Velocity-Free Distributed Optimization Algorithms for Second-Order Multiagent Systems

This article concerns the distributed optimization problem of dynamical systems using partial state information. Such a problem is motivated by the fact that optimization missions are often subject to dynamical constraints and sensor malfunction. To cooperatively deal with the optimization problem f...

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Bibliographic Details
Published in:IEEE transactions on control of network systems Vol. 11; no. 4; pp. 1911 - 1923
Main Authors: Zou, Yao, Huang, Yi, Xia, Kewei, Huang, Bomin, Dong, Xiaofei, Meng, Ziyang
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
Algorithms
Communication
Continuity (mathematics)
Convex functions
Cost function
Design optimization
Distributed optimization
Dynamical systems
event-based communication
Heuristic algorithms
Multi-agent systems
Multiagent systems
Network topology
Networked control systems
Optimization algorithms
second-order systems
Topology optimization
Velocity
velocity-free feedback
ISSN:2325-5870, 2372-2533
Online Access:Get full text
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Summary:This article concerns the distributed optimization problem of dynamical systems using partial state information. Such a problem is motivated by the fact that optimization missions are often subject to dynamical constraints and sensor malfunction. To cooperatively deal with the optimization problem for second-order multiagent systems in the absence of velocity information, we design two velocity-free distributed optimization algorithms over different communication topologies. First, for the case of the continuous communication, a fully velocity-free distributed optimization algorithm is designed by leveraging novel auxiliary dynamics, and each local cost function is just required to be convex. It is shown that all the agents are capable of achieving rendezvous on one of the optimal solutions of interest despite the absence of velocity information. Next, to relieve the communication burden, a modified velocity-free distributed optimization algorithm is designed by introducing an event-based communication mechanism, where all the local cost functions are required to be strongly convex. Particularly, a communication trigger condition is built such that the undesirable Zeno phenomenon is circumvented. Also, an adaptive gain is introduced to make the modified optimization algorithm fully distributed. Simulations are finally given to verify the optimization performance.
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ISSN:2325-5870
2372-2533
DOI:10.1109/TCNS.2024.3371550