文章基本信息

标题：Policy Iteration and Event-Triggered Robust Adaptive Dynamic Programming for Large-Scale Systems ⁎
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作者：Fuyu Zhao ; Weinan Gao ; Tengfei Liu 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：14
页码：376-381
DOI：10.1016/j.ifacol.2021.10.383
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, an event-triggered robust optimal control approach is proposed for large-scale systems with both parametric and dynamic uncertainties through robust adaptive dynamic programming, policy iteration, and small-gain techniques. By using the input and output data, the unmeasurable states are reconstructed instead of constructing a Luenberger observer. Starting from an admissible control policy, an event-based feedback control policy is learned to save the communication resources and reduce the number of control updates. The closed-loop stability and the convergence of the proposed algorithm are analyzed by using Lyapunov and small-gain techniques. A practical example of multimachine power systems with governor controllers is given to demonstrate the effectiveness of the proposed method.
关键词：KeywordsOutput-feedbackevent-triggered controlrobust adaptive dynamic programming (RADP)small-gain theorylarge-scale system