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Journal articles on the topic 'Non-linear predictive control'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Willis, M. J., M. T. Tham, G. A. Montague, and A. J. Morris. "Non-Linear Predictive Control." IFAC Proceedings Volumes 24, no. 1 (1991): 69–74. http://dx.doi.org/10.1016/s1474-6670(17)51298-7.

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2

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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3

Kouvaritakis, B., M. Cannon, and J. A. Rossiter. "Non-linear model based predictive control." International Journal of Control 72, no. 10 (1999): 919–28. http://dx.doi.org/10.1080/002071799220650.

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4

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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5

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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6

Hu, X. B., and W. H. Chen. "Model predictive control for non-linear missiles." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 221, no. 8 (2007): 1077–89. http://dx.doi.org/10.1243/09596518jsce394.

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7

Cannon, M., B. Kouvaritakis, Y. I. Lee, and A. C. Brooms. "Efficient non-linear model based predictive control." International Journal of Control 74, no. 4 (2001): 361–72. http://dx.doi.org/10.1080/00207170010010597.

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8

Zhang, Runfan, Diyi Chen, Wei Yao, Duoduo Ba, and Xiaoyi Ma. "Non-linear fuzzy predictive control of hydroelectric system." IET Generation, Transmission & Distribution 11, no. 8 (2017): 1966–75. http://dx.doi.org/10.1049/iet-gtd.2016.1300.

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9

Bequette, B. Wayne. "Non-Linear Model Predictive Control: A Personal Retrospective." Canadian Journal of Chemical Engineering 85, no. 4 (2007): 408–15. http://dx.doi.org/10.1002/cjce.5450850403.

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10

Baffi, G., J. Morris, and E. Martin. "Non-Linear Model Based Predictive Control Through Dynamic Non-Linear Partial Least Squares." Chemical Engineering Research and Design 80, no. 1 (2002): 75–86. http://dx.doi.org/10.1205/026387602753393240.

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11

Sieberg, Philipp Maximilian, and Dieter Schramm. "Central Non-Linear Model-Based Predictive Vehicle Dynamics Control." Applied Sciences 11, no. 10 (2021): 4687. http://dx.doi.org/10.3390/app11104687.

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Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially or fully automated driving functions, the occupants’ perception of safety and ride comfort changes. Both aspects are focused even more and have to be enhanced. In general, research on vehicle dynamics control systems is a field that has already been well researched. With regard to the mentioned aspects, however, a central control structure features

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12

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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13

Liu, G. P., and V. Kadirkamanathan. "Predictive control for non-linear systems using neural networks." International Journal of Control 71, no. 6 (1998): 1119–32. http://dx.doi.org/10.1080/002071798221515.

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14

Magni, L. "On robust tracking with non-linear model predictive control." International Journal of Control 75, no. 6 (2002): 399–407. http://dx.doi.org/10.1080/00207170110115626.

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15

Grimble, Michael John, and Pawel Majecki. "Non-linear predictive generalised minimum variance state-dependent control." IET Control Theory & Applications 9, no. 16 (2015): 2438–50. http://dx.doi.org/10.1049/iet-cta.2015.0356.

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16

Sørensen, P. H., M. Nørgaard, O. Ravn, and N. K. Poulsen. "Implementation of neural network based non-linear predictive control." Neurocomputing 28, no. 1-3 (1999): 37–51. http://dx.doi.org/10.1016/s0925-2312(98)00114-3.

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17

Liu, C., W.-H. Chen, and J. Andrews. "Explicit non-linear model predictive control for autonomous helicopters." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 226, no. 9 (2011): 1171–82. http://dx.doi.org/10.1177/0954410011418585.

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18

Leducq, Denis, Jacques Guilpart, and Gilles Trystram. "Non-linear predictive control of a vapour compression cycle." International Journal of Refrigeration 29, no. 5 (2006): 761–72. http://dx.doi.org/10.1016/j.ijrefrig.2005.12.005.

