Convergence analysis of ILC input sequence for underdetermined linear systems  

作　　者：Dong SHEN Jian HAN Youqing WANG 

机构地区：[1]College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China

出　　处：《Science China(Information Sciences)》2017年第9期282-284,共3页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.61673045,61304085,61374099;Beijing Natural Science Foundation(Grant No.4152040)

摘　　要：Dear editor, Iterative learning control （ILC） is a kind of intelli- gent control strategy, applied to those systems that could complete a given task over a finite time inter- val and repeat it agMn and again. Since introduced in 1984, it has gained fast developments during the past three decades [1]. In order to analyze the tracking performance of ILC, it is conventional to prove that the actual input sequence converges to the desired input along the iteration axis in most literature. This idea is intuitive as the output is driven by the input, thus better input convergence to the desired input leads to better output tracking performance. However, to ensure the convergence of the input sequence, it is usually assumed that the correlation matrix from the input to the out- put, i.e., the coupling matrix CB, provided that the system is （A, B, C）, is of full-column rank [2,3]. This condition means that a unique desired input could be solved according to the desired trajec- tory. Therefore, algorithms could be designed to find the unique solution. As a consequence of this requirement, the dimension of the outputs should be not less than the dimension of the inputs when a general multi-input-multi-output （MIMO） system is taken into account.Dear editor, Iterative learning control （ILC） is a kind of intelli- gent control strategy, applied to those systems that could complete a given task over a finite time inter- val and repeat it agMn and again. Since introduced in 1984, it has gained fast developments during the past three decades [1]. In order to analyze the tracking performance of ILC, it is conventional to prove that the actual input sequence converges to the desired input along the iteration axis in most literature. This idea is intuitive as the output is driven by the input, thus better input convergence to the desired input leads to better output tracking performance. However, to ensure the convergence of the input sequence, it is usually assumed that the correlation matrix from the input to the out- put, i.e., the coupling matrix CB, provided that the system is （A, B, C）, is of full-column rank [2,3]. This condition means that a unique desired input could be solved according to the desired trajec- tory. Therefore, algorithms could be designed to find the unique solution. As a consequence of this requirement, the dimension of the outputs should be not less than the dimension of the inputs when a general multi-input-multi-output （MIMO） system is taken into account.

关 键 词：In Convergence analysis of ILC input sequence for underdetermined linear systems ILC 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]