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Journal articles on the topic 'Predictive control systems'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Aulia, Lathifatul, Widowati Widowati, R. Heru Tjahjana, and Sutrisno Sutrisno. "MODELING PREDICTIVE TRACKING CONTROL FOR MAX-PLUS LINEAR SYSTEMS IN MANUFACTURING." Journal of Fundamental Mathematics and Applications (JFMA) 3, no. 2 (2020): 133–47. http://dx.doi.org/10.14710/jfma.v3i2.8605.

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Discrete event systems, also known as DES, are class of system that can be applied to systems having an event that occurred instantaneously and may change the state. It can also be said that a discrete event system occurs under certain conditions for a certain period because of the network that describes the process flow or sequence of events. Discrete event systems belong to class of nonlinear systems in classical algebra. Based on this situation, it is necessary to do some treatments, one of which is linearization process. In the other hand, a Max-Plus Linear system is known as a system that

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12

Ouyang, H., G. P. Liu, D. Rees, and W. Hu. "Predictive control of networked non-linear control systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 3 (2007): 453–63. http://dx.doi.org/10.1243/09596518jsce271.

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13

Zhao, Y. B., D. Rees, and G. P. Liu. "Improved predictive control approach to networked control systems." IET Control Theory & Applications 2, no. 8 (2008): 675–81. http://dx.doi.org/10.1049/iet-cta:20070363.

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14

Yang, Rongni, Guo-Ping Liu, Peng Shi, Clive Thomas, and Michael V. Basin. "Predictive Output Feedback Control for Networked Control Systems." IEEE Transactions on Industrial Electronics 61, no. 1 (2014): 512–20. http://dx.doi.org/10.1109/tie.2013.2248339.

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15

Shields, Bobby L., Eric J. Barth, and Michael Goldfarb. "Predictive Control for Time-Delayed Switching Control Systems." Journal of Dynamic Systems, Measurement, and Control 128, no. 4 (2006): 999–1004. http://dx.doi.org/10.1115/1.2363204.

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A methodology is proposed for the control of switching systems characterized by linear system dynamics with a time delay in the input channel. The method incorporates a state predictor that at each switching period determines the effect that the next control input will have on the future output of the system, and chooses the input that will take the system closest to the desired future state. The resulting control action is suboptimal, but is computationally tractable and shown to provide a bounded tracking error for stable plants. The proposed predictive control methodology is demonstrated on

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16

Xia, Yuanqing, Wen Xie, Bo Liu, and Xiaoyun Wang. "Data-driven predictive control for networked control systems." Information Sciences 235 (June 2013): 45–54. http://dx.doi.org/10.1016/j.ins.2012.01.047.

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17

Liu, Bo, Yuanqing Xia, Magdi S. Mahmoud, Harris Wu, and Shisheng Cui. "New Predictive Control Scheme for Networked Control Systems." Circuits, Systems, and Signal Processing 31, no. 3 (2011): 945–60. http://dx.doi.org/10.1007/s00034-011-9359-9.

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18

Onat, Ahmet, A. Teoman Naskali, and Emrah Parlakay. "Model Based Predictive Networked Control Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 13000–13005. http://dx.doi.org/10.3182/20080706-5-kr-1001.02198.

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19

Niemi, A. J., L. Tian, and R. Ylinen. "Model predictive control for grinding systems." Control Engineering Practice 5, no. 2 (1997): 271–78. http://dx.doi.org/10.1016/s0967-0661(97)00236-0.

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20

Goggos, V., and R. E. King. "Stochastic predictive control of mechatronic systems." Mechatronics 7, no. 2 (1997): 129–40. http://dx.doi.org/10.1016/s0957-4158(96)00042-6.

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21

Brunner, Florian David, and Frank Allgöwer. "Approximate Predictive Control of Polytopic Systems." IFAC Proceedings Volumes 47, no. 3 (2014): 11060–66. http://dx.doi.org/10.3182/20140824-6-za-1003.00546.

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22

Aboukheir, Hanna. "Predictive Control of Fractional Order Systems." IFAC Proceedings Volumes 45, no. 13 (2012): 622–26. http://dx.doi.org/10.3182/20120620-3-dk-2025.00152.

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23

Sun, Jian, Guo-Ping Liu, Jie Chen, and David Rees. "Networked predictive control for Hammerstein systems." Asian Journal of Control 13, no. 2 (2010): 265–72. http://dx.doi.org/10.1002/asjc.281.

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24

Mhaskar, Prashant, Nael H. El-Farra, and Panagiotis D. Christofides. "Hybrid predictive control of process systems." AIChE Journal 50, no. 6 (2004): 1242–59. http://dx.doi.org/10.1002/aic.10115.

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25

Zhang, Zhiming, John Anthony Rossiter, Lei Xie, and Hongye Su. "Predictive functional control for integrator systems." Journal of the Franklin Institute 357, no. 7 (2020): 4171–86. http://dx.doi.org/10.1016/j.jfranklin.2020.01.026.

