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Related Concept Videos

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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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
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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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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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Related Experiment Video

Updated: Jan 8, 2026

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
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Boundary-locked event-triggered mechanism-based adaptive flocking control for multi-USV systems.

Yong Hao1, Bo Cheng1, Kuo Hu2

  • 1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China.

ISA Transactions
|December 18, 2025
PubMed
Summary

This study introduces an adaptive flocking control law for unmanned surface vehicle swarms (USVS) to improve coordination. The novel approach enhances flock cohesion, reduces communication load, and boosts robustness against disturbances.

Keywords:
Echo state networkEvent-triggered controlFlocking controlUnmanned surface vehicle

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Area of Science:

  • Marine robotics
  • Control systems engineering
  • Artificial intelligence

Background:

  • Unmanned surface vehicle swarms (USVS) are vital for marine operations but face coordination challenges.
  • Flocking cohesion, communication, and uncertainty hinder effective USVS deployment.
  • Existing control strategies struggle with dynamic marine environments and communication constraints.

Purpose of the Study:

  • To develop an event-based adaptive flocking control law for USVS.
  • To enhance flock cohesion and ensure network connectivity.
  • To reduce communication overhead and improve system robustness against uncertainties.

Main Methods:

  • Designed an integrated dual-mode potential function (APF and DCPF) for flock cohesion and connectivity.
  • Implemented a boundary-locked event-triggered (BLET) mechanism with MIET and MTIT to minimize communication.
  • Incorporated a reinforcement learning-based echo state network (RLESN) for adaptive control and disturbance rejection.

Main Results:

  • The proposed control law effectively enhances flock cohesion and maintains connectivity.
  • The BLET mechanism significantly reduces communication load while ensuring system stability.
  • The RLESN successfully compensates for unmodeled dynamics and external disturbances, improving robustness.

Conclusions:

  • The developed event-based adaptive flocking control law offers a robust and efficient solution for USVS coordination.
  • The integrated approach addresses key challenges in flock cohesion, communication, and uncertainty management.
  • Simulation results validate the superiority of the proposed methodology over existing methods.