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Implementation of Iterative Learning Control Schemes on Maglev System
S. Bablani, A. Dey, V.K. Elumalai,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 700
Pages: 1419 - 1430
Iterative Learning Control (ILC) aim is to improve control performance through iterations. ILC enhances transient behavior of the system, without knowing entire dynamics of plant model. Applied in feedforward path, it learns from past iteration and improves current iteration. Conventional ILC shows instability for exogenous disturbances, thus various ILC schemes are studied. In this paper, configurations of Iterative Learning Control (ILC) scheme namely, current cycle feedback (CCF), previous cycle feedback (PCF), Previous and Current cycle feedback (PCCF) are analyzed, intended to improve trajectory tracking and comparison of results are made under, Nominal Case, External Disturbance and Model Uncertainty on Magnetic levitation system through HIL Mathematical model of Maglev, obtained using Newton second law. To stabilize the open loop unstable system a PIV feedback controller is implemented. © 2021, Springer Nature Singapore Pte Ltd.
About the journal
JournalData powered by TypesetLecture Notes in Electrical Engineering
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH