モバイルロボットの軌道を追跡するためのインテリジェント・アダプティブ・フラクション・オーダー・コントローラー
Mohammad A Jaradat1, Khaled S Hatamleh2, Mohammad Hayajneh3
1Mechanical Engineering Department, American University of Sharjah, Sharjah, United Arab Emirates; Mechanical Engineering Department, Jordan University of Science & Technology, Irbid, 22110, Jordan.
ISA transactions
|February 18, 2026
まとめ
インテリジェントのアダプティブのフラクショナル・オーダー・フル・ステート・フィードバック・コントローラ (FOFSC) は,ディファレンシャル・ドライブのロボットの軌道追跡を改善します. この新しいアプローチは,自律的な配送システムにおけるパフォーマンスの向上のために制御の利点を最適化します.
科学分野:
- ロボット工学と制御システム
- 人工知能 (AI) とは,人工知能 (AI) のことです.
- メカトロニクス メカトロニクス
背景:
- 自動運転の車輪付きモバイルロボットは,配送システムなどのタスクに不可欠です.
- 精密な軌道追跡制御は,これらのロボットにとって重要な課題です.
- 分数順序制御は,メモリと非局所性により,整数順序制御よりも利点があります.
研究 の 目的:
- 拡張されたディファレンシャルドライブロボット (DDR) の軌道を追跡するために,インテリジェントの適応分数順のフルステートフィードバックコントローラ (FOFSC) を提案する.
- 提案されたFOFSCのパフォーマンスを整数順序のフルステートフィードバックコントローラ (IOFSC) と比較する.
- グレー・ウルフ・オプティマイゼーション (GWO) を使用した適応的および非適応的最適化アプローチを評価する.
主な方法:
- グレイ・ウルフ・オプティマイゼーション (GWO) を利用したFOFSCの開発. ゲイン・チューニング.
- アダプティブ (オンラインのゲインアップデート) と非アダプティブ (オフラインのゲインチューニング) の両方の最適化戦略の実装.
- QBot 2e ロボットプラットフォームでのシミュレーションと実験的検証を使用して,IOFSCとの比較分析.
主要な成果:
- インテリジェント・アダプティブFOFSCは,IOFSCと比較して優れた軌道追跡性能を示した.
- 主要なパフォーマンスの改善には,より速い収束速度と追跡エラーの最小化が含まれます.
- 適応型FOFSCは,干渉を効果的に処理し,再調節なしに軌道の変化に適応しました.
結論:
- 提案されているインテリジェント・アダプティブFOFSCは,ディファレンシャルドライブロボットの軌道追跡を大幅に改善します.
- 分数順位の制御,特に適応的構成では,自律システムのための堅牢なソリューションを提供します.
- GWOアルゴリズムは,ロボットのナビゲーションを改善するためにコントローラパラメータを効果的に最適化します.
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