不确定随机非线性时滞系统的自适应神经网络跟踪控制

Adaptive Neural Tracking Control for Uncertain Stochastic Nonlinear Systems with Time-Varying Delays

针对一类具有严格反馈形式的随机非线性时变时滞系统的自适应神经跟踪控制问题,本文利用神经网络参数化和反推(backstepping)方法,构造出一类自适应神经网络状态反馈控制器。仿真结果表明,这种自适应控制器保证闭环系统的所有变量概率意义下有界,并使系统的输出跟踪参考信号,跟踪误差收敛到原点的一个充分小的邻域之内,仿真验证了该方法的有效性。

This paper addresses the problem of adaptive neural control for a class of uncertain stochastic nonlinear strict-feedback systems with time-varying delays. A novel adaptive neural control scheme is pres- ented by using a novel Lyapunov-Krasovskii functional and backstepping. The proposed adaptive controller guarantees that all the variables in the closed-loop system are semi-globally stochastic bounded while the the tracking error between the system output and the desiredreference signal converges to a small enough neighborhood of origin.