Robust adaptive neural control for a class of perturbed nonlinear systems with unmodeled dynamics and output disturbances

Abstract

AbstractFor a nonlinear system with unmodeled dynamics and output disturbances, an adaptive neural network based control strategy is designed. For the unknown output interference, this article introduces a new module between the system output and the tracking signal for the first time. By constructing a new state, system becomes a new system without unknown output interference. In order to alleviate the major influence caused by the system dynamic disturbance and unknown time delay, appropriate Lyapunov–Krasovskii functions, and backstepping technology are employed. In designing some adaptive methods, the unmodeled dynamics, disturbances, and unknown time‐varying virtual coefficients are processed. Unknown function is approximated by neural network. All signals are semi‐global uniform ultimate boundedness. Simulation effectively verifies the control strategy.