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

Ricardo Binz (1) , Stanislav Aranovskiy (1, 2)

1 IETR - Institut d'Électronique et des Technologies du numéRique
2 CentraleSupélec

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.

Keywords: Iterative learning control; Furuta Pendulum; mechatronic systems; observability

Domains: Engineering Sciences [physics]; Automatic

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⟩