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Volume 6 Issue 5
Sep.  2019

IEEE/CAA Journal of Automatica Sinica

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Adedapo Odekunle, Weinan Gao and Yebin Wang, "Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1108-1115, Sept. 2019. doi: 10.1109/JAS.2019.1911678
Citation: Adedapo Odekunle, Weinan Gao and Yebin Wang, "Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1108-1115, Sept. 2019. doi: 10.1109/JAS.2019.1911678

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

doi: 10.1109/JAS.2019.1911678
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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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    Highlights

    • A novel data-driven control approach is proposed to solve global robust optimal regulation problems.
    • Considering the presence Both static and dynamic uncertainties in the partially linear systems, a robust optimal controller is developed in terms of reinforcement learning and small gain theory.
    • It is rigorously ensured that the system in closedloop with the developed controller is robust to the uncertainties.

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