暗黙のライアプノフ法とスクレロノミックなラグランジアン力学に基づくニューラルネットワークによる不確実なオートローダーの強固な追跡制御
Hao Zheng1, Yufei Guo1, Zhaohui Wang1
1Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China.
ISA transactions
|September 5, 2025
まとめ
この研究では,ベース振動時の安定性を高め,タンクオートローダーの新しい制御戦略を導入しています. この方法はニューラルネットワークとライアプノフの安定性理論を使用して,安全性と性能を改善します.
科学分野:
- ロボットと制御システム
- 機械工学
- 人工知能
背景:
- 自動充填機は,主要戦車 (MBT) の運用に不可欠であり,弾薬の転送と充填を管理します.
- MBTのベース振動は不確実性をもたらし,オートローダーの安定性と正確な軌道の追跡に挑戦します.
- 既存の制御戦略はしばしば振動に関する事前の知識を必要とし,モデルの不正確さと闘う.
研究 の 目的:
- MBTの自動充填器のための新しい軌道追跡制御戦略を開発する.
- 基本の振動とモデル不正確さの不確実性に対処するために.
- 自動ロードシステムの安定性と性能を向上させる.
主な方法:
- 軌道追跡のための計算トルクメソッド (CTM) の実装.
- 逆ダイナミクスを近似するために,スクレロノミックなラグランジアン力学によるニューラルネットワークの開発.
- ベース振動の不確実性を管理するための暗黙のリヤプノフベースの安定器の設計.
- 閉ループシステムの安定性を証明するリヤプノフ理論の応用.
主要な成果:
- 提案された制御戦略は,ベースオシレーションによる不確実性を効果的に処理します.
- 従来の方法と比較して優れた軌道追跡精度を示した.
- 広範なシミュレーションと ハードウェア実験で検証され 頑丈さと有効性を確認しました
結論:
- 新しいCTMベースの制御戦略は,MBTのオートローダーの安定性と性能を大幅に改善します.
- ニューラルネットワークとリアプノフベースの安定化は ダイナミックな環境のための強力なソリューションを提供します.
- この発見は,より信頼性の高い,より正確な自動化されたシステムへの道を切り開きます.
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