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Robust Linear Output Regulation Using Extended State Observer

Author

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  • Mehran Hosseini-Pishrobat
  • Jafar Keighobadi
  • Atta Oveisi
  • Tamara Nestorović

Abstract

This paper presents a disturbance rejection-based solution to the problem of robust output regulation. The mismatch between the underlying plant and its nominal mathematical model is formulated by two disturbance classes. The first class is assumed to be generated by an autonomous linear system while for the second class no specific dynamical structure is considered. Accordingly, the robustness of the closed-loop system against the first disturbance class is achieved by following the internal model principle. On the other hand, in the framework of disturbance rejection control, an extended state observer (ESO) is designed to approximate and compensate for the second class, i.e., unstructured disturbances. As a result, the proposed output regulation method can deal with a vast range of uncertainties. Finally, the stability of the closed-loop system based on the proposed compound controller is carried out via Lyapunov and center manifold analyses, and some results on the robust output regulation are drawn. A representative simulation example is also presented to show the effectiveness of the control method.

Suggested Citation

  • Mehran Hosseini-Pishrobat & Jafar Keighobadi & Atta Oveisi & Tamara Nestorović, 2018. "Robust Linear Output Regulation Using Extended State Observer," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, August.
  • Handle: RePEc:hin:jnlmpe:4095473
    DOI: 10.1155/2018/4095473
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    Cited by:

    1. Sun, Yuchen & Ma, Shuping, 2021. "Output regulation of switched singular systems based on extended state observer approach," Applied Mathematics and Computation, Elsevier, vol. 399(C).
    2. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).

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