Distributed optimization for a class of high‐order nonlinear multiagent systems with unknown dynamics

Summary In this paper, we study a distributed optimization problem for a class of high‐order multiagent systems with unknown dynamics. In comparison with existing results for integrators or linear agents, we need to overcome the difficulties brought by the unknown nonlinearities and the optimization...

Full description

Saved in:
Bibliographic Details
Published in:International journal of robust and nonlinear control Vol. 28; no. 17; pp. 5545 - 5556
Main Author: Tang, Yutao
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 25.11.2018
Subjects:
adaptive control
Computer simulation
distributed optimization
embedded design
Integrators
Multiagent systems
Nonlinear systems
Optimization
parameter convergence
Parameter estimation
unknown dynamics
ISSN:1049-8923, 1099-1239
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary In this paper, we study a distributed optimization problem for a class of high‐order multiagent systems with unknown dynamics. In comparison with existing results for integrators or linear agents, we need to overcome the difficulties brought by the unknown nonlinearities and the optimization requirement. For this purpose, we employ an embedded control‐based design and first convert this problem into an output stabilization problem. Then, two kinds of adaptive controllers are given for these agents to drive their outputs to the global optimal solution under some mild conditions. Finally, we show that the estimated parameter vector converges to the true parameter vector under some well‐known persistence of excitation condition. The efficacy of these algorithms was verified by a simulation example.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.4330