Output-feedback MPC for Robotic Systems under Bounded Noise

Lenka Kuklišová Pavelková, Květoslav Belda

2021

Abstract

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.

Paper Citation

in Harvard Style

Kuklišová Pavelková L. and Belda K. (2021). Output-feedback MPC for Robotic Systems under Bounded Noise. In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-522-7, pages 574-582. DOI: 10.5220/0010557705740582

in Bibtex Style

@conference{icinco21,
author={Lenka Kuklišová Pavelková and Květoslav Belda},
title={Output-feedback MPC for Robotic Systems under Bounded Noise},
booktitle={Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2021},
pages={574-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010557705740582},
isbn={978-989-758-522-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Output-feedback MPC for Robotic Systems under Bounded Noise
SN - 978-989-758-522-7
AU - Kuklišová Pavelková L.
AU - Belda K.
PY - 2021
SP - 574
EP - 582
DO - 10.5220/0010557705740582