Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

查看全文 作　　者：Adedapo [1]Odekunle;Weinan [1]Gao;Yebin [2]Wang

机构地区：[1]the Department of Electrical and Computer Engineering,Allen.E.Paulson College of Engineering and Computing,Georgia Southern University,Statesboro,GA 30460 USA;[2]Mitsubishi Electric Research Laboratories,Cambridge,MA 02139 USA

出　　处：《IEEE/CAA Journal of Automatica Sinica》索引2019年第6卷第5期,共8页

摘　　要：In this paper, a data-driven control approach is developed by reinforcement learning (RL) to solve the global robust optimal output regulation problem (GROORP) of partially linear systems with both static uncertainties and nonlinear dynamic uncertainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncertainties. The closed-loop system is ensured to be input-to-output stable regarding the static uncertainty as the external input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-output stability for the closed-loop system and thus solves the original GROORP. Simulation results validates the efficacy of the proposed methodology. 关键词：ROBUST control output regulation REINFORCEMENT learning small-gain theory

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Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

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