Abstract
In this paper, a class of uncertain chaotic systems preceded by unknown backlash nonlinearity is investigated. Combining backstepping technique with fuzzy neural network identifying, an adaptive backstepping fuzzy neural controller (ABFNC) for uncertain chaotic systems with unknown backlash is proposed. The proposed ABFNC system is comprised of a fuzzy neural network identifier (FNNI) and a robust controller. The FNNI is the principal controller utilized for online estimation of the unknown nonlinear function. The robust controller is used to attenuate the effects of the approximation error so that the stability and control performance of the closed-loop adaptive system is achieved always. Finally, simulation results show that the ABFNC can achieve favorable tracking performances.









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Acknowledgments
The first author is grateful to the support of the Youth Foundation of Sichuan Provincial Education Department (No. 11ZB097), the Talents Project of Sichuan University of Science and Engineering (No. 2011RC07), the Key project of Artificial Intelligence Key Laboratory of Sichuan Province (No. 2011RZJ02), the Science and Technology Key Project of Zigong (No. 2012D09), the Cultivation Project of Sichuan University of Science and Engineering (No. 2012PY19), and the Innovation Group Build Plan for the Universities in Sichuan (No. 13TD0017). The second author would like to thank the support of National Natural Science Foundation of China (No. 61363082).
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Lin, D., Liu, H., Song, H. et al. Fuzzy neural control of uncertain chaotic systems with backlash nonlinearity. Int. J. Mach. Learn. & Cyber. 5, 721–728 (2014). https://doi.org/10.1007/s13042-013-0218-9
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DOI: https://doi.org/10.1007/s13042-013-0218-9