Adaptive output-feedback neural tracking control for uncertain switched MIMO nonlinear systems with time delays
Jie Kong, Ben Niu, Zhenhua Wang, Ping Zhao, Wenhai Qi
Abstract
This paper investigates the problem of adaptive output-feedback neural tracking control for a class of uncertain switched multiple-input multiple-output (MIMO) nonstrict-feedback nonlinear systems with time delays. It should be emphasised that the design for the considered system is quite difficult due to its unknown factors caused by the unknown system coefficients and the unknown functions. In our proposed design procedure, neural networks (NNs) are introduced to identify the unknown nonlinear functions and a valid hypothesis is used to deal with the unknown system coefficients. Then, the developed switched filter can be utilised to estimate the unmeasured system states. On the basis of the backstepping technique and the common Lyapunov function (CLF) approach, an adaptive neural controller is constructed for each subsystem. It is proved that all signals existing in the switched closed-loop system are ultimately bounded under arbitrary switching and each system output can track the corresponding target trajectory within a small bounded error. Finally, simulation results are presented to illustrate the efficiency of the proposed control method.