Journal of Robotics, Networking and Artificial Life

Volume 7, Issue 1, June 2020, Pages 1 - 5

Design of a Data-Driven Multi PID Controllers using Ensemble Learning and VRFT

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: kinoshita-takuya@hiroshima-u.ac.jp
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 Atlantis Press SARL.
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/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
7 - 1
Pages
1 - 5
Publication Date
2020/05
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.k.200512.014How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
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/).

Cite this article

TY  - JOUR
AU  - Takuya Kinoshita
AU  - Yuma Morota
AU  - Toru Yamamoto
PY  - 2020
DA  - 2020/05
TI  - Design of a Data-Driven Multi PID Controllers using Ensemble Learning and VRFT
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 1
EP  - 5
VL  - 7
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.k.200512.014
DO  - https://doi.org/10.2991/jrnal.k.200512.014
ID  - Kinoshita2020
ER  -