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関連する概念動画

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
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In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
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モデルフリー・アダプティブ・コントロールに基づくマルチエージェント・システムの障害回避制御

Libang Yin, Liwei An, Yue Hong

    IEEE transactions on cybernetics
    |August 25, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    この研究は,マルチエージェント制御のための新しいデータ主導のアプローチを提示し,衝突回避と障害物ナビゲーションを強化します. この方法は,安全な軌道の計画と適応的な減速を確実なエージェント形成制御のために保証します.

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    科学分野:

    • ロボット
    • 制御システム工学
    • 人工知能

    背景:

    • マルチエージェントシステムは,特に非線形制御シナリオでは,衝突と障害物の回避で課題に直面します.
    • 既存の方法はしばしば複雑なモデルまたは集中制御を必要とし,スケーラビリティと適応性を制限します.
    • ダイナミックな環境でより柔軟で堅固なソリューションの可能性を秘めている.

    研究 の 目的:

    • 非線形マルチエージェントシステムにおける衝突と障害の回避のためのデータ主導の協力的な出力制御戦略を開発する.
    • 障害物をダイナミックに回避できる安全な基準軌道の計画方法を設計する.
    • アダプティブな衝突防止のために入力-出力情報のみを使用する分散トラッキングコントローラを作成します.

    主な方法:

    • 安全な基準軌道の計画方法は,障害物を回避するために不安定な軌道のセグメントを動的に投影します.
    • ダイナミック・バリア機能を持つ分散トラッキング・コントローラは,入力・出力データを用いて設計されています.
    • 障害物の回避と安全な航行を保証する十分な条件が導き出される.

    主要な成果:

    • 提案された戦略により,エージェント・フォーメーションは,静止障害や移動障害を効果的に回避できます.
    • この方法では 組織内の個々のエージェントの衝突を防ぐことができます
    • エージェントは 望ましい軌道をたどりながら 整合性を保ちます

    結論:

    • データを駆動した協力的な出力アプローチは,非線形マルチエージェント制御における衝突と障害の回避のための効果的な解決策を提供します.
    • 計画された軌道の計画と適応的な制御方法は,安全で効率的な航行を保証します.
    • シミュレーションの結果は,複雑なシナリオでの戦略の堅実性とパフォーマンスを検証します.