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

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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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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang, Singapore

    Zhao Xu, Qing Song & Danwei Wang

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  1. Zhao Xu
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  2. Qing Song
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  3. Danwei Wang
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Correspondence to Zhao Xu.

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Xu, Z., Song, Q. & Wang, D. Recurrent neural tracking control based on multivariable robust adaptive gradient-descent training algorithm. Neural Comput & Applic 21, 1745–1755 (2012). https://doi.org/10.1007/s00521-011-0618-2

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  • DOI: https://doi.org/10.1007/s00521-011-0618-2

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