期刊
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
卷 7, 期 4, 页码 954-964出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2020.1003225
关键词
Adaptive dynamic programming (ADP); critic neural network; data-based; fault tolerant control (FTC); particle swarm optimization (PSO)
资金
- National Natural Science Foundation of China [61533017, 61973330, 61773075, 61603387]
- Early Career Development Award of SKLMCCS [20180201]
- State Key Laboratory of Synthetical Automation for Process Industries [2019-KF-23-03]
In this paper, a data-based fault tolerant control (FTC) scheme is investigated for unknown continuous-time (CT) affine nonlinear systems with actuator faults. First, a neural network (NN) identifier based on particle swarm optimization (PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network (PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation (HJBE) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.
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