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Adaptive Neural Network Control for a DC Motor System with Dead-Zone

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Abstract

In this paper, an adaptive control strategy for tracking of a direct-current (DC) motor system with a dead-zone is developed. The main contribution of the developed scheme is that we successfully integrate an asymmetric barrier Lyapunov function approach to relax the requirements on the initial conditions. The unknown functions in the DC system are approximated by using the radial basis function neural networks (RBFNN). It is shown that the DC motor can follow a selected trajectory and all the signals are guaranteed to be bounded. Simulation results are provided to confirm the effectiveness of the proposed control.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grants 61074014, 61104017, 51179019, the Program for Liaoning Excellent Talents in University under Grant LJQ2011064; and the Natural Science Foundation of Liaoning Province.

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Authors and Affiliations

  1. College of Science, Liaoning University of Technology, Jinzhou, Liaoning, 121001, China

    Lei Liu & Yan-Jun Liu

  2. Faculty of Science and Technology, University of Macau, Macau, China

    C. L. Philip Chen

Authors
  1. Lei Liu
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  2. Yan-Jun Liu
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  3. C. L. Philip Chen
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Correspondence to Yan-Jun Liu.

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Liu, L., Liu, YJ. & Chen, C.L.P. Adaptive Neural Network Control for a DC Motor System with Dead-Zone. Nonlinear Dyn 72, 141–147 (2013). https://doi.org/10.1007/s11071-012-0698-2

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  • DOI: https://doi.org/10.1007/s11071-012-0698-2

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