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Journal articles on the topic 'Nonlinear predictive control'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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2

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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3

Katende, Edward, Arthur Jutan, and Rob Corless. "Quadratic Nonlinear Predictive Control." Industrial & Engineering Chemistry Research 37, no. 7 (1998): 2721–28. http://dx.doi.org/10.1021/ie970754v.

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4

Zhang, Mengwei, Zhixiang Lin, Haiyang Huang, and Tianhong Zhang. "Design and verification of model predictive control for micro-turboshaft engine." Advances in Mechanical Engineering 11, no. 12 (2019): 168781401989019. http://dx.doi.org/10.1177/1687814019890198.

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In this article, a nonlinear model predictive control algorithm for a micro-turboshaft engine is designed. The control effect is verified by a bench test. First, a micro-turboshaft engine test bench is built, and the open-loop control experiment was carried out on it. Based on experiment data, a linear parameter varying prediction model is established. Then, by online rolling optimization based on multistep output prediction, together with feedback correction, a nonlinear model predictive control algorithm is obtained. The influence of algorithm parameters on the control effect is studied, and

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5

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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6

Xu, Zhi Cheng, Bin Zhu, and Qing Bin Jiang. "Application of Neural Network for Nonlinear Predictive Control." Advanced Materials Research 562-564 (August 2012): 1964–67. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1964.

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A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear pro

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7

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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8

Crassidis, John L., F. Landis Markley, Tobin C. Anthony, and Stephen F. Andrews. "Nonlinear Predictive Control of Spacecraft." Journal of Guidance, Control, and Dynamics 20, no. 6 (1997): 1096–103. http://dx.doi.org/10.2514/2.4191.

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9

Xi, Yu-Geng, Fan Wang, and Guo-Hua Wu. "Nonlinear Multi-Model Predictive Control." IFAC Proceedings Volumes 29, no. 1 (1996): 2359–64. http://dx.doi.org/10.1016/s1474-6670(17)58026-x.

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10

Paulusová, Jana, Štefan Kozák, and Jakub Grošek. "Nonlinear Model-Based Predictive Control." IFAC Proceedings Volumes 36, no. 18 (2003): 171–75. http://dx.doi.org/10.1016/s1474-6670(17)34664-5.

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11

Christofides, Panagiotis D., and Nael H. El-Farra. "Economic nonlinear model predictive control." Journal of Process Control 24, no. 8 (2014): 1155. http://dx.doi.org/10.1016/j.jprocont.2014.06.012.

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12

Soeterboek, A. R. M., H. B. Verbruggen, J. M. Wissing, and A. J. Koster. "Predictive Control of Nonlinear Processes." IFAC Proceedings Volumes 24, no. 1 (1991): 363–68. http://dx.doi.org/10.1016/s1474-6670(17)51346-4.

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13

Abu el Ata-Doss, S., and M. Fliess. "Nonlinear Predictive Control by Inversion." IFAC Proceedings Volumes 22, no. 3 (1989): 119–24. http://dx.doi.org/10.1016/s1474-6670(17)53620-4.

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14

AllgÖWer, F. "Editorial: Nonlinear model predictive control." IEE Proceedings - Control Theory and Applications 152, no. 3 (2005): 257–58. http://dx.doi.org/10.1049/ip-cta:20059060.

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15

Kashiwagi, Hiroshi, and Yun Li. "Nonparametric nonlinear model predictive control." Korean Journal of Chemical Engineering 21, no. 2 (2004): 329–37. http://dx.doi.org/10.1007/bf02705416.

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16

Tatjewski, Piotr. "Nonlinear Predictive Control of Manipulator Arms." Pomiary Automatyka Robotyka 27, no. 2 (2023): 47–58. http://dx.doi.org/10.14313/par_248/47.

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The subject of the article are predictive control algorithms (of MPC type – Model Predictive Control) for rigid manipulator arms. MPC with a state-space model and with the latest disturbance and modeling error suppression technique was applied, which avoids dynamic disturbance modeling or resorting to additional disturbance cancellation techniques, such as SMC. First of all, the most computationally efficient MPC-NPL (Nonlinear Prediction and Linearization) algorithms are considered, in two versions: the first with constrained QP (Quadratic Programming) optimization and the second with explici

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17

Ławryńczuk, Maciej, and Piotr Tatjewski. "Nonlinear predictive control based on neural multi-models." International Journal of Applied Mathematics and Computer Science 20, no. 1 (2010): 7–21. http://dx.doi.org/10.2478/v10006-010-0001-y.

