The Robust Iterative Learning Control of Networked Control Systems with Varying References
作者单位：Service Robots Laboratory of Shandong Universiy School of Control Science and Engineering Shandong University
会议名称：《第25届中国控制与决策会议》
主办单位：IEEE;NE Univ;IEEE Ind Elect Chapter;IEEE Harbin Sect Control Syst Soc Chapter;Guizhou Univ;IEEE Control Syst Soc;Syst Engn Soc China;Chinese Assoc Artificial Intelligence;Chinese Assoc Automat;Tech Comm Control Theory;Chinese Assoc Aeronaut;Automat Control Soc;Chinese Assoc Syst Simulat;Simulat Methods & Modeling Soc;Intelligent Control & Management Soc
会议日期：2013年
学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程]
基 金：Supported by National Natural Science Foundation of China (61075092, 61104009) Natural Science Foundation of Shandong Province (ZR2011FM011, ZR2010AM007) Independent Innovation Foundation of Shandong University (2010TB022, 2011JC017)
关 键 词：Iterative learning control Networked control system Convergence Robustness Iterative varying references.
摘 要：In this paper, the iterative learning control （ILC） is applied to the networked control system （NCS） with iterative varying references and time-delayed states. A robust PD type ILC learning law is discussed including both feedback and feedforward terms. And a P type reference updating law is applied to improve the convergence speed. The convergence conditions are derived in both frequency and time domains. The learning gains in the updating law guarantee the convergence of the control strategy, and can be adjusted to improve the convergence speed to a large extent. A number of simulation results are provided to validate the concepts and the tuning rules are summarized as well.