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Journal articles on the topic 'Model Predictve Control'

Author: Grafiati

Published: 10 May 2025

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1

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 (April 30, 2025): 165–72. https://doi.org/10.6113/tkpe.2025.30.2.165.

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2

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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3

白家納, 白家納, and 黃崇能 Pachara Opattrakarnkul. "以深度學習模式估測控制之駕駛輔助系統的研發." 理工研究國際期刊 12, no. 1 (April 2022): 015–24. http://dx.doi.org/10.53106/222344892022041201002.

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<p>Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance to enhance safety and driving comfort by adjusting vehicle velocity to maintain a safe distance between the host vehicle and the preceding vehicle. Generally, using model predictive control (MPC) in ACC systems provides high responsiveness and lower discomfort by solving real-time constrained optimization problems but results in computational load. This paper presents an architecture of deep learning based on model predictive control in ACC systems to avoid real-time optimization problems required by MPC, which in turn, reduces computational load. The learning dataset is acquired from the simulation data of the input/output of the MPC controller. We designed the proposed deep learning controller using long short-term memory networks (LSTMs) and simulated it in MATLAB/Simulink using the vehicle’s characteristics from the advanced vehicle simulator (ADVISOR). Finally, the safety and driving comfort are compared with the PID-based control to demonstrate the performance of the proposed deep-learning architecture.</p> <p> </p>

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4

Qin Shuo, 秦硕. "精密透镜系统的模型预测热控方法." Laser & Optoelectronics Progress 59, no. 17 (2022): 1722006. http://dx.doi.org/10.3788/lop202259.1722006.

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5

SZABOLCSI, Róbert. "MODEL PREDICTIVE CONTROL APPLIED IN UAV FLIGHT PATH TRACKING MISSIONS." Review of the Air Force Academy 17, no. 1 (May 24, 2019): 49–62. http://dx.doi.org/10.19062/1842-9238.2019.17.1.7.

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6

G.S.S.S.S.V., Krishna Mohan. "Auto-tuning Smith-predictive Control of Delayed Processes based on Model Reference Adaptive Controller." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1224–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201597.

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7

G P, Athira, and Riya Mary Francis. "Control of Totally Refluxed Reactive Distillation Column Using Model Predictive Controller." International Journal of Scientific Engineering and Research 3, no. 8 (August 27, 2015): 31–35. https://doi.org/10.70729/ijser15385.

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8

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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9

Shamim, Nimat, Subrina Sultana Noureen, Argenis Bilbao, Anitha Sarah Subburaj, and Stephen Bayne. "A Comparative Study of Vector Control and Model Predictive Control Technique for Grid Connected Battery System." International Journal of Research and Engineering 4, no. 12 (January 5, 2018): 287–95. http://dx.doi.org/10.21276/ijre.2018.5.1.1.

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10

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 (May 28, 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 operating constraints take the form of functions defined over a probability space, and the resulting technique is called stochastic MPC. Our research over the past 10 years has focused on predictive control design methods that systematically handle uncertain forecasts in autonomous and semiautonomous systems. In the first part of this article, we present an overview of the approach we use, its main advantages, and its challenges. In the second part, we present our most recent results on data-driven predictive control. We show how to use data to efficiently formulate stochastic MPC problems and autonomously improve performance in repetitive tasks. The proposed framework is able to handle a large set of predicted scenarios in real time and learn from historical data.

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11

Sharma, Ravindra, and Chandrakant Sharma. "Mitigating Nonlinear Harmonics in Diesel Electrical Ship Network by Model Predictive Control." International Journal of Science and Research (IJSR) 13, no. 10 (October 5, 2024): 510–15. http://dx.doi.org/10.21275/sr241005223632.

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12

Tran, Ngoc Son, Khac Lai Lai, and Phuong Nam Dao. "A Novel Model Predictive Control for an Autonomous Four-Wheel Independent Vehicle." International Journal of Mechanical Engineering and Robotics Research 13, no. 5 (2024): 509–15. http://dx.doi.org/10.18178/ijmerr.13.5.509-515.

