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Relevant bibliographies by topics / Predictive control systems

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Academic literature on the topic 'Predictive control systems'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Predictive control systems"

1

Rosolia, Ugo, Xiaojing Zhang, and Francesco Borrelli. "Data-Driven Predictive Control for Autonomous Systems." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 259–86. http://dx.doi.org/10.1146/annurev-control-060117-105215.

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In autonomous systems, the ability to make forecasts and cope with uncertain predictions is synonymous with intelligence. Model predictive control (MPC) is an established control methodology that systematically uses forecasts to compute real-time optimal control decisions. In MPC, at each time step an optimization problem is solved over a moving horizon. The objective is to find a control policy that minimizes a predicted performance index while satisfying operating constraints. Uncertainty in MPC is handled by optimizing over multiple uncertain forecasts. In this case, performance index and o

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Flor Unda, Omar. "Adaptive control systems for solar collectors." Athenea 2, no. 4 (2021): 19–25. http://dx.doi.org/10.47460/athenea.v2i4.18.

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En este trabajo se presentan las estrategias de control del flujo de aceite mediante la técnica de Control Predictivo basado en Modelo, para el mecanismo de control del campo de colectores solares cilindros parabólicos. Se analiza el comportamiento dinámico del sistema con el uso del modelo matemático, una técnicade control self-tunning y controlador predictivo basado en modelo para el control de plantas tipo ACUREX. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel. References [1]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1997. Nonlin

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BOUKABOU, ABDELKRIM, ABDELHAMID CHEBBAH, and NOURA MANSOURI. "PREDICTIVE CONTROL OF CONTINUOUS CHAOTIC SYSTEMS." International Journal of Bifurcation and Chaos 18, no. 02 (2008): 587–92. http://dx.doi.org/10.1142/s0218127408020501.

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In this paper, a predictive control method is suggested for chaos control in continuous-time systems. This method combines the delayed feedback control of high order continuous-time chaotic systems with the prediction-based method of discrete-time chaotic systems. Moreover, we give necessary and sufficient conditions for exponential stabilization of unstable fixed points by the proposed method. Both control performance and system sensitivity to initial conditions of this approach are demonstrated by numerical simulations.

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Jones, Colin N., and Eric Kerrigan. "Predictive control for embedded systems." Optimal Control Applications and Methods 36, no. 5 (2015): 583–84. http://dx.doi.org/10.1002/oca.2213.

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Xia, Yuanqing, Li Li, Guo-Ping Liu, and Peng Shi. "H∞predictive control of networked control systems." International Journal of Control 84, no. 6 (2011): 1080–97. http://dx.doi.org/10.1080/00207179.2011.592219.

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Wu, Jing, Liqian Zhang, and Tongwen Chen. "Model predictive control for networked control systems." International Journal of Robust and Nonlinear Control 19, no. 9 (2009): 1016–35. http://dx.doi.org/10.1002/rnc.1361.

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Bahan, Т. H., V. P. Boun, O. S. Bunke, and K. O. Nadeliaev. "PREDICTIVE CONTROL IN CONTROL SYSTEMS OF MICROCLIMATE." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 1, no. 1 (2024): 91–96. http://dx.doi.org/10.32782/2663-5941/2024.1.1/14.

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Sathiyanarayanan, T. "Direct Predictive Power Control of Grid Tied Distributed Generator Systems." International Journal of Science and Research (IJSR) 11, no. 5 (2022): 586–91. http://dx.doi.org/10.21275/sr22506083745.

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Zhang, Yingwei, Xue Chen, and Renquan Lu. "Performance of Networked Control Systems." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/382934.

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Data packet dropout is a special kind of time delay problem. In this paper, predictive controllers for networked control systems (NCSs) with dual-network are designed by model predictive control method. The contributions are as follows. (1) The predictive control problem of the dual-network is considered. (2) The predictive performance of the dual-network is evaluated. (3) Compared to the popular networked control systems, the optimal controller of the new NCSs with data packets dropout is designed, which can minimize infinite performance index at each sampling time and guarantee the closed-lo

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Boukabou, A., and N. Mansouri. "Neural Predictive Control of Unknown Chaotic Systems." Nonlinear Analysis: Modelling and Control 10, no. 2 (2005): 95–106. http://dx.doi.org/10.15388/na.2005.10.2.15125.

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In this work, a neural networks is developed for modelling and controlling a chaotic system based on measured input-output data pairs. In the chaos modelling phase, a neural network is trained on the unknown system. Then, a predictive control mechanism has been implemented with the neural networks to reach the close neighborhood of the chosen unstable fixed point embedded in the chaotic systems. Effectiveness of the proposed method for both modelling and prediction-based control on the chaotic logistic equation and Hénon map has been demonstrated.

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Dissertations / Theses on the topic "Predictive control systems"

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Scokaert, Pierre O. M. "Constrained predictive control." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359508.

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Chang, Anton On Tak. "Multivariable predictive control." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336179.

