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Relevant bibliographies by topics / Model predictive control

Academic literature on the topic 'Model predictive control'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 September 2024

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Dissertations / Theses
Books
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Journal articles on the topic "Model predictive control"

1

Wieber, Pierre-Brice. "Model Predictive Control for Biped Walking Motion Generation." Journal of the Robotics Society of Japan 32, no. 6 (2014): 503–7. http://dx.doi.org/10.7210/jrsj.32.503.

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Ohshima, Masahiro, Iori Hashimoto, Takeichiro Takamatsu, and Hiromu Ohno. "Model predictive control with disturbance prediction." KAGAKU KOGAKU RONBUNSHU 13, no. 5 (1987): 589–95. http://dx.doi.org/10.1252/kakoronbunshu.13.589.

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Magni, L. "Nonlinear Model Predictive Control: Control and Prediction Horizon." IFAC Proceedings Volumes 33, no. 13 (June 2000): 213–18. http://dx.doi.org/10.1016/s1474-6670(17)37192-6.

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Ding, Baocang, Marcin T. Cychowski, Yugeng Xi, Wenjian Cai, and Biao Huang. "Model Predictive Control." Journal of Control Science and Engineering 2012 (2012): 1–2. http://dx.doi.org/10.1155/2012/240898.

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van den Boom, J. J. "Model predictive control." Control Engineering Practice 10, no. 9 (September 2002): 1038–39. http://dx.doi.org/10.1016/s0967-0661(02)00061-8.

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Alamir, Mazen, and Frank Allgöwer. "Model Predictive Control." International Journal of Robust and Nonlinear Control 18, no. 8 (2008): 799. http://dx.doi.org/10.1002/rnc.1266.

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Muske, Kenneth R., and James B. Rawlings. "Model predictive control with linear models." AIChE Journal 39, no. 2 (February 1993): 262–87. http://dx.doi.org/10.1002/aic.690390208.

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Hewing, Lukas, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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Abstract:

Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC with learning methods, for which we consider three main categories. Most of the research addresses learning for automatic improvement of the prediction model from recorded data. There is, however, also an increasing interest in techniques to infer the parameterization of the MPC controller, i.e., the cost and constraints, that lead to the best closed-loop performance. Finally, we discuss concepts that leverage MPC to augment learning-based controllers with constraint satisfaction properties.

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Carron, Andrea, and Melanie N. Zeilinger. "Model Predictive Coverage Control." IFAC-PapersOnLine 53, no. 2 (2020): 6107–12. http://dx.doi.org/10.1016/j.ifacol.2020.12.1686.

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Mårtensson, Karl, and Andreas Wernrud. "Dynamic Model Predictive Control." IFAC Proceedings Volumes 41, no. 2 (2008): 13182–87. http://dx.doi.org/10.3182/20080706-5-kr-1001.02233.

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Dissertations / Theses on the topic "Model predictive control"

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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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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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Books on the topic "Model predictive control"

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Camacho, E. F., and C. Bordons. Model Predictive control. London: Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5.

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Zhang, Ridong, Anke Xue, and Furong Gao. Model Predictive Control. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0083-7.

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Camacho, Eduardo F., and Carlos Bordons. Model Predictive Control. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8.

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Kouvaritakis, Basil, and Mark Cannon. Model Predictive Control. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24853-0.

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Camacho, E. F. Model predictive control. London: Springer, 2003.

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Camacho, E. F. Model predictive control. 2nd ed. New York: Springer, 2004.

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1962-, Bordons C., ed. Model predictive control. Berlin: Springer, 1999.

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Allgöwer, Frank. Nonlinear Model Predictive Control. Basel: Birkhäuser Basel, 2000.

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Allgöwer, Frank, and Alex Zheng, eds. Nonlinear Model Predictive Control. Basel: Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8407-5.

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Ellis, Matthew, Jinfeng Liu, and Panagiotis D. Christofides. Economic Model Predictive Control. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41108-8.

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Book chapters on the topic "Model predictive control"

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Camacho, Eduardo F., and Carlos Bordons. "Generalized Predictive Control." In Model Predictive Control, 51–83. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-3398-8_4.

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Camacho, E. F., and C. Bordons. "Generalized Predictive Control." In Model Predictive control, 47–79. London: Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5_4.

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Camacho, E. F., and C. Bordons. "Model Predictive Controllers." In Model Predictive control, 13–30. London: Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5_2.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Model Predictive Control Based on Extended State Space Model." In Model Predictive Control, 17–27. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_2.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Introduction." In Model Predictive Control, 1–14. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_1.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Industrial Application." In Model Predictive Control, 109–25. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_10.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Further Ideas on MPC and PFC Using Relaxed Constrained Optimization." In Model Predictive Control, 127–37. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_11.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Predictive Functional Control Based on Extended State Space Model." In Model Predictive Control, 29–35. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_3.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Model Predictive Control Based on Extended Non-minimal State Space Model." In Model Predictive Control, 37–50. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_4.

