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• W2974516277 abstract "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." @default.
• W2974516277 created "2019-09-26" @default.
• W2974516277 creator A5023127460 @default.
• W2974516277 creator A5037634397 @default.
• W2974516277 date "2019-09-16" @default.
• W2974516277 modified "2023-09-27" @default.
• W2974516277 title "Adaptive iterative learning control for switched discrete-time systems with stochastic measurement noise" @default.
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• W2974516277 doi "https://doi.org/10.1177/0142331219867838" @default.
• W2974516277 hasPublicationYear "2019" @default.
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