非线性时滞系统的智能迭代学习控制

针对一类单输入单输出非线性时滞系统，提出了一种自适应神经网络迭代学习控制方案，神经网络用来逼近未知非线性函数和未知非线性时滞函数，放宽了传统迭代学习控制对非线性函数和非线性时滞函数的限制，拓展了迭代学习控制的应用范围．采用Lyapunov—Krasovskii函数和利用反演（Backstepping）技术设计神经网络学习律和控制律，基于Lyapunov稳定性理论，证明了闭环系统所有信号半全局一致最终有界，通过调节设计参数可以实现对目标轨线任意精度的跟踪．

Intelligent iterative learning control for nonlinear time-delay systems

Adaptive neural network iterative learning control scheme is presented for a class of single-input-single-output nonlinear time-delay systems. Unknown nonlinear function vectors and unknown nonlinear time-delay functions are approximated by two neural networks, respectively, such that the requirements on the unknown nonlinear functions and the unknown nonlinear time-delay functions are relaxed. The neural network learning laws and control laws are designed by using appropriate Lyapunov-Krasovskii function and backstepping technology. Furthermore, based on Lyapunov theory, all signals in the closed loop system are guaranteed to be semiglobally uniformly ultimately bounded and the output of the system is proved to converge to a small neighborhood of the desired trajectory.