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19

Panjapornpon, C., and M. Soroush. "Shortest-prediction-horizon non-linear model-predictive control with guaranteed asymptotic stability." International Journal of Control 80, no. 10 (2007): 1533–43. http://dx.doi.org/10.1080/00207170601186200.

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20

Zhang, Haoran, and Emmanuel Prempain. "A Non-Linear Offset-Free Model Predictive Control Design Approach." Actuators 13, no. 8 (2024): 322. http://dx.doi.org/10.3390/act13080322.

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This paper presents a non-linear model predictive control approach for offset-free tracking and the rejection of piece-wise constant disturbances. The approach involves augmenting the system’s state vector with the integral of the tracking error, enabling the design of a non-linear model predictive controller for this augmented system. Nominal closed-loop stability is enforced thanks to a terminal equality constraint and proven by a Lyapunov argument. Compared to the existing offset-free approaches in the literature, our method offers greater simplicity, as it does not rely on linear approxima

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21

GONDHALEKAR, Ravi, and Jun-ichi IMURA. "Performance Measures in Model Predictive Control with Non-linear Prediction Horizon Time-discretization." Transactions of the Society of Instrument and Control Engineers 43, no. 10 (2007): 883–91. http://dx.doi.org/10.9746/ve.sicetr1965.43.883.

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22

Thilker, Christian Ankerstjerne, Hjörleifur G. Bergsteinsson, Peder Bacher, Henrik Madsen, Davide Calì, and Rune G. Junker. "Non-linear Model Predictive Control for Smart Heating of Buildings." E3S Web of Conferences 246 (2021): 09005. http://dx.doi.org/10.1051/e3sconf/202124609005.

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Smart and flexible operation of components in district heating systems can play a crucial role in integrating larger shares of renewable energy sources in energy systems. Buildings are one of the crucial components that will enable flexibility in the district heating by using intelligent operation. Recent work suggests that such improved operation at the same time can increase thermal comfort and lower economic costs. We have digitalised the heating system in a Danish school by adding IoT devices, such as smart thermostats and temperature sensors to demonstrate the possibilities of making buil

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23

Su, Baili, Guoyuan Qi, and Barend Jacobus Van Wyk. "Output feedback predictive control for uncertain non-linear switched systems." International Journal of Modelling, Identification and Control 17, no. 3 (2012): 195. http://dx.doi.org/10.1504/ijmic.2012.049686.

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24

Sahed, Oussama Ait, Kamel Kara, and Abousoufyane Benyoucef. "Artificial bee colony-based predictive control for non-linear systems." Transactions of the Institute of Measurement and Control 37, no. 6 (2014): 780–92. http://dx.doi.org/10.1177/0142331214546796.

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25

EL ATA-DOSS, S. ABU, A. COÏC, and M. FLIESS. "Non-linear predictive control by inversion: discontinuities for critical behaviour." International Journal of Control 55, no. 6 (1992): 1521–33. http://dx.doi.org/10.1080/00207179208934298.

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26

Reble, M., R. M. Esfanjani, S. K. Y. Nikravesh, and F. Allgower. "Model predictive control of constrained non-linear time-delay systems." IMA Journal of Mathematical Control and Information 28, no. 2 (2010): 183–201. http://dx.doi.org/10.1093/imamci/dnq029.

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27

Grimble, M. J., and P. Majecki. "Polynomial approach to non-linear predictive generalised minimum variance control." IET Control Theory & Applications 4, no. 3 (2010): 411–24. http://dx.doi.org/10.1049/iet-cta.2009.0043.

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28

Liu, Guo-Ping. "Design and analysis of networked non-linear predictive control systems." IET Control Theory & Applications 9, no. 11 (2015): 1740–45. http://dx.doi.org/10.1049/iet-cta.2014.1198.

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29

Cao, Gang, Edmund M. K. Lai, and Fakhrul Alam. "Gaussian process model predictive control of unknown non-linear systems." IET Control Theory & Applications 11, no. 5 (2017): 703–13. http://dx.doi.org/10.1049/iet-cta.2016.1061.

