Robust adaptive neural tracking control for a class of nonlinear systems with unmodeled dynamics using disturbance observer

This paper is concerned with an adaptive neural tracking control for a class of strict-feedback nonlinear systems subject to unmodeled dynamics, system uncertainties, completely unknown external disturbance and input dead zone. An adaptive neural control method combined with backstepping technique and the radial basis function neural networks (RBFNNs) is proposed for the systems under consideration. In recursive backstepping designs, a dynamic signal is introduced to cope with the unmodeled dynamics, a disturbance observer is employed to approximate the unknown disturbance and the dead zone equalled to the sum of the simple linear system and the partial bounded disturbance. It is shown that by using Lyapunov methods, the developed control scheme can ensure semi-globally uniformly ultimately bounded (SGUUB) of all signals within the closed-loop systems. Simulation results are presented to illustrate the validity of the approach.