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Event-triggered control for distributed time-varying optimization.

Haojin Li1, Xiaodong Cheng2, Peter van Heijster2

  • 1Department of Mathematics, Harbin Institute of Technology, Weihai, 264209, China.

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|October 4, 2025
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Summary
This summary is machine-generated.

This study introduces an event-triggered distributed neurodynamic approach for real-time optimization problems. It conserves energy and reduces computation by optimizing communication and eliminating Hessian matrix inversion.

Keywords:
Distributed neurodynamic approachDistributed time-varying optimizationEvent-triggered scheme

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Area of Science:

  • Control Systems
  • Optimization Theory
  • Distributed Computing

Background:

  • Distributed time-varying optimization problems (DTOP) require efficient coordination among agents.
  • Existing methods often incur high communication and computational costs.
  • Real-time optimization and consensus are critical for many applications.

Purpose of the Study:

  • To propose a novel event-triggered (ET) distributed neurodynamic (DND) approach for DTOP.
  • To achieve real-time global cost function optimization and agent state consensus.
  • To reduce communication energy and computational complexity.

Main Methods:

  • Integration of a distributed controller with ET schemes for agent communication.
  • Development of a DND approach that avoids Hessian matrix inverse computation.
  • Application to a battery charging problem case study.

Main Results:

  • The proposed ET-DND approach effectively optimizes global cost functions in real time.
  • Agent states are successfully steered toward consensus.
  • Significant energy conservation and computational cost reduction were demonstrated.

Conclusions:

  • The ET-DND approach offers an efficient solution for DTOP.
  • It provides a practical method for real-time optimization with reduced resource usage.
  • The approach is validated by its successful application to battery charging optimization.