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Nonlinear predictive control based on neural multi-modelsThis paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discus

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18

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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19

Ławryńczuk, Maciej. "Efficient Nonlinear Predictive Control Based on Structured Neural Models." International Journal of Applied Mathematics and Computer Science 19, no. 2 (2009): 233–46. http://dx.doi.org/10.2478/v10006-009-0019-1.

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Efficient Nonlinear Predictive Control Based on Structured Neural ModelsThis paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction accuracy than the corresponding cla

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20

Ławryńczuk, Maciej. "A Family of Model Predictive Control Algorithms With Artificial Neural Networks." International Journal of Applied Mathematics and Computer Science 17, no. 2 (2007): 217–32. http://dx.doi.org/10.2478/v10006-007-0020-5.

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A Family of Model Predictive Control Algorithms With Artificial Neural NetworksThis paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine

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21

Zheng, Qiangang, Yong Wang, Fengyong Sun, and Haibo Zhang. "Aero-engine direct thrust control with nonlinear model predictive control based on linearized deep neural network predictor." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 3 (2019): 330–37. http://dx.doi.org/10.1177/0959651819853395.

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A novel nonlinear model predictive control method for aero-engine direct thrust control is proposed to improve engine response ability and reduce computational complexity of nonlinear model predictive control. The control objective of the proposed method is the thrust directly instead of the measurable parameters. The linearized model based on online sliding window deep neural network is proposed as predictive model. The online sliding window deep neural network has strong fitting capacity for nonlinear object and adopted to fitting the transient process of engine. The back propagation is adop

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22

Yang, Tao, and Dan Dan Song. "Vehicle Stability Control Study Based on Neural Network Predictive Method." Applied Mechanics and Materials 734 (February 2015): 295–98. http://dx.doi.org/10.4028/www.scientific.net/amm.734.295.

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A control method is proposed to improve vehicle yaw stability based on neural network predictive. A vehicle steering model using neural network control strategy is set up, at the same time, the nonlinear predictive functional control using the neural network model is developed for control of high-nonlinear system. New structure of neural network multi-step prediction that is different from cascade or parallel is given. The results illustrate that the nonlinear predictive functional control using neural network model is more effective for control nonlinear system than PID control. A simulation

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23

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 (2024): 510–15. http://dx.doi.org/10.21275/sr241005223632.

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24

Cristea, Smaranda, and César de Prada. "A STABILISING NONLINEAR PREDICTIVE CONTROL STRATEGY." IFAC Proceedings Volumes 40, no. 12 (2007): 216–21. http://dx.doi.org/10.3182/20070822-3-za-2920.00036.

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25

Gerksic, Samo, Dani Juricic, Stanko Strmcnik, and Drago Matko. "Wiener model based nonlinear predictive control." International Journal of Systems Science 31, no. 2 (2000): 189–202. http://dx.doi.org/10.1080/002077200291307.

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26

Long, C. E., P. K. Polisetty, and E. P. Gatzke. "Globally Optimal Nonlinear Model Predictive Control." IFAC Proceedings Volumes 37, no. 9 (2004): 83–88. http://dx.doi.org/10.1016/s1474-6670(17)31798-6.

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27

Katende, Edward, and Arthur Jutan. "Nonlinear Predictive Control of Complex Processes." Industrial & Engineering Chemistry Research 35, no. 10 (1996): 3539–46. http://dx.doi.org/10.1021/ie9507282.

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28

Pham, D. T., and K. C. J. Cheung. "A nonlinear model predictive control scheme." IFAC Proceedings Volumes 32, no. 2 (1999): 3802–7. http://dx.doi.org/10.1016/s1474-6670(17)56649-5.

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29

Sistu, P. B., R. S. Gopinath, and B. W. Bequette. "Computational issues in nonlinear predictive control." Computers & Chemical Engineering 17, no. 4 (1993): 361–66. http://dx.doi.org/10.1016/0098-1354(93)80027-k.

