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Control Systems01:10

Control Systems

1.4K
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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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...
984
Conservation of Energy in Control Volume01:14

Conservation of Energy in Control Volume

908
Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
For steady flow systems, the time derivative of the stored energy becomes zero since there is no energy accumulation within the control volume. This simplifies the energy equation to:
908
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...
416
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

178
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
178
Control Systems: Applications01:25

Control Systems: Applications

736
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...
736

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Interactive and Visualized Online Experimentation System for Engineering Education and Research
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貯蔵庫コンピューティングを用いたダイナミックシステムの適応制御

Swarnendu Mandal1, Swati Chauhan2, Umesh Kumar Verma2

  • 1International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan.

Chaos (Woodbury, N.Y.)
|September 4, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,ダイナミックシステムの適応制御のための貯蔵庫コンピューティングを使用するデータ主導の方法を導入します. シミュレーションと現実の電子回路で検証された最小限のトレーニングデータを使用して,正確な制御をターゲットにすることができます.

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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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科学分野:

  • 複雑なシステム
  • 非線形動力学
  • 機械学習

背景:

  • ダイナミックなシステムは,しばしば望ましい状態を達成するために適応制御戦略を必要とします.
  • 貯水池コンピューティングは,複雑なシステムからタイムシリーズデータを処理するための強力なフレームワークを提供します.

研究 の 目的:

  • ダイナミックシステムのデータ駆動型適応制御技術を開発し実証する.
  • システムパラメータを予測し,制御信号を生成するためにリザーバーコンピューティングを活用する.
  • 様々なシステムアトラクターと初期条件における制御スキームの有効性を検証する.

主な方法:

  • タイムシリーズのデータからシステムのパラメータを予測するモデルをトレーニングするために貯水池コンピューティングを使用します.
  • 予測されたシステムパラメータに基づいてフィードバック制御信号を開発する.
  • ダイナミック・システムをターゲット状態に導くために制御信号を適用する.
  • 物理的なロスラーシステム回路で数値シミュレーションと実装を通じてアプローチを検証する.

主要な成果:

  • 貯水池コンピューティングのアプローチは,タイムシリーズのデータからシステムパラメータをうまく予測します.
  • 開発された制御信号は,ダイナミックシステムを任意のターゲットアトラクターに効果的に駆動します.
  • この方法は,様々なアトラクタータイプと初期条件で頑丈さを証明しています.
  • ロスラーシステムの電子回路での成功は,実用性を確認しています.

結論:

  • 貯蔵庫コンピューティングを駆使した,提案されたデータ駆動型適応制御方法は,ダイナミックなシステムに効率的で汎用的なアプローチを提供します.
  • このテクニックは最小限のトレーニングデータを必要とし,現実世界のアプリケーションに実用的です.
  • この研究は 機械学習による複雑なシステムの 制御戦略の発展に道を開きます