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Shead, Leo R. E. "Predictive control of nonsquare systems." Thesis, University of Sheffield, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.505568.

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Ouyang, Hua. "Networked predictive control systems : control scheme and robust stability." Thesis, University of South Wales, 2007. https://pure.southwales.ac.uk/en/studentthesis/networked-predictive-control-systems(9c6178d7-e6a4-420b-b35f-2d62d35ff5b0).html.

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Networked predictive control is a new research method for Networked Control Systems (NCS), which is able to handle network-induced problems such as time-delay, data dropouts, packets disorders, etc. while stabilizing the closed-loop system. This work is an extension and complement of networked predictive control methodology. There is always present model uncertainties or physical nonlinearity in the process of NCS. Therefore, it makes the study of the robust control of NCS and that of networked nonlinear control system (NNCS) considerably important. This work studied the following three proble

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Yoon, Tae-Woong. "Robust adaptive predictive control." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359527.

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Chow, Chi-Ming. "Predictive control with constraints." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320170.

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O'Brien, Marie. "Predictive control of urban wastewater systems." Thesis, University of Strathclyde, 2006. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21658.

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Within recent years, technological advances and stricter regulatory requirements have seen the increased use of automation and instrumentation within the wastewater treatment industry. As a result, advanced control strategies are required, to fully exploit the potential of these complex systems in addressing water quality concerns. Model based control strategies can be appropriate within the multivariable constrained wastewater system. In particular, the inherent model based nature of this approach can be valuable in the prediction of the treatment plant effluent quality required over a consid

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Mu, Huiying. "Predictive control of linear uncertain systems." Thesis, Imperial College London, 2007. http://hdl.handle.net/10044/1/8515.

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Predictive control is a very useful tool in controlling constrained systems, since the constraints can be satisfied explicitly by the optimisations. Sets, namely, reachable sets, controllable sets, invariant sets, etc, play fundamental roles in designing predictive control strategies for uncertain systems. Meanwhile, in addition to the commonly assumed boundedness of the uncertainty, the explicit use of its stochastic properties can lead to improvement in system response. This thesis is concerned with robust set theories, mainly for reachable sets, with applications to time-optimal control; an

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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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Williams, Grevin Carlton. "Auxiliary measurements in predictive control." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357555.

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Books on the topic "Predictive control systems"

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Kanjilal, P. P. Adaptive prediction and predictive control. P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Ocampo-Martinez, Carlos. Model Predictive Control of Wastewater Systems. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-353-4.

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Karer, Gorazd. Predictive Approaches to Control of Complex Systems. Springer Berlin Heidelberg, 2013.

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Mosca, E. Optimal, predictive, and adaptive control. Prentice Hall, 1995.

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A, Linkens D., ed. Generalised predictive control and bioengineering. Taylor & Francis, 1998.

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Karer, Gorazd, and Igor Škrjanc. Predictive Approaches to Control of Complex Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33947-9.

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1930-, Mayne David Q., ed. Model predictive control: Theory and design. Nob Hill Pub., 2009.

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Zou, Yuanyuan, and Shaoyuan Li. Distributed Cooperative Model Predictive Control of Networked Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6084-0.

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Li, Shaoyuan, and Yi Zheng. Distributed Model Predictive Control for Plant-Wide Systems. John Wiley & Sons, Singapore Pte. Ltd, 2015. http://dx.doi.org/10.1002/9781118921579.

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Yaramasu, Venkata, and Bin Wu. Model Predictive Control of Wind Energy Conversion Systems. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119082989.

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Book chapters on the topic "Predictive control systems"

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Camacho, E. F., and C. Bordons. "Model Predictive Control and Hybrid Systems." In Model Predictive control. Springer London, 2007. http://dx.doi.org/10.1007/978-0-85729-398-5_10.

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López, César Pérez. "Robust Predictive Control." In MATLAB Control Systems Engineering. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0289-0_4.

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Bemporad, Alberto, and Davide Barcelli. "Decentralized Model Predictive Control." In Networked Control Systems. Springer London, 2010. http://dx.doi.org/10.1007/978-0-85729-033-5_5.

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Sunan, Huang, Tan Kok Kiong, and Lee Tong Heng. "Adaptive Predictive Control of a Class of SISO Non-Linear Systems." In Applied Predictive Control. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3725-2_7.

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Patan, Krzysztof. "Model Predictive Control." In Studies in Systems, Decision and Control. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11869-3_4.

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Bemporad, Alberto, and Manfred Morari. "Predictive Control of Constrained Hybrid Systems." In Nonlinear Model Predictive Control. Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8407-5_4.

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Grüne, Lars, and Jürgen Pannek. "Numerical Optimal Control of Nonlinear Systems." In Nonlinear Model Predictive Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-501-9_10.

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Grüne, Lars, and Jürgen Pannek. "Discrete Time and Sampled Data Systems." In Nonlinear Model Predictive Control. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-501-9_2.

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Grüne, Lars, and Jürgen Pannek. "Numerical Optimal Control of Nonlinear Systems." In Nonlinear Model Predictive Control. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46024-6_12.

