title: Ultra-fast formation control of high-order discrete-time multi-agent systems based on multi-step predictive mechanism.
abstract: This paper deals with the ultra-fast formation control problem of high-order discrete-time multi-agent systems. Using the local neighbor-error knowledge, a novel ultra-fast protocol with multi-step predictive information and self-feedback term is proposed. The asymptotic convergence factor is improved by a power of q+1 compared to the routine protocol. To some extent, the ultra-fast algorithm overcomes the influence of communication topology to the convergence speed. Furthermore, some sufficient conditions are given herein. The ones decouple the design of the synchronizing gains from the detailed graph properties, and explicitly reveal how the agent dynamic and the communication graph jointly affect the ultra-fast formationability. Finally, some simulations are worked out to illustrate the effectiveness of our theoretical results.
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, PR China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning Province 110819, PR China. Electronic address: 677zwl@163.com.
authors.forename: Wenle
authors.initials: W
authors.lastname: Zhang
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, PR China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning Province 110819, PR China.
authors.forename: Jianchang
authors.initials: J
authors.lastname: Liu
authors.affiliation: College of Information Science and Engineering, Northeastern University, Shenyang, PR China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning Province 110819, PR China.
authors.forename: Honghai
authors.initials: H
authors.lastname: Wang
keywords: Discrete-time multi-agent system; Distributed control; Formation control; Multi-step predictive mechanism; Self-feedback