el sistema también cuenta con una estrategia de control predictivo, que permite pronosticar las condiciones meteorológicas y las demandas de calefacción futuras, para ajustar el funcionamiento de todo el conjunto de elementos, considerando esta información. El sistema propuesto demostró ser efectivo en distintos tipos de climas y viviendas.
To increase the use of energy that comes from renewables is important to fight against climate change. However, their deployment leads to significant challenges due to the intermittence in their generation and the mismatch between energy demand and supply. In that sense, this PhD thesis is framed in two proposals to increase the performance of photovoltaic panels in heating systems integrated in the building sector. On the one hand, the system considers a thermal energy storage tank, which allows to store the energy produced by the panels during the solar hours, in order to consume it along the peak demand periods. On the other hand, the system also takes into account a model predictive control strategy, which enables to forecast weather conditions and future heating demands, to adjust the operation of all the elements. The proposed system demonstrated a good and effective behaviour in different climate conditions and buildings.
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Simon, Daniel. "Model Predictive Control in Flight Control Design : Stability and Reference Tracking". Licentiate thesis, Linköpings universitet, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103742.
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Aircraft are dynamic systems that 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 are becoming increasingly important as the performance and complexity of the controlled systems is constantly increasing. It is especially important in the design of control systems for fighter aircraft. These require maximum control performance in order to have the upper hand in a dogfight or when they have to outmaneuver an enemy missile. Therefore pilots often maneuver the aircraft very close to the limit of what it is capable of, and an automatic system (called flight envelope protection system) against violating the restrictions is a necessity. In other application areas, nonlinear optimal control methods have been successfully used to solve this but in the aeronautical industry, these methods have not yet been established. One of the more popular methods that are well suited to handle constraints is Model Predictive Control (MPC) and it is used extensively in areas such as the process industry and the refinery industry. Model predictive control means in practice that the control system iteratively solves an advanced optimization problem based on a prediction of the aircraft's future movements in order to calculate the optimal control signal. The aircraft's operating limitations will then be constraints in the optimization problem. In this thesis, we explore model predictive control and derive two fast, low complexity algorithms, one for guaranteed stability and feasibility of nonlinear systems and one for reference tracking for linear systems. In reference tracking model predictive control for linear systems we build on the dual mode formulation of MPC and our goal is to make minimal changes to this framework, in order to develop a reference tracking algorithm 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 several methods to approximate the nonlinear constraints have been proposed in the literature, many working in an ad hoc fashion, resulting in conservatism, or worse, inability to guarantee recursive feasibility. Also several methods work in an iterative manner which can be quit time consuming making them inappropriate for fast real time applications. In this thesis we propose a method to handle the nonlinear constraints, using a set of dynamically generated local inner polytopic approximations. The main benefits of the proposed method is that while computationally cheap it still can guarantee recursive feasibility and convergence.The series name "Linköping studies in science and technology. Licentiate Thesis" is incorrect. The correct series name is "Linköping studies in science and technology. Thesis".
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Megías, Jiménez David. "Robustness aspects of Model Predictive Control". Doctoral thesis, Universitat Autònoma de Barcelona, 2000. http://hdl.handle.net/10803/32173.
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Model, Model-based or Receding-horizon Predictive Control (MPC or RHPC) is a successful and mature control strategy which has gained the widespread acceptance of both academia and industry. The basis of these control laws, which have been reported to handle quite complex dynamics, is to perform predictions of the system to be controlled by means of a model. A control profile is then computed to minimise some cost function defined in terms of the predictions and the hypothesised controls.
It was soon realised that the first few predictive controllers failed to fulfil essential properties, such as the stability of the nominal closed-loop system. In addition, it was noticed that the discrepancies between the model and the true process, referred to as system uncertainty, can seriously affect the achieved performance. The robustness problem should, thus, be addressed. In this thesis, the problems of nominal stability and robustness are reviewed and investigated. In particular, the accomplishment of constraint specifications in the presence of various sources of uncertainty is a major objective of the methods developed throughout this PhD research.
