Convergence properties of two networked iterative learning control schemes for discrete-time systems with random packet dropout
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发布时间：2025-04-30
发布时间：2025-04-30
论文名称：Convergence properties of two networked iterative learning control schemes for discrete-time systems with random packet dropout
发表刊物：International Journal of Systems Science
摘要：This paper addresses convergence issue of two networked iterative learning control (NILC) schemes for a class of discrete-time nonlinear systems with random packet dropout occurred in input and output channels and modelled as 0-1 Bernoulli-type random variable. In the two NILC schemes, the dropped control input of the current iteration is substituted by the synchronous input used at the previous iteration, whilst for the dropped system output, the first replacement strategy is to replace it by the synchronous pre-given desired trajectory and the second one is to substitute it by the synchronous output used at the previous iteration. By the stochastic analysis technique, we analyse the convergence properties of two NILC schemes. It is shown that under appropriate constraints on learning gain and packet dropout probabilities, the tracking errors driven by the two schemes are convergent to zero in the expectation sense along iteration direction, respectively. Finally, illustrative simulations are carried out to manifest the validity and effectiveness of the results.
合写作者：Jian Liu, Xiaoe Ruan
卷号：49（12）
页面范围：2682-2694
是否译文：否
发表时间：2018-07-15