Dynamic event-triggered adaptive control for electro-hydraulic servomechanism.

Journal: ISA transactions
Published Date: May 23, 2025

Abstract

This paper investigates the adaptive robust control of electro-hydraulic servomechanisms subject to restricted data communication, unmeasurable state variables, and modeling uncertainties. A novel dynamic event-triggered adaptive robust control algorithm is proposed, which integrates a finite-time extended state observer (FTESO) with Pi-sigma fuzzy neural networks (PSFNN). In the developed framework, a PSFNN-enhanced FTESO is employed to simultaneously estimate both unmeasurable states and modeling uncertainties. To alleviate communication burdens, a dynamic event-triggering mechanism with the observed state deviation of the FTESO at adjacent triggering moments and virtual tracking errors as inputs is developed. Within the finite-time backstepping control architecture, an adaptive robust control law is systematically constructed for the electro-hydraulic servomechanism. Comparative simulations demonstrate that the proposed algorithm achieves rapid position tracking error convergence with reduced data transmission.

Authors

  • Chao Shen
    Department of Epidemiology, School of Public Health, Soochow University, Suzhou 215123, China.
  • Jianxin Zhu
    State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Hunan, 410083, China; The National Enterprise R&D Center, Sunward Intelligence Equipment Co., Ltd., Hunan, 410100, China. Electronic address: 134204@csu.edu.cn.

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