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Journal articles on the topic 'Model predictive control'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 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 (2025): 165–72. https://doi.org/10.6113/tkpe.2025.30.2.165.

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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

Veselý, Vojtech. "Stable Model Predictive Control Design: Sequential Approach." Journal of Electrical Engineering 62, no. 2 (2011): 99–103. http://dx.doi.org/10.2478/v10187-011-0016-0.

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Stable Model Predictive Control Design: Sequential Approach The paper addresses the problem of output feedback stable model predictive control design with guaranteed cost. The proposed design method pursues the idea of sequential design for N prediction horizon using one-step ahead model predictive control design approach. Numerical examples are given to illustrate the effectiveness of the proposed method.

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13

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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14

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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15

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 (2007): 1007–18. http://dx.doi.org/10.1243/09596518jsce417.

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16

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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17

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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18

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

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19

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

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20

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

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21

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

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22

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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23

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

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24

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

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25

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

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26

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

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27

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

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28

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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29

Brüdigam, Tim, Johannes Teutsch, Dirk Wollherr, Marion Leibold, and Martin Buss. "Probabilistic model predictive control for extended prediction horizons." at - Automatisierungstechnik 69, no. 9 (2021): 759–70. http://dx.doi.org/10.1515/auto-2021-0025.

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Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods

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30

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

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31

Kim, Bo-Ah, Young Seop Son, Seung-Hi Lee, and Chung Choo Chung. "Model Predictive Control Using Dual Prediction Horizons for Lateral Control." IFAC Proceedings Volumes 46, no. 10 (2013): 280–85. http://dx.doi.org/10.3182/20130626-3-au-2035.00054.

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32

de Costa Sousa, J. M., and M. Setnes. "Fuzzy predictive filters in model predictive control." IEEE Transactions on Industrial Electronics 46, no. 6 (1999): 1225–32. http://dx.doi.org/10.1109/41.808014.

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33

Huang, Chang Yuan, and Hai Peng Pan. "Practical Research on Predictive Fuzzy-PID Control in Reactor Temperature Control." Applied Mechanics and Materials 313-314 (March 2013): 355–58. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.355.

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Against the characteristics of the temperature in reactor such as time-delay, time-varying and difficulty to build a precise mathematical model in the chemical industry. Through the analysis of dynamic characteristics of the controlled object, the method of fuzzy-PID control was designed based on a predictive model. According to the detected temperature signal, the output deviation of the controller and the on-line identification of prediction model, this algorithm gains the predictive value which uses a generalized predictive model and the fuzzy-PID control. Then compare the predictive value

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34

Chen, Zhenbin, Jiaqin Lai, Peixin Li, Omar I. Awad, and Yubing Zhu. "Prediction Horizon-Varying Model Predictive Control (MPC) for Autonomous Vehicle Control." Electronics 13, no. 8 (2024): 1442. http://dx.doi.org/10.3390/electronics13081442.

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The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the proximal policy optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. We established a state space related t

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35

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 (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, wh

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36

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

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37

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

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38

Soroush, Masoud, and Masoud Nikravesh. "Shortest-Prediction Horizon Nonlinear Model Predictive Control 1." IFAC Proceedings Volumes 29, no. 1 (1996): 5817–22. http://dx.doi.org/10.1016/s1474-6670(17)58611-5.

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39

Valluri, Sairam, Masoud Soroush, and Masoud Nikravesh. "Shortest-prediction-horizon non-linear model-predictive control." Chemical Engineering Science 53, no. 2 (1998): 273–92. http://dx.doi.org/10.1016/s0009-2509(97)00284-4.

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40

van der Burg, M. W., and P. Djavdan. "Model Predictive Averaging Level Control using Disturbance Prediction." IFAC Proceedings Volumes 28, no. 12 (1995): 219–24. http://dx.doi.org/10.1016/s1474-6670(17)45425-5.

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41

Rezaei, Amir, and Jeffrey B. Burl. "Prediction of Vehicle Velocity for Model Predictive Control." IFAC-PapersOnLine 48, no. 15 (2015): 257–62. http://dx.doi.org/10.1016/j.ifacol.2015.10.037.

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42

Giwa, Abdulwahab, Abel Adekanmi Adeyi, and Saidat Olanipekun Giwa. "Control of a Reactive Distillation Process Using Model Predictive Control Toolbox of MATLAB." International Journal of Engineering Research in Africa 30 (May 2017): 167–80. http://dx.doi.org/10.4028/www.scientific.net/jera.30.167.

