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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.
An adaptive neural output feedback controller is designed for a class of stochastic time-delay systems with unknown control directions. First, the state observer is used to estimate the unmeasured state of the system. Secondly, an appropriate Lyapunov-Krasovskii function is selected to eliminate unknown delays on the system The Nussbaum-type function is used to deal with the unknown control direction of the system, the unknown nonlinear function is approximated by neural network, and the complexity problem in controller design is solved by using dynamic surface control (DSC). Finally, through the Lyapunov stability Theory, constructing a robust adaptive neural network output feedback controller can ensure that all signals in a closed-loop system are uniformly bounded eventually and the tracking error converges to a small value in the sense of second- or fourth-order moments. The validity of the proposed method is verified.