貯水池の水位制御のための結合粒子群の最適化と強化学習

Oana Niculescu-Faida¹, Catalin Popescu¹

¹Hydrotechnical Engineering Department, System Engineering-Automation and Applied Informatics Domain, Faculty of Hydrotechnics, Technical University of Civil Engineering Bucharest, Blvd. Lake Tei No. 122-124, Sector 2, 020396 Bucharest, Romania.

Sensors (Basel, Switzerland)

|August 28, 2025

まとめ

この研究は,補強学習と粒子群の最適化を組み合わせた貯水池の水位の高度な適応制御システムを提示していますこのシステムは成功裏に実装され,実際のアプリケーションで効果的な洪水緩和が実証されました.

科学分野:

エンジニアリング
環境科学
制御システム

背景:

産業用 PID コントローラーは安定性がありますが,動作の変更により頻繁に再調整する必要があります.
貯水池の水位調節は周辺地域の洪水防止に不可欠です.

研究の目的:

貯水池の水位調節のための最適な自動システムを開発する.
PID コントローラーのパラメータを適応的に調整することで,洪水制御を強化します.

主な方法:

強化学習と粒子群最適化 (PSO) を組み合わせた新しい最適化方法です.
補強学習のフィットネス機能を強化するために,PSOを導くために数学式が開発されました.
シミュレーションはMATLABとPythonを使って行われました.

主要な成果:

開発された制御システムは,強化学習とPSOの利点を効果的に組み合わせ,その欠点を最小限に抑えました.
シミュレーションは非常に良い結果をもたらし,システムの性能を検証しました.
このシステムは成功裏に導入され,洪水防止の実用的な能力を実証しました.

結論:

アダプティブPIDコントローラパラメータ調整システムは,貯水池の水位管理のための堅牢なソリューションを提供します.
開発されたダムの最適自動化システムは,ルーマニアでより広範な利用のために実装され,適応されるべきです.
この研究は,インテリジェント・オートメーションによる洪水対策戦略の改善に寄与しています.

キーワード:

コントロールシステム粒子群の最適化補強学習

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