摘要
针对基于迭代学习的故障估计器方法,提出一种基于扩张状态观测器(ESO)思想的迭代学习算法,以提高虚拟故障的收敛速度.该算法将ESO的输出误差非线性反馈机制用于迭代学习过程,利用故障估计器当前输出残差的非线性函数修正下次迭代时的虚拟故障值.对所建立的故障估计器的收敛性进行理论分析,并在此基础上进行了仿真实验.仿真结果表明,所提出的算法具有良好的收敛速度和故障估计精度.
For the fault estimator based on the iterative learning theory, an iterative learning algorithm based on the extended states observer（ESO） is proposed to improve the convergence speed of the virtual fault. In this algorithm, the nonlinear feedback mechanism of the ESO is transplanted to iterative learning processes, that is, the nonlinear function of the current output residual is used to adjust the value of the virtual fault in the next iteration. The theoretical convergence analysis of the proposed fault estimator is proven, based on which some simulation experiments are conducted. The obtained results show the favorable convergence speed and the fault estimation precision of the proposed method.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第3期546-550,共5页
Control and Decision
基金
国防预研基金项目(513270203)
武器装备预研重点基金项目(9140A27020211JB3402)
关键词
迭代学习
故障估计
扩张状态观测器
虚拟故障
iterative learning
fault estimation
extended state observer（ESO）
virtual fault
