Robust adaptive backstepping control for a class of uncertain nonlinear systems based on disturbance observers

Abstract

In this paper, a novel robust adaptive control scheme for a class of uncertain nonlinear systems is proposed using disturbance observer and backstepping method. Firstly, a disturbance observer is developed using radial basis function (RBF) neural network. The parameter updated law of the RBF neural network is given for monitoring subsystem disturbance well. The robust adaptive control scheme is then presented with backstepping method based on the designed disturbance observer. Semiglobal uniform ultimate boundedness (SGUUB) of all signals in the closed-loop uncertain nonlinear system is achieved. The closed-loop system stability analysis shows that semiglobal uniform boundedness of all signals is guaranteed by appropriately choosing design parameters. Finally, two examples are taken to illustrate the effectiveness of the proposed control scheme.