Global adaptive neural tracking control of nonlinear MIMO systems

Abstract

This paper addresses the globally stable tracking control problem of a class of uncertain multiple-input–multiple-output nonlinear systems. By employing the radial basis function neural networks to compensate for the system uncertainties, a novel switching controller is developed. The key features of the proposed control scheme are presented as follows. First, to design the desired adaptive neural controller successfully, an nth-order smoothly switching function is constructed originally. Second, the number of the neural networks and the adaptive parameters is reduced by adopting the direct adaptive approach, so a simplified controller is designed and it is easy to implement in practice. By utilizing the special properties of the affine terms of the considered systems, the singularity problem of the controller is completely avoided. Finally, the overall controller guarantees that all the signals in the closed-loop system are globally uniformly ultimately bounded and the system output converges to a small neighborhood of the reference trajectory by appropriately choosing the design parameters. A simulation example is given to illustrate the effectiveness of the proposed control scheme.