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This work is centered on developing a novel Model Predictive Control (MPC) for Four-Wheel Independent (FWID) vehicles to achieve trajectory tracking effectiveness, which is difficult to satisfy due to the changing the optimization solution after each time period. By using linearization technique for FWID model and eliminating the term of dynamic uncertainty in the tracking error model, the nominal linear Discrete Time System (DTS) is achieved to develop the proposed MPC strategy, which leverages the Luenberger observer to obtain the predictive model and extends for obtaining the Output feedback MPC scheme. On the other hand, the appropriate optimization problem is given at each time instant to guarantee the stability of the closed loop system under the designed MPC law without the consideration of terminal region as well as terminal controller, which have been considered in the previous researches. The unification between the optimization problem in MPC scheme and the tracking problem is validated by the Lyapunov function-based analysis with the inequality estimations. The efficiency of the proposed MPC law for FWID vehicles is clarified through a simulation study.

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13

Shamraev, A. D., and S. A. Kolyubin. "Bioinspired and Energy-Efficient Convex Model Predictive Control for a Quadruped Robot." Nelineinaya Dinamika 18, no. 5 (2022): 0. http://dx.doi.org/10.20537/nd221214.

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Animal running has been studied for a long time, but until now robots cannot repeat the same movements with energy efficiency close to animals. There are many controllers for controlling the movement of four-legged robots. One of the most popular is the convex MPC. This paper presents a bioinspirational approach to increasing the energy efficiency of the state-of-the-art convex MPC controller. This approach is to set a reference trajectory for the convex MPC in the form of an SLIP model, which describes the movements of animals when running. Adding an SLIP trajectory increases the energy efficiency of the Pronk gait by 15 percent over a range of speed from 0.75 m/s to 1.75 m/s.

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14

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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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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15

Norquay, Sandra J., Ahmet Palazoglu, and JoséA Romagnoli. "Model predictive control based on Wiener models." Chemical Engineering Science 53, no. 1 (January 1998): 75–84. http://dx.doi.org/10.1016/s0009-2509(97)00195-4.

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16

Fruzzetti, K. P., A. Palazoğlu, and K. A. McDonald. "Nolinear model predictive control using Hammerstein models." Journal of Process Control 7, no. 1 (February 1997): 31–41. http://dx.doi.org/10.1016/s0959-1524(97)80001-b.

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17

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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18

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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19

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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20

Ulum, Zaiful. "Model Predictive Control Based Kalman Filter for Active Suspension Design of Light Rail Vehicles." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (February 28, 2020): 261–67. http://dx.doi.org/10.5373/jardcs/v12sp3/20201261.

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21

Choi, Yoonsuk, Wonwoo Lee, and Jinwoo Yoo. "A Variable Horizon Model Predictive Control Based on Curvature Properties of Vehicle Driving Path." Transaction of the Korean Society of Automotive Engineers 29, no. 12 (December 2, 2021): 1147–59. http://dx.doi.org/10.7467/ksae.2021.29.12.1147.

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22

Yan Kuan, 闫宽, 张聪 Zhang Cong, 陈绪兵 Chen Xubing, 李明超 Li Mingchao, 方杰 Fang Jie, and 叶冬 Ye Dong. "激光软钎焊系统中半导体激光器温度模型预测控制设计." Acta Optica Sinica 44, no. 14 (2024): 1414001. http://dx.doi.org/10.3788/aos240578.

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23

Fauvel, Clément, Kritchai Witheephanich, Alan McGibney, Susan Rea, and Suzanne Lesecq. "Generating Models for Model Predictive Control in Buildings." Proceedings 2, no. 15 (August 23, 2018): 1137. http://dx.doi.org/10.3390/proceedings2151137.

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There are strong policy drivers for the promotion of energy efficiency in buildings. In the literature, Model Predictive Control (MPC) is seen as a promising solution to deal with the energy management problem in buildings. Model identification is the primary task involved in the design of MPC control and defining the good level of complexity for the thermal dynamic model is a critical question. This paper focuses on the development of reliable models that can be used to support the deployment of (Distributive (Di)) MPC application.

