Skip to main content
Have a personal or library account? Click to login
Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system Cover

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

International Journal of Applied Mathematics and Computer Science
Volume 21 (2011): Issue 1 (March 2011)
By:  and    
Open Access
|Mar 2011
References

References

  1. Al-Holou, N., Lahdhiri, T., Joo, D. S., Weaver, J. and Al-Abbas, F. (2002). Sliding mode neural network inference fuzzy logic control for active suspension system,(2): 234-245.
    Open DOISearch in Google Scholar
  2. Biglarbegian, M., Melek, W. and Golnaraghi, F. (2008). A novel neuro-fuzzy controller to enhance the performance of vehicle semi-active suspension systems,(8): 691-711.
    Open DOISearch in Google Scholar
  3. Boukezzoula, R., Galichet, S. and Foulloy, L. (2007). Fuzzy feedback linearizing controller and its equivalence with fuzzy nonlinear internal model control structure,(2): 233-248, DOI: 10.2478/v10006-007-0021-4.
    Open DOISearch in Google Scholar
  4. Boutalis, Y. S. (2004). Neural network approaches for feedback linearization,(1): 15-26.
    Search in Google Scholar
  5. Buckner, G. D., Schuetze, K. T. and Beno, J. H. (2001). Intelligent feedback linearization for active vehicle suspension control,(4): 727-733.
    Open DOISearch in Google Scholar
  6. Cao, J., Liu, H., Li, P. and Brown, D. (2008). State of the art in vehicle active suspension adaptive control systems based on intelligent methodologies,(3): 392-405.
    Open DOISearch in Google Scholar
  7. Chantranuwathana, S. and Peng, H. (2004). Adaptive robust force control for vehicle active suspension,(2): 83-102.
    Open DOISearch in Google Scholar
  8. Christophe, L., Swevers, J. and Sas, P. (2005). Robust linear control of an active suspension on a quarter car test-rig,(5): 577-586.
    Open DOISearch in Google Scholar
  9. Dahunsi, O. A., Pedro, J. O. and Nyandoro, O. T. (2009). Neural network-based model predictive control of a servo-hydraulic vehicle suspension system,, pp. 1-6, DOI: 10.1109/AFRCON.2009.5308111.
    Open DOISearch in Google Scholar
  10. Dahunsi, O. A., Pedro, J. O. and Nyandoro, O. T. (2010a). Neural network-based PID control of a servo-hydraulic vehicle suspension system,, pp. 1-6.
    Search in Google Scholar
  11. Dahunsi, O. A., Pedro, J. O. and Nyandoro, O. T. (2010b). System identification and neural network based PID control of servo-hydraulic vehicle suspension system,1(3): 93-105.
    Open DOISearch in Google Scholar
  12. D'Amato, F. J. and Viassolo, D. E. (2000). Fuzzy control for active suspensions,(1): 897-920.
    Open DOISearch in Google Scholar
  13. Deng, J., Becerra, V. M. and Stobart, R. (2009). Input constraints handling in an MPC/feedback linearization scheme,(2): 219-232, DOI: 10.2478/v10006-009-0018-2.
    Open DOISearch in Google Scholar
  14. Du, H. and Zhang, N. (2008). Multiobjective static output feedback control design for vehicle suspensions,(1): 228-239.
    Open DOISearch in Google Scholar
  15. Du, H. and Zhang, N. (2009). Static output feedback control for electrohydraulic active suspensions via T-S fuzzy model approach,(5): 051004-1-051004-11.
    Open DOISearch in Google Scholar
  16. Ehtiwesh, I. A. S. and Dorovic, Z. (2009). Comparative analysis of different control strategies for electro-hydraulic servo systems,(6): 906-909.
    Search in Google Scholar
  17. Eski, I. and Yildrim, S. (2009). Vibration control of vehicle active suspension system using a new robust neural network control system,(5): 778-793.
    Open DOISearch in Google Scholar
  18. Fallah, M. S., Bhat, R. and Xie, W. (2009).∞ robust control of active suspensions: A practical point of view,, pp. 1385-1390.
