Disturbance observer based adaptive neural control of uncertain MIMO nonlinear systems with unmodeled dynamics

Abstract This paper investigates the disturbance observer-based adaptive neural tracking control of a class of multiple-input multiple-output (MIMO) systems in the presence of unmodeled dynamics, system uncertainties, time varying 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, neural network (NN) is employed for uncertainty approximation. The disturbance observer is developed to provide efficient learning of the compounded disturbance which includes the effect of time varying disturbance, neural network approximation error. 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. This paper is novel at least in the two aspects: (1) disturbance observer based tracking control method is developed for MIMO nonlinear systems with unmodeled dynamics and (2) the strong coupled terms is considered in this paper where the interconnections are functions of all states, which is a more general form than existing related results.