Distributed Optimization Control for High-Order Nonlinear Multiagent Systems: A Fully Distributed Event-Triggered Control Algorithm

This article investigates the fully distributed optimization control problem for high-order nonlinear multiagent systems (MASs) with unknown nonlinearities. Initially, a fully distributed optimal signal estimator with adaptive gains is developed, which ensures that its output asymptotically converge...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 12; no. 19; pp. 41033 - 41045
Main Authors: Zhang, Jing, Yue, Huarong, Xia, Jianwei, Park, Ju H.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Backstepping
Closed loops
Control algorithms
Control systems
Control theory
Cost function
Distributed algorithms
Distributed optimization
Energy consumption
Event detection
Event triggered control
Feedback control
fully distributed algorithm
Heuristic algorithms
Multi-agent systems
Multiagent systems
Nonlinear control
nonlinear multiagent systems
Nonlinear systems
Nonlinearity
Optimization
Resource management
Tracking problem
Uncertainty
Vectors
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary:This article investigates the fully distributed optimization control problem for high-order nonlinear multiagent systems (MASs) with unknown nonlinearities. Initially, a fully distributed optimal signal estimator with adaptive gains is developed, which ensures that its output asymptotically converges to the optimal solution of the optimization problem without requiring any global information. Subsequently, the distributed optimization problem is transformed into a reference-tracking problem. By employing backstepping technique, a distributed control protocol is designed for each agent, enabling the outputs of all agents to asymptotically track the estimator's output. It is shown that the boundedness of all signals in closed-loop systems and the distributed optimization objective can be successfully achieved. Furthermore, to reduce continuous communication among agents, the optimal signal estimator is redesigned by introducing a dynamic event-triggered mechanism (DETM), making it not only a fully distributed algorithm but also one that minimizes energy consumption. Finally, the effectiveness of the algorithm is validated through numerical and practical simulations.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3591437