Authors
Takuya Kinoshita*, Yuma Morota, Toru Yamamoto
Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima city, Hiroshima, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Takuya Kinoshita
Received 6 November 2019, Accepted 17 March 2020, Available Online 20 May 2020.
DOI
https://doi.org/10.2991/jrnal.k.200512.014How to use a DOI?
Keywords
Data-driven control; PID control; ensemble learning
Abstract
Data-driven control has been proposed for directly calculating control parameters using experimental data. Specifically, the Virtual Reference Feedback Tuning (VRFT) has been proposed for linear time-invariant systems. In the field of machine learning, the ensemble learning was proposed to improve the accuracy of prediction by using multiple learners. In this study, a design scheme of data-driven controllers using the ensemble learning and VRFT is newly proposed for linear time-varying systems. The ensemble learning can divide the linear time-varying system into some sections that can be regarded locally as linear time-invariant systems.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).