First of all, controllers which guarantee nominal stability, such as the CRHPC and the GPC∞, are highlighted and formulated, and 1-norm counterparts are obtained. The robustness of these strategies in the unconstrained case has been analysed, and it has been concluded that the infinite horizon approach often leads to more convenient performance and robustness results for typical choices of the tuning knobs. Then the constrained case has been undertaken, and min-max controllers based on the global uncertainty approach have been formulated for both 1-norm and 2-norm formulations. For these methods, a band updating algorithm has been suggested to modify the assumed uncertainty bounds on-line. Although both formulations provide similar results, which overcome the classical approach to robustness when constraints are specified, the 1-norm controllers are computationally more efficient, since the optimal control move sequence can be computed with a standard LP problem.
Finally, a refinement of the min-max approach which includes the notion that feedback is present in the receding-horizon implementation of predictive controllers, termed as feedback min-max MPC, is shown to overcome some of the drawbacks of the standard min-max approach.El 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.
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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.
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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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Asadi, Fatemeh. "Self-organized distributed model predictive control". Thesis, University of Bristol, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.720820.
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Khosravi, Sara. "Constrained model predictive control of hypnosis". Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56230.
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This thesis investigates the design and performance of a model predictive controller (MPC) for the automatic control of hypnosis. It constitutes the first step towards automatic control of anesthesia with constraints on important parameters such as drug concentrations in the body and hemodynamic variables such as blood pressure. The literature suggests, that closed-loop control of anesthesia can significantly reduce drug consumption and lessen recovery times, thus improving the safety and quality of anesthesia care while reducing costs. However, automation of anesthesia is challenging because of shortcomings associated with drug-response modeling, in particular limited data for children and disagreement between published models, inadequate predictive capacity of models owing to inclusion of monitor dynamics in the models, and significant inter/intra patient variability and uncertainty in models. The first part of this thesis introduces a new approach to dose-response modeling and presents models with different clinical end-points for propofol in children and adults. This thesis also presents a new monitor-decoupledmodel of propofol pharmacodynamics (PD) where the monitor model is clearly excluded from the identified PD. The second part of the thesis concentrates on design of a constrained MPC for hypnosis. While the anesthesia closed-loop concept has already been investigated in the past, there is still a need for a closed-loop control system that explicitly includes robustness in the design step, allows constraints on drug concentrations and physiological parameters, and can incorporate multivariable control of multi drug and multi sensor systems. In this thesis, robust MPC controllers are presented for closed-loop control of depth of hypnosis in adults and children. Robustness in the presence of inter-patient variability is taken into account in the controller design. A novel idea is introduced on how to define and implement physiological constraints in closed-loop control of hypnosis using MPC with a parallel PKPD model. Evaluation of the proposed MPC meets the design specifications and shows that the required robustness against patient uncertainty is achieved and the proposed safety constrained control strategy can potentially reduce the risk of under/over-dosing for most patients by providing controller enforced safety bounds without sacrificing the performance of the closed-loop control system.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Curinga, Florian. "Autonomous racing using model predictive control". Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-222801.
