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Conference papers

Iterative Learning Control Strategy for a Furuta Pendulum System with Variable-Order Linearization

Abstract : We consider Iterative Learning Control for the Furuta Pendulum nonlinear mechanical system, where the goal is to learn the input torque such that the pendulum angle follows a reference. We show that the linearization of the considered system is of variable trajectorydependent order and thus some existing solutions do not apply. We propose a novel method based on the observability matrix inversion allowing to deal with the variable-order minimum realization. The applicability of the proposed method is illustrated with simulations.
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Conference papers
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https://hal-centralesupelec.archives-ouvertes.fr/hal-03405347
Contributor : Stanislav Aranovskiy Connect in order to contact the contributor
Submitted on : Wednesday, October 27, 2021 - 11:06:36 AM
Last modification on : Wednesday, April 27, 2022 - 4:34:33 AM
Long-term archiving on: : Friday, January 28, 2022 - 6:36:46 PM

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Binz Aranovskiy 2021 Iterative...
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  • HAL Id : hal-03405347, version 1

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Ricardo Binz, Stanislav Aranovskiy. Iterative Learning Control Strategy for a Furuta Pendulum System with Variable-Order Linearization. Modeling, Estimation and Control Conference, Oct 2021, Austin, United States. ⟨hal-03405347⟩

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