An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

›› 2009, Vol. 17 ›› Issue (1): 100-107.

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

LU Mei^1,2, JIN Chengbo³, SHAO Huihe¹

1. Institute of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Department of Fieldbus, Shanghai Automation Instrument Co., Ltd., Shanghai 200233, China;
3. Department of Product Design, Shanghai GaoKe Valve Manufactory Co., Ltd., Shanghai 200092, China

Received:2007-08-21 Revised:2008-08-06 Online:2009-01-28 Published:2009-01-28
Supported by:
Supported by the National High Technology Research and Development Program of China(2004AA412050)

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

陆妹^1,2, 金成波³, 邵惠鹤¹

1. Institute of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Department of Fieldbus, Shanghai Automation Instrument Co., Ltd., Shanghai 200233, China;
3. Department of Product Design, Shanghai GaoKe Valve Manufactory Co., Ltd., Shanghai 200092, China

通讯作者: LU Mei, E-mail: lumei7718@163.com
基金资助:
Supported by the National High Technology Research and Development Program of China(2004AA412050)

Abstract

Abstract: A finite horizon predictive control algorithm, which applies a saturated feedback control law as its local control law, is presented for nonlinear systems with time-delay subject to input constraints.In the algorithm, N free control moves, a saturated local control law and the terminal weighting matrices are solved by a minimization problem based on linear matrix inequality(LMI) constraints online.Compared with the algorithm with a nonsaturated local law, the presented algorithm improves the performances of the closed-loop systems such as feasibility and optimality.This model predictive control(MPC) algorithm is applied to an industrial continuous stirred tank reactor(CSTR) with explicit input constraint.The simulation results demonstrate that the presented algorithm is effective.

Key words: predictive control, fuzzy system, time-delay, saturated control law

摘要： A finite horizon predictive control algorithm,which applies a saturated feedback control law as its local control law,is presented for nonlinear systems with time-delay subject to input constraints.In the algorithm,N free control moves,a saturated local control law and the terminal weighting matrices are solved by a minimization problem based on linear matrix inequality(LMI) constraints online.Compared with the algorithm with a nonsaturated local law,the presented algorithm improves the performances of the closed-loop systems such as feasibility and optimality.This model predictive control(MPC) algorithm is applied to an industrial continuous stirred tank reactor(CSTR) with explicit input constraint.The simulation results demonstrate that the presented algorithm is effective.

关键词: predictive control, fuzzy system, time-delay, saturated control law

LU Mei, JIN Chengbo, SHAO Huihe. An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process[J]. , 2009, 17(1): 100-107.

陆妹, 金成波, 邵惠鹤. An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process[J]. , 2009, 17(1): 100-107.

References

1 Kothare, M.V., Balakrishan, V., Morari, M., “Robust constrained model predictive control using linear matrix inequality”, Automatica,32 (10), 1361-1379 (1996).
2 Cuzzola, F.A., Geromel, J.C., Morari, M., “An improved approach for constrained robust model predictive control”, Automatica, 38,1183-1189 (2002).
3 Ding, B.C., Xi, Y.G., Li, S.Y., “A synthesis approach of on-line constrained robust model predictive control”, Automatica, 40, 163-167 (2004).
4 Cao, Y.Y., Frank, P.M., “Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach”, IEEE Trans. Fuzzy Syst., 8 (2), 200-211 (2000).
5 Cao, Y.Y., Frank, P.M., “Stability analysis and synthesis of nonlinear time-delay systems via linear Takagi-Sugeno Fuzzy model”, Fuzzy Sets Syst., 124, 213-229 (2001).
6 Hu, X.B., Chen, W.H., “Model predictive control for constrained systems with uncertain state-delays”, Int. J. Robust Nonlinear Contr.,14, 1421-1432 (2004).
7 Jeong, S.C., Park, P.G., “Constrained MPC algorithm for uncertain time-varying systems with state-delay”, IEEE Trans. Automat. Contr., 50 (2), 257-263 (2005).
8 Zhang, J., “Study on model predictive control algorithms for time-delay systems”, Ph.D. Thesis, Harbin Univ. of Technology, China (2004).
9 Su, C.L., Wang, S.Q., “Robust model predictive control for discrete uncertain nonlinear systems with time-delay via fuzzy model”, J. Zhejiang Univ., Science A, 7 (10), 1723-1732 (2006).
10 Wang, D., Zhou, D.H., Jin, Y.H., “Active fault tolerant control of a class of nonlinear time-delay processes”, Chin. J. Chem. Eng., 12 (1),60-65 (2004).
11 Bao, Z.J., Pi, D.Y., Sun, Y.X., “Nonlinear model predictive control based on support vector machine with multi-kernal”, Chin. J. Chem. Eng., 15 (5), 691-697 (2007).
12 Zhu, Q.X., Li, C.F., “Dimensionality reduction with input training neural network and its application in chemical process modelling”, Chin. J. Chem. Eng., 14 (5), 597-603 (2006).
13 Takagi, T., Sugeno, M., “Fuzzy identification of systems and its applications to modeling and control”, IEEE Trans. Syst., Man, Cybern., 15, 116-132 (1985).
14 Huang, Y.L., Lou, H.H., Gong, J.P., Edgar, T.F., “Fuzzy model predictive control”, IEEE Trans. Fuzzy Syst., 8, 665-678 (2000).
15 Mollow, S., Babuska, R., Abonyi, J., Verbruggen, H.B., “Effective optimization for fuzzy model predictive control”, IEEE Trans. Fuzzy Syst., 12, 661-675 (2000).
16 Hu, T.S., Lin, Z.L., Control Systems with Actuator Saturation: Analysis and Design, Birkhauser, Boston (2001).
17 Cao, Y.Y., Lin, Z.L., Shamash, Y., “Set invariance analysis and gain-scheduling control for LPV systems subjected to actuator saturation”, Syst. Control Lett., 46, 137-151 (2002).
18 Limon, M.D., Gomes, S.J.J.M., Camacho, E.F., Alamo, T., “Improved MPC design based on Saturating Controls laws”, European J. Contr., 11 (2), 112-122 (2005).
19 Jamal, D., Jacques, B., “Parameter dependent Lyapunov functions for discrete time systems with time varying parameter uncertainties”, Syst. Control Lett., 43, 355-359 (2001).
20 Schuurmans, J., Rossiter, J.A., “Robust predictive control using tight sets of predicted states”, IEE Proceedings of Control Theory and Applications, 147 (1), 13-18 (2000).

