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Networked Iterative Learning Control for Nonlinear Switched Discrete-time Systems with Random Measurement Packet Losses

机译:具有随机测量丢包的非线性切换离散时间系统的网络迭代学习控制

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For nonlinear switched discrete-time systems with random measurement packet losses modeled by a Bernoulli-type stochastic sequence, this paper presents a P-type networked Iterative Learning Control (ILC) algorithm with an attenuating forgetting factor. In this ILC scheme, the random measurement packet losses are replaced by the desired output data. Under a given switching rule, the convergence of ILC tracking error in mathematical expectation in each of subsystems is proved by mathematical induction, and the convergent condition of the proposed networked P-type ILC algorithm is given. An illustrative simulation is used to verify the effectiveness of the proposed ILC algorithm.
机译:对于以伯努利型随机序列为模型的具有随机测量分组丢失的非线性切换离散时间系统,本文提出了一种具有衰减遗忘因子的P型网络迭代学习控制(ILC)算法。在这种ILC方案中,随机测量数据包丢失被所需的输出数据代替。在给定的切换规则下,通过数学归纳证明了每个子系统在数学期望中的ILC跟踪误差的收敛性,并给出了网络P型ILC算法的收敛条件。使用说明性仿真来验证所提出的ILC算法的有效性。

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