On-line Tuning PID Parameters in Idle-speed Engine based on Radial Basis Function Neural Network

カテゴリ: 部門大会

論文No: OS6-7

グループ名: 【C】平成21年電気学会電子・情報・システム部門大会講演論文集

発行日: 2009/09/03

タイトル(英語): On-line Tuning PID Parameters in Idle-speed Engine based on Radial Basis Function Neural Network

著者名: 曹建磊 (早稲田大学),申芝仙 (早稲田大学),宮崎 道雄(関東学院大学),李羲頡 (早稲田大学)

著者名(英語): Jian-Lei Cao(Waseda University),Ji-Sun Shin(Waseda University),Michio Miyazaki(Kanto Gakuin University),Hee-hyol Lee(Waseda University)

キーワード: Radial Basis Function|Neural Network|Idle Speed Control|PID

要約(日本語): On-line Tuning PID Parameters in Idle-speed Engine based on Radial Basis Function Neural Network

PID controllers are widely used in industrial process control because of their simple structure and robustness. But traditional PID controller with fixed parameters can hardly adapt to time varying system. In order to improve the performance,several schemes of self-tuning PID controllers were proposed.

Radial Basic Function neural network is powerful computational tools, this network form a special structure ,which has advantages of the simple structure ,faster learning algorithms, better approximation capabilities.

In this research, A adaptive PID controller based on RBF network is proposed. We use the self-tuning ability of RBF to automatically tune and modify the PID parameters on-line.

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