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30

Haber, R. "Predictive control of nonlinear dynamic processes." Applied Mathematics and Computation 70, no. 2-3 (1995): 169–84. http://dx.doi.org/10.1016/0096-3003(94)00122-k.

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31

Marti, Kurt. "Stochastic Nonlinear Model Predictive Control (SNMPC)." PAMM 8, no. 1 (2008): 10775–76. http://dx.doi.org/10.1002/pamm.200810775.

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32

Ebert, Wolfram. "Nonlinear predictive control: theory and practice." Automatica 40, no. 6 (2004): 1101–2. http://dx.doi.org/10.1016/j.automatica.2004.01.006.

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33

Morari, M., and U. Maeder. "Nonlinear offset-free model predictive control." Automatica 48, no. 9 (2012): 2059–67. http://dx.doi.org/10.1016/j.automatica.2012.06.038.

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34

Cannon, Mark. "Efficient nonlinear model predictive control algorithms." Annual Reviews in Control 28, no. 2 (2004): 229–37. http://dx.doi.org/10.1016/j.arcontrol.2004.05.001.

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35

Yu, Shuyou, Encong Sheng, Yajing Zhang, Yongfu Li, Hong Chen, and Yi Hao. "Efficient Nonlinear Model Predictive Control of Automated Vehicles." Mathematics 10, no. 21 (2022): 4163. http://dx.doi.org/10.3390/math10214163.

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In this paper, an efficient model predictive control (MPC) of velocity tracking of automated vehicles is proposed, in which a reference signal is given a priori. Five degree-of-freedom vehicle dynamics with nonlinear tires is chosen as the prediction model, in which coupling characteristics of longitudinal and lateral dynamics are taken into account. In order to balance computational burden and prediction accuracy, Koopman operator theory is adopted to transform the nonlinear model into a global linear model. Then, the global linear model is used in the design of MPC to reduce online computati

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36

Lakshmi N, Sridhar. "Bifurcation Analysis and Multiobjective Nonlinear Model Predictive Control of Drug Addiction Models." Journal of Cardiovascular Medicine and Cardiology 11, no. 4 (2024): 096–102. https://doi.org/10.17352/2455-2976.000215.

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Bifurcation analysis and nonlinear model predictive control were performed on drug addiction models. Rigorous proof showing the existence of bifurcation (branch) points is presented along with computational validation. It is also demonstrated (both numerically and analytically) that the presence of the branch points was instrumental in obtaining the Utopia solution when the multiobjective nonlinear model prediction calculations were performed. Bifurcation analysis was performed using the MATLAB software MATCONT while the multi-objective nonlinear model predictive control was performed by using

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37

Vojtesek, Jiri, Petr Dostal, and Vladimir Bobal. "Control of Nonlinear System – Adaptive and Predictive Control." IFAC Proceedings Volumes 42, no. 11 (2009): 898–903. http://dx.doi.org/10.3182/20090712-4-tr-2008.00147.

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38

Tatjewski, Piotr. "Supervisory predictive control and on-line set-point optimization." International Journal of Applied Mathematics and Computer Science 20, no. 3 (2010): 483–95. http://dx.doi.org/10.2478/v10006-010-0035-1.

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Supervisory predictive control and on-line set-point optimizationThe subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on

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39

Jumaa Alkurawy, Lafta E., and Khalid G. Mohammed. "Model predictive control of magnetic levitation system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5802. http://dx.doi.org/10.11591/ijece.v10i6.pp5802-5812.

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In this work, we suggest a technique of controller design that applied to systems based on nonlinear. We inform the sufficient conditions for the stability of closed loop system. The asymptotic stability of equilibrium and the nonlinear controller can be applied to improvement the stability of Magnetic Levitation system(MagLev). The MagLev nonlinear nodel can be obtained by state equation based on Lagrange function and Model Predictive Control has been used for MagLev system.

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40

Lafta, E. Jumaa Alkurawy, and G. Mohammed Khalid. "Model predictive control of magnetic levitation system." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5802–12. https://doi.org/10.11591/ijece.v10i6.pp5802-5812.

