Double-channel event-triggered adaptive tracking control of nonstrict-feedback nonlinear systems

Abstract

This paper develops a novel way to control nonstrict-feedback nonlinear systems by using only one approximator, namely a neural network (NN) or a fuzzy logic system (FLS), in the first step of backstepping, such that the computational simplicity is ensured. The bounded property of basis functions solves the “algebraic loop” problem. The double-channel event-triggered control (ETC) is completed both in sensor-to-controller (SC) and controller-to-actuator (CA) channels, where the separate triggering conditions are fabricated. To solve the “jumps of virtual control laws (JVCL)” problem bedeviling the backstepping combined with ETC, this paper opens an avenue while guaranteeing the closed-loop stability with the aid of undetermined virtual control laws.