Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points

作     者：Yu Liu Rong-Hu Chi Zhong-Sheng Hou 

作者机构：School of Automation and Electronic Engineering Qingdao University of Science and Technology Advanced Control Systems Lab School of Electronics and Information Engineering Beijing Jiaotong University 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2015年第12卷第3期

页      面：266-272页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China(Nos.61374102,61433002 and 61120106009) High Education Science&Technology Fund Planning Project of Shandong Province of China(No.J14LN30) 

主　　题：Adaptive terminal iterative learning control neural network initial state learning iteration-varying terminal desired points ini 

摘      要：Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.