FLC-Based Adaptive Neuro-Fuzzy Inference System for Enhancing the Traveling Comfort

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)


In this paper, pioneering adaptive neuro-fuzzy inference system (ANFIS) which is trained with the data obtained from well-known intelligent control technique fuzzy logic controller (FLC) for half-car (HC) model is proposed to improve the traveling comfort. In automobile industries, the traveling performance of the vehicle is tested at the design stage by simulating the vehicle response to various road excitations under different loading conditions. In this work, the disturbance from the road is assumed to be a dual bump. Initially, a FLC is designed to give better performance. Secondly, a flexible machine learning approach artificial neural network (ANN) that is trained with the FLC data by considering the performance measure as mean square error (MSE) is designed and used. At last, an ANFIS with the adaptive and generalizing features of ANN and intelligence of FLC is used for control purpose. In the modeling of system, simulation with and without controllers is carried out in MATLAB/Simulink environment. A comparison is made among the responses of the system with these controllers, and it shows that the system with FLC-based ANFIS gives significant reduction of the body acceleration (BA) and thus improves the traveling comfort.


Adaptive neuro-fuzzy inference system Fuzzy logic controller Artificial neural network Mean square error Body acceleration 


  1. 1.
    C.-S. Ting, T.-H.S. LI, F.C. Kung, Design of fuzzy controller for active suspension system. J. Mechatron. 5(4), 365–383 (1995)Google Scholar
  2. 2.
    S.I. Ihsana, M. Ahmadianb, W.F. Farisa, E.D. Blancardb, Ride performance analysis of half-car model for semi-active system using RMS as performance criteria. Shock Vibr. 16, 593–605 (2009)CrossRefGoogle Scholar
  3. 3.
    S. Turkay, H. Akcay, Influence of tire damping on mixed H2/H synthesis of half-car active suspensions. J. Sound Vib. 322, 15–28 (2009)CrossRefGoogle Scholar
  4. 4.
    R.A. Irani, R.J. Bauer, A. Warkentin, A dynamic terramechanic model for small lightweight vehicles with rigid wheels and grousers operating in sandy soil. J. Terrramech. 48, 307–318 (2011)CrossRefGoogle Scholar
  5. 5.
    J. Cao, H. Liu, P. Li, D.J. Brown, State of the art in vehicle active suspension adaptive control systems based on intelligent methodologies. IEEE Trans. Intell. Transp. Syst. 9(3), 392–405 (2008)Google Scholar
  6. 6.
    A. Faheem, F. Alam, V. Thomas, The suspension dynamic analysis for a quarter car model and half car model, in 3rd BSME-ASME International Conference on Thermal Engineering, Dhaka (2006), pp. 20–22Google Scholar
  7. 7.
    C.-J. Huang, J.-S. Lin, C.-C. Chen, Road-adaptive algorithm design of half-car active suspension system. Expert Syst. Appl. 37 (2010)Google Scholar
  8. 8.
    C.Y. Tang, G. Zhao, H. LI, S.W. Zhou, Research on suspension system based on genetic algorithm and neural network control, in 2009 Second International Conference on Intelligent Computation Technology and Automation (2009), pp. 468–471Google Scholar
  9. 9.
    K. Rajeswari, P. Lakshmi, Control of active suspension using fuzzy logic, in International Conference on Modeling and Simulation, Coimbatore, 27–29 Aug 2007Google Scholar
  10. 10.
    K. Rajeswari, P. Lakshmi, Adaptive neuro-fuzzy controller for vehicle suspension systems, in International Conference on System Dynamics and Control—ICSDC (2010), pp. 134–140Google Scholar
  11. 11.
    N. Yagiz, L.E. Sakman, R. Guclu, Different control applications on a vehicle using fuzzy logic control. Sadhana 33, 15–25 (2008)Google Scholar
  12. 12.
    J. Campos, F.L. Lewis, L. Davis, S. Ikenaga, Backstepping based fuzzy logic control of active vehicle system, in Proceedings of the American Control Conference Chicago, Illinois, June (2000)Google Scholar
  13. 13.
    N.E. Nawa, T. Furuhashi, T. Hashiyama, Y. Uchikawa, A study on the discovery of relevant fuzzy rules using pseudobacterial genetic algorithm. IEEE Trans. Ind. Electron. 46(6) (1999)Google Scholar
  14. 14.
    R. Krtolic, H. Chan, U. Orguner, H. Hrovat, A two-time-scale analysis of active suspension control of a 2D/4DOF half-car model, in Proceedings of American Control Conference Seattle, Washington (1995), pp. 1162–1168Google Scholar
  15. 15.
    N. Al-Holou, T. Lahdhiri, D.S. Joo, J. Weaver, F. Al-Abbas, Sliding mode neural network inference fuzzy logic control for active suspension systems. IEEE Trans. Fuzzy Syst. 10(2) (2002)Google Scholar
  16. 16.
    J. Xu, J. Fei, Neural network predictive control of vehicle suspension, in 2010 IEEE Conference (2010) 978-1-4244-7618-3/10Google Scholar
  17. 17.
    J. Kalkkuhl, K.J. Hunt, H. Fritz, FEM-based neural-network approach to nonlinear modeling with application to longitudinal vehicle dynamics control. IEEE Trans. Neural Netw. 10(4) (1999)Google Scholar
  18. 18.
    Y. Li, W. Sun, J. Huang, L. Zheng, Y. Wang, Effect of vertical and lateral coupling between tyre and road on vehicle rollover vehicle system dynamics. Int. J. Veh. Mech. Mobility 1–26 (2013)Google Scholar
  19. 19.
    P. Solatian, S.H. Abbasi, F. Shabaninia, Simulation study of flow control based on PID ANFIS controller for non-linear process plants. Am. J. Intell. Syst. 2(5), 104–110 (2012)CrossRefGoogle Scholar
  20. 20.
    M.A. Eltantawie, Decentralized neuro-fuzzy control for half car with semi-active suspension system. Int. J. Autom. Technol. 13(3), 423–431 (2012)CrossRefGoogle Scholar
  21. 21.
    M.V.C. Rao, V. Prahlad, A tunable fuzzy logic controller for vehicle suspension systems. Fuzzy Sets Syst. 85, 11–21 (1997)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of EEECollege of Engineering GuindyChennaiIndia

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