Adaptive Neural Network Control for Uncertain Nonlinear Systems with Asymptotic Stability Guarantees

Abstract

A neuroadaptive control framework for the nonlinear uncertain dynamical systems is developed in this paper. The proposed framework is Lyapunov-Based and unlike standard neural network (NN) controllers guaranteeing ultimate bounded ness, the framework guarantees asymptotic stability of the closed-loop system The neuroadaptive controllers are constructed without requiring explicit knowledge of the system dynamics, a recurrent neural network (NN) is used to approximate the unknown nonlinear plant. To provide good accuracy in identification of unknown model parameters, an online adaptive law is proposed to adapt the consequent part of the NN. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.