基于迭代学习的线性不确定重复系统间歇性故障估计

Iterative Learning Based Intermittent Fault Estimation for a Class of Linear Uncertain Repeated Systems

针对一类带有不确定参数项的线性重复系统间歇性故障估计问题,本文提出一种基于迭代学习的故障估计算法.该算法通过设计基于迭代学习的故障估计器和状态观测器,构造李雅普诺夫方程和优化函数证明该算法的鲁棒性和收敛性,并通过线性矩阵不等式,求解出算法中的观测器增益矩阵和迭代学习参数矩阵.区别于其他观测器方法,本文中的方法利用上一次基于迭代学习观测器输出和系统实际输出产生的残差信号,对本次的故障信号进行跟踪估计,从而准确地估计出故障的幅值和形状.仿真结果说明了该算法的有效性和准确性.

This paper presents a novel observer-based fault estimation method using the iterative leaning scheme for linear uncertain repeated systems where the parameter uncertainties are time-varying. Firstly, a state observer is constructed to monitor the system status and the Lyapunov function is utilized to ensure the stability of the system. After providing the design of a robust monotonically convergence for the error system, an optimal function is presented to ensure the iterative learning law is applicable to systems. Meanwhile, two Lemmas and a reasonable assumption are utilized to linearize the nonlinear terms in our initial results. The LMI toolbox is utilized to obtain the results of the learning gain. In addition,the theoretical results have been verified through simulation tests. It has also shown that the proposed iterative learning based approach can be applicable to more general linear uncertain repeated systems.