Disturbance-observer-based adaptive NN control for a class of MIMO discrete-time nonlinear strict-feedback systems with dead zone

Abstract

In this paper, based on a disturbance observer (DO), an adaptive neural network (ANN) tracking control scheme is proposed for the multi-input and multi-output (MIMO) strict-feedback discrete-time system (SFDTS). The unknown nonlinear functions, dead-zone input and external disturbance are all considered in the studied SFDTS. Before starting to design the controller, the MIMO SFDTS is transformed into a maximum N-step ahead predictor to solve the noncausal problem. Then, the backstepping method is successfully used to design the control scheme for the new system. The unknown nonlinear functions are approximated by radial basis function neural networks. The external disturbance is estimated based on the DO, and the ANN controller is designed on the basis of the outputs of the DO. By applying the Lyapunov stability theory, all the signals in the whole closed-loop system are ensured bounded. Finally, a numerical simulation is provided to verify the validity of the proposed control scheme.