首页> 外文期刊>Journal of Mathematical Analysis and Applications >Comments on 'Decentralized iterative learning control for a class of large scale interconnected dynamical systems' by Hansheng Wu [J. Math. Anal. Appl. 327 (2007) 233-245]
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Comments on 'Decentralized iterative learning control for a class of large scale interconnected dynamical systems' by Hansheng Wu [J. Math. Anal. Appl. 327 (2007) 233-245]

机译:吴汉生对“一类大规模互联动力学系统的分散式迭代学习控制”的评论[J.数学。肛门应用327(2007)233-245]

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摘要

In the reference paper Wu (2007) [1], a decentralized iterative learning control (ILC) algorithm is proposed aiming at achieving the asymptotic convergence of output errors for a class of linear time-varying large scale interconnected dynamic systems. The key points in the analysis of this decentralized ILC are the adoption of a specific time-weighted norm, and the use of a property of nondecreasing real functions that leads to the cancellation of interactions between subsystems. Here, on the one hand, we show that there exists a derivation problem in the proof, thus the asymptotic convergence property cannot be obtained. On the other hand, we provide an alternative analysis method, the classical contraction-mapping based ILC analysis with the lambda-norm, to demonstrate that the decentralized ILC algorithm is still valid and able to achieve the asymptotic convergence.
机译:在参考文献Wu(2007)[1]中,提出了一种分散式迭代学习控制(ILC)算法,旨在实现一类线性时变大型互联动态系统的输出误差渐近收敛。分析此分散式ILC的关键点是采用特定的时间加权规范,以及使用不减少实函数的属性,这会导致子系统之间的交互作用被取消。在这里,一方面,我们证明证明中存在一个导数问题,因此无法获得渐近收敛性。另一方面,我们提供了另一种分析方法,即基于经典压缩映射的带有lambda范数的ILC分析,以证明去中心化ILC算法仍然有效并且能够实现渐近收敛。

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