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Robust adaptive leader-following consensus control for a class of nonlinear multi-agent systems
Wen G.-X.2; Chen C.L.P.2
2013
Source PublicationProceedings - 2013 Chinese Automation Congress, CAC 2013
Pages491-496
AbstractThis paper presents a robust adaptive neural consensus tracking control design for a class of nonlinear multi-agent systems with unknown nonlinear dynamic function. A Radial Basis Function Neural Network (RBFNN) is used as a universal approximation to reduce the model uncertainties coming from uncertain nonlinearities and to improve tracking performance. One main advantage of the proposed control approach is that the robustness of the nonlinear multi-agent systems is improved. Finally, it is prove the consensus tracking error convergence to a small neighborhood by Lyapnuov stability theory. A simulation is used to demonstrate the effectiveness of the developed scheme. © 2013 IEEE.
Keywordconsensus tracking control neural network nonlinear multi-agent systems robust adaptive control
DOI10.1109/CAC.2013.6775784
URLView the original
Language英語
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Document TypeConference paper
专题University of Macau
Affiliation1.UMacau Research Institute
2.Universidade de Macau
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GB/T 7714
Wen G.-X.,Chen C.L.P.. Robust adaptive leader-following consensus control for a class of nonlinear multi-agent systems[C],2013:491-496.
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