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Design of fuzzy iterative learning fault-tolerant control for batch processes with time-varying delays
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Design of fuzzy iterative learning fault-tolerant control for batch processes with time-varying delays

Author: Limin Wang Affiliation: College of Mathematics and Statistics, Hainan Normal University, Haikou, China; School of Information and Control Engineering, Liaoning Shihua University, Fushun, China; Bingyun Li Affiliation: School of Information and Control Engineering, Liaoning Shihua University, Fushun, China; Jingxian Yu Affiliation: School of Information and Control Engineering, Liaoning Shihua University, Fushun, China; Ridong Zhang Affiliation: The Belt and Road Information Research Institute, Automation College, Hangzhou Dianzi University, Hangzhou, China; Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, , Hong Kong; Furong Gao Affiliation: Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, , Hong Kong
Edition/Format: Article Article : English
Publication:Optimal Control Applications and Methods, v39 n6 (November/December 2018): 1887-1903
Summary:
In this paper, a new two-dimensional (2D) fuzzy composite iterative learning fault-tolerant control strategy using a 2D Takagi-Sugeno fuzzy model is proposed for batch processes with time delay and actuator faults. Firstly, based on the local-sector nonlinearity method, a 2D Takagi-Sugeno fuzzy model representing the nonlinear batch process with actuator faults is constructed with a series of linear models and  Read more...
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Details

Document Type: Article
All Authors / Contributors: Limin Wang Affiliation: College of Mathematics and Statistics, Hainan Normal University, Haikou, China; School of Information and Control Engineering, Liaoning Shihua University, Fushun, China; Bingyun Li Affiliation: School of Information and Control Engineering, Liaoning Shihua University, Fushun, China; Jingxian Yu Affiliation: School of Information and Control Engineering, Liaoning Shihua University, Fushun, China; Ridong Zhang Affiliation: The Belt and Road Information Research Institute, Automation College, Hangzhou Dianzi University, Hangzhou, China; Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, , Hong Kong; Furong Gao Affiliation: Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, , Hong Kong
ISSN:0143-2087
Language Note: English
Unique Identifier: 7910671694
Notes: Limin Wang, College of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China; or School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China.Email: wanglimin0817@163.comRidong Zhang, The Belt and Road Information Research Institute, Automation College, Hangzhou Dianzi University, Hangzhou 310018, China; or Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong.Email: zrd-el@163.com
Number of Figures: 7
Number of Words: 10662
Awards:
Responsibility: WANG et al.

Abstract:

In this paper, a new two-dimensional (2D) fuzzy composite iterative learning fault-tolerant control strategy using a 2D Takagi-Sugeno fuzzy model is proposed for batch processes with time delay and actuator faults. Firstly, based on the local-sector nonlinearity method, a 2D Takagi-Sugeno fuzzy model representing the nonlinear batch process with actuator faults is constructed with a series of linear models and nonlinear membership functions. Then, a 2D fuzzy feedback control-based iterative learning fault-tolerant control strategy is proposed under the constructed model. Using the 2D Lyapunov stability theory, sufficient conditions for system asymptotic stability are given. The fault-tolerant control law is then designed, guaranteeing system asymptotic stability along time and batches even if the system fails. Finally, the traditional control algorithm, the pure iterative learning control algorithm, and the feedback control-based iterative learning control algorithm proposed in this paper are compared on the level control of the three-tank system, which proves the effectiveness of the proposed method.

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