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24

Hovd, M., J. H. Lee, and M. Morari. "Truncated step response models for model predictive control." Journal of Process Control 3, no. 2 (May 1993): 67–73. http://dx.doi.org/10.1016/0959-1524(93)80002-s.

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25

Pannocchia, Gabriele, and James B. Rawlings. "Disturbance models for offset-free model-predictive control." AIChE Journal 49, no. 2 (February 2003): 426–37. http://dx.doi.org/10.1002/aic.690490213.

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26

Safdar, Rahmat Ullah, Muwahida Liaquat, Syed M. Tahir Zaidi, and Muhammad Usman Akram. "Model Predictive Control: Taking the Idea of Artificial Pancreas a Step forward for Diabetes Management." International Journal of Pharma Medicine and Biological Sciences 10, no. 4 (October 2021): 142–47. http://dx.doi.org/10.18178/ijpmbs.10.4.142-147.

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27

S. Guedes, Felipe, Nady Rocha, Alvaro M. Maciel, and Alfeu Joãozinho Sguarezi Filho. "Finite-Set Model Predictive Direct Power Control for DFIG with Reduced Number of Voltage Vectors." Eletrônica de Potência 28, no. 01 (March 23, 2023): 1–11. http://dx.doi.org/10.18618/rep.2023.1.0025.

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28

Lyu, Zehao, Xiang Wu, Jie Gao, and Guojun Tan. "An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches." Energies 14, no. 19 (October 4, 2021): 6342. http://dx.doi.org/10.3390/en14196342.

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The control performance of the finite control set model predictive current control (FCS-MPCC) for the interior permanent magnet synchronous machine (IPMSM) depends on the accuracy of the mathematical model. A novel robust model predictive current control method based on error compensation is proposed in order to reduce the parameter sensitivity and improve the current control robustness. In this method, the equivalent parameters are obtained from the known voltage and current information at the past time and the error between the predicted current and the actual current at the present time, which is utilized in the two-step prediction process to compensate the parameter mismatch error. Finally, the optimal voltage vector is selected by the cost function. The proposed method is compared with the traditional model predictive current control method through experiments. The experimental results show the effectiveness of the proposed method.

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29

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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30

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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31

Gawthrop, P. J., and L. Wang. "Intermittent model predictive control." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 7 (November 2007): 1007–18. http://dx.doi.org/10.1243/09596518jsce417.

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32

Ohshima, Masahiro. "III. Model Predictive Control." IEEJ Transactions on Electronics, Information and Systems 116, no. 10 (1996): 1089–93. http://dx.doi.org/10.1541/ieejeiss1987.116.10_1089.

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33

LING, Keck-Voon, Jan MACIEJOWSKI, and WU Bing-Fang. "MULTIPLEXED MODEL PREDICTIVE CONTROL." IFAC Proceedings Volumes 38, no. 1 (2005): 574–79. http://dx.doi.org/10.3182/20050703-6-cz-1902.00496.

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34

PATWARDHAN, ASHUTOSH A., JAMES B. RAWLINGS, and THOMAS F. EDGAR. "NONLINEAR MODEL PREDICTIVE CONTROL." Chemical Engineering Communications 87, no. 1 (January 1990): 123–41. http://dx.doi.org/10.1080/00986449008940687.

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35

Bravo, J. M., C. G. Varet, and E. F. Camacho. "Interval Model Predictive Control." IFAC Proceedings Volumes 33, no. 6 (May 2000): 57–62. http://dx.doi.org/10.1016/s1474-6670(17)35448-4.

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36

Arulalan, Gomatam R., and Pradeep B. Deshpande. "Simplified model predictive control." Industrial & Engineering Chemistry Research 26, no. 2 (February 1987): 347–56. http://dx.doi.org/10.1021/ie00062a029.

