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Neural Computing and Applications

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01.10.2012 | Original Article

Recurrent neural tracking control based on multivariable robust adaptive gradient-descent training algorithm

verfasst von: Zhao Xu, Qing Song, Danwei Wang

Erschienen in: Neural Computing and Applications | Ausgabe 7/2012

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Abstract

In this paper, a recurrent neural network (RNN) based robust tracking controller is designed for a class of multiple-input-multiple-output discrete time nonlinear systems. The RNN is used in the closed-loop system to estimate online unknown nonlinear system function. A multivariable robust adaptive gradient-descent training algorithm is developed to train RNN. The weight convergence and system stability are proven in the sense of Lyapunov function. Simulation results are presented for a two-link robot tracking control problem.

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Titel: Recurrent neural tracking control based on multivariable robust adaptive gradient-descent training algorithm
verfasst von: Zhao Xu
Qing Song
Danwei Wang
Publikationsdatum: 01.10.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 7/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0618-2

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