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This research work has been carried out to investigate the application of the Model Predictive Control Toolbox contained in MATLAB in controlling a reactive distillation process used for the production of a biodiesel, the model of which was obtained from the work of Giwa et al.1. The optimum values of the model predictive control parameters were obtained by running the mfile program written for the implementation of the control simulation varying the model predictive control parameters (control horizon and prediction horizon) and recording the corresponding integral squared error (ISE). Therea

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43

Srikshana, Sasidaran, R. Adithya, Raja V. Haris, and M.P.Anbarasi. "Recent Trends in Model Predictive Control." International Journal of Innovative Science and Research Technology 7, no. 2 (2022): 249–54. https://doi.org/10.5281/zenodo.6323081.

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In this paper we are going to present the recent trends of model predictive control (MPC) and its techniques are used in modern world. MPC forecasts plant output behavior using a plant model. The MPC controller solves the optimization problem across the prediction horizon while adhering to the constraints at the current phase. This can be used in non-linear problems and it is more precise when compare to the linear controller such as PID.

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44

Yin, Fang Chen, Geng Sheng Ma, Ya Feng Ji, Zhong Ping Li, and Dian Hua Zhang. "Explicit Indirect Predictive Control Algorithm in the Application of AWC Control." Advanced Materials Research 926-930 (May 2014): 1344–47. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1344.

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Using the characteristics of prediction model, rolling optimization and feedback correction, a AWC system based on explicit indirect predictive control was designed, and its control performance was simulated based on a hot strip continuous mill. The results show that explicit indirect predictive control achieves better control effects than the normal PID on response time and steady precision with matching model; when model mismatching is caused by inaccuracy of plastic coefficient and pure delay time, the normal PID is overshot or even oscillation, but the control performance of the explicit i

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45

Askari, Masood, Mahmoud Moghavvemi, Haider Almurib, and M. A. Haidar Ahmed. "Stability of soft-constrained finite horizon model predictive control." IEEE Transactions on Industry Applications 53, no. 6 (2017): 5883–92. https://doi.org/10.1109/TIA.2017.2718978.

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This paper delves into the intriguing realm of soft-constrained model predictive control (MPC) and focuses on the critical aspect of stability. A notable revelation surfaces: the seemingly infinite horizon problem of soft-constrained MPC can find resolution by transforming it into a finite horizon MPC quandary, given the prediction horizon exceeds a specific upper bound. The standout contribution of this study lies in presenting a systematic procedure to compute this upper bound for the prediction horizon, effectively ensuring stability in soft-constrained MPC systems. This procedure serves as

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46

Jianhong, Wang, and Ricardo A. Ramirez-Mendoza. "Application of Interval Predictor Model Into Model Predictive Control." WSEAS TRANSACTIONS ON SYSTEMS 20 (January 6, 2022): 331–43. http://dx.doi.org/10.37394/23202.2021.20.38.

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In this paper, interval prediction model is studied for model predictive control (MPC) strategy with unknown but bounded noise. After introducing the family of models and some basic information, some computational results are presented to construct interval predictor model, using linear regression structure whose regression parameters are included in a sphere parameter set. A size measure is used to scale the average amplitude of the predictor interval, then one optimal model that minimizes this size measure is efficiently computed by solving a linear programming problem. The active set approa

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47

Wang, Bo, Muhammad Shahzad, Xianglin Zhu, Khalil Ur Rehman, and Saad Uddin. "A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation." Sensors 20, no. 11 (2020): 3335. http://dx.doi.org/10.3390/s20113335.

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l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used

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48

He, Guofeng, Shicheng Zheng, Yanfei Dong, Guojiao Li, and Wenjie Zhang. "Model Predictive Voltage Control of Uninterruptible Power Supply Based on Extended-State Observer." Energies 15, no. 15 (2022): 5489. http://dx.doi.org/10.3390/en15155489.

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Finite-set model predictive controls have been widely used in inverter control because of the flexible target control and no need of a modulation unit. However, the mismatching of prediction model parameters produces prediction errors, resulting in a significant decline in the performance of finite-set model predictive controls. Aiming at the problem of model parameter mismatch, an extended-state observer was proposed to accurately estimate the disturbance of the system in this paper, and the obtained disturbance value was added to a finite-set model predictive control controller to compensate

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49

Chrif, Labane, and Zemalache Meguenni Kadda. "Aircraft Control System Using Model Predictive Controller." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 259. http://dx.doi.org/10.11591/tijee.v15i2.1538.

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This paper concerns the application of model-based predictive control to the longitudinal and lateral mode of an aircraft in a terrain following task. The predictive control approach was based on a quadratic cost function and a linear state space prediction model with input and state constraints. The optimal control was obtained as the solution of a quadratic programming problem defined over a receding horizon. Closed-loop simulations were carried out by using the linear aircraft model. This project thesis provides a brief overview of Model Predictive Control (MPC).A brief history of industria

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50

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 (2015): 31–35. https://doi.org/10.70729/ijser15385.

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