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On convergence of extended state observers for nonlinear systems with non-differentiable uncertainties.
Wu, Xiang; Lu, Qun; She, Jinhua; Sun, Mingxuan; Yu, Li; Su, Chun-Yi.
Afiliação
  • Wu X; College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
  • Lu Q; School of Aeronautical Engineering, Taizhou University, Jiaojiang 318000, Zhejiang, China. Electronic address: landgod1@126.com.
  • She J; School of Engineering, Tokyo University of Technology, Hachioji, Tokyo, 192-0982, Japan.
  • Sun M; College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
  • Yu L; College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
  • Su CY; School of Aeronautical Engineering, Taizhou University, Jiaojiang 318000, Zhejiang, China; Department of Mechanical, Industrial, and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
ISA Trans ; 136: 590-604, 2023 May.
Article em En | MEDLINE | ID: mdl-36503618
ABSTRACT
The analysis of convergence of extended state observers (ESOs) requires the total disturbance to be differentiable. However, this requirement is not satisfied in many control engineering practices. In this paper, we attempt to analyze the convergence of ESOs for nonlinear systems with non-differentiable uncertainties. A decomposition method is first presented to divide the non-differentiable total disturbance into a differentiable signal and a bounded but non-differentiable signal. Based on this decomposition, we give out the convergence of both nonlinear and linear ESOs (NLESO/LESO), low- and high-power ESOs (LPESO/HPESO), and fixed-time ESO (FxESO). We also derive the explicit formulas for the estimation errors of these ESOs. Simulations and experiments demonstrate the correctness of the analysis results.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article