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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.
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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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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Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Control System Problem01:21

Control System Problem

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In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
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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.
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Effects of feedback01:24

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Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
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Related Experiment Video

Updated: Jun 19, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
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Revisiting kinetic Monte Carlo algorithms for time-dependent processes: From open-loop control to feedback control.

Supraja S Chittari1, Zhiyue Lu1

  • 1Department of Chemistry, University of North Carolina-Chapel Hill, Chapel Hill, North Carolina 27599, USA.

The Journal of Chemical Physics
|July 25, 2024
PubMed
Summary
This summary is machine-generated.

We developed a new method for simulating stochastic systems with feedback control, improving accuracy and avoiding common simulation errors. This approach offers a unified view of existing algorithms and a powerful tool for complex system dynamics.

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Area of Science:

  • Computational Physics
  • Chemical Kinetics
  • Stochastic Processes

Background:

  • Simulating stochastic systems with feedback control presents significant challenges due to intricate dynamics.
  • Existing methods for open-loop control often fall short when applied to feedback-controlled systems.

Purpose of the Study:

  • To introduce a novel single-step-trajectory probability analysis for time-dependent stochastic systems.
  • To provide a unified perspective and alternative proofs for existing kinetic Monte Carlo (KMC) algorithms.
  • To develop an accurate KMC algorithm for feedback-controlled stochastic systems.

Main Methods:

  • Developed a single-step-trajectory probability analysis framework.
  • Revisited and provided unified proofs for time-dependent KMC algorithms under open-loop control.
  • Introduced a novel feedback-controlled KMC algorithm based on trajectory probability analysis.

Main Results:

  • The trajectory probability analysis offers a unified proof for existing open-loop KMC algorithms.
  • The novel feedback-controlled KMC algorithm accurately simulates systems with state-dependent external control.
  • The new method successfully avoids the artificial Zeno effect seen in other simulation approaches.

Conclusions:

  • The trajectory probability analysis provides a unified perspective on KMC algorithms for stochastic systems.
  • This work presents a powerful and accurate tool for simulating stochastic systems with feedback control.
  • The developed algorithm enhances the simulation fidelity of complex, controlled dynamic systems.