    Search in Google Scholar
  19. Feng, J. Z., Li, J. and Yu, F. (2003). GA-based PID and fuzzy logic control for active vehicle suspension system,(4): 181-191.
    Search in Google Scholar
  20. Fiahlo, I. and Balas, G. J. (2002). Road adaptive active suspension using linear parameter-varying gain-scheduling,(1): 43-54.
    Open DOISearch in Google Scholar
  21. Gao, B., Tilley, D. G., Williams, R. A., Bean, A. and Donahue, J. (2006). Control of hydropneumatic active suspension based on a non-linear quarter-car model,(1): 75-31.
    Open DOISearch in Google Scholar
  22. Gao, Z. (2002). From linear to nonlinear control means: A practical progression,(2): 177-189.
    Open DOISearch in Google Scholar
  23. Garces, F. R., Becerra, V. M., Kambhampti, C. and Warwick, K. (2003)., Springer, London.
    Open DOISearch in Google Scholar
  24. Gaspar, P., Szaszi, I. and Bokor, J. (2003). Active suspension design using linear parameter varying control,(2): 206-221.
    Open DOISearch in Google Scholar
  25. Goodwin, G. C., Rojas, O. and Takata, H. (2001). Nonlinear control via generalized feedback linearization using neural networks,(2): 79-88.
    Open DOISearch in Google Scholar
  26. Hada, M. K., Menon A. and Bhave S. Y. (2007). Optimisation of an active suspension force controller using genetic algorithm for random input,(5): 691-706.
    Open DOISearch in Google Scholar
  27. Hagan, M. T. and Demuth, H. B. (1999). Neural networks for control,, pp. 1642-1656.
    Search in Google Scholar
  28. Hassanzadeh, I., Alizadeh, G., Shiirjoposht, N. P. and Hashemzadeh, F. (2010). A new optimal nonlinear approach to half car active suspension control,(1): 78-84.
    Open DOISearch in Google Scholar
  29. He, Y. and McPhee, J. (2005). A design methodology for mechatronic vehicles: Application of multidisciplinary optimization, multibody dynamics and genetic algorithms,(10): 697-733.
    Open DOISearch in Google Scholar
  30. Hrovat, D. (1997). Survey of advanced suspension developments and related optimal control applications,(10): 1781-1817.
    Open DOISearch in Google Scholar
  31. Isidori, A. (1989)., Springer-Verlag, Berlin.
    Open DOISearch in Google Scholar
  32. Jelali, M. and Kroll, A. (2003)., Springer-Verlag, London.
    Open DOISearch in Google Scholar
  33. Jin, Y. and Yu, D. J. (2008). Adaptive neuron control using an integrated error approach with application to active suspensions,(3): 329-335.
    Open DOISearch in Google Scholar
  34. Kar, I. and Behera, L. (2009). Direct adaptive neural control for affine nonlinear systems,(2): 756-764.
    Open DOISearch in Google Scholar
  35. Koshkouei, A. J. and Burnham, K. J. (2008). Sliding mode controllers for active suspensions,, pp. 1-6.
    Search in Google Scholar
  36. Kumar, M. S. (2008). Development of active suspension system for automobiles using PID controller,, pp. 1472-1477.
    Search in Google Scholar
  37. Kumar, M. S. and Vijayarangan, S. (2007). Analytical and experimental studies on active suspension system of light passenger vehicle to improve ride comfort,(3): 34-41.
    Search in Google Scholar
  38. Kuo, Y. and Li, T. S. (1999). GA-based fuzzy PI/PD controller for automotive active suspension system,(6): 88-100.
    Open DOISearch in Google Scholar
  39. Lufty, O. F., Noor, S. B. M., Marhaban, M. H. and Abbas, K. A. (2009). A geneticatlly trained adaptive neuro-fuzzy inference system network utilized as a PID-like feedback controller for non-linear systems,(3): 289-429.
    Open DOISearch in Google Scholar
  40. Nakkarat, P. and Kuntanapreeda, S. (2009). Observer-based backstepping force control of an electrohydraulic actuator,(8): 895-902.
