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Output-feedback MPC for Robotic Systems under Bounded Noise
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SYSNO ASEP 0543771 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Output-feedback MPC for Robotic Systems under Bounded Noise Author(s) Kuklišová Pavelková, Lenka (UTIA-B) ORCID
Belda, Květoslav (UTIA-B) RID, ORCIDNumber of authors 2 Source Title Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics. - Setúbal : Scitepress, 2021 / Gusikhin O. ; Nijmeijer H. ; Madani K. - ISSN 2184-2809 - ISBN 978-989-758-522-7 Pages s. 574-582 Number of pages 9 s. Publication form Online - E Action International Conference on Informatics in Control, Automation and Robotics 2021 /18./ Event date 06.07.2021 - 08.07.2021 VEvent location Setúbal (online) Country PT - Portugal Event type WRD Language eng - English Country PT - Portugal Keywords model predictive control ; output-feedback control ; robot manipulator ; state estimation ; Bayes methods ; bounded uncertainty Subject RIV BC - Control Systems Theory OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Institutional support UTIA-B - RVO:67985556 DOI 10.5220/0010557705740582 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022
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