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Control Systems01:10

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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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Open and closed-loop control systems01:17

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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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Conservation of Energy in Control Volume01:14

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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:
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Feedback control systems01:26

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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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Time-Domain Interpretation of PD Control01:07

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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.
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Control Systems: Applications01:25

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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.
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Control adaptativo de los sistemas dinámicos mediante la computación de depósitos

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
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Resumen
Este resumen es generado por máquina.

Este estudio introduce un método basado en datos que utiliza la computación de reservorios para el control adaptativo de sistemas dinámicos. Permite un control preciso de los estados objetivo utilizando datos de entrenamiento mínimos, validados en simulaciones y circuitos electrónicos del mundo real.

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Área de la Ciencia:

  • Sistemas complejos
  • Dinámica no lineal
  • Aprendizaje automático

Sus antecedentes:

  • Los sistemas dinámicos a menudo requieren estrategias de control adaptativas para lograr los estados deseados.
  • La computación de depósitos ofrece un marco poderoso para procesar datos de series temporales de sistemas complejos.

Objetivo del estudio:

  • Desarrollar y demostrar una técnica de control adaptativo basado en datos para sistemas dinámicos.
  • Aprovechar la computación del depósito para predecir los parámetros del sistema y generar señales de control.
  • Validar la eficacia del sistema de control a través de diversos atrayentes del sistema y condiciones iniciales.

Principales métodos:

  • Utilizando la computación de reservorios para entrenar un modelo que prediga los parámetros del sistema a partir de datos de series temporales.
  • Desarrollo de una señal de control de retroalimentación basada en los parámetros previstos del sistema.
  • Aplicación de la señal de control para guiar el sistema dinámico hacia un estado objetivo.
  • Validación del enfoque a través de simulaciones numéricas y implementación en un circuito físico del sistema Rössler.

Principales resultados:

  • El enfoque de computación de reservorios predice con éxito los parámetros del sistema a partir de datos de series temporales.
  • La señal de control desarrollada impulsa efectivamente los sistemas dinámicos hacia atractores de destino arbitrarios.
  • El método demuestra robustez en varios tipos de atractores y condiciones iniciales.
  • El éxito de la aplicación en un circuito electrónico del sistema Rössler confirma su aplicabilidad práctica.

Conclusiones:

  • El método de control adaptativo basado en datos propuesto, impulsado por la computación de reservorios, ofrece un enfoque eficiente y versátil para sistemas dinámicos.
  • La técnica requiere un mínimo de datos de entrenamiento, lo que la hace práctica para aplicaciones del mundo real.
  • Este trabajo allana el camino para estrategias de control avanzadas en sistemas complejos a través del aprendizaje automático.