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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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Reinforcement learning-based pinning control for synchronization suppression in complex networks.

Kaiwen Li1, Liufei Yang2, Chun Guan1

  • 1Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.

Heliyon
|August 7, 2024
PubMed
Summary
This summary is machine-generated.

This study uses reinforcement learning to suppress synchronization in complex networks. A hybrid pinning strategy effectively controls network synchronization, offering insights for optimization.

Keywords:
Complex networksPinning controlReinforcement learningSynchronization suppression

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Area of Science:

  • Complex networks
  • Network synchronization
  • Control theory

Background:

  • Synchronization is common in complex networks, but excessive synchronization can be detrimental.
  • Desynchronization techniques are crucial for managing unwanted synchronization.
  • Understanding and controlling synchronization is vital across various scientific domains.

Purpose of the Study:

  • To investigate reinforcement learning-based pinning control strategies for synchronization suppression.
  • To analyze the effectiveness of these strategies in different network structures (global coupling, small-world, scale-free).
  • To evaluate the impact of controlled node ratios and key node selection on suppression performance.

Main Methods:

  • Utilizing the Proximal Policy Optimization algorithm for reinforcement learning.
  • Applying pinning control strategies to Watts-Strogatz small-world and Barabási-Albert scale-free networks.
  • Investigating node control ratios and employing the LeaderRank algorithm for key node identification.

Main Results:

  • Demonstrated the effectiveness of reinforcement learning-based pinning control across various network coupling schemes.
  • Identified a critical ratio of pinned nodes necessary for efficient synchronization suppression.
  • Showcased the superior performance of a novel hybrid pinning strategy.

Conclusions:

  • Reinforcement learning-based pinning control is an effective method for suppressing network synchronization.
  • The proposed hybrid pinning strategy offers enhanced performance in controlling synchronization.
  • Findings provide valuable insights for optimizing and managing synchronization in complex systems.