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