Data-based fault tolerant control for affine nonlinear systems through particle swarm optimized neural networks

被引：119

作者：

Lin, Haowei ^{[1
]}

Zhao, Bo ^{[2
]}

Liu, Derong ^{[1
]}

Alippi, Cesare ^{[3
,4
]}

机构：

[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China

[2] Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China

[3] Politecn Milan, Dipartimento Elettron & Informaz, I-20133 Milan, Italy

[4] Univ Svizzera Italiana, Lugano, Switzerland

来源：

IEEE-CAA JOURNAL OF AUTOMATICA SINICA | 2020年 / 7卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Adaptive dynamic programming (ADP); critic neural network; data-based; fault tolerant control (FTC); particle swarm optimization (PSO); TRACKING CONTROL; HJB SOLUTION; CONVERGENCE; ALGORITHM; STABILITY; DESIGN;

D O I：

10.1109/JAS.2020.1003225

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：954 / 964

页数：11

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