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Grüne, Lars, and Jürgen Pannek. "Discrete Time and Sampled Data Systems." In Nonlinear Model Predictive Control. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46024-6_2.

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Conference papers on the topic "Predictive control systems"

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Beger, Severin, and Sandra Hirche. "A Robust Model Predictive Control Method for Networked Control Systems." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886340.

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Sun, Jian, Jie Chen, and Lihua Dou. "Networked predictive control for linear systems with unknown communication delay." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915219.

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Adelipour, Saeed, Mohammad Haeri, and Gabriele Pannocchia. "Decentralized Robust Model Predictive Control for Multi-Input Linear Systems." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516722.

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Mussi Brugnolli, Mateus, and Bruno Augusto Angélico. "Explicit Model Predictive Control for Inverted Pendulum Systems." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1523.

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Model Predictive Control is a control technique that has been greatly investigated in recent years. It has the versatility of different types of models for the prediction of the system and aptitude to handle the system constraints. In the last decade, the multi-parametric optimization has been applied to the control theory that allowed for the MPC optimization to be performed offline, which was denominated as explicit Model Predictive Control. This work investigates the application of this control technique in Inverted Pendulum systems, which are commonly used as didactic control systems. The

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Spiller, Mark, Fateme Bakhshande, and Dirk Söffker. "Adaptive Neural Network Based Predictive Control of Nonlinear Systems With Slow Dynamics." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22358.

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Abstract In this paper a data-driven approach for model-free control of nonlinear systems with slow dynamics is proposed. The system behavior is described using a local model respectively a neural network. The network is updated online based on a Kalman filter. By predicting the system behavior two control approaches are discussed. One is obtained by calculating a control input from the one step ahead prediction equation using least squares, the other is obtained by solving a standard linear model predictive control problem. The approaches are tested on a constrained nonlinear MIMO system with

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Crassidis, John, and F. Markley. "Predictive filtering for nonlinear systems." In Guidance, Navigation, and Control Conference. American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-3775.

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Qu, Zukun, Boutaieb Dahhou, and Gilles Roux. "Fault tolerant control system based on subspace predictive control and multiple model predictive control." In 2016 Conference on Control and Fault-Tolerant Systems (SysTol). IEEE, 2016. http://dx.doi.org/10.1109/systol.2016.7739834.

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Hadi Rezaei, Mohammad, and P. Jabehdar Maralani. "Application of hierarchical control in Generalized Predictive Control." In Advanced Systems (ICIAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icias.2010.5716237.

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Oshin, Alex, Hassan Almubarak, and Evangelos Theodorou. "Differentiable Robust Model Predictive Control." In Robotics: Science and Systems 2024. Robotics: Science and Systems Foundation, 2024. http://dx.doi.org/10.15607/rss.2024.xx.003.

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Zhang, Jianhua, Ting Zhang, Mingming Lin, Guolian Hou, and Kang Li. "Multiple model predictive control for organic rankine cycle (ORC) based waste heat energy conversion systems." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737577.

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Reports on the topic "Predictive control systems"

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Haves, Phillip, Brandon Hencey, Francesco Borrell, et al. Model Predictive Control of HVAC Systems: Implementation and Testing at the University of California, Merced. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/988177.

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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task

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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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Ajjarapu, Venkataramana, Amarsagar Matavalam, Alok Kumar Bharati, et al. Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1785126.

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Kushner, Mark. DOE Plasma Science Center - Predictive Control of Plasma Kinetics: Multi-Phase and Bounded Systems (Final Report DE-SC0001939). Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1837426.

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Apiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317444.

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The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow a

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Cohen, Herbert E. Prediction of Input Control for Time Invariant Open Loop Combat-Control System. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada261510.

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Kalashnikova, Irina, Srinivasan Arunajatesan, Matthew Franklin Barone, Bart Gustaaf van Bloemen Waanders, and Jeffrey A. Fike. Reduced Order Modeling for Prediction and Control of Large-Scale Systems. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1177206.

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Shin, Insub, and Alexander H. Levis. Performance Prediction of Real-Time Command, Control, and Communications (C3) Systems. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada388093.

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Konsam, Manis Kumar, Amanda Thounajam, Prasad Vaidya, Gopikrishna A, Uthej Dalavai, and Yashima Jain. Machine Learning-Enhanced Control System for Optimized Ceiling Fan and Air Conditioner Operation for Thermal Comfort. Indian Institute for Human Settlements, 2024. http://dx.doi.org/10.24943/mlcsocfacotc6.2023.

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This paper proposes and tests the implementation of a sustainable cooling approach that uses a machine learning model to predict operative temperatures, and an automated control sequence that prioritises ceiling fans over air conditioners. The robustness of the machine learning model (MLM) is tested by comparing its prediction with that of a straight-line model (SLM) using the metrics of Mean Bias Error (MBE) and Root Mean Squared Error (RMSE). This comparison is done across several rooms to see how each prediction method performs when the conditions are different from those of the original ro

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