基于自适应神经网络滑模观测器的容错控制  

Fault-tolerant control based on adaptive neural network sliding mode observer

作　　者：杨正银 胡健[1] 姚建勇[1] 沙英哲 宋秋雨 YANG Zheng-yin;HU Jian;YAO Jian-yong;SHA Ying-zhe;SONG Qiu-yu(College of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学机械工程学院,江苏南京210094

出　　处：《浙江大学学报（工学版）》2022年第8期1656-1665,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(51975294);高性能复杂制造国家重点实验室开放课题基金资助项目(Kfkt2019–11);中央高校基本科研业务费专项资金资助项目(30920010009)。

摘　　要：针对机电伺服系统可能发生的故障,提出基于自适应神经网络滑模观测器的快速终端滑模容错控制策略.在自适应滑模观测器中引入神经网络估计故障,以提高故障发生时观测器的状态估计精度和故障检测准确性.利用观测器的状态估计值进行状态重构,结合参数自适应技术和快速终端滑模控制方法设计主动容错控制器.针对参数不确定性设计参数自适应率进行估计,并利用前馈补偿技术补偿故障和参数不确定性.针对未知上界的扰动设计具有自适应增益的鲁棒项.利用Lyapunov定理证明所提出的控制方法可以实现系统有界稳定,大量仿真和实验结果验证了控制器在系统发生故障时具有良好的容错能力、控制精度和响应速度.A fast terminal sliding mode fault-tolerant control strategy based on adaptive neural network sliding mode observer was proposed for the possible failure of electromechanical servo system.The neural network was introduced into the adaptive sliding mode observer to estimate the fault,so as to improve the accuracy of state estimation and fault diagnosis.An active fault-tolerant controller was designed by using the state estimation value of the observer,combining the parameter adaptive technology and the fast terminal sliding mode control method.The parameter adaptive rate was designed to estimate the parameter uncertainty,and the feedforward compensation technology was used to compensate the fault and parameter uncertainty.A robust term with adaptive gain was designed to overcome the disturbance of unknown upper bound.Using Lyapunov theorem,it is proved that the proposed control method can achieve bounded stability of the system.A large number of simulation and experimental results verify that the controller has good fault tolerance,control accuracy and response speed in case of system failure.

关 键 词：容错控制 神经网络 自适应滑模观测器 故障估计 快速终端滑模 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]