Discrete-time linear systems with switching in the repetitive mode are considered. The systems are subjected to random disturbances, and the measurements are corrupted by additive noises. Two iterative learning control design methods are proposed. Both of the methods involve an auxiliary 2D model in the form of a discrete repetitive process. The first method is based on the dissipativity conditions established for the auxiliary model with a special choice of the supply rate and storage function. This choice allows finding a control law (in the general case, nonlinear) that ensures the convergence of the learning process. The second method adopts a linear iterative learning control update law of a given form, while the convergence of the learning process is ensured by the stability conditions of the auxiliary 2D model. The structure of both control laws includes a stationary Kalman filter. The stability conditions are obtained using the divergent method of vector Lyapunov functions. An example is given to demonstrate the capabilities and features of the new method.

考虑了在重复模式下切换的离散线性系统。该系统受到随机干扰，并且测量值被附加噪声破坏。提出了两种迭代学习控制设计方法。两种方法都涉及离散重复过程形式的辅助2D模型。第一种方法基于为辅助模型建立的耗散条件，并特别选择了供给速率和存储函数。这种选择允许找到一个控制律（通常是非线性的），以确保学习过程的收敛性。第二种方法采用给定形式的线性迭代学习控制更新定律，而辅助2D模型的稳定性条件确保了学习过程的收敛性。两种控制律的结构都包括一个固定的卡尔曼滤波器。使用向量Lyapunov函数的发散方法获得稳定性条件。给出一个示例来演示新方法的功能和特性。