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関連する概念動画

Open and closed-loop control systems01:17

Open and closed-loop control systems

984
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
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Control Systems01:10

Control Systems

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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
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Controller Configurations01:22

Controller Configurations

149
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
149
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

147
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.
In the absence...
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Feedback control systems01:26

Feedback control systems

416
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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Control Systems: Applications01:25

Control Systems: Applications

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Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The...
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モデルの予測制御に基づく入力制約を持つモジュール式再構成可能な操作器のイベントトリガー最適制御

Fan Zhou1, Yifan Zhang1, Tianhao Ma2

  • 1School of Electrical and Electronic Engineering, Changchun University of Technology, 130012, Changchun, China.

ISA transactions
|September 4, 2025
PubMed
まとめ

この研究は,モデル予測制御 (MPC) を使用したモジュール再構成マニピュレータ (MRM) のイベントトリガー最適制御を導入します. この方法は,トルク制限と適応力のあるダイナミックプログラミングにより,性能と頑丈性を向上させ,同時に安全性を確保します.

キーワード:
適応型ダイナミックプログラミングイベントトリガー制御モデル予測制御再構成可能なモジュール式操作機

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科学分野:

  • ロボットと制御システム
  • 自動化における人工知能
  • 先進的な制御理論

背景:

  • モジュール式再構成マニピュレータ (MRM) は,適応可能な構造のため,複雑な制御課題を提示します.
  • 既存の制御方法は,分散的な調整とモデル不確実性に対する強さで苦労します.
  • 投入の制限によって安全性を確保することは,実際のMRMアプリケーションにとって非常に重要です.

研究 の 目的:

  • イベントトリガーの最適制御戦略を開発する.
  • システムの性能,強度,安全性を向上させるため
  • 分散制御とMRMのモデル不正確さの課題に対処する.

主な方法:

  • 分散型予測制御 (MPC) のアプローチは,グローバルフレームワークによって調整されるモジュール固有のタスクにMRM制御を分解します.
  • ハイパーボリックタンゲント関数は,安全上の危険を防ぐために入力トルクの制約に使用されます.
  • アダプティブ・ダイナミック・プログラミング (ADP) は,MPCと統合され,モデリングエラーに対する強度が向上します.
  • ハミルトン-ジャコビ-ベルマン (HJB) 方程式を最適制御ソリューションにするために,クリティカルニューラルネットワーク (NN) が利用されます.
  • Lyapunovの安定性理論は,軌道の追跡エラーの均一な最終境界性 (UUB) を保証するために適用されます.

主要な成果:

  • 提案されたイベントトリガーのMPC方法は,MRMの軌道の追跡エラーを大幅に削減します.
  • 効率的で分散された制御戦略によって資源の消費は最小限に抑えられます.
  • 制限されたトルク機能が強化され,運転の安全性が向上します.
  • ADPとNNの統合はシステムの安定性を向上させました

結論:

  • 開発されたイベントトリガーの最適制御方法は,MRMに対する堅牢で効率的な解決策を提供します.
  • このアプローチは,複雑なロボットシステムの性能,安全性,適応性を効果的にバランスとします.
  • この研究は,モジュラーで再構成可能なロボットプラットフォームの 最先端の制御を進めている.