A new framework of consensus protocol design for complex multi-agent systems

This paper aims to find a simple but efficient method for consensus protocol design. This paper presents two consensus protocols to solve the consensus problem of complex multi-agent systems that consist of inhomogeneous subsystems. The limitations of current studies are analyzed, and a novel model based on transfer functions is presented. This model can be used to describe both homogeneous and inhomogeneous multi-agent systems in a unified framework. Based on this model, two sufficient and necessary conditions for the consensus of complex multi-agent systems have been obtained. One is for the systems without any external input, and the other is for the systems with the same external input. Then, two corresponding distributed consensus protocols are presented. Considering that the complex multi-agent systems may require different outputs sometimes, the relationship between inputs and outputs is analyzed. Finally, some simulations are given to demonstrate the performance and effectiveness of the proposed approaches.

[1]  K. Brown,et al.  Graduate Texts in Mathematics , 1982 .

[2]  Randy A. Freeman,et al.  Multi-Agent Coordination by Decentralized Estimation and Control , 2008, IEEE Transactions on Automatic Control.

[3]  John L. Junkins,et al.  Spacecraft Formation Flying Control using Mean Orbit Elements , 2000 .

[4]  Ahmad Afshar,et al.  New approaches for distributed sensor networks consensus in the presence of communication time delay , 2009, 2009 Chinese Control and Decision Conference.

[5]  Kouhei Ohnishi,et al.  Multi-robot teleoperation under dynamically changing network topology , 2009, 2009 IEEE International Conference on Industrial Technology.

[6]  Remo Guidieri.  Res , 1995, RES: Anthropology and Aesthetics.

[7]  Kagan Tumer,et al.  Improving Air Traffic Management with a Learning Multiagent System , 2009, IEEE Intelligent Systems.

[8]  Brian D. O. Anderson,et al.  Non-hierarchical UAV formation control for surveillance tasks , 2008, 2008 American Control Conference.

[9]  Gordon F. Royle,et al.  Algebraic Graph Theory , 2001, Graduate texts in mathematics.

[10]  Zhiqiang Li,et al.  A Cooperative Spectrum Sensing Consensus Scheme in Cognitive Radios , 2009, IEEE INFOCOM 2009.

[11]  Lin Huang,et al.  Consensus of Multiagent Systems and Synchronization of Complex Networks: A Unified Viewpoint , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[12]  Linghui Liu,et al.  A Centralized Approach to Flight Conflict Resolution In Multi-Agent System , 2009, 2009 First International Workshop on Education Technology and Computer Science.

[13]  Reza Olfati-Saber,et al.  Flocking for multi-agent dynamic systems: algorithms and theory , 2006, IEEE Transactions on Automatic Control.

[14]  Rodolphe Sepulchre,et al.  Synchronization in networks of identical linear systems , 2009, Autom..

[15]  Guangming Xie,et al.  Output agreement in high-dimensional multi-agent systems , 2009, 2009 American Control Conference.

[16]  K. Khorasani,et al.  Adaptive formation control of UAVs in the presence of unknown vortex forces and leader commands , 2006, 2006 American Control Conference.