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Open and closed-loop control systems01:17

Open and closed-loop control systems

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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.
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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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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...
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Related Experiment Video

Updated: Jun 14, 2025

Management of Respiratory Motion Artefacts in 18F-fluorodeoxyglucose Positron Emission Tomography using an Amplitude-Based Optimal Respiratory Gating Algorithm
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Off-Axis Integral Cavity Carbon Dioxide Gas Sensor Based on Machine-Learning-Based Optimization.

Pengbo Li1,2, Guanyu Lin1, Jianbo Chen3

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

This study introduces an Extreme Learning Machine model with a CIC filter for more accurate atmospheric carbon dioxide (CO2) detection. The new method enhances instrument performance and is successfully deployed in a real-world monitoring station.

Keywords:
greenhouse gasmachine learningoff-axis integrating cavity output spectrumtrace gas detection

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Instrumental Analysis

Background:

  • Accurate atmospheric carbon dioxide (CO2) detection is crucial for monitoring the greenhouse effect.
  • Conventional off-axis integrating cavity detection systems face challenges with computational intensity and environmental sensitivity.

Purpose of the Study:

  • To develop a more efficient and accurate method for detecting atmospheric CO2.
  • To improve the performance of off-axis integrating cavity detection systems.

Main Methods:

  • An Extreme Learning Machine (ELM) model was integrated with a cascaded integrator comb (CIC) filter into an off-axis integrating cavity system.
  • The performance of the ELM-CIC model was evaluated based on detection limit, accuracy, and root mean square deviation.
  • The proposed method was deployed in a daily atmospheric CO2 monitoring station near an industrial area.

Main Results:

  • The ELM-CIC model demonstrated improved instrument performance, including a lower detection limit and enhanced accuracy.
  • Appropriate parameter tuning was shown to be effective in optimizing the system's performance metrics.
  • The system successfully performed daily atmospheric CO2 concentration detection in a field deployment.

Conclusions:

  • The integration of an ELM model with a CIC filter offers a significant advancement in atmospheric CO2 detection technology.
  • This novel approach addresses the limitations of conventional methods, providing a more robust and precise monitoring solution.
  • The successful field deployment validates the practical applicability of the ELM-CIC method for continuous environmental monitoring.