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推進システムの故障下での火星登山車両の共同軌道の再計画:学習ベースの低最適のウォームスタートアプローチ
IEEE transactions on neural networks and learning systems
|August 21, 2025
まとめ
この研究では,推進システムの故障時に火星登山車両の軌道を再計画するための新しい方法が紹介されています. このアプローチは,飛行経路と軌道を迅速に最適化し,計算効率を向上させることで,ミッションの成功を保証します.
科学分野:
- 航空宇宙工学
- 天体力学
- 欠陥を許容する制御システム
背景:
- 火星への登山車 (MAV) のミッションは,推進システムの故障によるリスクに直面します.
- 従来の軌道の再計画方法は,タイムリーな故障対応には遅すぎます.
- ミッションを成功させるには 迅速で信頼性の高い軌道調整が必要です
研究 の 目的:
- 推進システムの故障を経験するMAVのサブ最適の共同軌道再計画 (SJTR) 方法を開発する.
- 飛行中の異常の後,軌道と軌道の再計画の速度と精度を向上させるため.
- システムの故障に対するMAVの打ち上げミッションの強さを高める.
主な方法:
- ターゲットの軌道と飛行軌跡の合体凸最適化問題を立案した.
- 再決定の原則の遵守を保証するために,ターミナル制約に対する罰則係数を適用します.
- 学習ベースのウォームスタートスキームを開発し,より迅速な収束と正確な軌道決定のためにディープニューラルネットワーク (DNN) を使用しました.
主要な成果:
- SJTRの方法は,軌道再設計のための簡潔で迅速な解決策を提供します.
- DNNベースのウォートスタートスキームは,収束と軌道型決定の精度を大幅に高めます.
- 数値シミュレーションは,質量流量と特定の衝動の低下に対する方法の信頼性を確認した.
- 既存の方法と比較して少なくとも49.5%高い計算効率を達成しました.
結論:
- 提案されたSJTR方法は,推進システムの故障があるMAVの打ち上げミッションのための信頼性の高い計算効率の良いソリューションです.
- 学習ベースのウォームスタート・スキームは,軌道再設計のパフォーマンスを効果的に改善します.
- このアプローチは火星への登山ミッションの 安全性と成功確率を高めます
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