Observer based adaptive dynamic programming for fault tolerant control of a class of nonlinear systems

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• 作者：Bo Zhao^a ; ^{zhaobo@ia.ac.cn} ; Derong Liu ; ^b ; ^{derong@ustb.edu.cn} ; Yuanchun Li^c ; ^{liyc@ccut.edu.cn}
• 关键词：Adaptive dynamic programming ; Fault tolerant control ; Policy iteration ; Nonlinear systems ; Fault observer ; Neural network
• 刊名：Information Sciences
• 出版年：2017
• 出版时间：April 2017
• 年：2017
• 卷：384
• 期：Complete
• 页码：21-33
• 全文大小：1581 K
• 卷排序：384

文摘

This paper develops a novel fault tolerant control (FTC) scheme for a class of nonlinear systems with actuator failures based on adaptive dynamic programming (ADP). The estimated actuator failure from a fault observer is utilized to construct an improved performance index function that reflects the failure, regulation and control simultaneously. By employing a proper performance index function, the FTC problem can be transformed into an optimal control problem. By using policy iteration, the Hamilton–Jacobi–Bellman equation can be solved by constructing a critic neural network. Then, the approximated optimal controller can be derived directly. The closed-loop system is guaranteed to be uniformly ultimately bounded via the Lyapunov stability theorem. The effectiveness of the developed FTC scheme is demonstrated by two simulation examples. The significant contribution of the proposed strategy lies in that the well-known ADP method is extended to solving the FTC problem.