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arXiv · 2206.10266

Data-driven observer design for an inertia wheel pendulum with static friction

L. Ecker·M. Schöberl
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Abstract

An indirect data-driven state observer design approach for the inertia wheel pendulum considering static friction of the actuated inertia disc is presented. The frictional forces occurring in a real laboratory setup are characterized by the Stribeck effect as well as the transition between two different dynamic behaviors, sticking and non-sticking. These switching nonlinear dynamics are identified with various machine learning methodologies in a data-driven manner, i.e., the unsupervised separation and feature clustering of measured state trajectories into two dynamic classes, and the supervised classification of a state-dependent switching condition. The identified system with the interior switching-structure of two dynamics is combined with a moving horizon estimator.

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L. Ecker, M. Schöberl. 2022-06-21. Data-driven observer design for an inertia wheel pendulum with static friction. https://doi.org/10.1016/j.ifacol.2023.01.071

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