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ニューラル・アダプティブ・フィニット・タイム・コンセンサス・トラッキング シングラ・マルチエージェント・システムとアクチュエータ・アタック
IEEE transactions on neural networks and learning systems
|February 16, 2026
まとめ
この研究は,アクチュエータ攻撃に直面している単一のマルチエージェントシステムのための有限時間コンセンサストラッキング (FTCT) を扱っています. 新しいニューラル・アダプティブ・プロトコルは,複雑なネットワークの脅威にもかかわらず,システムの安定性と正確な追跡を保証します.
科学分野:
- 制御理論 制御理論
- ネットワーク化されたシステム
- 人工知能 (AI) とは,人工知能 (AI) のことです.
背景:
- アクチュエーター攻撃は,複雑なネットワークシステムに重大な脅威をもたらし,安定性とパフォーマンスを損なう.
- 時間が異なる遅延を持つ単一のマルチエージェントシステムは,調整された制御を達成する上でユニークな課題を提示します.
- 有限時間コンセンサストラッキング (FTCT) は,代理人の間で迅速かつ安定した合意を確保するために不可欠です.
研究 の 目的:
- アクチュエーター攻撃下にある単一マルチエージェントシステムの有限時間コンセンサストラッキング (FTCT) 制御問題を調査する.
- 時間の変動による遅延や外部からの干渉に対応できる堅実な制御戦略を開発する.
- 敵対的な行動にもかかわらず,ネットワーク化されたシステムの安定性と信頼性の高いパフォーマンスを保証します.
主な方法:
- フォロワーエージェントの状態を近似し,アクチュエータ攻撃下でスライディングモードの表面を設計するために,状態オブザーバーが提案されています.
- ニューラル・アダプティブに分散型FTCTプロトコルとアクチュエータ・アタック・アセスメント・プロトコルの新型は,放射線ベースの機能ニューラルネットワークを用いて設計されています.
- パーティショニング戦略によるスライディングモード制御は,単一のマルチエージェントシステムでFTCTを分析するために使用されます.
主要な成果:
- 限られた時間間隔で単一のマルチエージェントシステムでFTCTのための十分な条件と解決戦略が提供されています.
- 提案された神経適応プロトコルは,アクチュエータ攻撃を効果的に推定し,コンセンサストラッキングを確保します.
- シミュレーションの例は,提案されたFTCT制御戦略の有効性を検証します.
結論:
- 開発されたFTCT制御戦略は,アクチュエータ攻撃に敏感な単一のマルチエージェントシステムに有効です.
- ニューラルネットワークとスライディングモード制御の組み合わせは,複雑なネットワーク化されたシステムに強力なソリューションを提供します.
- この研究は,安全で効率的なマルチエージェントシステムの制御のための理論的枠組みと実践的な検証を提供します.
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