文章基本信息

标题：On Stabilizing Control of Gaussian Processes for Unknown Nonlinear Systems
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作者：Yuji Ito ; Kenji Fujimoto ; Yukihiro Tadokoro 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：15385-15390
DOI：10.1016/j.ifacol.2017.08.1861
语种：English
出版社：Elsevier
摘要：AbstractThis paper proposes a novel controller design to stabilize Gaussian process (GP) dynamical models for partially unknown nonlinear systems. The unknown system is identified as the GP model. Because existing GP models are difficult to analyze in terms of controller design, a novel GP identification method is proposed that obtains the state dependent coefficient matrix of the system, making the controller design more efficient. The GP model is represented as a piecewise linear system with a bounded uncertainty that is derived based on the characteristics of the GP. Extending quadratic stability theory derives a novel stabilizing controller for such systems.
关键词：KeywordsBayesian methodsNonparametric methodsAsymptotic stabilizationStability of nonlinear systemsRobust controlUncertainty descriptions