文章基本信息

标题：Bayesian Optimization-based Modular Indirect Adaptive Control for a Class of Nonlinear Systems
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作者：Mouhacine Benosman ; Amir-massoud Farahmand
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：13
页码：253-258
DOI：10.1016/j.ifacol.2016.07.960
语种：English
出版社：Elsevier
摘要：AbstractWe study in this paper the problem of adaptive trajectory tracking control for a class of nonlinear systems with parametric uncertainties. We propose to usea modular adaptive approach,where we first design a robust nonlinear state feedback which renders the closed loop input-to-state stable (ISS). The input is considered to be the estimation error of the uncertain parameters, and the state is considered to be the closed-loop output tracking error. We augment this robust ISS controller with a model-free learning algorithm to estimate the model uncertainties. We implement this method with a Bayesian optimization-based method called Gaussian Process Upper Confidence Bound (GP-UCB). The combination of the ISS feedback and the learning algorithms gives alearning-based modular indirect adaptive controller.We test the efficiency of this approach on a two-link robot manipulator example, under noisy measurements conditions.