Dynamic Event-Triggered Control for Nonlinear Stochastic Systems With Unknown Measurement Sensitivity

Abstract

This paper focuses on the adaptive output feedback control problem for nonlinear stochastic systems with unknown measurement sensitivity based on dynamic event-triggered mechanism (ETM). Different from the existing works, a novel adaptive output feedback control algorithm is proposed for unknown measurement sensitivity (its sign and bounds are unknown) by means of Nussbaum-type function. First, a reduce-order dynamic gain K-filter is proposed to reconstruct the unmeasurable state variable. Second, a tangent-type barrier Lyapunov function with a predefined-time performance function is established to constrain system output into the given region in a predefined time. Third, a dynamic ETM is put forward to reduce trigger times, and then the controller is designed accordingly. Based on the Lyapunov stability theory, it is proved that system state variables converge to zero in probability and other signals of the closed-loop system are bounded in probability. Finally, the validity of the proposed algorithm is demonstrated by the numerical simulation on a single-link manipulator.