A NEW NEURAL NETWORK-BASED ADAPTIVE ILC FOR NONLINEAR DISCRETE-TIME SYSTEMS WITH DEAD ZONE SCHEME  

作　　者：Ronghu CHI Zhongsheng HOU 

机构地区：[1]Institute of Autonomous Navigation and Intelligent Control, School of Automation and Electrical Engineering,Qingdao University of Science and Technology, Qingdao 266042, China. [2]Advanced Control Systems Laboratory, School of Electronics and Information Engineering, Beijing JiaotongUniversity, Beijing 100044, China.

出　　处：《Journal of Systems Science & Complexity》2009年第3期435-445,共11页系统科学与复杂性学报（英文版）

基　　金：supported by General Program (60774022);State Key Program (60834001) of National Natural Science Foundation of China;Doctoral Foundation of Qingdao University of Science & Technology (0022324)

摘　　要：By introducing a deadwzone scheme, a new neural network based adaptive iterative learning control （ILC） （NN-AILC） scheme is presented for nonlinear discrete-time systems, where the NN weights are time-varying. The most distinct contribution of the proposed NN-AILC is the relaxation of the identical conditions of initial state and reference trajectory, which are common requirements in traditional ILC problems. Convergence analysis indicates that the tracking error converges to a bounded ball, whose size is determined by the dead-zone nonlinearity. Computer simulations verify the theoretical results.

关 键 词：Adaptive control iterative learning control neural network non-identical initial condition non-identical trajectory. 

分 类 号：TP271.8[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]