Adaptive tracking control for input delayed MIMO nonlinear systems

Abstract

An adaptive control scheme combined with backstepping, radial basis function (RBF) neural networks is proposed for the output tracking control problem of a class of MIMO nonlinear systems with input delay and disturbances. Neural networks are employed to estimate the unknown continuous functions. The control scheme ensures that the closed-loop system is semi-globally uniformly ultimately bounded (SGUUB). The tracking error is proved to be bounded and ultimately converges to an adequately small compact set. The feasibility is investigated by a simulation example.