Output-feedback MPC for Robotic Systems under Bounded Noise

0543771 - ÚTIA 2022 RIV PT eng C - Konferenční příspěvek (zahraniční konf.)
Kuklišová Pavelková, Lenka - Belda, Květoslav
Output-feedback MPC for Robotic Systems under Bounded Noise.
Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics. Setúbal: Scitepress, 2021 - (Gusikhin, O.; Nijmeijer, H.; Madani, K.), s. 574-582. ISBN 978-989-758-522-7. ISSN 2184-2809.
[International Conference on Informatics in Control, Automation and Robotics 2021 /18./. Setúbal (online) (PT), 06.07.2021-08.07.2021]
Institucionální podpora: RVO:67985556
Klíčová slova: model predictive control * output-feedback control * robot manipulator * state estimation * Bayes methods * bounded uncertainty
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://library.utia.cas.cz/separaty/2021/AS/kuklisova-0543771.pdf

The paper presents an output-feedback model predictive control applied to the motion control of a dynamic model of a parallel kinematic machine. The controlled system is described by a stochastic linear discrete-time model with bounded disturbances. An approximate uniform Bayesian filter provides set state estimates. The choice of the specific point estimate from this set is a part of the optimization. The cost function includes penalties on the tracking error and the actuation effort respecting increments. Illustrative examples show the effectiveness of the proposed approach and provide a comparison with previous results.
Trvalý link: http://hdl.handle.net/11104/0321662