Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable

Hanqiao Huang; Haoyu Cheng; Ruijia Song; Gonghao Sun; Yangwang Fang; Guan Huang

doi:10.1155/2022/8339634

Fault Detection Filter Design and Optimization for Switched Systems with All Modes Unstable

Comput Intell Neurosci. 2022 Apr 4:2022:8339634. doi: 10.1155/2022/8339634. eCollection 2022.

Authors

Hanqiao Huang¹, Haoyu Cheng¹, Ruijia Song², Gonghao Sun^{1

3}, Yangwang Fang¹, Guan Huang⁴

Affiliations

¹ Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an, China.
² School of Astronautics, Northwestern Polytechnical University, Xi'an, China.
³ Shanghai Electro-Mechanical Engineering Institute, Shanghai, China.
⁴ China Electronics Standardization Institute, Beijing, China.

Abstract

This problem of intelligent switched fault detection filter design is investigated in this article. Firstly, the mode-dependent average dwell time (MDADT) method is applied to generate the time-dependent switching signal for switched systems with all subsystems unstable. Afterwards, the switched fault detection filter is proposed for the generation of residual signal, which consists of dynamics-based filter and learning-based filter. The MDADT method and multiple Lyapunov function (MLF) method are employed to guarantee the stability and prescribed attenuation performance. The parameters of dynamics-based filter are given by solving a series of linear matrix inequalities. To improve the transient performance, the deep reinforcement learning is introduced to design learning-based filter in the framework of actor-critic. The output of learning-based filter can be viewed as uncertainties of dynamics-based filter. The deep deterministic policy gradient algorithm and nonfragile control are adopted to guarantee the stability of algorithm and compensate the external disturbance. Finally, simulation results are given to illustrate the effectiveness of the method in the paper.