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Zhang, Ridong, Anke Xue, and Furong Gao. "Predictive Functional Control Based on Extended Non-minimal State Space Model." In Model Predictive Control, 51–57. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0083-7_5.

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Conference papers on the topic "Model predictive control"

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Romero, Angel, Yunlong Song, and Davide Scaramuzza. "Actor-Critic Model Predictive Control." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 14777–84. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610381.

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Irwin, G. W. "Predictive control using multiple model networks." In IEE Colloquium on Model Predictive Control: Techniques and Applications Day 1. IEE, 1999. http://dx.doi.org/10.1049/ic:19990533.

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Sandoz, D. J. "Innovation in industrial model predictive control." In IEE Colloquium on Model Predictive Control: Techniques and Applications Day 2. IEE, 1999. http://dx.doi.org/10.1049/ic:19990542.

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Ordys, A. W. "Predictive control in power generation." In IEE Colloquium on Model Predictive Control: Techniques and Applications Day 2. IEE, 1999. http://dx.doi.org/10.1049/ic:19990545.

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Roberts, P. D. "A brief overview of model predictive control." In IEE Colloquium on Model Predictive Control: Techniques and Applications Day 1. IEE, 1999. http://dx.doi.org/10.1049/ic:19990529.

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Mayne, D. "Model predictive control: the challenge of uncertainty." In IEE Colloquium on Model Predictive Control: Techniques and Applications Day 1. IEE, 1999. http://dx.doi.org/10.1049/ic:19990534.

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Sadowska, Anna, Leo Steenson, and Magnus Hedlund. "Model-Predictive Control of a Compliant Hydraulic System." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516830.

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Sandoz, D. J. "Innovation in industrial model predictive control." In IEE Seminar on Practical Experiences with Predictive Control. IEE, 2000. http://dx.doi.org/10.1049/ic:20000117.

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Veres, S. M. "Synergy of predictive control and identification." In IEE Colloquium on Model Predictive Control: Techniques and Applications Day 1. IEE, 1999. http://dx.doi.org/10.1049/ic:19990532.

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Becerra, V. M. "Integrating predictive control and economic optimisation." In IEE Colloquium on Model Predictive Control: Techniques and Applications Day 2. IEE, 1999. http://dx.doi.org/10.1049/ic:19990540.

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Reports on the topic "Model predictive control"

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Baum, C. C., K. L. Buescher, V. Hanagandi, R. Jones, and K. Lee. Adaptive model predictive control using neural networks. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10178912.

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Castanon, David A., and Jerry M. Wohletz. Model Predictive Control for Dynamic Unreliable Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada409519.

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B. Wayne Bequette and Priyadarshi Mahapatra. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants. Office of Scientific and Technical Information (OSTI), August 2010. http://dx.doi.org/10.2172/1026486.

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Li, Dinggen, and Yang Ye. The Control of Air-Fuel Ratio of the Engine Based on Model Predictive Control. Warrendale, PA: SAE International, October 2012. http://dx.doi.org/10.4271/2012-32-0050.

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Ollerenshaw, Douglas, and Mark Costello. Model of Predictive Control of a Direct-Fire Projectile Equipped With Canards. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada432823.

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Bouffard, Patrick. On-board Model Predictive Control of a Quadrotor Helicopter: Design, Implementation, and Experiments. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada572108.

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Aswani, Anil, Humberto Gonzalez, S. S. Sastry, and Claire Tomlin. Statistical Results on Filtering and Epi-convergence for Learning-Based Model Predictive Control. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada558989.

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Bryson, Joshua, and Benjamin Gruenwald. Linear Parameter Varying (LPV) Model Predictive Control (MPC) of a High-Speed Projectile. Aberdeen Proving Ground, MD: DEVCOM Army Research Laboratory, September 2021. http://dx.doi.org/10.21236/ad1150280.

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Ponciroli, R., and R. Vilim. Analyses of Model-Based Predictive Control for a S-CO2 Brayton Cycle Power Converter. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1962742.

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Haves, Phillip, Brandon Hencey, Francesco Borrell, John Elliot, Yudong Ma, Brian Coffey, Sorin Bengea, and Michael Wetter. Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced. Office of Scientific and Technical Information (OSTI), June 2010. http://dx.doi.org/10.2172/988177.

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