未知の非線形厳格なフィードバックシステムのデータ駆動バックステッピング制御
IEEE transactions on cybernetics
|September 3, 2025
まとめ
この研究は,厳格なフィードバックシステムのためのデータ駆動のバックステップ制御 (DBC) メソッドを導入します. オンラインモデルを避け,オフラインデータと新しいコントローラを使用してシステムの安定性を確保します.
科学分野:
- 制御システム工学
- ロボット
- 機械学習
背景:
- 未知のダイナミクスを持つ厳格なフィードバックシステムの追跡制御は困難です.
- 既存の方法はしばしばオンラインモデルと仮定に依存し,適用性を制限しています.
- オンラインモデル識別を回避するデータ主導の制御戦略が必要である.
研究 の 目的:
- 不明なダイナミクスを持つ厳格なフィードバックシステムに対して,データ主導のバックステップ制御 (DBC) のアプローチを提案する.
- データ駆動型ダイナミック・サーフェス・コントロール (DDSC) 方法により,DBCにおける複雑性爆発の問題に対処する.
- 提案されたデータベースの制御戦略の有効性を検証する.
主な方法:
- オフラインデータを用いたデータ駆動の連続時間ライアプノフ方程式リターンコントローラを開発した.
- データ駆動型LMIを利用したデータ駆動型ダイナミック表面制御 (DDSC) のアプローチを提案した.
- 半グローバル指数的な安定性と半グローバルに均一に最終的な境界 (UUB) のエラーシステムを保証します.
主要な成果:
- データ駆動バックステッピング制御 (DBC) アプローチは,オフラインデータから未知のダイナミクスを成功裏に特定しました.
- データ駆動型ダイナミック・サーフェス・コントロール (DDSC) は,複雑性の爆発問題を緩和しました.
- DBCとDDSCは,半グローバル指数的な安定性と半グローバルUUBエラーシステムを実証した.
結論:
- 提案されたデータ主導の制御方法は,未知のダイナミクスを持つ厳格なフィードバックシステムに効果的な解決策を提供します.
- これらのアプローチは,オンライン近似モデルの必要性を排除し,制御設計を簡素化します.
- シミュレーションの例は,データ主導の制御戦略の優越性と有効性を確認しています.
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