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37

Yeo, Yeong K., and Dennis C. Williams. "Bilinear model predictive control." Industrial & Engineering Chemistry Research 26, no. 11 (November 1987): 2267–74. http://dx.doi.org/10.1021/ie00071a017.

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38

Edgar, T. F., J. P. Gong, H. H. Lou, and Y. L. Huang. "Fuzzy model predictive control." IEEE Transactions on Fuzzy Systems 8, no. 6 (2000): 665–78. http://dx.doi.org/10.1109/91.890326.

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39

Leva, Alberto, Federico Mattia Benzi, Virna Magagnotti, and Giulia Vismara. "Sporadic Model Predictive Control." IFAC-PapersOnLine 50, no. 1 (July 2017): 4887–92. http://dx.doi.org/10.1016/j.ifacol.2017.08.740.

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40

Bemporad, Alberto, and David Muñoz de la Peña. "Multiobjective model predictive control." Automatica 45, no. 12 (December 2009): 2823–30. http://dx.doi.org/10.1016/j.automatica.2009.09.032.

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41

Ling, Keck Voon, Jan Maciejowski, Arthur Richards, and Bing Fang Wu. "Multiplexed model predictive control." Automatica 48, no. 2 (February 2012): 396–401. http://dx.doi.org/10.1016/j.automatica.2011.11.001.

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42

Carrasco, Diego S., and Graham C. Goodwin. "Feedforward model predictive control." Annual Reviews in Control 35, no. 2 (December 2011): 199–206. http://dx.doi.org/10.1016/j.arcontrol.2011.10.007.

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43

Camacho, Eduardo F., and Carlos Bordons. "Distributed model predictive control." Optimal Control Applications and Methods 36, no. 3 (March 20, 2015): 269–71. http://dx.doi.org/10.1002/oca.2167.

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44

Bakhtadze, N., A. Chereshko, D. Elpashev, I. Yadykin, R. Sabitov, and G. Smirnova. "Associative Model Predictive Control." IFAC-PapersOnLine 56, no. 2 (2023): 7330–34. http://dx.doi.org/10.1016/j.ifacol.2023.10.346.

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45

Yang, Yuanqing, and Baocang Ding. "Model predictive control for LPV models with maximal stabilizable model range." Asian Journal of Control 22, no. 5 (March 25, 2019): 1940–50. http://dx.doi.org/10.1002/asjc.2070.

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46

Cymbalák, Dávid, Ondrej Kainz, and František Jakab. "Extended Object Tracking and Stream Control Model Based on Predictive Evaluation Metric of Multiple-Angled Streams." International Journal of Computer Theory and Engineering 7, no. 5 (October 2015): 343–48. http://dx.doi.org/10.7763/ijcte.2015.v7.983.

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47

Schimperna, Irene, and Lalo Magni. "Recurrent Equilibrium Network models for Nonlinear Model Predictive Control." IFAC-PapersOnLine 58, no. 18 (2024): 226–31. http://dx.doi.org/10.1016/j.ifacol.2024.09.035.

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48

Roubos, J. A., S. Mollov, R. Babuška, and H. B. Verbruggen. "Fuzzy model-based predictive control using Takagi–Sugeno models." International Journal of Approximate Reasoning 22, no. 1-2 (September 1999): 3–30. http://dx.doi.org/10.1016/s0888-613x(99)00020-1.

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49

Jung, Marvin, Paulo Renato da Costa Mendes, Magnus Önnheim, and Emil Gustavsson. "Model Predictive Control when utilizing LSTM as dynamic models." Engineering Applications of Artificial Intelligence 123 (August 2023): 106226. http://dx.doi.org/10.1016/j.engappai.2023.106226.

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50

Henriksson, Erik, Daniel E. Quevedo, Henrik Sandberg, and Karl Henrik Johansson. "Self-Triggered Model Predictive Control for Network Scheduling and Control1." IFAC Proceedings Volumes 45, no. 15 (2012): 432–38. http://dx.doi.org/10.3182/20120710-4-sg-2026.00132.

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