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Conference Papers Year : 2021

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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Dates and versions

hal-03405347 , version 1 (27-10-2021)

Identifiers

  • HAL Id : hal-03405347 , version 1

Cite

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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