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26

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 (2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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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

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27

Soeterboek, Ronald. "Predictive control." Automatica 30, no. 7 (1994): 1213–15. http://dx.doi.org/10.1016/0005-1098(94)90217-8.

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28

Albi, Giacomo, and Lorenzo Pareschi. "Selective model-predictive control for flocking systems." Communications in Applied and Industrial Mathematics 9, no. 2 (2018): 4–21. http://dx.doi.org/10.2478/caim-2018-0009.

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Abstract In this paper the optimal control of alignment models composed by a large number of agents is investigated in presence of a selective action of a controller, acting in order to enhance consensus. Two types of selective controls have been presented: an homogeneous control filtered by a selective function and a distributed control active only on a selective set. As a first step toward a reduction of computational cost, we introduce a model predictive control (MPC) approximation by deriving a numerical scheme with a feedback selective constrained dynamics. Next, in order to cope with the

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29

Chen, Zai Ping, and Xue Wang. "Research on Networked Control Systems Based on Adaptive Predictive Control." Applied Mechanics and Materials 441 (December 2013): 833–36. http://dx.doi.org/10.4028/www.scientific.net/amm.441.833.

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According to the random time-delay exist in sensor-controller channel and controller-actuator channel in networked control systems, an adaptive predictive control strategy was proposed. In this control strategy, an improved generalized predictive control algorithm is adopted to compensate the networked random time-delay. In addition, using the recursive least squares with a variable forgetting factor algorithm to indentify the model parameters of controlled object on-line, through the way, it could adjust the systems with unknown parameters adaptively. Simulation results show that the adaptive

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30

Muratbakeev, Eduard, Yuriy Kozhubaev, Diana Novak, Elena Kuzmenko, and Yiming Yao. "Research of Control Systems and Predictive Diagnostics of Electric Motors." Symmetry 17, no. 5 (2025): 751. https://doi.org/10.3390/sym17050751.

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Nowadays, electric motors are an integral part of most modern electromechanical systems that are used in industry. It follows that industrial processes are becoming more dependent on their efficiency. If faults in electric motors are not rectified, they can lead to malfunctions and accidents, as well as production downtime. Symmetry of a three-phase system means that the voltage and current in the three phase conductors are equal to each other, with a period of 120°. Asymmetry occurs if one of these conditions or both conditions are violated at the same time. In most cases, asymmetry is caused

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31

Chen, Wen-Hua, Donald J. Ballance, and Peter J. Gawthrop. "Optimal control of nonlinear systems: a predictive control approach." Automatica 39, no. 4 (2003): 633–41. http://dx.doi.org/10.1016/s0005-1098(02)00272-8.

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32

Mutha, Rajendra K., William R. Cluett, and Alexander Penlidis. "Nonlinear model-based predictive control of control nonaffine systems." Automatica 33, no. 5 (1997): 907–13. http://dx.doi.org/10.1016/s0005-1098(96)00220-8.

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33

Schweizer, Jörg, and Michael Peter Kennedy. "Predictive Poincaré control: A control theory for chaotic systems." Physical Review E 52, no. 5 (1995): 4865–76. http://dx.doi.org/10.1103/physreve.52.4865.

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34

Shen, Qing, Weihua Gui, Ying Xiong, and Chunhua Yang. "Predictive control and scheduling codesign in network control systems." Journal of Control Theory and Applications 8, no. 2 (2010): 239–44. http://dx.doi.org/10.1007/s11768-010-7277-1.

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35

Yang, Zhaohua, Wen Jiang, Xia Liu, and Zhenqiang Qi. "Predictive control of aircraft control systems for maneuverable target." Journal of the Franklin Institute 355, no. 18 (2018): 9036–52. http://dx.doi.org/10.1016/j.jfranklin.2016.10.001.

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36

Nozdrenkov, Valerii, Andrii Pavlov, Galyna Oleksiienko, Oleksandr Zhuravlov, and Yurii Zhuravlov. "Predictive control of heating systems using IoT and predictive analytics." Bulletin of the National Technical University "KhPI". Series: Energy: Reliability and Energy Efficiency, no. 2 (9) (December 29, 2024): 47–56. https://doi.org/10.20998/eree.2024.2(9).317625.

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The article explores the principles and methods of predictive control for heating systems using Internet of Things (IoT) technologies and predictive analytics to optimize energy consumption and maintain indoor temperature stability. The proposed model is designed to control the heating process in buildings by incorporating heat loss dynamics and leveraging external temperature forecasting. IoT sensors and external data from the OpenWeatherMap cloud service gather real-time environmental and system data. Predictive algorithms process this data to proactively generate control signals that adjust

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37

Zhongda, Tian, Li Shujiang, Wang Yanhong, and Wang Xiangdong. "Mixed-kernel least square support vector machine predictive control based on improved free search algorithm for nonlinear systems." Transactions of the Institute of Measurement and Control 40, no. 16 (2018): 4382–96. http://dx.doi.org/10.1177/0142331217748193.

