Adaptive NN Consensus Control for a Class of Nonlinear Multi-agent Time-Delay Systems

Author

Guo-Xing Wen ; Chen, C.L.P.

Author_Institution

Dept. of Comput. & Inf. Sci., Univ. of Macau, Macau, China

fYear

2013

fDate

13-16 Oct. 2013

Firstpage

4941

Lastpage

4946

Abstract

This paper studies an adaptive neural consensus control for a class of nonlinear multi-agent time delay systems. The Radial Basis Function Neural Networks (RBFNN) are utilized to approximate the unknown nonlinear function of system dynamic. Based on Lyapunov analysis method, it is proven that the nonlinear multi-agent system is stable and the consensus errors converge to a small neighborhood of zero. In contrast to the existing results, the advantage of the developed scheme is that the influence of time delay on the nonlinear multi-agent systems is eliminated. The effectiveness of the developed scheme is illustrated by a simulation example.

Keywords

Lyapunov methods; adaptive control; decentralised control; delay systems; neurocontrollers; nonlinear control systems; radial basis function networks; Lyapunov analysis method; RBFNN; adaptive NN consensus control; adaptive neural consensus control; consensus errors; nonlinear multiagent time delay systems; radial basis function neural networks; system dynamic; unknown nonlinear function; Artificial neural networks; Delay effects; Eigenvalues and eigenfunctions; Function approximation; Multi-agent systems; consensus control; neural network; nonlinear multiagent systems; state time delay;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on

Conference_Location

Manchester

Type

conf

DOI

10.1109/SMC.2013.844

Filename

6722595