Tracking of Unicycle Robots Using Event-Based MPC With Adaptive Prediction Horizon

Sun, Zhongqi and Xia, Yuanqing and Dai, Li and Campoy Cervera, Pascual (2020). Tracking of Unicycle Robots Using Event-Based MPC With Adaptive Prediction Horizon. "IEEE/ASME Transactions on Mechatronics", v. 25 (n. 2); pp. 739-749. ISSN 1083-4435. https://doi.org/10.1109/TMECH.2019.2962099.

Description

Title: Tracking of Unicycle Robots Using Event-Based MPC With Adaptive Prediction Horizon
Author/s:
  • Sun, Zhongqi
  • Xia, Yuanqing
  • Dai, Li
  • Campoy Cervera, Pascual
Item Type: Article
Título de Revista/Publicación: IEEE/ASME Transactions on Mechatronics
Date: April 2020
ISSN: 1083-4435
Volume: 25
Subjects:
Freetext Keywords: Adaptive prediction horizon; event-triggered control; model predictive control (MPC); self-triggered control; unicycle robots
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In this article, we propose two event-based model predictive control (MPC) schemes with adaptive prediction horizon for tracking of unicycle robots with additive disturbances. The schemes are able to reduce the computational burden from two aspects: reducing the frequency of solving the optimization control problem (OCP) to relieve the computational load and decreasing the prediction horizon to decline the computational complexity. Event-triggering and self-triggering mechanisms are developed to activate the OCP solver aperiodically, and a prediction horizon update strategy is presented to decrease the dimension of the OCP in each step. The proposed schemes are tested on a networked platform to show their efficiency.

More information

Item ID: 64117
DC Identifier: http://oa.upm.es/64117/
OAI Identifier: oai:oa.upm.es:64117
DOI: 10.1109/TMECH.2019.2962099
Official URL: https://ieeexplore.ieee.org/document/8941320
Deposited by: Memoria Investigacion
Deposited on: 28 Oct 2020 16:22
Last Modified: 28 Oct 2020 16:22
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