Dynamic event-triggered adaptive control for electro-hydraulic servomechanism

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.