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.

本文研究了一类具有时滞的不确定切换多输入多输出(MIMO)非严格反馈非线性系统的自适应输出反馈神经跟踪控制问题。需要强调的是，所考虑系统的设计是相当困难的，因为它的未知因素是由未知系统系数和未知函数引起的。在我们提出的设计程序中，引入了神经网络 (NN) 来识别未知的非线性函数，并使用有效的假设来处理未知的系统系数。然后，可以利用开发的开关滤波器来估计未测量的系统状态。在backstepping技术和通用李雅普诺夫函数（CLF）方法的基础上，为每个子系统构建了一个自适应神经控制器。证明了切换闭环系统中存在的所有信号在任意切换下最终都是有界的，每个系统输出都可以在很小的有界误差内跟踪相应的目标轨迹。最后，给出了仿真结果来说明所提出的控制方法的效率。