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Relevant bibliographies by topics / Model predictive control

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Academic literature on the topic 'Model predictive control'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Model predictive control"

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Gwon, Jun, Jong-Seok Kim, Young-Seok Lee, Oluleke Babayomi, and Ki-Bum Park. "A Model-Free Predictive Control for IPMSM Drive Based on Finite Control Set." TRANSACTIONS OF KOREAN INSTITUTE OF POWER ELECTRONICS 30, no. 2 (2025): 165–72. https://doi.org/10.6113/tkpe.2025.30.2.165.

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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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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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Magni, L. "Nonlinear Model Predictive Control: Control and Prediction Horizon." IFAC Proceedings Volumes 33, no. 13 (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 (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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Nabil, Farah, H. N. Talib M., Ibrahim Z., et al. "Analysis and investigation of different advanced control strategies for high-performance induction motor drives." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 6 (2020): 3303~3314. https://doi.org/10.12928/TELKOMNIKA.v18i6.15342.

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Induction motor (IM) drives have received a strong interest from researchers and industry particularly for high-performance AC drives through vector control method. With the advancement in power electronics and digital signal processing (DSP), high capability processors allow the implementation of advanced control techniques for motor drives such as model predictive control (MPC). In this paper, design, analysis and investigation of two different MPC techniques applied to IM drives; the model predictive torque control (MPTC) and model predictive current control (MPCC) are presented. The two te

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Alakkad, Moataz M. A., Md. Hairul Nizam Talib, Zulhani Rasin, Jurifa Mat Lazi, Muhammad Haziq Md. Jamal, and Zainuddin Mat Isa. "Overview: Model predictive control techniques for controlling induction motor based on vector control." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 4 (2024): 2049–57. https://doi.org/10.11591/ijpeds.v15.i4.pp2049-2057.

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This paper presents a comprehensive review of electric induction motor (IM) drive systems. It conducts an evaluation and critical analysis of modern control techniques aimed at enhancing induction motors or IM drive performance, drawing insights from a systematic literature survey. This review paper introduces the mathematical and dynamic models of induction motors and control via two-level inverter drives. Furthermore, the paper offers an extensive review of model predictive control (MPC) for induction motors which is considered a vector control (VC) technique. The MPC are subdivision based o

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

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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 performan

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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-ba

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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

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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

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Books on the topic "Model predictive control"

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Camacho, E. F., and C. Bordons. Model Predictive control. 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. 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. 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. 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. Springer, 2003.

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

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

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Daniilidis, Aris, Lars Grüne, Josef Haunschmied, and Gernot Tragler, eds. Model Predictive Control. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85256-5.

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

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

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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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610381.

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Kopp, Fionna B., and Francesco Borrelli. "Data-Driven Multi-Modal Learning Model Predictive Control." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886732.

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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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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), 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. Defense Technical Information Center, 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), 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. SAE International, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. Defense Technical Information Center, 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. DEVCOM Army Research Laboratory, 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), 2016. http://dx.doi.org/10.2172/1962742.

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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject

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