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

Adedapo Odekunle; Weinan Gao; Yebin Wang

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

机译：不确定部分线性系统的数据驱动全局鲁棒最优输出调节

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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.

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《自动化学报（英文版）》 |2019年第5期|1108-1115|共8页
作者
Adedapo Odekunle; Weinan Gao; Yebin Wang;
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Department of Electrical and Com-puter Engineering Allen. E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA 30460 USA;

Mitsubishi Electric Research Laboratories Cambridge MA 02139 USA;

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收录信息中国科学引文数据库(CSCD);
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正文语种 eng
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Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

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