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Autonomous vehicles are expected to have a significant impact on our societies by freeinghumans from the driving task, and thus eliminating the human factor in one of themost dangerous places: roads. As a matter of facts, road kills are one of the largest sourceof human deaths and many countries put the decrease of these casualties as one of their toppriorities. It is expected that autonomous vehicles will dramatically help in achieving that.Moreover, using controllers to optimize both the car behaviour on the road and higher leveltraffic management could reduce traffic jams and increase the commuting speed overall.To minimize road kills, an approach is to design controllers that would handle the car atits limits of handling, by integrating complex dynamics such as adherence loss it is possibleto prevent the car from leaving the road. A convenient setup to evaluate this type ofcontrollers is a racing context: a controller is steering a car to complete a track as fast aspossible without leaving the road and by brining the car to its limits of handling.In this thesis, we design a controller for an autonomous vehicle with the goal of driving itfrom A to B as fast as possible. This is the main motivation in racing applications. Thecontroller should steer the car with the goal to minimize the racing time.This controller was designed within the model predictive controller (MPC) framework,where we used the concept of road-aligned model. In contrast with the standard mpc techniques,we use the objective function to maximize the progress along the reference path,by integrating a linear model of the vehicle progression along the centerline. Combinedwith linear vehicle model and constraints, a optimization problem providing the vehiclewith the future steering and throttle values to apply is formulated and solved with linearprogramming in an on-line fashion during the race. We show the effectiveness of our controllerin simulation, where the designed controller exhibits typical race drivers behavioursand strategies when steering a vehicle along a given track. We ultimately confront it withsimilar controllers from the literature, and derive its strength and weaknesses compared tothem.Autonoma 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.
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Cheng, Qifeng. "Robust & stochastic model predictive control". Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:89da4934-9de7-4142-958e-513065189518.
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In the thesis, two different model predictive control (MPC) strategies are investigated for linear systems with uncertainty in the presence of constraints: namely robust MPC and stochastic MPC. Firstly, a Youla Parameter is integrated into an efficient robust MPC algorithm. It is demonstrated that even in the constrained cases, the use of the Youla Parameter can desensitize the costs to the effect of uncertainty while not affecting the nominal performance, and hence it strengthens the robustness of the MPC strategy. Since the controller u = K x + c can offer many advantages and is used across the thesis, the work provides two solutions to the problem when the unconstrained nominal LQ-optimal feedback K cannot stabilise the whole class of system models. The work develops two stochastic tube approaches to account for probabilistic constraints. By using a semi closed-loop paradigm, the nominal and the error dynamics are analyzed separately, and this makes it possible to compute the tube scalings offline. First, ellipsoidal tubes are considered. The evolution for the tube scalings is simplified to be affine and using Markov Chain model, the probabilistic tube scalings can be calculated to tighten the constraints on the nominal. The online algorithm can be formulated into a quadratic programming (QP) problem and the MPC strategy is closed-loop stable. Following that, a direct way to compute the tube scalings is studied. It makes use of the information on the distribution of the uncertainty explicitly. The tubes do not take a particular shape but are defined implicitly by tightened constraints. This stochastic MPC strategy leads to a non-conservative performance in the sense that the probability of constraint violation can be as large as is allowed. It also ensures the recursive feasibility and closed-loop stability, and is extended to the output feedback case.
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Hartley, Edward Nicholas. "Model predictive control for spacecraft rendezvous". Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609090.
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Larsen, 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.
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For the past couple of years researchers around theworld have tried to develop fully autonomous vehicles. One of theproblems that they have to solve is how to navigate in a dynamicworld with ever-changing variables. This project was initiated tolook into one scenario of the path planning problem; overtakinga human driven vehicle. Model Predictive Control (MPC) hashistorically been used in systems with slower dynamics but withadvancements in computation it can now be used in systems withfaster dynamics. In this project autonomous vehicles controlledby MPC were simulated in Python based on the kinematic bicyclemodel. Constraints were posed on the overtaking vehicle suchthat the two vehicles would not collide. Results show that anovertake, that keeps a proper distance to the other vehicle andfollows common traffic laws, is possible in certain scenarios.Under 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
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Noorian, Farzad. "Risk Management using Model Predictive Control". Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14282.
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Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%.
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Huang, Yang y 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.
