Premium
Consensus analysis for leader‐following multi‐agent systems with second‐order individual dynamics and arbitrary sampling
Author(s) -
Dai MingZhe,
Xiao Feng,
Wei Bo
Publication year - 2017
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3799
Subject(s) - robustness (evolution) , protocol (science) , computer science , sampling (signal processing) , regular polygon , graph , position (finance) , consensus , algebraic graph theory , mathematical optimization , multi agent system , mathematics , control theory (sociology) , topology (electrical circuits) , control (management) , theoretical computer science , combinatorics , artificial intelligence , alternative medicine , filter (signal processing) , chemistry , pathology , biochemistry , geometry , computer vision , medicine , gene , finance , economics
Summary This paper performs a consensus analysis of leader‐following multi‐agent systems with multiple double integrators in the framework of sampled‐data control. Both single‐leader and multiple‐leader scenarios are considered under the assumption of networks with detectable position‐like state information. The coordination tasks are accomplished by a given protocol with the robustness against the change of sampling periods. The sampling periods can be chosen to be of an arbitrary fixed length or large time‐varying length. Under the proposed protocol, we achieve two objectives: (i) in the single leader‐subgroup case, all followers reach an agreement with leaders on states asymptotically and (ii) in the multiple leader‐subgroup case, each follower converges to some convex combination of the final states of all leaders. It is shown that the final state configuration of the convex combination is uniquely determined by the underlying interaction topology, which can be any weakly connected graph. Compared with the existing results on leader‐following networks, the consensus problem and the containment problem are solved in a unified framework with large sampling periods. Some numerical experiments are conducted to illustrate the dynamic behavior of all agents with this protocol. Copyright © 2017 John Wiley & Sons, Ltd.