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http://dx.doi.org/10.5302/J.ICROS.2016.16.0074

Neural-Network-based Consensus Tracking of Second-Order Multi-Agent Systems With Unknown Heterogeneous Nonlinearities  

Choi, Yun Ho (School of Electrical and Electronics Engineering, Chung-Ang University)
Yoo, Sung Jin (School of Electrical and Electronics Engineering, Chung-Ang University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.22, no.6, 2016 , pp. 477-482 More about this Journal
Abstract
This paper presents a simple approximation-based design approach for consensus tracking of heterogeneous second-order nonlinear systems under a directed network. All nonlinearities of followers are assumed to be unknown and non-identical. In the controller design procedure, graph-independent error surfaces are used and an unimplementable intermediate controller for each follower is designed at the first design step. Then, by adding and subtracting a graph-based term at the second step, the actual controller for each follower is designed by using one neural network employed to estimate a lumped and distributed nonlinearity. Therefore, the proposed local controller for each follower has a simpler structure than existing approximation-based consensus tracking controllers for multi-agent systems with unmatched nonlinearities.
Keywords
consensus tracking; nonlinear multi-agent systems; neural networks; unmatched heterogeneous nonlinearities;
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Times Cited By KSCI : 2  (Citation Analysis)
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