Dynamic event-triggered adaptive control for electro-hydraulic servomechanism

• Published in: ISA transactions Vol. 164; pp. 34 - 45
• Main Authors: Shen, Chao, Zhu, Jianxin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2025
• Subjects: Adaptive robust control; Dynamic event-triggered mechanism; Electro-hydraulic servomechanism; Finite-time extended state observer; Pi-sigma fuzzy neural networks; Electro-hydraulic servomechanism; Pi-sigma fuzzy neural networks; Finite-time extended state observer; Adaptive robust control; Dynamic event-triggered mechanism
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2025.05.027

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. Considering the adaptive robust control of electro-hydraulic servomechanism with modeling uncertainties and restricted data communication, a dynamic event-triggered adaptive PSFNN backstepping control (DEAPBC) is developed to guarantee control accuracy while releasing the communication bandwidth. •Unmeasurable state variables, modeling uncertainties, and restricted data communication are considered in controller design.•Finite-time extended state observer and Pi-sigma fuzzy neural networks are designed for estimating state variables and modeling uncertainties.•Novel dynamic event-triggering mechanism is constructed to reduce redundant data transmission.•Tracking errors of electro-hydraulic servomechanism converge in finite time while releasing communication bandwidth.