Event-Triggered Adaptive Asymptotic Tracking Control for Stochastic Non-Linear Systems With Unknown Hysteresis: A New Switching Threshold Approach

Abstract

This paper proposes a novel event-triggered adaptive asymptotic tracking control (ATC) method for stochastic non-linear systems with unknown hysteresis. Firstly, in order to reduce the depletion of network resources while optimizing the asymptotic tracking performance of the system, a switching threshold mechanism (STM)-based event-triggered control (ETC) strategy is adopted. Secondly, a first-order filter is utilized to address the problem of the contradiction between event-triggered mechanism (ETM) output and rate-dependent hysteresis actuator input. By incorporating an enhanced backstepping technique and a bounded estimation method, it is rigorously demonstrate that the system achieves zero tracking error, effectively compensates for unknown hysteresis, and ensures that all closed-loop signals remain bounded in probability. Meanwhile, the Zeno phenomenon is excluded. Finally, the effectiveness and superiority of the proposed control scheme are verified by the simulation results.