Event-triggered neuroadaptive output-feedback control for nonstrict-feedback nonlinear systems with given performance specifications

This paper focuses on the event-triggered neuroadaptive output-feedback tracking control issue for nonstrict-feedback nonlinear systems with given performance specifications. By constructing a neural observer to estimate unmeasurable states, novel controller is presented together piecewise threshold rule. The mechanism has two thresholds reduce communication resources between and actuator. salient features of are fourfold: (1) error can converge preassigned small region at predesigned converging mode within prescribed time, time independent initial conditions system. (2) strict constraint value relaxed largely via an improved speed function. (3) complexity our algorithm be reduced since there no signal in trigger condition. (4) Command-filtered technology filtering compensating applied address “explosion complexity” problem. Furthermore, Lyapunov stability analysis demonstrates that under controller, all signals closed-loop system semiglobally bounded, Zeno behavior ruled out strictly. Numerical simulations finally provided illustrate scheme.