Effect of Fuzzy Logic Based-Skyhook Policy with Particle Swarm Optimization for Semi-Active Ride Comfort

  • Abstract
  • Keywords
  • References
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  • Abstract

    This paper presents the effect of the fuzzy logic based-skyhook policy tuned using particle swarm optimization (FLSP-PSO) for semi-active ride comfort of quarter vehicle model. Spencer model was used to represent the magnetorheological damper model and its behavior was investigated in the form of force-displacement and force-velocity characteristics. The fuzzy logic control adopted with the skyhook policy based on Sugeno-type fuzzy was used to enhance the ride performance. An intelligent evolutionary algorithm known as the particle swarm optimization was also adapted in the proposed controller to compute the fuzzy gain scaling. The performance of the FLSP-PSO controller is compared to other controller responses. The effect of the PSO techniques to optimize the FLPS parameters gives a better performance and able to improve the vehicle ride comfort than its counterparts.



  • Keywords

    Fuzzy logic, Particle swarm optimization, Ride comfort, Semi-active suspension, Skyhook

  • References

      [1] Guglielmino E, Sireteanu T, Stammers C, Ghita, G & Giuclea M, Semi-active Suspension Control. Verlag, London, (2008).

      [2] Sammier D, Sename O & Dugard L (2010), Skyhook and H8 Control of Semi-active Suspensions : Some Practical Aspects. International Journal of vehicle mechanics and mobility, 39(4), 279–308.

      [3] Taskin Y, Haciaglu Y & Yagiz N (2017), Experimental evaluation of a fuzzy logic controller on a quarter car test rig. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(7), 2433–2445.

      [4] Aggarwal ML (2014), Fuzzy logic control of a semi-active quarter car system. International Journal of Mechanical, Aerospace, Industrial and Mechatronic Engineering, 8(1), 154–158.

      [5] Cseko LH, Kvasnica M & Lantos B (2015), Explicit mpc-based rbf neural network controller design with discrete-time actual kalman filter for semiactive suspension. IEEE Transactions on Control Systems Technology,23(5), 1736–1753.

      [6] Zapateiro M, Luo N, Karimi HR & Vehí J (2009), Vibration control of a class of semiactive suspension system using neural network and backstepping techniques,.Mechanical Systems and Signal Processing, 23(6),1946–1953.

      [7] Khiavi AM, Mirzaei M & Hajimohammadi S (2013), A new optimal control law for the semi-active suspension system considering the nonlinear magneto-rheological damper model. Journal of Vibration and Control, 20(14), 2221–2233.

      [8] Nugroho PW, Du H, Li WH (2012), A new adaptive fuzzy-hybrid control strategy of semi-active suspension with magneto-rheological damper, 4th International Conference on Computational Methods, pp. 1–9,

      [9] Jianwei Y, Jie L, Zhixuan J & Huigang Z (2010), Fuzzy-PID control system simulation of the semi-active vehicle suspension, 2010 International Conference on Digital Manufacturing & Automation, No. 1, pp. 772-775.

      [10] Karnopp D, Crosby MJ & Harwood RA (1974), Vibration control using semi-active force generators. Journal of Engineering for Industry, 96(2), 619–626.

      [11] Ubaidillah Hudha K & Jamaluddin H (2011), Simulation and experimental evaluation on a skyhook policy-based fuzzy logic control for semi-active suspension system. Int. J. Structural Engineering, 2(3), 243–272.

      [12] Magdanela L (1997), Adapting the gain of an FLC with genetic algorithms. International Journal of Approximate Reasoning, 17(97), 327–349.

      [13] Marcelo TA, Rafikov M & Balthazar JM (2009), An Intelligent Controller Design for Magnetorheological Damper Based on a Quarter-Car, Journal of Vibration and Control, 15(12), 1907–1920.

      [14] Mahala MK, Gadkari P & Deb A (2009), Mathematical models for designing vehicles for ride comfort. Proceeding of the 2nd International Conference on Research into Design,1, pp. 168–175.

      [15] Azar BF, Rahbari NM & Talatahari S (2011), Seisimic mitigation of tall building using magnetorheological dampers. Asian Journal of Civil Engineering, 12(5), 637–649.

      [16] Spencer BF, Dyke SJ, Sain MK and Carlson JD (1997), Phenomenological model of a magnetorheological damper, Journal of Engineering Mechanics, pp. 1–23.

      [17] Craft MJ, Buckner GD & Anderson RD (2003), Semi-active vehicle shock absorbers: design and experimental evaluations, Proceedings of SPIE – The International Society for Optical Engineering, pp. 577–588.

      [18] Kennedy J & Eberhart R (1995), Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942–1948.

      [19] Tighzert L, Fonlupt C & Mendil B (2017), A set of new compact firefly algorithms. Swarm and Evolutionary Computation, 11, 1–24.

      [20] Dwivedi AK, Ghosh S & Londhe ND (2016), Low power FIR filter design using modified multi-objective artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 55, 58–69.

      [21] Sedghizadeh S & Beheshti S (2018), Particle swarm optimization based fuzzy gain scheduled subspace predictive control. Engineering Applications of Artificial Intelligence, 67(2), 331–344.




Article ID: 28152
DOI: 10.14419/ijet.v7i4.36.28152

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