具有快变时滞的1阶非线性参数化系统自适应迭代学习控制

Adaptive Iterative Learning Control for First-Order Nonlinearly Parameterized Systems with Fast-Time-Varying Delays

针对一类具有未知快变时滞的1阶非线性参数化系统,提出了一种自适应迭代学习控制方案.为克服未知快变时滞不确定项给控制器设计带来的困难,提出了一种新型的指数型Lyapunov-Krasovskii泛函.通过对系统进行参数化,设计了控制器和未知时变参数的自适应迭代学习律.通过构造一个指数型Lyapunov-Krasovskii复合能量函数,证明了所有信号的有界性和跟踪误差的收敛性.最后通过仿真算例验证了所提出算法的有效性.

An adaptive iterative learning control scheme is proposed for a class of first-order nonlinearly parameterized systems with unknown fast-time-varying delays. With respect to the uncertainties of the unknown fast-time-varying delays, a novel exponential-type Lyapunov-Krasovskii function is proposed to overcome the difficulty in designing the controller. The controller and adaptive iterative learning law of unknown time-varying parameters are designed by parameterizing the system. The boundedness of all signals and the convergence of tracking errors are proved by constructing an exponential-type Lyapunov-Krasovskii-like composite energy function. The effectiveness of the proposed control algorithms is verified by a simulation example.