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Control Systems01:10

Control Systems

1.8K
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...
1.8K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.6K
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...
1.6K
PD Controller: Design01:26

PD Controller: Design

615
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
615
Feedback control systems01:26

Feedback control systems

685
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...
685
Control Systems: Applications01:25

Control Systems: Applications

1.1K
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...
1.1K
PID Controller01:19

PID Controller

644
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
644

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Video Experimental Relacionado

Updated: Jan 15, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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Sistema de control de autoaprendizaje definido por software para robots industriales mediante aprendizaje por

Junhyuck Moon1, Minji Kim1, Taeung Lee2

  • 1Department of Artificial Intelligence, Kyung Hee University, 1732 Deogyeong-daero, Yongin-si, 17104, Republic of Korea.

Scientific reports
|January 13, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un sistema de control de autoaprendizaje para la fabricación, que integra la detección de anomalías y el aprendizaje por refuerzo. Permite que el equipo se adapte de forma autónoma a nuevas tareas y condiciones, mejorando la flexibilidad y los tiempos de respuesta.

Palabras clave:
aprendizaje por refuerzodetección de anomalíasrobots industrialessistemas de control definidos por softwareautomatización de la fabricacióninteligencia artificial en la industria

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Área de la Ciencia:

  • Automatización de la fabricación
  • Inteligencia artificial en la industria
  • Ingeniería de sistemas de control

Sus antecedentes:

  • La fabricación moderna exige sistemas adaptables para las necesidades cambiantes de producción.
  • Los controles actuales basados en software tienen limitaciones en la respuesta en tiempo real y el manejo de estados inesperados del equipo.
  • A menudo se requieren operadores humanos cualificados, lo que supone un cuello de botella.

Objetivo del estudio:

  • Proponer un novedoso sistema de control de autoaprendizaje para equipos de fabricación.
  • Permitir que los equipos existentes se adapten a nuevas tareas y condiciones anómalas mediante actualizaciones de software.
  • Mejorar la flexibilidad del sistema y reducir la dependencia de los operadores humanos.

Principales métodos:

  • Integración de algoritmos de detección de anomalías y aprendizaje por refuerzo (RL).
  • Utilización de un entorno virtual para entrenar modelos de RL en diversos estados anómalos del equipo.
  • Desarrollo de un algoritmo de detección de anomalías para activar modelos de control preentrenados para estados específicos.

Principales resultados:

  • El sistema propuesto adaptó con éxito equipos existentes a nuevas tareas y estados mediante actualizaciones de software.
  • La detección de anomalías y la conmutación del modelo de control se produjeron en 1,5 segundos.
  • Se realizó la validación en un robot SCARA en condiciones simuladas de sobrecorriente sin sensores adicionales.

Conclusiones:

  • El sistema de control de autoaprendizaje mejora eficazmente la flexibilidad y la capacidad de respuesta de la fabricación.
  • La integración de la detección de anomalías y el RL ofrece una solución viable para la adaptación autónoma de equipos.
  • Este enfoque reduce la necesidad de sensores especializados y la intervención humana en entornos de fabricación dinámicos.