出力飽和の非線形システムのクラスのためのデータ駆動のポイントツーポイント有限イテレーション学習制御
IEEE transactions on cybernetics
|August 28, 2025
まとめ
この研究は,未知の非線形システムにおける正確な点から点への追跡 (PTP) のためのデータ駆動アルゴリズムを導入します. 新しいFINITE ITERATION LEARNING CONTROL (FILC) メソッドは,制限されたトラッキングエラーを効率的に確保します.
科学分野:
- 制御システム工学
- 非線形動力学
- 機械学習
背景:
- 出力飽和の未知の非線形離散時間系を制御することは大きな課題です.
- 既存の制御方法は,多くの実用的なアプリケーションでは利用できない正確なシステムモデルを必要とします.
研究 の 目的:
- 出力飽和の未知の非線形離散時間システムにおける精密なポイントツーポイント (PTP) トラッキングのためのデータ駆動制御アルゴリズムを開発する.
- システムに関する事前の知識なしに,有限な回数での制限されたトラッキングエラーを達成します.
主な方法:
- データを駆動する新しいFINITE ITERATION LEARNING CONTROL (FILC) アルゴリズムが提案されています.
- このアルゴリズムは,時間領域における再帰的な進化を用いて,システムの入力-出力関係を導きます.
- イテラティブドメインのダイナミック・リニアライゼーション技術は,ダイナミックなデータ駆動モデルを確立します.
- 微分エラーのパワーに基づく有限の繰り返し学習戦略は,収束を保証します.
主要な成果:
- 提案されたFILCアルゴリズムは,未知の非線形離散時間システムのPTPトラッキング制御を効果的に扱います.
- 制限されたトラッキングエラーは,限られた回数で達成されます.
- 理論的証明はアルゴリズムの有限反復収束を確認する.
- シミュレーション結果は,提案されたメソッドの実用的な有効性を検証します.
結論:
- システムモデルが未知である複雑な制御問題に対して,データ駆動型FILCアプローチは堅実な解決策を提供します.
- この方法は,出力飽和下で正確な追跡制御を達成するための効率的で理論的に健全な方法を提供します.
- この研究は,非線形システムの制御工学の進歩におけるデータ駆動技術の可能性を示しています.
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