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30

Zamarreño, J. M., and P. Vega. "Neural Predictive Control Application to a Highly Non-Linear System." IFAC Proceedings Volumes 29, no. 1 (1996): 1002–7. http://dx.doi.org/10.1016/s1474-6670(17)57795-2.

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31

Costa, A. C., L. A. C. Meleiro, and R. Maciel Filho. "Non-linear predictive control of an extractive alcoholic fermentation process." Process Biochemistry 38, no. 5 (2002): 743–50. http://dx.doi.org/10.1016/s0032-9592(02)00205-4.

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32

Oliveira, Gustavo H. C., and José R. H. Secchi. "A non-linear predictive control scheme for nonholonomic mobile robots." IFAC Proceedings Volumes 36, no. 17 (2003): 449–54. http://dx.doi.org/10.1016/s1474-6670(17)33435-3.

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33

Zamarreño, J. M., and P. Vega. "Neural predictive control. Application to a highly non-linear system." Engineering Applications of Artificial Intelligence 12, no. 2 (1999): 149–58. http://dx.doi.org/10.1016/s0952-1976(98)00055-4.

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34

Wang, Ye, Vicenç Puig, and Gabriela Cembrano. "Non-linear economic model predictive control of water distribution networks." Journal of Process Control 56 (August 2017): 23–34. http://dx.doi.org/10.1016/j.jprocont.2017.05.004.

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35

Shi, Jingzhuo, and Bo Liu. "Non-linear Generalized Predictive Control of Traveling-wave Ultrasonic Motor." Electric Power Components and Systems 40, no. 11 (2012): 1229–45. http://dx.doi.org/10.1080/15325008.2012.689414.

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36

Costa, Aline C., and Rubens Maciel Filho. "Non-Linear Predictive Control of a Three-Phase Catalytic Reactor." Canadian Journal of Chemical Engineering 81, no. 5 (2008): 1109–18. http://dx.doi.org/10.1002/cjce.5450810525.

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37

Balan, Radu, Vistrian Maties, Olimpiu Hancu, and Sergiu Stan. "A Model Predictive Control Algorithm Applied To Non-Linear Processes." PAMM 6, no. 1 (2006): 797–98. http://dx.doi.org/10.1002/pamm.200610378.

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38

Hanema, Jurre, Roland Tóth, and Mircea Lazar. "Stabilizing non‐linear model predictive control using linear parameter‐varying embeddings and tubes." IET Control Theory & Applications 15, no. 10 (2021): 1404–21. http://dx.doi.org/10.1049/cth2.12131.

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39

Al-Qaisy, Muayad A. Shehab. "Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor." Tikrit Journal of Engineering Sciences 19, no. 3 (2012): 41–57. http://dx.doi.org/10.25130/tjes.19.3.05.

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In this article, multi-input multi-output (MIMO) linear model predictive controller (LMPC) based on state space model and nonlinear model predictive controller based on neural network (NNMPC) are applied on a continuous stirred tank reactor (CSTR). The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID) strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are use

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40

Colin, G., Y. Chamaillard, G. Bloch, and A. Charlet. "Exact and Linearized Neural Predictive Control: A Turbocharged SI Engine Example." Journal of Dynamic Systems, Measurement, and Control 129, no. 4 (2007): 527–33. http://dx.doi.org/10.1115/1.2745881.

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This paper describes a real-time control method for non-linear systems based on model predictive control. The model used for the prediction is a neural network because of its ability to represent non-linear systems, its ability to be differentiated, and its simplicity of use. The feasibility and the performance of the method, based on on-line linearization, are demonstrated on a turbocharged spark-ignited engine application, where the simulation models used are very accurate and complex. The results, first in simulation and then on a test bench, show the implementation of the proposed control

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41

Wu, Jinhui, Zhehao Jin, Andong Liu, and Li Yu. "Non-linear model predictive control for visual servoing systems incorporating iterative linear quadratic Gaussian." IET Control Theory & Applications 14, no. 14 (2020): 1989–94. http://dx.doi.org/10.1049/iet-cta.2019.1399.