    Open DOISearch in Google Scholar
  41. Norgaard, M., Ravn, O., Poulsen, N. K. and Hansen, L. K. (2003)., Springer, Boston, MA.
    Search in Google Scholar
  42. O'Dwyer, A. (2006)., Imperial College Press, London.
    Open DOISearch in Google Scholar
  43. Pedro, J. O. (2003). Design and performance of an active vehicle suspension system,, pp. 203-209.
    Search in Google Scholar
  44. Pedro, J. O. (2007).-LQG/LTR controller design for active suspension systems,(2): 32-41.
    Search in Google Scholar
  45. Pedro, J. O. and Mgwenya, T. R. (2004). LQR control of a full - car active suspension with actuator dynamics,, pp. 1-9.
    Search in Google Scholar
  46. Poursamad, A. (2009). Adaptive feedback linearization control of antilock braking system using neural networks,(5): 767-773.
    Open DOISearch in Google Scholar
  47. Poussot-Vassal, C., Sename, O., Dugard, L., Gaspar, P., Szabo, Z. and Bokor, J. (2006). Multi-objective qLPV∞/control of a half vehicle,, pp. 1-6.
    Search in Google Scholar
  48. Ryu, S., Kim, Y. and Park, Y. (2008). Robust∞ preview control of an active suspension system with norm-bounded uncertainties,(5): 585-592.
    Open DOISearch in Google Scholar
  49. Salem, A. A. A. and Aly, A. A. (2009). Fuzzy control of a quarter-car suspension systems,(53): 258-263.
    Search in Google Scholar
  50. Seo, J., Venugopal, R. and Kenne, J. (2007). Feedback linearization based control of a rotational hydraulic drive,(12): 1495-1507.
    Open DOISearch in Google Scholar
  51. Sharkawy, A. B. (2005). Fuzzy and adaptive fuzzy control for the automobiles' active suspension system,(10): 795-806.
    Open DOISearch in Google Scholar
  52. Shen, X. and Peng, H. (2003). Analysis of active suspension systems with hydraulic actuators,, pp. 1-10.
    Search in Google Scholar
  53. Shi, J., Li, X. W. and Zhang, J. W. (2010). Feedback linearization and sliding mode control for active hydropneumatic suspension of a special-purpose vehicle,(3): 171-181.
    Search in Google Scholar
  54. Shirahatt, A., Prasad, P. S. S., Panzade, P. and Kulkarni, M. M. (2008). Optimal design of passenger car suspension for ride and road holding,(1): 66-76.
    Open DOISearch in Google Scholar
  55. He, S., Reif, K. and Unbehauen, R. (1998). A neural approach for control of nonlinear systems with feedback linearization,(6): 1409-1421.
    Open DOISearch in Google Scholar
  56. Slotine, J. J. and Li, W. (1991)., Prentice Hall, Englewood Cliffs, NJ.
    Search in Google Scholar
  57. Yagiz, N., Sakman, L. E. and Guclu, R. (2008). Different control applications on a vehicle using fuzzy logic control,(1): 15-25.
    Open DOISearch in Google Scholar
  58. Yesildirek, A. and Lewis, F. L. (1995). Feedback linearization using neural networks,(11): 1659-1664.
    Open DOISearch in Google Scholar
  59. Yoshimura, T. and Teramura, I. (2005). Active suspension control of a one-wheel car model using single input rule modules fuzzy reasoning and a disturbance observer,(4): 251-256.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.2478/v10006-011-0010-5 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Journal RSS Feed
Language: English
Page range: 137 - 147
Published on: Mar 28, 2011
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
neural networks,
direct adaptive control,
feedback linearization control,
PID control,
ride comfort,
suspension system,
servo-hydraulics
Related subjects:
Mathematics,
Applied mathematics

© 2011 Jimoh Pedro, Olurotimi Dahunsi, published by University of Zielona Góra
This work is licensed under the Creative Commons License.

Volume 21 (2011): Issue 1 (March 2011)
Previous article
Next article