Adaptive iterative learning control for switched discrete-time systems with stochastic measurement noise
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发布时间：2025-04-30
发布时间：2025-04-30
论文名称：Adaptive iterative learning control for switched discrete-time systems with stochastic measurement noise
发表刊物：Transactions of the Institute of Measurement and Control
摘要：This paper investigates an adaptive iterative learning control (AILC) scheme for a class of switched discrete-time linear systems with stochastic measurement noise. For the case when the subsystems dynamics are unknown and the switching rule is arbitrarily fixed, the iteration-wise input-output data-based system lower triangular matrix estimation is derived by means of minimizing an objective function with a gradient-type technique. Then, the AILC is constructed in an interactive form with system matrix estimation for the switched linear systems to track the desired trajectory. Based on the derivation of the boundedness of the estimation error of system matrix, by virtue of norm theory and statistics technique, the tracking error and the covariance matrix of the tracking error are derived to be bounded, respectively. Finally, the AILC concept is extended to nonlinear systems by utilizing linearization techniques. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.
合写作者：Yan Geng, Xiaoe Ruan
卷号：42(2)
页面范围：259-271
是否译文：否
发表时间：2019-12-30