迭代扩张状态观测器及其在迭代学习控制中的应用

Iterative extended state observer and its application in iterative learning control

针对一类迭代学习控制(ILC)系统的不确定项,根据时域中扩张状态观测器的思想,提出迭代域中线性迭代扩张状态观测器(LIESO),该线性迭代扩张状态观测器可以利用迭代过程的跟踪误差给出迭代学习控制系统的不确定项的显式估计.给出了基于该估计的迭代学习控制算法,并应用类Lyapunov方法证明其收敛性.仿真结果表明,所提出的迭代学习控制算法是有效的,应用迭代扩张状态观测器可以大幅度提高迭代学习效率.

For the uncertainty in a class of iterative learning control（ILC） systems, a linear iterative extended state observer（LIESO） in the iterative domain is presented based on the thought of extended state observer in time domain.This LIESO can estimate explicitly the uncertainty of the ILC system according to the tracking error during the process of iterations. The ILC algorithm based on the estimation of the uncertainty is presented, whose convergence is proved by using Lyapunov-like approach. Simulation results show the effectiveness of the proposed ILC algorithm and the iterative learning efficiency can be improved so much by using LIESO.