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42

He, De-Feng, Hua Huang, and Qiu-Xia Chen. "Stabilizing model predictive control of time-varying non-linear systems using linear matrix inequalities." IMA Journal of Mathematical Control and Information 33, no. 1 (2014): 21–35. http://dx.doi.org/10.1093/imamci/dnu022.

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43

Poursafar, N., M. Haeri, and H. D. Taghirad. "Model predictive control of non-linear discrete time systems: a linear matrix inequality approach." IET Control Theory & Applications 4, no. 10 (2010): 1922–32. http://dx.doi.org/10.1049/iet-cta.2009.0650.

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44

Klaučo, Martin, Ľuboš Čirka, and Juraj Kukla. "Non-linear model predictive control of conically shaped liquid storage tanks." Acta Chimica Slovaca 11, no. 2 (2018): 141–46. http://dx.doi.org/10.2478/acs-2018-0020.

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Abstract This paper deals with the analysis and design of a model predictive control (MPC) strategy used in connection with level control in conically shaped industrial liquid storage tanks. The MPC is based on a non-linear dynamic model describing changes of the liquid level concerning changes in the inlet flow of the liquid. Euler discretization of the dynamic system was applied to transform con-tinuous time dynamics to its discrete-time counterpart used in non-linear MPC (NMPC) design. By means of a simulation case study, NMPC has been shown to track the changes of the liquid level, hence p

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45

Shokrollahi, Ali, and Saeed Shamaghdari. "Robust H∞ model predictive control for constrained Lipschitz non-linear systems." Journal of Process Control 104 (August 2021): 101–11. http://dx.doi.org/10.1016/j.jprocont.2021.06.007.

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46

Suriyakala, S., T. Hariharasudhan, and Dr D. Prince Winston. "Identification and Control of Non-Linear System Using Model Predictive controller." YMER Digital 21, no. 03 (2022): 165–73. http://dx.doi.org/10.37896/ymer21.03/19.

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The modeling of level and temperature process is the most common problems in the process industry. In this paper system identification is performed for a hybrid tank system. Hybrid tank is an example for highly non-linear system. This system has two inputs heater current and flow and the outputs are level and temperature. The Main aim of this paper is to maintain level and temperature at a desired value. Input flow is measured using turbine flow meter. The output temperature is measured using RTD. The level is measured using differential pressure transmitter (DPT). The simulation is performed

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47

AZAKI, Zakeye, Antoine GIRARD, and Sorin OLARU. "Predictive and Symbolic Control: Performance and Safety for Non-linear Systems." IFAC-PapersOnLine 55, no. 16 (2022): 290–95. http://dx.doi.org/10.1016/j.ifacol.2022.09.039.

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48

Sun, Qinglin, Na Dong, Zengqiang Chen, and Zhuzhi Yuan. "A modified neural network based predictive control for non-linear systems." International Journal of Modelling, Identification and Control 8, no. 2 (2009): 91. http://dx.doi.org/10.1504/ijmic.2009.029020.

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49

Wang, Bin, Lan Yang, Fengjiao Wu, and Diyi Chen. "Fuzzy predictive functional control of a class of non-linear systems." IET Control Theory & Applications 13, no. 14 (2019): 2281–88. http://dx.doi.org/10.1049/iet-cta.2018.5903.

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50

Ghoumari, M. Y. El, H. J. Tantau, D. Megas, and J. Serrano. "REAL TIME NON LINEAR CONSTRAINED MODEL PREDICTIVE CONTROL OF A GREENHOUSE." IFAC Proceedings Volumes 35, no. 1 (2002): 61–65. http://dx.doi.org/10.3182/20020721-6-es-1901.01319.

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