Optimal tracking consensus for swarm systems with leader-following switching topologies
Qian Zhang1, Cheng Wang2, Miao Zhao2,3
1Shaanxi Police College, Xi'an, 710021, People's Republic of China. zhangqian04010705@163.com.
Scientific Reports
|October 21, 2025
Summary
This study introduces a new strategy for leader-following swarm systems to achieve optimal tracking consensus. The method balances tracking performance with control energy, ensuring efficient system operation despite changing network structures.
Area of Science:
- Robotics and Control Systems
- Distributed Systems and Swarm Intelligence
- Optimization Theory
Background:
- Swarm systems with leader-following topologies face challenges in achieving optimal tracking consensus.
- Existing strategies may not efficiently balance tracking performance with control energy consumption.
- Dynamic changes in network topology (switching topologies) complicate consensus achievement.
Purpose of the Study:
- To develop a novel tracking strategy for optimal tracking consensus in swarm systems with switching topologies.
- To introduce an optimization index that balances tracking performance and control energy.
- To establish conditions for achieving optimal tracking consensus and analyze system constraints.
Main Methods:
- A new tracking strategy incorporating an optimization index (tracking performance and control energy terms).
- Introduction of a local interaction matrix and a dual nonsingular transformation.
- Derivation of sufficient conditions for optimal tracking consensus and analysis of topology switching constraints.
Main Results:
- Sufficient conditions for optimal tracking consensus were established.
- A constraint relationship between topology switching and control parameters was determined.
- An explicit expression for the minimum optimization index value was derived, dependent only on initial conditions.
Conclusions:
- The proposed strategy effectively achieves optimal tracking consensus in leader-following swarm systems with switching topologies.
- The optimization index provides a quantifiable balance between performance and energy efficiency.
- The findings offer a robust framework for designing control strategies in dynamic swarm environments.
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