TY - GEN
T1 - Data-Driven Observer Design for an Inertia Wheel Pendulum with Static Friction
AU - Ecker, Lukas
AU - Schöberl, Markus
PY - 2022/11/1
Y1 - 2022/11/1
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85146768730
U2 - 10.1016/j.ifacol.2023.01.071
DO - 10.1016/j.ifacol.2023.01.071
M3 - Conference proceedings
VL - 55
T3 - IFAC-PapersOnLine
SP - 193
EP - 198
BT - Preprints 1st IFAC Workshop on Control of Complex Systems
ER -