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A Fuzzy Skyhook Algorithm Using Piecewise Linear Inverse Model

Cho Jeong-Mok    (Dept. of Control & Instrumentation Eng. Changwon National University   ); Yoo Bong-Soo    (Dept. of Control & Instrumentation Eng. Changwon National University   ); Joh Joong-Seon    (Dept. of Control & Instrumentation Eng. Changwon National University  );
  • 초록

    In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model has been constructed by using piecewise linear damping force model. In this paper, the fuzzy logic control based on heuristic knowledge is combined with the skyhook control. And it is simulated for a quarter car model. The acceleration of the sprung mass is included in the premise part of the fuzzy rules to reduce the vertical acceleration RMS value of the sprung mass. Then scaling factors and membership functions are tuned using genetic algorithm to obtain optimal performance.


  • 주제어

    Fuzzy Sky-hook .   Inverse Model .   Genetic Algorithm .   Quarter-car Model .   ER Damper.  

  • 참고문헌 (10)

    1. S. Hwang, S. Heo, H. Kim, and K. Lee, 'Vehicle Dynamic Analysis and Evaluation of Continuously Controlled Semiactive Suspensions Using Hardware-in the-loop Simulation', Vehicle System Dynamics, Vol. 27, No. 5-6, pp. 423-434, June 1997 
    2. D. H. Wang and W. H. Liao, 'Modeling and control of magneto-rheological fluid dampers using neural networks', Smart Materials and Structures 14, pp. 111-126, 2005 
    3. C. L. Nikias and M. R. Raghuveer, 'Bisprctrurn Estimation', Proceeding of IEEE, Vol. 75, No.7, pp. 869-891, 1987 
    4. C. L. Nikias and J. M. Mendel, 'Signal Processing with High Order Spectra', IEEE Signal Processing Magazine, pp. 10-37, 1993 
    5. L. A. Zadeh, 'Fuzzy Sets', Information and Control, Vol.8, pp.338-353, 1965 
    6. P.Q. Xia, 'An inverse model of MR damper using optimal neural network and system identification', Journal of Sound and vibration 266, pp. 1009-1023, 2003 
    7. S. B. Choi, S. K. Lee, and Y. P. Park, 'A hysteresis model for the field-dependent damping force of a magneto-rheological damper', Journal of Sound and Vibration 245(2), pp. 375-383, 2001 
    8. G. P. Frost, M. N. Howell, and Q. H. Wu, 'Moderated Reinforcement Learning of Active and Semiactive Vehicle Suspension Control Laws', Proceedings of the Institution of Mechanical Engineers, Part 1, Vol. 210, No. 14, pp.249-257, 1996 
    9. Y. Liu, F. Gordaninejad, C. Evrensel, S. Karakas, and U. Dogruer, 'Experimental study on fuzzy skyhook control of a vehicle suspension system using a magneto-rheological fluid damper', Proceedings of SPIE on Smart Structures and Materials, Vol.5388, pp. 338-347, 2004 
    10. A. K. Cater, 'Transient Motion Control of Passive and Semiactive Damping for Vehicle Suspensions', Master of Science Thesis, Virginia Polytechnic and State University, Blacksburg, Virginia, July 1998 

 저자의 다른 논문

  • Cho Jeong-Mok (2)

    1. 2006 "Inverse Model Control of An ER Damper System" International journal of fuzzy logic and intelligent systems : IJFIS 6 (1): 64~69    
    2. 2006 "Fuzzy Skyhook Control of A Semi-active Suspension System" International journal of fuzzy logic and intelligent systems : IJFIS 6 (2): 121~126    
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