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Adaptive neural control and learning of affine nonlinear systems

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Abstract

This paper presents deterministic learning from adaptive neural network control of affine nonlinear systems with completely unknown system dynamics. Thanks to the learning capability of radial basis function, neural network (NN), stable adaptive NN controller is designed for the unknown affine nonlinear systems. The designed adaptive NN controller is rigorously shown that learning of the unknown closed-loop system dynamics can be achieved during the stable control process because partial persistent excitation condition of some internal signals in the closed-loop system is satisfied. Subsequently, neural learning controller using the knowledge obtained from deterministic learning is constructed to achieve closed-loop stability and improve control performance. Numerical simulation is provided to show the effectiveness of the proposed control scheme.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant No. 60934001, No. 61225014, and No. 61075082.

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Authors and Affiliations

  1. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Yuxiang Wu & Cong Wang

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  1. Yuxiang Wu
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  2. Cong Wang
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Correspondence to Cong Wang.

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Wu, Y., Wang, C. Adaptive neural control and learning of affine nonlinear systems. Neural Comput & Applic 25, 309–319 (2014). https://doi.org/10.1007/s00521-013-1488-6

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  • DOI: https://doi.org/10.1007/s00521-013-1488-6

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