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In this work, we suggest a technique of controller design that applied to systems based on nonlinear. We inform the sufficient conditions for the stability of closed loop system. The asymptotic stability of equilibrium and the nonlinear controller can be applied to improvement the stability of magnetic levitation system (MagLev). The MAgLev nonlinear nodel can be obtained by state equation based on Lagrange function and model predictive control has been used for MagLev system.

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41

MUTHA, R. K., W. R. CLUETT, and A. PENLIDIS. "Modifying the Prediction Equation for Nonlinear Model-Based Predictive Control." Automatica 34, no. 10 (1998): 1283–87. http://dx.doi.org/10.1016/s0005-1098(98)00082-x.

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42

Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, and Jaroslav Hlava. "Model Predictive Control for Autonomous Driving Vehicles." Electronics 10, no. 21 (2021): 2593. http://dx.doi.org/10.3390/electronics10212593.

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The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subj

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43

Fedorová, Kristína, Peter Bakaráč, and Michal Kvasnica. "Agile manoeuvres using model predictive control." Acta Chimica Slovaca 12, no. 1 (2019): 136–41. http://dx.doi.org/10.2478/acs-2019-0019.

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Abstract This paper shows how model predictive control (MPC) can be used to perform agile manoeuvres in a pendulum-on-a-cart system, which is an abstraction of many mechanical systems commonly used in the industry, such as cranes. Specifically, the problem of moving a cart on which a pendulum is mounted using a free joint is rapidly moved from one position to another one while mitigating the swings of the pendulum. To achieve this goal, an optimization-based MPC strategy is employed that selects the control moves while minimizing the chosen cost function and, simultaneously, enforcing constrai

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44

Tian, Yuqiang, Bin Wang, Peng Chen, and Ying Yang. "A state estimator–based nonlinear predictive control for a fractional-order Francis hydraulic turbine governing system." Journal of Vibration and Control 26, no. 11-12 (2019): 1068–80. http://dx.doi.org/10.1177/1077546319891633.

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A nonlinear predictive control scheme with a state estimator for a fractional-order hydraulic turbine governing system is studied in this article. First, a more practical fractional-order model of the hydraulic turbine governing system is introduced. Second, according to the Grünwald–Letnikov definition of fractional calculus, the fractional-order hydraulic turbine governing system is transformed into a nonlinear discrete model. Third, based on the Takagi–Sugeno fuzzy theory, a fuzzy prediction model of the fractional-order hydraulic turbine governing system is presented. Furthermore, a state

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45

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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46

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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47

Chen, Wen-Hua, Donald J. Ballance, and Peter J. Gawthrop. "Nonlinear generalised predictive control and optimal dynamical inversion control." IFAC Proceedings Volumes 32, no. 2 (1999): 2540–45. http://dx.doi.org/10.1016/s1474-6670(17)56432-0.

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48

Xu, F., H. Chen, X. Gong, and Y. F. Hu. "Engine Idle Speed Control Using Nonlinear Model Predictive Control." IFAC Proceedings Volumes 46, no. 21 (2013): 171–76. http://dx.doi.org/10.3182/20130904-4-jp-2042.00119.

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49

Hartwich, Arndt, Martin Schlegel, Lynn Würth, and Wolfgang Marquardt. "Adaptive control vector parameterization for nonlinear model-predictive control." International Journal of Robust and Nonlinear Control 18, no. 8 (2008): 845–61. http://dx.doi.org/10.1002/rnc.1246.

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50

Kudryavtseva, I. A., and K. S. Petrov. "Helicopter control by nonlinear model predictive control with constraints." Modelling and Data Analysis 15, no. 2 (2025): 89–109. https://doi.org/10.17759/mda.2025150205.

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<p><strong>Context and relevance.</strong> The paper presents the controller synthesis problem for a helicopter moving under constraints. Helicopter dynamics is described by 6 DoF model that is complimented by the measurement equations. The problem is being solved supposing that the hard control constraints are imposed to specify technical characteristics of the controller. <strong>Objective.</strong> The goal is to find the control input to achieve a required target output over a finite time period. <strong>Hypothesis.</strong> Taking the non-linearit

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