Adaptive adjustment of iterative learning control gain matrix in harsh noise environment  

作　　者：Bingqiang Li Hui Lin Hualing Xing 

机构地区：[1]School of Automation,Northwestern Polytechnical University [2]School of Water Resources and Architectural Engineering,Northwest A&F University

出　　处：《Journal of Systems Engineering and Electronics》2013年第1期128-134,共7页系统工程与电子技术（英文版）

基　　金：supported by the Specialized Research Fund for the Doctoral Program of Higher Education(20106102110032)

摘　　要：For the robustness problem of open-loop P-type iterative learning control under the influence of measurement noise which is inevitable in actual systems, an adaptive adjustment algorithm of iterative learning nonlinear gain matrix based on error amplitude is proposed and two nonlinear gain functions are given. Then with the help of Bellman-Gronwall lemma, the robustness proof is derived. At last, an example is simulated and analyzed. The results show that when there exists measurement noise, the proposed learning law adjusts the learning gain matrix on line based on error amplitude, thus can make a compromise between learning convergence rate and convergence accuracy to some extent： the fast convergence rate is achieved with high gain in initial learning stage, the strong robustness and high convergence accuracy are achieved at the same time with small gain in the end learning stage, thus better learning results are obtained.For the robustness problem of open-loop P-type iterative learning control under the influence of measurement noise which is inevitable in actual systems, an adaptive adjustment algorithm of iterative learning nonlinear gain matrix based on error amplitude is proposed and two nonlinear gain functions are given. Then with the help of Bellman-Gronwall lemma, the robustness proof is derived. At last, an example is simulated and analyzed. The results show that when there exists measurement noise, the proposed learning law adjusts the learning gain matrix on line based on error amplitude, thus can make a compromise between learning convergence rate and convergence accuracy to some extent： the fast convergence rate is achieved with high gain in initial learning stage, the strong robustness and high convergence accuracy are achieved at the same time with small gain in the end learning stage, thus better learning results are obtained.

关 键 词：iterative learning control open-loop P-type learninglaw nonlinear gain measurement noise robustness. 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]