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Dissertations / Theses on the topic 'Supervisory model predictive control'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

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.

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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 contr

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2

Boussemart, Yves 1980. "Predictive models of procedural human supervisory control behavior." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/79543.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.<br>Page 150 blank. Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 138-149).<br>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.

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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.

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To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energ

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4

Abraham, 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.

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Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both

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5

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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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 performan

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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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8

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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9

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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11

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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12

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-ba

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13

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

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14

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

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15

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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<p>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 s

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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.

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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 I

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17

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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19

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.<br>Includes bibliographical references (p. 203-209).<br>(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

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20

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

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21

Bell, Geoffrey Laurence. "Robust model predictive control design." Thesis, Imperial College London, 2000. http://hdl.handle.net/10044/1/7450.

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22

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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23

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 su

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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

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25

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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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

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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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28

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 k

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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

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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 c

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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 cas

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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.<br>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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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 controlle

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36

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 com

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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 choic

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Al, Seyab Rihab Khalid Shakir. "Nonlinear model predictive control using automatic differentiation." Thesis, Cranfield University, 2006. http://hdl.handle.net/1826/1491.

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Although nonlinear model predictive control (NMPC) might be the best choice for a nonlinear plant, it is still not widely used. This is mainly due to the computational burden associated with solving online a set of nonlinear differential equations and a nonlinear dynamic optimization problem in real time. This thesis is concerned with strategies aimed at reducing the computational burden involved in different stages of the NMPC such as optimization problem, state estimation, and nonlinear model identification. A major part of the computational burden comes from function and derivative evaluati

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Ma, Yudong. "Model Predictive Control for Energy Efficient Buildings." Thesis, University of California, Berkeley, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3593911.

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<p> The 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. </p><p> Commercial buildings are plants that process air in o

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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.<br>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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<p>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

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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.

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Löfberg, 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.

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Controlling a system with control and state constraints is one of the most important problems in control theory, but also one of the most challenging. Another important but just as demanding topic is robustness against uncertainties in a controlled system. One of the most successful approaches, both in theory and practice, to control constrained systems is model predictive control (MPC). The basic idea in MPC is to repeatedly solve optimization problems on-line to find an optimal input to the controlled system. In recent years, much effort has been spent to incorporate the robustness problem i

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Friedbaum, Jesse Robert. "Model Predictive Linear Control with Successive Linearization." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7063.

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Robots have been a revolutionizing force in manufacturing in the 20th and 21st century but have proven too dangerous around humans to be used in many other fields including medicine. We describe a new control algorithm for robots developed by the Brigham Young University Robotics and Dynamics and Robotics Laboratory that has shown potential to make robots less dangerous to humans and suitable to work in more applications. We analyze the computational complexity of this algorithm and find that it could be a feasible control for even the most complicated robots. We also show conditions for a sys

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Bereza-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.

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This thesis investigates the potential advantages and disadvantages of using adistributed control approach to land an autonomous drone on an autonomousboat. The expected advantages include better utilisation of computational resources,as well as increased robustness towards communication delays. Inthis context, distributed control means that separate computers on the droneand boat are both involved in computing the control inputs to the system. Thisstands in contrast to an existing centralised algorithm where all computationsfor finding the control input are performed on the drone. Two new alg

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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.

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This master thesis studies the implementation of a Robust MPC controllerin marine vessels on different tasks. A tube based MPC is designed based onsystem linearization around the target point guaranteeing local input to statestability of the respective linearized version of the original nonlinear system.The method is then applied to three different tasks: Dynamic positioningon which recursive feasibility of the nominal MPC is also guaranteed, Speed-Heading control and trajectory tracking with the Line of sight algorithm.Numerical simulation is then provided to show technique’s effectiveness.<b

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Breger, Louis Scott 1979. "Model predictive control for formation flying spacecraft." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17758.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.<br>Includes bibliographical references (p. 105-114).<br>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

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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.

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We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC) is presented as a suitable method to iteratively improve an overtaking manoeuvre each time it is performed. We present recent extensions to the LMPC framework to make it applicable to overtaking. Furthermore, we also present two alternative modelling approaches with the intention of reducing computational complexity of the optimization problems solved by the controller. All proposed frameworks are built f

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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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