入力型Virtual Internal Model Tuningによるノンパラメトリック制御器のデータ駆動更新

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2023/02/01

タイトル(英語): Data-driven Update of Non-parametric Controller by Input-oriented Virtual Internal Model Tuning

著者名: 鈴木　元哉（電気通信大学 大学院情報理工学研究科／デンセーシステム研究所）

著者名(英語): Motoya Suzuki (Graduate School of Informatics and Engineering, The University of Elector-communications/Dense System Laboratory)

キーワード: データ駆動制御，VIMT，インパルス応答　　data-driven control，VIMT，impulse response

要約(英語): Input-oriented virtual internal model tuning can tune the feedback controller by using one-shot experiment data This method can realize desired closed-loop responses when the orders of numerator and denominator of the feedback controller is adequate. However, it is difficult to determine the orders of numerator and denominator of the feedback controller when the controlled object is unknown. From this reason, input-oriented virtual internal model tuning is expanded to non-parametric controllers. Proposed methods can obtain the feedback controller which is parametrized by the impulse response of the controlled object. The impulse response is estimated by Ridge regression. The proposed method can realize good controllers because over-learning is not occurred by Ridge regression. The validity of the proposed method is verified via numerical simulation and experiment verification. From verification results, the input-oriented VIMT based on least square methods can not realize desired closed-loop response because of over-learning. Proposed method can realize desired control response even when the controlled object is unknown.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.2 （2023） 特集：エネルギー分野へ適用されたAI・IoT技術

本誌掲載ページ: 201-208 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/2/143_201/_article/-char/ja/