Robust adaptive radial‐basis function neural network‐based backstepping control of a class of perturbed nonlinear systems with unknown system parameters

Abstract

SummaryIn this article, the robust adaptive output tracking control problem is addressed for a class of nonlinear systems with nonlinear dynamics and unknown system parameters. The nonlinear dynamics including internal parameter uncertainties and external disturbances are formulated as time‐varying state/input‐dependent perturbations. Radial‐basis function neural networks (RBFNNs) are developed to approximate the perturbations. A robust adaptive RBFNN‐based output feedback control strategy against the perturbations is developed by using backstepping technique with immeasurable states and without knowing any system parameter. Based on Lyapunov stability theorem, the asymptotic output tracking results of the closed‐loop nonlinear system are obtained in the presence of perturbations, immeasurable states, and unknown system parameters. The efficacy of the proposed adaptive RBFNN‐based output feedback control strategy is validated by simulation in a DC–DC buck converter system.