Adaptive distributed optimization algorithms for Euler–Lagrange systems

This paper investigates the distributed optimization problem of a group of Euler–Lagrange (EL) systems subject to unavailable inertial parameters. A local cost function is assigned to each agent and the sum of all the local cost functions is considered as the global one. Under widely used assumption...

Full description

Saved in:
Bibliographic Details
Published in:Automatica (Oxford) Vol. 119; p. 109060
Main Authors: Zou, Yao, Meng, Ziyang, Hong, Yiguang
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.09.2020
Subjects:
Distributed algorithm
Distributed optimization
Euler–Lagrange system
Gain adaptation
Distributed optimization
Gain adaptation
Euler–Lagrange system
Distributed algorithm
ISSN:0005-1098, 1873-2836
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper investigates the distributed optimization problem of a group of Euler–Lagrange (EL) systems subject to unavailable inertial parameters. A local cost function is assigned to each agent and the sum of all the local cost functions is considered as the global one. Under widely used assumptions, an adaptive distributed algorithm is proposed such that all the agent states converge to the specified point minimizing the global cost function in a cooperative manner. In particular, by introducing a novel auxiliary system with adaptive gains, the proposed optimization algorithm is privacy-preserving such that no actual state of any agent is necessary for other agents. Moreover, the proposed optimization algorithm is fully distributed in the sense that the optimization objective is achieved without knowledge of global graph information, explicit global cost function as well as strongly convex and Lipschitz constants associated with all local cost functions. Numerical simulations are illustrated to validate the theoretical results.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2020.109060