Adaptive learning based fault tolerant control for uncertain nonlinear systems

Qinmin Yang, Bingnan Liu, Zhiwen Yu
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Abstract

This paper introduces a fault tolerant controller design for nonlinear unknown systems with multiple actuators and bounded disturbance. The controller consists of an adaptive learning-based control law and a switching function mechanism. The adaptive control law is implemented by a two-layer neural network and the switching function is designed to automatically search for the correct switching vector to turn off the unknown faulty actuator if there is any. The stability of the system output under the occurrence of actuator failure is proved through standard Lyapunov approach, while the other signals are guaranteed to be bounded. The theoretical result is substantiated by a simulation example with a continuous stirred tank reactor.
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基于自适应学习的不确定非线性系统容错控制
介绍了一种具有多作动器和有界扰动的非线性未知系统的容错控制器设计。该控制器由基于自适应学习的控制律和切换函数机构组成。采用双层神经网络实现自适应控制律,设计开关函数,在存在未知故障致动器时,自动搜索正确的开关向量关闭未知故障致动器。通过标准李雅普诺夫方法证明了在执行器失效时系统输出的稳定性,同时保证了其他信号的有界性。通过连续搅拌槽式反应器的仿真算例验证了理论结果。
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