Fuzzy force learning controller of flexible wiper system
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  • 作者:Ali Zolfagharian ; P. Valipour ; S. E. Ghasemi
  • 关键词:Automotive wiper ; System identification ; Intelligent control ; Multi ; objective genetic algorithm
  • 刊名:Neural Computing & Applications
  • 出版年:2016
  • 出版时间:February 2016
  • 年:2016
  • 卷:27
  • 期:2
  • 页码:483-493
  • 全文大小:1,079 KB
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  • 作者单位:Ali Zolfagharian (1)
    P. Valipour (2)
    S. E. Ghasemi (3)

    1. Department of System Dynamics and Control, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
    2. Department of Textile and Apparel, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
    3. Young Reseachers and Elite Club, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
  • 刊物类别:Computer Science
  • 刊物主题:Simulation and Modeling
  • 出版者:Springer London
  • ISSN:1433-3058
文摘
Wiper blade of automobile is among those types of flexible system that is required to be operated in quite high velocity to be efficient in high load conditions. This causes some annoying noise and deteriorated vision for occupants. The modeling and control of vibration and low-frequency noise of an automobile wiper blade using soft computing techniques are focused in this study. The flexible vibration and noise model of wiper system are estimated using artificial intelligence system identification approach. A PD-type fuzzy logic controller and a PI-type fuzzy logic controller are combined in cascade with active force control (AFC)-based iterative learning (IL). A multi-objective genetic algorithm is also used to determine the scaling factors of the inputs and outputs of the PID-FLC as well as AFC-based IL gains. The results from the proposed controller namely fuzzy force learning (FFL) are compared with those of a conventional lead–lag-type controller and the wiper bang–bang input. Designing controllers based on classical methods could become tedious, especially for systems with high-order model. In contrast, FFL controller design requires only tuning of some scaling factors in the control loop and hence is much simpler and efficient than classical design methods. Keywords Automotive wiper System identification Intelligent control Multi-objective genetic algorithm
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