Design of a Data-Driven Multi PID Controllers using Ensemble Learning and VRFT
- 10.2991/jrnal.k.200512.014How to use a DOI?
- Data-driven control; PID control; ensemble learning
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.
- © 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 - 2020/05/20 TI - Design of a Data-Driven Multi PID Controllers using Ensemble Learning and VRFT JO - Journal of Robotics, Networking and Artificial Life SP - 68 EP - 72 VL - 7 IS - 1 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.k.200512.014 DO - 10.2991/jrnal.k.200512.014 ID - Kinoshita2020 ER -