具有输入时滞的随机非线性系统的自适应神经网络控制

考虑一类具有输入时滞的随机非线性系统的自适应神经网络控制问题。通过定义含输入积分项的设计变量，将输入时滞系统转变为非时滞系统。结合神经网络控制、积分中值定理与Decoupled Backstepping技巧，针对该类系统提出一套自适应控制策略。所提出的控制器保证闭环系统的所有信号皆4阶矩半全局一致最终有界, 并且跟踪误差收敛于原点附近的小邻域内。仿真实验结果验证了所提出控制策略的有效性。

Adaptive Neural Control for a Class of Stochastic Nonlinear Systems with Input Delay

This paper considers the problem of adaptive neural control for a class of uncertain stochastic nonlinear systems with input delay. By defining the design variable with the input integral term, the input delayed system is converted to the non delayed systems. And then, an adaptive neural control scheme is presented by combining the neural network approximation, integral mean value theorem and the decoupled backstepping technique. It is shown that the proposed controller can guarantee the 4 moment semi globally uniformly ultimately boundedness for all the signals in the closed loop system. Moreover, the tracking error can converge to a small neighborhood around the origin. A numerical example is given to illustrate the effectiveness of the control scheme.