Disturbance observer-based control with adaptive neural network for unknown nonlinear system

Abstract

This paper presents an adaptive neural network controller based on a disturbance observer to compensate the disturbance caused by neural network approximation for a class of unknown nonlinear systems. The proposed adaptive neural network control with an updated parameters mechanism is not subject to the restriction of compact set assumption for satisfying the universal approximation property. The neural network approximation error can be compensated online through the proposed disturbance observer. The proposed method eliminates the need to obtain an exact system model before applying the ideal controller. The effectiveness of the proposed method is validated by numerical simulations.