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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/108195

    Title: Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems
    Authors: Hsu, Chun-Fei
    Keywords: Antilock braking system (ABS);Intelligent sliding-mode control;Exponential reaching;law;Fuzzy neural network;Functional neural network
    Date: 2016-08-01
    Issue Date: 2016-11-08 02:10:25 (UTC+8)
    Publisher: Springer U K
    Abstract: The purpose of the antilock braking system (ABS) is to regulate the wheel longitudinal slip at its optimum point in order to generate the maximum braking force; however, the vehicle braking dynamic is highly nonlinear. To relax the requirement of detailed system dynamics, this paper proposes an intelligent exponential sliding-mode control (IESMC) system for an ABS. A functional recurrent fuzzy neural network (FRFNN) uncertainty estimator is designed to approximate the unknown nonlinear term of ABS dynamics, and the parameter adaptation laws are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Since the outputs of the functional expansion unit are used as the output weights of the FRFNN uncertainty estimator, the FRFNN can effectively capture the input–output dynamic mapping. In addition, a nonlinear reaching law, which contains an exponential term of sliding surface to smoothly adapt the variations of sliding surface, is designed to reduce the level of the chattering phenomenon. Finally, the simulation results demonstrate that the proposed IESMC system can achieve robustness slip tracking performance in different road conditions.
    Relation: Neural Computing and Applications 27(6) pp.1463-1475
    DOI: 10.1007/s00521-015-1946-4
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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