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Magnetic Bearing Systems have been receiving a great deal of research attention for the past decades. Its inherent nonlinearity and open-loop instability are challenges for controller design. This thesis investigates and designs model predictive control strategy for an experimental Active Magnetic Bearing (AMB) laboratory system. A host-target development environment of real-time control system with hardware in the loop (HIL) is implemented. In this thesis, both continuous and discrete time model predictive controllers are studied. In the first stage, local MPC controllers are applied to control the AMB system; and in the second stage, concept of supervisory controller design is then investigated and implemented. Contributions of the thesis can be summarized as follows; 1. A Discrete time Model Predictive Controller has been developed and applied to the active magnetic bearing system. 2. A Continuous time Model Predictive Controller has been developed and applied to the active magnetic bearing system. 3. A frequency domain identification method using FSF has been applied to pursue model identification with respect to local MPC and magnetic bearing system. 4. A supervisory control strategy has been applied to pursue a two stages model predictive control of active magnetic bearing system.
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Marín, Lahoz Juan. "Development of a predictive model of impulse control disorder in parkinson’s disease using clinical, neuropsichological, genetic and neurophysiological data as risk markers". Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/669347.
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Los trastornos de control de impulsos (TCI) son una complicación frecuente del tratamiento de la enfermedad de Parkinson (EP), particularmente del uso de agonistas de dopamina (AD). Los TCI en la EP se han estudiado durante dos décadas. Sin embargo, la evidencia prospectiva aún es escasa y no hay modelos predictivos disponibles. Esta tesis consta de cuatro trabajos que abordan estas lagunas del conocimiento.
En el primer trabajo evaluamos la asociación entre impulsividad y TCI incidente. No confirmamos la asociación sospechada, aunque encontramos una asociación significativa entre la impulsividad y la severidad de los TCI (p = 0.001). La falta de asociación entre la impulsividad y la presencia de TCI se confirmó longitudinalmente en el cuarto trabajo.
El segundo trabajo fue un estudio de supervivencia prospectivo longitudinal para evaluar la causalidad de la asociación entre la depresión y el TCI. Los pacientes con EP que estaban deprimidos tenían aproximadamente el doble de riesgo de desarrollar TCI (p <0,001). Esta asociación fue específica e independiente del uso de AD y otros posibles factores de confusión. Además, los pacientes con depresión refractaria tenían un riesgo aún mayor (p = 0,001).
El tercero fue un estudio de casos y controles anidado en un estudio prospectivo longitudinal. Estudiamos el metabolismo cerebral a usando PET de 18 FDG. Comparamos pacientes con TCI de nuevo inicio con pacientes apareados sin TCI de la misma cohorte. Los pacientes con TCI mostraron un metabolismo más alto en amplias áreas corticales (p <0.05 corregido para FWE) Los resultados fueron los mismos utilizando el análisis voxelwise y el análisis intracortical. También demostramos que no había diferencias estructurales tanto en el grosor cortical y como en la segmentación subcortical. Usando un grupo de controles sanos apareados, descubrimos que el metabolismo más alto encontrado en los pacientes con TCI es en realidad preservación, dado que no se encontraron diferencias del metabolismo cortical entre los pacientes con EP y TCI y los controles sanos, mientras que los pacientes con EP sin TCI mostraron hipometabolismo en comparación con los controles sanos.
El objetivo del cuarto estudio fueron marcadores que pudieran diferenciar a los pacientes con EP con riesgo alto de TCI y aquellos con riesgo bajo. En particular, estudiamos la “feedback related negativity” (FRN), un marcador neurofisiológico del procesamiento de recompensas. Encontramos que este marcador era diferente en los pacientes que desarrollaron TCI en los siguientes tres años en comparación con aquellos que no lo hicieron (p = 0.001). Además, desarrollamos dos modelos para la predicción de TCI: uno utilizando sólo datos clínicos y el otro incluyendo también la FRN basal. El modelo que incluyó la FRN desempeñó significativamente mejor (p = 0.003). Los pacientes identificados gracias a la FRN como de alto riesgo tenían un riesgo diez veces mayor de desarrollar TCI durante los tres años siguientes que los identificados como de bajo riesgo.
