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Adaptive data based neural network leader-follower control of multi-agent networks

conference contribution
posted on 2011-01-01, 00:00 authored by Sui Yang KhooSui Yang Khoo, J Yin, B Wang, S Zhao, Z Man
In this paper, we propose a data based neural network leader-follower control for multi-agent networks where each agent is described by a class of high-order uncertain nonlinear systems with input perturbation. The control laws are developed using multiple-surface sliding control technique. In particular, novel set of sliding variables are proposed to guarantee leader-follower consensus on the sliding surfaces. Novel switching is proposed to overcome the unavailability of instantaneous control output from the neighbor. By utilizing RBF neural network and Fourier series to approximate the unknown functions, leader-follower consensus can be reached, under the condition that the dynamic equations of all agents are unknown. An O(n) data based algorithm is developed, using only the network’s measurable input/output data to generate the distributed virtual control laws. Simulation results demonstrate the effectiveness of the approach.

History

Event

IEEE Industrial Electronics Society Conference (37th : 2011 : Melbourne, Vic.)

Pagination

3924 - 3929

Publisher

IEEE

Location

Melbourne, Vic.

Place of publication

Piscataway, N.J.

Start date

2011-11-07

End date

2011-11-10

ISSN

1553-572X

ISBN-13

9781612849720

Language

eng

Publication classification

E1 Full written paper - refereed; E Conference publication

Copyright notice

2011, IEEE

Title of proceedings

IECON 2011 : Proceedings of the 37th Annual Conference on IEEE Industrial Electronics Society