Fei Li, Wei Shen, and Xiaoyu Su
Electro-hydraulic servo system, event-triggered, adaptive gradient descent optimisation algorithm, adaptive robust sliding-mode control, position tracking
To address unmodelled nonlinear friction and external disturbances in electro-hydraulic servo systems (EHSSs), as well as the chattering and limited tracking accuracy arising from the fixed learning rate in conventional adaptive robust sliding-mode control (ARSMC), this paper proposes an event-triggered adaptive robust sliding- mode control strategy incorporating adaptive gradient-descent optimisation and Kalman filtering (EO KF EARCS). A Kalman filter is employed to recursively estimate and suppress unknown dynamics and measurement noise; an adaptive gradient-descent algorithm, coupled with an event-triggering mechanism, dynamically adjusts the learning rate-accelerating convergence of control gains while reducing online computational load. Global asymptotic stability of the closed-loop system is rigorously established via a Lyapunov-based analysis. Simulation studies demonstrate that, compared with traditional ARSMC, EO KF EARCS delivers superior tracking precision and stability for both sinusoidal and square-wave reference inputs, achieving RMSEs of 0.0335 rad/s and 0.1162 rad/s – reductions of 75.9% and 79.1%, respectively – while also exhibiting faster convergence and smoother control inputs.
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