En conclusión, encontramos evidencias que respalda la depresión como un factor de riesgo de TCI en la EP, evidencias de preservación metabólica cerebral entre los pacientes con EP que tienen TCI, evidencias que respalda el papel del procesamiento de recompensas para el desarrollo de TCI y evidencia que sugiere que la impulsividad debe descartarse como un factor de riesgo para TCI. Por último, demostramos que en el contexto del Parkinson, el desarrollo de TCI puede predecirse, y, por lo tanto, probablemente puede evitarse.Impulse control disorders (ICD) are a common complication of Parkinson’s disease (PD) treatment, particularly of dopamine agonist (DA) use. ICD in PD have been studied for two decades. Nonetheless, prospective evidence is still scarce and predictive models are lacking. This thesis consists of four works addressing these gaps. In the first work we evaluated the association between impulsivity and incident ICD. We did not confirm the suspected association although we found a significant association between impulsivity and ICD severity (p=0.001). The lack of association between impulsivity and ICD presence was confirmed longitudinally in the fourth work. The second work was a longitudinal prospective survival study to evaluate whether the association between depression and ICD was causal. We found that depressed PD patients had approximately double risk of developing ICD (p<0.001). This association was specific and independent from DA use and other potential confounders. Besides, patients with refractory depression had an even higher risk (p=0.001). The third was a case-control study nested in a longitudinal prospective study. We studied brain metabolism via 18 FDG PET. We compared patients with new onset ICD with matched patients free of ICD from the same cohort. ICD patients showed higher metabolism in widespread cortical areas (p<0.05 FWE corrected). The results were the same using voxelwise analysis and intracortical analysis. We also showed that there were no structural differences using cortical thickness and subcortical segmentation. Using a group of matched healthy controls, we found that the higher metabolism found in ICD patients should be regarded as preservation because no cortical metabolic differences were found between PD‑ICD patients and healthy controls, while PD‑nonICD patients showed hypometabolism when compared with healthy controls. The fourth study targeted markers that could differentiate PD patients at high risk of ICD and those at low risk. Particularly we targeted the feedback related negativity (FRN) a neurophysiological marker of reward processing. We found this marker to be different in patients who developed ICD within the subsequent three years compared with those who did not (p=0.001). Furthermore, we developed two models for ICD prediction: one used only clinical data and the other also included the baseline FRN. The model including the FRN performed significantly better (p=0.003). Patients identified using the FRN as high risk had a risk ten times higher for the next three years than those identified as low risk. In conclusion, we found evidence that backs depression as a risk factor for ICD in PD, evidence of brain metabolic preservation among PD patients with ICD, evidence that supports the role of reward processing for ICD development and evidence that suggests that impulsivity should be discarded as a risk factor for ICD. Lastly, we showed that the development of ICD can be predicted in PD patients and therefore, probably can be avoided.
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Munoz, Carpintero Diego Alejandro. "Strategies in robust and stochastic model predictive control". Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f6bce71-f91f-4d5a-998f-295eff5b089a.
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The presence of uncertainty in model predictive control (MPC) has been accounted for using two types of approaches: robust MPC (RMPC) and stochastic MPC (SMPC). Ideal RMPC and SMPC formulations consider closed-loop optimal control problems whose exact solution, via dynamic programming, is intractable for most systems. Much effort then has been devoted to find good compromises between the degree of optimality and computational tractability. This thesis expands on this effort and presents robust and stochastic MPC strategies with reduced online computational requirements where the conservativeness incurred is made as small as conveniently possible. Two RMPC strategies are proposed for linear systems under additive uncertainty. They are based on a recently proposed approach which uses a triangular prediction structure and a non-linear control policy. One strategy considers a transference of part of the computation of the control policy to an offline stage. The other strategy considers a modification of the prediction structure so that it has a striped structure and the disturbance compensation extends throughout an infinite horizon. An RMPC strategy for linear systems with additive and multiplicative uncertainty is also presented. It considers polytopic dynamics that are designed so as to maximize the volume of an invariant ellipsoid, and are used in a dual-mode prediction scheme where constraint satisfaction is ensured by an approach based on a variation of Farkas' Lemma. Finally, two SMPC strategies for linear systems with additive uncertainty are presented, which use an affine-in-the-disturbances control policy with a striped structure. One strategy considers an offline sequential design of the gains of the control policy, while these are variables in the online optimization in the other. Control theoretic properties, such as recursive feasibility and stability, are studied for all the proposed strategies. Numerical comparisons show that the proposed algorithms can provide a convenient compromise in terms of computational demands and control authority.
