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Chaos control of the permanent magnet synchronous motor with time-varying delay by using adaptive sliding mode control based on DSC

机译:基于DSC的自适应滑模控制时变时滞永磁同步电动机的混沌控制。

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摘要

This paper focuses on the problem of chaos control for the permanent magnet synchronous motor with chaotic oscillation, unknown dynamics and time-varying delay by using adaptive sliding mode control based on dynamic surface control. To reveal the mechanism of motor system and facilitate controller design, the dynamic behavior of the system is investigated. Nonlinear items of system model, upper bounds of time delays and their derivatives are taken as unknown in the overall process. A RBF neural network with an adaptive law, which eliminates restrictions on accurate model and parameters, is employed to cope with unknown dynamics. In order to solve issues such as chaotic oscillation, 'explosion of complexity' of backstepping, and chattering associated with sliding mode control, a sliding mode controller is developed within the framework of dynamic surface control by the hybrid of adaptive technology and RBF neural network. In addition, an appropriate Lyapunov function is employed to demonstrate the system stability. Finally, the feasibility of the proposed scheme is testified by simulation. (C) 2018 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译:通过基于动态表面控制的自适应滑模控制,研究了具有混沌振荡,未知动力学和时变时滞的永磁同步电动机的混沌控制问题。为了揭示电动机系统的机理并简化控制器设计,研究了系统的动态行为。系统模型的非线性项,时延上限及其导数在整个过程中被视为未知。具有自适应定律的RBF神经网络消除了对精确模型和参数的限制,可用于应对未知的动力学。为了解决诸如混沌振荡,后推的“复杂性爆炸”以及与滑模控制相关的颤动等问题,通过自适应技术和RBF神经网络的混合,在动态表面控制的框架内开发了一种滑模控制器。另外,采用适当的Lyapunov函数来演示系统稳定性。最后,通过仿真验证了该方案的可行性。 (C)2018富兰克林研究所。由Elsevier Ltd.出版。保留所有权利。

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  • 来源
    《Journal of the Franklin Institute》 |2018年第10期|4147-4163|共17页
  • 作者

    Luo Shaohua; Gao Ruizhen;

  • 作者单位

    Huaiyin Inst Technol, Jiangsu Key Lab Adv Mfg Technol, Huaian 223003, Peoples R China;

    Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China;

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  • 正文语种 eng
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