基于最新信息的网络非线性系统迭代学习控制

2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI:10.1109/CHICC.2015.7260114

D. Shen, Yun Xu

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引用次数: 2

摘要

针对测量损失为随机序列的非线性网络系统，构造了迭代学习控制算法。当相应的测量值可用时，算法定期更新，如果相应的数据包丢失，则使用以前迭代中最新的可用数据包进行更新。严格证明了算法的收敛性，并通过实例仿真验证了算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Iterative learning control for networked nonlinear systems using latest information

The iterative learning control (ILC) algorithm is constructed for networked nonlinear systems with random measurement losses modeled by a stochastic sequence. The algorithm updates regularly when the corresponding measurement is available, while updates with the latest available packet from previous iterations if the corresponding one is lost. The almost sure convergence is strictly proved, and illustrative simulations verify the effectiveness of the proposed algorithm.

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来源期刊

2015 34th Chinese Control Conference (CCC)

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