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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.
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This study presents a detailed description of a cost function-based predictive control strategy called Finite Control Set Model Predictive Control (FCS-MPC) and its applications to the control of power electronics converters. The basic concepts, operating principles and general properties of this control technique have been explained. The analysis is performed on two different power converter topologies: traditional three-phase Voltage Source Inverter (VSI) and Modular Multilevel Converter (MMC). In order to verify its capabilities MATLAB (SIMULINK) simulations have been performed for both cases.The design procedure of FCS-MPC is based on first, a discrete-time model of the system that is used to predict the behavior of the controlled variables for all the possible switching states of the converter and second, a cost function that should be defined according to the control requirements of the system. The switching state that minimizes the cost function will be selected to be applied to the converter at the next sampling time.FCS-MPC is a powerful control technique that has several advantages such as high accuracy, flexibility and stability, easy implementation, simple and understandable concepts, but the most important and exclusive feature of this control strategy is the inclusion of nonlinearities and system constraints in the cost function. As a result, all the control requirements can be considered by one controller at the same time.There are important factors, regarding FCS-MPC, that have been investigated in this study, such as:1)the effect of the cost function definition and the application of weighting factors2)the effect of discretization method and system model accuracy on the controller performance3)the effect of measurement errors on the controller robustness4)dynamic behavior of the controller and its response speed when a disturbance occurs in the system5)reference tracking capability of the controller
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Atić, 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.
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Thesis (M.S.)--West Virginia University, 2003.Title from document title page. Document formatted into pages; contains vii, 68 p. : ill. Includes abstract. Includes bibliographical references (p. 66-68).
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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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Schön, Tomas. "Identification for Predictive Control : A Multiple Model Approach". Thesis, Linköping University, Department of Electrical Engineering, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1050.
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Predictive control relies on predictions of the future behaviour of the system to be controlled. These predictions are calculated from a model of this system, thus making the model the cornerstone of the predictive controller. Furthermore predictive control is the only advanced control methodology that has managed to become widely used in the industry. The necessity of good models in the predictive control context can thus be motivated both from the very nature of predictive control and from its widespread use in industry.
This thesis is concerned with examining the use of multiple models in the predictive controller. In order to do this the standard predictive control formulation has been extended to incorporate the use of multiple models. The most general case of this new formulation allows the use of an individual model for each prediction horizon.
The models are estimated using measurements of the input and output sequences from the true system. When using this data to find a good model of the system it is important to remember the intended purpose of the model. In this case the model is going to be used in a predictive controller and the most important feature of the models is to deliver good k-step ahead predictions. The identification algorithms used to estimate the models thus strives for estimating models good at calculating these predictions.
Finally this thesis presents some complete simulations of these ideas showing the potential of using multiple models in the predictive control framework.
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Rice, Michael J. "Numerical and computational aspects of predictive control". Thesis, Loughborough University, 1999. https://dspace.lboro.ac.uk/2134/27111.
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Model Predictive Control (MPC) is an application of control that is highly popular due to its sensible approach and its ease of implementation. These qualities give MPC an advantage over Linear Quadratic (LQ) control, even though LQ will result in the optimal result where feasible. Recent advancements have resulted in greater computational power, which has given rise to the development of more complicated MPC algorithms, but there are instances when the complexity of the calculations involved will result in the amount of computations involved or ill-conditioning of the problem.
