EHA fault diagnosis and fault tolerant control based on adaptive neural network robust observer

被引：0
|
作者：
Zhao J. [1 ]
Hu J. [1 ]
Yao J. [1 ]
Zhou H. [2 ]
Wang J. [1 ]
Cao M. [1 ]
机构：
[1] School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing
[2] State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 05期
基金：
中国国家自然科学基金;
关键词：
electro-hydrostatic actuator; fault diagnosis; fault tolerant control; radial basis function neural network; robust observer;
D O I：
10.13700/j.bh.1001-5965.2021.0416
中图分类号：
学科分类号：
摘要：
Aiming at the characteristics of high power density, complex working conditions, high integration of components and the wide variety of faults of electro hydrostatic actuator (EHA), a fault diagnosis and fault-tolerant controller of electro-hydro actuator based on an adaptive neural network robust observer is designed. A robust observer is proposed to observe the internal state of the model. The uncertain parameters, such as elastic modulus of hydraulic system is estimated by adaptive law; and the nonlinear, such as friction disturbance is approximated by radial basis function (RBF) neural network. The feedforward compensation method is used to compensate the fault and parameter uncertainty, and the robust term is designed to overcome other disturbances. By using Lyapunov stability theorem, it is proved that the proposed controller can realize the bounded stability of the system in the presence of faults. The co-simulation results show that the proposed controller has higher control accuracy and robustness than the traditional proportional, integral and differential controller (PID) and adaptive robust controller (ARC). © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
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页码：1209 / 1221
页数：12
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