[1]	Shan Liu, Wenqi Zhong, Xi Chen, Li Sun, Lukuan Yang. Multiobjective economic model predictive control using utopia-tracking for the wet flue gas desulphurization system [J]. Chinese Journal of Chemical Engineering, 2023, 54(2): 343-352.
[2]	Jia Ren, Zengqiang Chen, Mingwei Sun, Qinglin Sun, Zenghui Wang. Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning [J]. Chinese Journal of Chemical Engineering, 2022, 49(9): 234-244.
[3]	Zhen Wang, Aimin An, Qianrong Li. Distributed model predictive control based on adaptive sampling mechanism [J]. Chinese Journal of Chemical Engineering, 2021, 39(11): 193-204.
[4]	Lukuan Yang, Wenqi Zhong, Li Sun, Xi Chen, Yingjuan Shao. Dynamic optimization oriented modeling and nonlinear model predictive control of the wet limestone FGD system [J]. Chinese Journal of Chemical Engineering, 2020, 28(3): 832-845.
[5]	Li Jia, Wendan Tan. Just-in-time learning based integrated MPC-ILC control for batch processes [J]. Chin.J.Chem.Eng., 2018, 26(8): 1713-1720.
[6]	Jianxin Wang, Na Yu, Mengqi Chen, Lin Cong, Lanyi Sun. Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies [J]. Chin.J.Chem.Eng., 2018, 26(5): 1087-1101.
[7]	Pengfei Cao, Xionglin Luo, Xiaohong Song. Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor [J]. , 2017, 25(9): 1230-1237.
[8]	Haokun Wang, Zuhua Xu, Jun Zhao, Aipeng Jiang. An optimal filter based MPC for systems with arbitrary disturbances [J]. , 2017, 25(5): 632-640.
[9]	Mahmoud M. Saafan, Mohamed M. Abdelsalam, Mohamed S. Elksas, Sabry F. Saraya, Fayez F. G. Areed. An adaptive neuro-fuzzy sliding mode controller for MIMO systems with disturbance [J]. , 2017, 25(4): 463-476.
[10]	Cuimei Bo, Jun Li, Lei Yang, Hui Yi, Jihai Tang, Xu Qiao. MPC of distillation column with side reactors for methyl acetate [J]. Chin.J.Chem.Eng., 2017, 25(12): 1798-1804.
[11]	Hongguang Pan, Weimin Zhong, Zaiying Wang. An on-line constraint softening strategy to guarantee the feasibility of dynamic controller in double-layered MPC [J]. Chin.J.Chem.Eng., 2017, 25(12): 1805-1811.
[12]	Baocang Ding, Hongguang Pan. Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance [J]. , 2016, 24(10): 1431-1441.
[13]	Kai Feng, Jiangang Lu, Jinshui Chen. Nonlinearmodel predictive control based on support vectormachine and genetic algorithm [J]. Chin.J.Chem.Eng., 2015, 23(12): 2048-2052.
[14]	Defeng He, Shiming Yu, Li Yu. Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes [J]. Chin.J.Chem.Eng., 2015, 23(10): 1662-1669.
[15]	Chen Chen, Zhihua Xiong, Yisheng Zhong. Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory [J]. , 2014, 22(7): 762-768.

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments