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. An improved speed transformation function is introduced to make the output tracking error converge to a preassigned small region at predesigned converging mode within preset finite time. Combining the variable separation method based on the structural property of radial basis function (RBF) and backstepping technology, the algebraic loop problem caused by the nonstrict-feedback structure is overcome. The 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-behaviour is ruled out strictly. Numerical simulations are finally provided to illustrate the presented control scheme.