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Journal articles on the topic 'Model predictive control'

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Author: Grafiati
Published: 4 June 2021
Last updated: 14 September 2024

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1

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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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

Magni, L. "Nonlinear Model Predictive Control: Control and Prediction Horizon." IFAC Proceedings Volumes 33, no. 13 (June 2000): 213–18. http://dx.doi.org/10.1016/s1474-6670(17)37192-6.

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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

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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13

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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14

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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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

Veselý, Vojtech. "Stable Model Predictive Control Design: Sequential Approach." Journal of Electrical Engineering 62, no. 2 (March 1, 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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26

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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27

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 (June 2013): 280–85. http://dx.doi.org/10.3182/20130626-3-au-2035.00054.

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28

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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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 (September 1, 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 are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.
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30

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 (April 11, 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 to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared them with an MPC with fixed prediction horizons. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong adaptability and trajectory tracking capability.
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31

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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32

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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33

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

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34

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

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35

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

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36

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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37

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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38

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 with the reference trajectory to get the deviation. Finally use this deviation and the change of the deviation to optimize the PID control parameters and attain the appropriate amount of system control. The simulation results show that the fuzzy-PID control based on prediction model has strong adaptability, good robustness, control accuracy and higher practical value.
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39

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). Thereafter, using the obtain optimum value of 5 and 15 for control horizon and prediction horizon respectively as well as a manipulated variable rate weight of 0.025 and an output variable rate weight of 1.10, various steps were applied to the setpoint of the controlled variable and the responses plotted. The results given by the simulations carried out by varying the model predictive control parameters (control horizon and prediction horizon) for the control of the system revealed that optimizing the control parameters is better than arbitrary choosing. Also, the simulation of the developed model predictive control system of the process showed that its performance was better than those used to control the same process using a proportional-integral-derivative (PID) controller tuned with Cohen-Coon and Ziegler-Nichols techniques. It has, thus, been discovered that the Model Predictive Control Toolbox of MATLAB can be applied successfully to control a reactive distillation process in order to obtain better performance than that obtained from a PID controller tuned with Cohen-Coon and Ziegler-Nichols methods.
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40

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 approach is applied to solve the linear programming problem, and based on these optimization variables, the predictor interval of the considered model with sphere parameter set can be directly constructed. As for choosing a fixed non-negative number in our given size measure, a better choice is proposed by using the Karush-Kuhn-Tucker (KKT) optimality conditions. In order to apply interval prediction model into model predictive control, the midpoint of that interval is substituted in a quadratic optimization problem with inequality constrained condition to obtain the optimal control input. After formulating it as a standard quadratic optimization and deriving its dual form, the Gauss-Seidel algorithm is applied to solve the dual problem and convergence of Gauss-Seidel algorithm is provided too. Finally simulation examples confirm our theoretical results.
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41

Faulwasser, Timm, Lars Grüne, and Matthias A. Müller. "Economic Nonlinear Model Predictive Control." Foundations and Trends® in Systems and Control 5, no. 1 (2018): 224–409. http://dx.doi.org/10.1561/2600000014.

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42

Ling, K. V., J. M. Maciejowski, J. Guo, and E. Siva. "Channel-Hopping Model Predictive Control." IFAC Proceedings Volumes 44, no. 1 (January 2011): 11417–22. http://dx.doi.org/10.3182/20110828-6-it-1002.01590.

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43

Adachi, Shuichi. "Introduction to Model Predictive Control." Journal of the Robotics Society of Japan 32, no. 6 (2014): 499–502. http://dx.doi.org/10.7210/jrsj.32.499.

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44

Houska, Boris, Dries Telen, Filip Logist, and Jan Van Impe. "Self-Reflective Model Predictive Control." SIAM Journal on Control and Optimization 55, no. 5 (January 2017): 2959–80. http://dx.doi.org/10.1137/15m1049865.

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45

Rakovic, Saša V., Basil Kouvaritakis, Mark Cannon, Christos Panos, and Rolf Findeisen. "Parameterized Tube Model Predictive Control." IEEE Transactions on Automatic Control 57, no. 11 (November 2012): 2746–61. http://dx.doi.org/10.1109/tac.2012.2191174.

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46

Geyer, Tobias, Nikolaos Oikonomou, Georgios Papafotiou, and Frederick D. Kieferndorf. "Model Predictive Pulse Pattern Control." IEEE Transactions on Industry Applications 48, no. 2 (March 2012): 663–76. http://dx.doi.org/10.1109/tia.2011.2181289.

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47

Grandia, Ruben, Farbod Farshidian, Alexey Dosovitskiy, Rene Ranftl, and Marco Hutter. "Frequency-Aware Model Predictive Control." IEEE Robotics and Automation Letters 4, no. 2 (April 2019): 1517–24. http://dx.doi.org/10.1109/lra.2019.2895882.

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48

Roset *, Bas, and Henk Nijmeijer. "Observer-based model predictive control." International Journal of Control 77, no. 17 (November 20, 2004): 1452–62. http://dx.doi.org/10.1080/00207170412331326855.

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49

Richards, A., and J. P. How. "Robust distributed model predictive control." International Journal of Control 80, no. 9 (September 2007): 1517–31. http://dx.doi.org/10.1080/00207170701491070.

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50

Gawthrop, Peter J., and Liuping Wang. "Constrained intermittent model predictive control." International Journal of Control 82, no. 6 (May 8, 2009): 1138–47. http://dx.doi.org/10.1080/00207170802474702.

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