An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

doi:10.1016/S1004-9541(13)60490-7

Chin.J.Chem.Eng. ›› 2013, Vol. 21 ›› Issue (4): 401-411.DOI: 10.1016/S1004-9541(13)60490-7

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

WANG Limin^1,2,3, CHEN Xi³, GAO Furong ^1,3,4

1 Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;
2 College of Sciences, Liaoning Shihua University, Fushun 113001, China;
3 Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;
4 Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

Received:2012-03-15 Revised:2012-05-03 Online:2013-04-26 Published:2013-04-28
Supported by:
Supported in part by NSFC/RGC joint Research Scheme (N-HKUST639/09), the National Natural Science Foundation of China (61104058, 61273101), Guangzhou Scientific and Technological Project (2012J5100032), Nansha district independent innovation project (201103003), China Postdoctoral Science Foundation (2012M511367, 2012M511368), and Doctor Scientific Research Foundation of Liaoning Province (20121046).

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

王立敏^1,2,3, 陈曦³, 高福荣^1,3,4

1 Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;
2 College of Sciences, Liaoning Shihua University, Fushun 113001, China;
3 Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;
4 Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

通讯作者: CHEN Xi, GAO Furong
基金资助:
Supported in part by NSFC/RGC joint Research Scheme (N-HKUST639/09), the National Natural Science Foundation of China (61104058, 61273101), Guangzhou Scientific and Technological Project (2012J5100032), Nansha district independent innovation project (201103003), China Postdoctoral Science Foundation (2012M511367, 2012M511368), and Doctor Scientific Research Foundation of Liaoning Province (20121046).

Abstract

Abstract: Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H_? performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.

Key words: two-dimensional Fornasini-Marchsini model, batch process, iterative learning control, linear matrix inequality, fault-tolerant guaranteed cost control

摘要： Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H_? performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.

关键词: two-dimensional Fornasini-Marchsini model, batch process, iterative learning control, linear matrix inequality, fault-tolerant guaranteed cost control

WANG Limin, CHEN Xi, GAO Furong . An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes[J]. Chin.J.Chem.Eng., 2013, 21(4): 401-411.

王立敏, 陈曦, 高福荣. An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes[J]. Chinese Journal of Chemical Engineering, 2013, 21(4): 401-411.

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An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

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