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Smooth Transition in Communication for Swarm Control With Formation Change

Dengxiu Yu, C. L. Philip Chen

2020IEEE Transactions on Industrial Informatics53 citationsDOI

Abstract

In this article, we propose a smooth transition in communication for swarm control with formation change. The communication topology in a swarm is automatically constructed by the relation-invariable persistent formation, which is a method of a self-organized system. Based on the communication topology, intelligent units in swarm systems can solve difficult tasks which would be hardly completed by a single intelligent unit. Intelligent units in the swarm are controlled to realize swarm behaviors, such as aggregation, dispersion, and switching formation when the swarm moves. The switching function in the traditional swarm control with formation change is step function and the transient switching may result in communication failure, transient velocity change, energy waste, and actuator damage. To overcome transient switching in traditional methods, we design smooth transition in communication. The proposed method guarantees the intelligent units could be connected in the communication, which can ensure the communication security of swarm system during the process of communication topology changing. Introduction of smooth switching function in swam control makes it difficult to design the controller. The swarm control with smooth transition in communication, which is based on sliding mode control, is designed and analyzed. Finally, a simulation is carried out to verify the effectiveness of the proposed approaches.

Topics & Concepts

Swarm behaviourComputer scienceSwarm roboticsFunction (biology)Transient (computer programming)Controller (irrigation)Topology (electrical circuits)Control engineeringControl theory (sociology)Distributed computingControl (management)EngineeringArtificial intelligenceBiologyEvolutionary biologyAgronomyElectrical engineeringOperating systemDistributed Control Multi-Agent SystemsModular Robots and Swarm IntelligenceNeural Networks Stability and Synchronization
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