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Júnior, José Genario de Oliveira. "Model predictive control applied to A 2-DOF helicopter". Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-11042018-082532/.
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This work presents an embedded model predictive control application to a 2-DOF Helicopter Process. The mathematical modeling of the plant is first presented along with an analysis of the linear model. Then, the incremental state-space representations used in the MPC formulation are derived. The MPC technique is then defined, along with how to rewrite the physical constraints into the problem formulation. After that, a discussion on the utilized Quadratic Programming solver is presented along with possible alternatives to it, showing some considerations on which matrices to calculate beforehand for an embedded application. Finally, system identification is performed and the experimental results are presented.Este trabalho apresenta uma aplicação de controle preditivo embarcado em um helicóptero de bancada com dois graus de liberdade. A modelagem matemática é apresentada, junto com uma análise do modelo linear obtido. São obtidas duas representações de modelos de espaço de estados considerando a entrada incremental, que serão usadas posteriormente para a formulação do controlador. Então, é definida a técnica de controle utilizada, juntamente com a inclusão das restrições físicas da planta na formulação do problema. Após isto, é feita uma discussão sobre qual solver para a programação quadrática utilizar, junto com algumas alternativas ao solver escolhido, bem como algumas considerações sobre a aplicação embarcada. Finalmente, são apresentados os resultados da identificação de sistemas aplicadas ao protótipo, bem como os resultados experimentais obtidos.
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Shekhar, Rohan Chandra. "Variable horizon model predictive control : robustness and optimality". Thesis, University of Cambridge, 2012. https://www.repository.cam.ac.uk/handle/1810/244210.
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Variable Horizon Model Predictive Control (VH-MPC) is a form of predictive control that includes the horizon length as a decision variable in the constrained optimisation problem solved at each iteration. It has been recently applied to completion problems, where the system state is to be steered to a closed set in finite time. The behaviour of the system once completion has occurred is not considered part of the control problem. This thesis is concerned with three aspects of robustness and optimality in VH-MPC completion problems. In particular, the thesis investigates robustness to well defined but unpredictable changes in system and controller parameters, robustness to bounded disturbances in the presence of certain input parameterisations to reduce computational complexity, and optimal robustness to bounded disturbances using tightened constraints. In the context of linear time invariant systems, new theoretical contributions and algorithms are developed. Firstly, changing dynamics, constraints and control objectives are addressed by introducing the notion of feasible contingencies. A novel algorithm is proposed that introduces extra prediction variables to ensure that anticipated new control objectives are always feasible, under changed system parameters. In addition, a modified constraint tightening formulation is introduced to provide robust completion in the presence of bounded disturbances. Different contingency scenarios are presented and numerical simulations demonstrate the formulation’s efficacy. Next, complexity reduction is considered, using a form of input parameterisation known as move blocking. After introducing a new notation for move blocking, algorithms are presented for designing a move-blocked VH-MPC controller. Constraints are tightened in a novel way for robustness, whilst ensuring that guarantees of recursive feasibility and finite-time completion are preserved. Simulations are used to illustrate the effect of an example blocking scheme on computation time, closed-loop cost, control inputs and state trajectories. Attention is now turned towards mitigating the effect of constraint tightening policies on a VH-MPC controller’s region of attraction. An optimisation problem is formulated to maximise the volume of an inner approximation to the region of attraction, parameterised in terms of the tightening policy. Alternative heuristic approaches are also proposed to deal with high state dimensions. Numerical examples show that the new technique produces substantially improved regions of attraction in comparison to other proposed approaches, and greatly reduces the maximum required prediction horizon length for a given application. Finally, a case study is presented to illustrate the application of the new theory developed in this thesis to a non-trivial example system. A simplified nonlinear surface excavation machine and material model is developed for this purpose. The model is stabilised with an inner-loop controller, following which a VH-MPC controller for autonomous trajectory generation is designed using a discretised, linearised model of the stabilised system. Realistic simulated trajectories are obtained from applying the controller to the stabilised system and incorporating the ideas developed in this thesis. These ideas improve the applicability and computational tractability of VH-MPC, for both traditional applications as well as those that go beyond the realm of vehicle manœuvring.
