随机时滞系统的神经网络输出反馈动态面控制

针对一类具有未知控制方向的随机时滞系统设计自适应神经输出反馈控制器.首先,利用状态观测器估计不可测量的系统状态;其次,选择合适的Lyapunov-Krasovskii函数消除未知延迟项对系统的影响,利用Nussbaum-type函数处理系统的未知控制方向问题,通过神经网络逼近未知的非线性函数,以及用动态表面控制(DSC)解决控制器设计中出现的复杂性问题;最后,通过Lyapunov稳定性理论,构造一个鲁棒自适应神经网络输出反馈控制器,可以保证闭环系统中所有信号在二阶或四阶矩意义下一致最终有界,跟踪误差能收敛到零值小的领域内.仿真实例验证了所提出方法的有效性.

Output-feedback neural DSC control for stochastic nonlinear time-delay systems

This paper deals with the problem concerned with output-feedback adaptive neural control for a class of stochastic nonlinear time-delay systems with unknown control directions. Firstly, the state observer is established for estimating the unmeasured states. Then, in the design procedure, an appropriate Lyapunov-Krasovskii functional is used to compensate the unknown time-delay terms, the Nussbaum-type gain function is used to handle the unknown control directions, the neural network is employed to approximate the unknown nonlinearities, and the dynamic surface control(DSC) technique is used to avoid the the problem of éxplosion of complexity'' in control design. Finally, a robust adaptive neural output feedback control scheme is constructed via the Lyapunov stability theory. It is shown that the designed controller can ensure that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded(SGUUB) and the tracking error converges to a small neighborhood of the origin. The example is presented to verify the effectiveness of the proposed scheme.