Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length
Iterative Learning Fault Estimation Design for Nonlinear System with Random Trial Length
复制标题
DOI：
10.1155/2017/1850737
复制
发表时间：
2017-11
期刊：
影响因子：
--
通讯作者：
Li Feng;Ke Zhang;Y. Chai;Shuiqing Xu;Zhimin Yang
中科院分区：
文献类型：
--
作者：
Li Feng;Ke Zhang;Y. Chai;Shuiqing Xu;Zhimin Yang
针对一类试验长度随机变化的非线性系统，设计了一种基于迭代学习的故障估计观测器。这是通过提出一种用于监控系统状态的状态观测器和一种用于在不精确系统模型的情况下进行故障估计的迭代学习律来实现的。定义了一个平均因子来处理随机试验长度引起的跟踪信息的缺失和冗余。通过收敛性分析，给出了故障信号估计的充分设计条件.通过求解所提出的约束条件，计算了观测器增益和迭代学习律指标.数值算例验证了该方法的有效性和优越性。
An iterative learning scheme-based fault estimation observer is designed for a class of nonlinear systems with randomly changed trial length. This is achieved by presenting a state observer for monitoring the system state and an iterative learning law for fault estimation in the presence of imprecise system model. An average factor is defined to deal with the lack and redundancy in tracking information caused by random trial length. Via the convergence analysis, sufficient design conditions are developed for estimation of fault signal. The observer gains and iterative learning law indexes are computed by solving the proposed conditions under - constraints. Numerical examples are presented to demonstrate the validity, the effectiveness, and the superiority of this method.