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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.
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Dave, Kedar Himanshu. "Inferential model predictive control using statistical tools". College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2585.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.Thesis 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.
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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.
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This 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.
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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.
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This work makes a detailed analysis on the principles and characteristics of Direct Power Control (DPC) and Model Predictive Control (MPC) applied to three-phase half-bridge PWM Rectifier. Model predictive control has merits of forecast and real-time optimization. MPC controller computes the optimal space vector of input voltage to the PWM rectifier in the dq frame and then this desired space vector is modulated via space vector pulse width modulation. The simulation results from Matlab/Simulink illustrate the flexibility and effectiveness of MPC-SVPWM approach.
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Kristoffersson, Ida. "Model Predictive Control of a Turbocharged Engine". Thesis, KTH, Reglerteknik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107508.
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Engine control becomes increasingly important in newer cars. It is therefore interesting to investigate if a relatively new control method as Model Predictive Control (MPC) can be useful in engine control in the future. One of the advantages of MPC is that it can handle contraints explicitly. In this thesis basics on turbocharged engines and the underlying theory of MPC is presented. Based on a nonlinear mean value engine model, linearized at multiple operating points, we then implement both a linear and a nonlinearMPC strategy and highlight implementation issues. The implemented MPC controllers calculate optimal wastegate position in order to track a requested torque curve and still make sure that the constraints on turbocharger speed and minimum and maximum opening of the wastegate are fulfilled.
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Gabrielsson, Fredrik. "Model Predictive Control of Skeboå Water system". Thesis, KTH, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98868.
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This thesis is a study of model predictive control of water levels and flows in a water system. The water system studied includes five lakes and six dams that are regulated manually by sluice-gates. The water is used in the papermaking process at Holmen Paper Mill in Hallstavik. The aim of this thesis is to find out how to control the water system when all dams are automated and to minimize the discharge of water from the system without risking production stops due to water shortage. To fulfil the aim, a simulation is made during a dry period with low amount of rain. The simulation is then compared to the same period but when the system was manually controlled. In this thesis two models of the water system are constructed, a simple linear model and a more complex non-linear model. In the linear model the channels between the lakes are assumed to be delays of water flow. In the non-linear model the same channels are described by Saint Venant equations of changes of flow and Manning’s equation on how water flow and the cross-section of a channel are related. In both models, the lakes are modelled as the change in volume with respect to time due to inflow to and outflow from the lake. The non-linear model is verified against measured water levels, flows, sluice-gate heights and precipitation to ensure that the model describes the water system well enough. The linear model is used in the model predictive controller to calculate the optimal outflow from the dams. The optimal outflows are then converted into optimum gate heights in the dams, which in turn are used as input to the non-linear model. The non-linear model is used to simulate the water system. The results from the simulation show that the control of the water system can significantly be improved. The conclusion of this thesis is that a lot more water can be saved when the system is automated and that the water levels in the lakes can be kept more stable with respect to a set reference level. The recommendation if only one dam is to be controlled initially is to start with the dam at Närdingen.
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Lundh, 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.
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This thesis discuss the possibility to position control a rotor levitated with active magnetic bearings. The controller type considered is model predictive control which is an online strategy that solves an optimization problem in every sample, making the model predictive controller computation-intense. Since the sampling time must be short to capture the dynamics of the rotor, very little time is left for the controller to perform the optimization. Different quadratic programming strategies are investigated to see if the problem can be solved in realtime. Additionally, the impact of the choices of prediction horizon, control horizon and terminal cost is discussed. Simulations showing the characteristics of these choises are made and the result is shown.Det 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.