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Many controlled objects in the actual industrial process are nonlinear systems, and the traditional control theory cannot achieve very good control effect. Based on swarm intelligence optimization algorithm, the nonlinear prediction and predictive control algorithm, this paper put forwards a nonlinear systems predictive control method based on the mixed-kernel least square support vector machine (LSSVM) model and improved free search (IFS) algorithm. The mixed-kernel LSSVM combines the advantages of radial basis function (RBF) and the Polynomial function, which can achieve a better prediction

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38

Lucia, Sergio, Markus Kögel, Pablo Zometa, Daniel E. Quevedo, and Rolf Findeisen. "Predictive control, embedded cyberphysical systems and systems of systems – A perspective." Annual Reviews in Control 41 (2016): 193–207. http://dx.doi.org/10.1016/j.arcontrol.2016.04.002.

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39

Liu, Jing, and Yu Chi Zhao. "Predictive Control Algorithm in the Application of Computer Control." Advanced Materials Research 756-759 (September 2013): 736–39. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.736.

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According to the error between the model predictive output and the future expected output, ILPC carries out an iterative learning and amending process on the current and the future control input vector, namely performs forecast, iterative amendment, forecast again, iterative amendment again repeatedly in the iterative domain. This paper gives a fast high-precision temperature control systems structure, characteristics and realization method. CARI model and the improved generalized predictive control method are used to improve the control precision of the system, and shorten the response time.

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40

v. MOHRENSCHILDT, MARTIN. "PREDICTIVE TRACES IN HYBRID SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 15, no. 02 (2005): 289–98. http://dx.doi.org/10.1142/s0218194005002373.

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Hybrid systems are ideal models of real control systems where the discrete controller interacts with the continuous system on a closed loop via A/D converters and digitally controlled actuators. We extend the notion of a predictive controller to hybrid systems by introducing the notion of predictive traces, the set of all possible traces of a hybrid system starting in some state and mode into the future. A control algorithm is developed that explores this set of predictive traces to determine the next control action.

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41

Li, He, Robert J. Frei, and Patrick M. Wensing. "Model Hierarchy Predictive Control of Robotic Systems." IEEE Robotics and Automation Letters 6, no. 2 (2021): 3373–80. http://dx.doi.org/10.1109/lra.2021.3061322.

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42

Rosolia, Ugo, and Aaron D. Ames. "Iterative Model Predictive Control for Piecewise Systems." IEEE Control Systems Letters 6 (2022): 842–47. http://dx.doi.org/10.1109/lcsys.2021.3086561.

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43

Camacho, E. F., D. R. Ramírez, D. Limón, D. Muñoz de la Peña, and T. Álamo,. "Model Predictive Control techniques for Hybrid Systems." IFAC Proceedings Volumes 42, no. 17 (2009): 1–13. http://dx.doi.org/10.3182/20090916-3-es-3003.00003.

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44

Ewald, Grzegorz, and Mietek A. Brdys. "Model Predictive Controller for Networked Control Systems." IFAC Proceedings Volumes 43, no. 8 (2010): 274–79. http://dx.doi.org/10.3182/20100712-3-fr-2020.00046.

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45

Marek, Kubalcik, and Bobal Vladimir. "Predictive control of multivariable time-delay systems." MATEC Web of Conferences 125 (2017): 02023. http://dx.doi.org/10.1051/matecconf/201712502023.

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46

Yin, Yanyan, and Fei Liu. "Constrained predictive control of nonlinear stochastic systems." Journal of Systems Engineering and Electronics 21, no. 5 (2010): 859–67. http://dx.doi.org/10.3969/j.issn.1004-4132.2010.05.021.

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47

Sotomayor, Oscar A. Z., and Darci Odloak. "PERFORMANCE ASSESSMENT OF MODEL PREDICTIVE CONTROL SYSTEMS." IFAC Proceedings Volumes 39, no. 2 (2006): 875–80. http://dx.doi.org/10.3182/20060402-4-br-2902.00875.

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48

Pham, Van Thang, Didier Georges, and Gildas Besançon. "Infinite-Dimensional Predictive Control for Hyperbolic Systems." SIAM Journal on Control and Optimization 52, no. 6 (2014): 3592–617. http://dx.doi.org/10.1137/110838200.

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49

Zhang, L., and B. Huang. "Robust Model Predictive Control of Singular Systems." IEEE Transactions on Automatic Control 49, no. 6 (2004): 1000–1006. http://dx.doi.org/10.1109/tac.2004.829634.

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50

Lazar, M., W. P. M. H. Heemels, S. Weiland, and A. Bemporad. "Stabilizing Model Predictive Control of Hybrid Systems." IEEE Transactions on Automatic Control 51, no. 11 (2006): 1813–18. http://dx.doi.org/10.1109/tac.2006.883059.

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