Robust iterative learning control for nonlinear systems with measurement disturbances  

作　　者：Xuhui BuI FashanYu Zhongsheng Hou Haizhu Yang 

机构地区：[1]School of Electrical Engineering & Automation, Henan Polytechnic University, Jiaozuo 454003, E R. China [2]Henan Provincial Open Laboratory for Control Engineering Key Discipline, Henan Polytechnic University, Jiaozuo 454003, P. R. China [3]Advanced Control Systems Laboratory, Beijing Jiaotong University, Beijing 100044, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2012年第6期906-913,共8页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (61203065; 60834001);the Program of Open Laboratory Foundation of Control Engineering Key Discipline of Henan Provincial High Education (KG 2011-10)

摘　　要：The iterative learning control （ILC） has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are pre-sented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement dis-turbances. The analysis is also supported by a numerical example.The iterative learning control （ILC） has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are pre-sented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement dis-turbances. The analysis is also supported by a numerical example.

关 键 词：iterative learning control （ILC） nonlinear system mea-surement disturbance iteration-varying disturbance. 

分 类 号：TP273.22[自动化与计算机技术—检测技术与自动化装置] TP271[自动化与计算机技术—控制科学与工程]