TY - GEN
T1 - Topology Optimization for Sampled-Data Consensus to Improve the Convergence Rate and Increase the Sampling Period
AU - Chen, Xinzhuang
AU - Gao, Shanshan
AU - Zhang, Shenggui
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - This paper considers the network topology optimization problem for first-order multi-agent systems (MASs) under the sampled-data consensus protocol, so that MASs can achieve a faster convergence rate and set a longer sampling period, which leads to less information exchanges for reaching consensus. An edge swapping operation (ESO) method is introduced to optimize the communication topology of MASs with undirected graphs as their communication topologies, which keeps the degree of each vertex constant. Based on properties of Laplacian eigenvectors, conditions are proposed for determining an effective ESO which can lead a graph with a larger algebraic connectivity and a smaller Laplacian spectral radius. Numerical results on random graphs show that only ten percent of ESOs are effective. Then an iterative algorithm for optimizing the communication topology for any given MAS is designed. Finally, simulations for MASs under graphs before and after optimization are given, which show that our approach is efficient.
AB - This paper considers the network topology optimization problem for first-order multi-agent systems (MASs) under the sampled-data consensus protocol, so that MASs can achieve a faster convergence rate and set a longer sampling period, which leads to less information exchanges for reaching consensus. An edge swapping operation (ESO) method is introduced to optimize the communication topology of MASs with undirected graphs as their communication topologies, which keeps the degree of each vertex constant. Based on properties of Laplacian eigenvectors, conditions are proposed for determining an effective ESO which can lead a graph with a larger algebraic connectivity and a smaller Laplacian spectral radius. Numerical results on random graphs show that only ten percent of ESOs are effective. Then an iterative algorithm for optimizing the communication topology for any given MAS is designed. Finally, simulations for MASs under graphs before and after optimization are given, which show that our approach is efficient.
KW - Algebraic connectivity
KW - Laplacian spectral radius
KW - Multi-agent systems
KW - Sampled-data consensus
KW - Topology optimization
UR - http://www.scopus.com/inward/record.url?scp=85130904096&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-9492-9_310
DO - 10.1007/978-981-16-9492-9_310
M3 - 会议稿件
AN - SCOPUS:85130904096
SN - 9789811694912
T3 - Lecture Notes in Electrical Engineering
SP - 3161
EP - 3170
BT - Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021
A2 - Wu, Meiping
A2 - Niu, Yifeng
A2 - Gu, Mancang
A2 - Cheng, Jin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2021
Y2 - 24 September 2021 through 26 September 2021
ER -