文章基本信息

标题：Stacked adaptive dynamic programming with unknown system model
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作者：Pavel Osinenko ; Thomas Göhrt ; Grigory Devadze 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：4150-4155
DOI：10.1016/j.ifacol.2017.08.803
语种：English
出版社：Elsevier
摘要：AbstractAdaptive dynamic programming is a collective term for a variety of approaches to infinite-horizon optimal control. Common to all approaches is approximation of the infinite-horizon cost function based on dynamic programming philosophy. Typically, they also require knowledge of a dynamical model of the system. In the current work, application of adaptive dynamic programming to a system whose dynamical model is unknown to the controller is addressed. In order to realize the control algorithm, a model of the system dynamics is estimated with a Kalman filter. A stacked control scheme to boost the controller performance is suggested. The functioning of the new approach was verified in simulation and compared to the baseline represented by gradient descent on the running cost.
关键词：KeywordsKalman filtersDynamic programmingOptimal control