Distributed Consensus of Multi-Agent Systems With Input Constraints: A Model Predictive Control Approach

The discrete-time double-integrator consensus problem is addressed for multi-agent systems with directed switching proximity topologies and input constraints. Some model predictive control protocols are developed to achieve stable consensus under the condition that the proximity graph has a directed...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2015-03, Vol.62 (3), p.825-834
Main Authors: Cheng, Zhaomeng, Zhang, Hai-Tao, Fan, Ming-Can, Chen, Guanrong
Format: Article
Language:English
Subjects:
Agents (artificial intelligence)
Computer simulation
Consensus
Control theory
Convergence
Degrees of freedom
Expert systems
input constraint
Mathematical models
Multi-agent systems
Predictive control
Protocols
Proximity
Sampling
Stacking
switching interaction network
Topology
Vectors
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Summary:The discrete-time double-integrator consensus problem is addressed for multi-agent systems with directed switching proximity topologies and input constraints. Some model predictive control protocols are developed to achieve stable consensus under the condition that the proximity graph has a directed spanning tree and the sampling period is sufficiently small. Moreover, the control horizon is extended to larger than one, which endows sufficient degrees of freedom to accelerate the convergence to consensus. Numerical simulations are conducted to show the effectiveness of the control algorithm.
ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2014.2367575