NDLI: A Supermodular Optimization Framework for Leader Selection Under Link Noise in Linear Multi-Agent Systems

Content Provider	IEEE Xplore Digital Library
Author	Clark, A. Bushnell, L. Poovendran, R.
Copyright Year	1963
Abstract	In many applications of multi-agent systems (MAS), a set of leader agents acts as control inputs to the remaining follower agents. In this paper, we introduce an analytical approach to selecting leader agents in order to minimize the total mean-square error of the follower agent states from their desired value in steady-state in the presence of noisy communication links. We show that, for a set of link weights based on the second-order noise statistics, the problem of choosing leaders in order to minimize this error can be solved using supermodular optimization techniques, leading to efficient algorithms that are within a provable bound of the optimum. We formulate two leader selection problems within our framework, namely the problem of choosing a fixed number of leaders to minimize the error, as well as the problem of choosing the minimum number of leaders to achieve a tolerated level of error. We study both leader selection criteria for different scenarios, including MAS with static topologies, topologies experiencing random link or node failures, switching topologies, and topologies that vary arbitrarily in time due to node mobility. In addition to providing provable bounds for all of these cases, simulation results demonstrate that our approach outperforms other leader selection methods, such as node degree-based and random selection methods, and provides comparable performance to current state of the art algorithms.
Sponsorship	IEEE Control Systems Society
Starting Page	283
Ending Page	296
Page Count	14
File Size	3462942
File Format	PDF
ISSN	00189286
Volume Number	59
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Optimization Lead Topology Network topology Mean square error methods Steady-state submodular optimization Dynamic networks leader-follower system leader selection link noise online algorithms
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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