Adaptive Linear Quadratic Regulator for Continuous-Time Systems With Uncertain Dynamics

查看全文 作　　者：Sumit Kumar [1,2]Jha;Shubhendu [1,3]Bhasin

机构地区：[1]IEEE;[2]Department of Electronics and Communication Engineering,Motilal Nehru National Institute of Technology Allahabad,Prayagraj-211004,India;[3]Department of Electrical Engineering,Indian Institute of Technology Delhi,New Delhi-110016,India

出　　处：《IEEE/CAA Journal of Automatica Sinica》索引2020年第7卷第3期,共9页

摘　　要：In this paper, adaptive linear quadratic regulator(LQR) is proposed for continuous-time systems with uncertain dynamics. The dynamic state-feedback controller uses inputoutput data along the system trajectory to continuously adapt and converge to the optimal controller. The result differs from previous results in that the adaptive optimal controller is designed without the knowledge of the system dynamics and an initial stabilizing policy. Further, the controller is updated continuously using input-output data, as opposed to the commonly used switched/intermittent updates which can potentially lead to stability issues. An online state derivative estimator facilitates the design of a model-free controller. Gradient-based update laws are developed for online estimation of the optimal gain. Uniform exponential stability of the closed-loop system is established using the Lyapunov-based analysis, and a simulation example is provided to validate the theoretical contribution. 关键词：ADAPTIVE optimal control continuous POLICY UPDATE linear QUADRATIC REGULATOR UNCERTAIN system dynamics

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Adaptive Linear Quadratic Regulator for Continuous-Time Systems With Uncertain Dynamics

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