Adaptive Dynamic Surface Output-Feedback Control for a Class of Hysteric Nonlinear Systems with Prespeeified Tracking Performance

In this paper, a high-gain observer based adaptive dynamic surface output-feedback control is proposed for a class of nonlinear systems preceded by unknown backlash-like hysteresis. The main features are 1) the RBF neural networks are employed to approximate the unknown smooth functions; 2) by using the proposed control scheme and the tracking error transformation functions, the tracking performance could be prespecified; 3) the derivative-explosion problem when the hysteresis is fused with backstepping design can be eliminated, which greatly simplifies the control law; 4) by combining with the estimation of vector norm of the unknown parameters, the computational burden is greatly reduced. Simulation results show the effectiveness of the proposed scheme.