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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Haowei Lin, Bo Zhao, Derong Liu and Cesare Alippi, "Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 954-964, July 2020. doi: 10.1109/JAS.2020.1003225
Citation: Haowei Lin, Bo Zhao, Derong Liu and Cesare Alippi, "Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 954-964, July 2020. doi: 10.1109/JAS.2020.1003225

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

doi: 10.1109/JAS.2020.1003225
Funds:  This work was supported in part by the National Natural Science Foundation of China (61533017, 61973330, 61773075, 61603387), the Early Career Development Award of SKLMCCS (20180201), and the State Key Laboratory of Synthetical Automation for Process Industries (2019-KF-23-03)
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  • 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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    Highlights

    • A data-based fault tolerant control scheme is investigated.
    • The unknown system dynamics is approximated by PSO-NN identifier.
    • The HJB equation is solved with a high successful rate by the PSOCNN.
    • The online fault tolerant control is shown to be optimal.

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