Skip to main content
SpringerLink
Log in

Supervisory recurrent fuzzy neural network control for vehicle collision avoidance system design

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper develops an intelligent method called supervisory recurrent fuzzy neural network (SRFNN) control to deal with the vehicle collision avoidance system (VCAS), which is an uncertain nonlinear model-free system. This SRFNN control system is composed of a recurrent fuzzy neural network (RFNN) controller and a supervisory controller. The RFNN controller is investigated to mimic an ideal controller, and the supervisory controller is designed to compensate for the approximation error between the RFNN controller and the ideal controller. This SRFNN control is employed to keep the VCAS within a safety range to avoid traffic accidences. The simulation results show the performance and effectiveness of the proposed control system are better than that obtained by formal formula-based control.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Matsuda S (1997) Vehicle collision preventing system. U. S. patent US 5,699,040

  2. Aufrere R, Gowdy J, Mertz C, Thorpe C, Wang CC, Yata T (2003) Perception for collision avoidance and autonomous driving. Mechatronics 13:1149–1161

    Article  Google Scholar 

  3. Mar J, Lin FJ (2001) An ANFIS controller for the car-following collision prevention system. IEEE Trans Vehi Tech 50(4):1106–1113

    Article  Google Scholar 

  4. Ganji B, Kouzani AZ (2012) Combined quasi-static backward modeling and look-ahead fuzzy control of vehicles. Expert Syst Appl 39(1):223–233

    Article  Google Scholar 

  5. Milanés V, Pérez J, Godoy J, Onieva E (2012) A fuzzy aid rear-end collision warning/avoidance system. Expert Syst Appl 39(10):9097–9107

    Article  Google Scholar 

  6. Qi Z, Tian Y, Shi Y (2012) Efficient railway tracks detection and turnouts recognition method using HOG features. Neural Comput Appl. doi:10.1007/s00521-012-0846-0

  7. Mon YJ, Chen RH, Luo KT (2002) Fuzzy logic based vehicle collision avoidance warning device. U. S. patent US 6,430,506 B1

  8. Mon YJ (2005) Integrated automotive safety system design by a fuzzy-neural-network. Int J Veh Auton Syst 3(2/3/4):176–197

    Google Scholar 

  9. Ge SS, Hang CC, Zhang T (1999) Adaptive neural network control of nonlinear systems by state and output feedback. IEEE Trans Syst Man Cybern Part B Cybern 29:818–828

    Article  Google Scholar 

  10. Zhihong M, Wu HR, Palaniswami M (1998) An adaptive tracking controller using neural networks for a class of nonlinear systems. IEEE Trans Neural Netw 9:947–1031

    Google Scholar 

  11. Chen YC, Teng CC (1995) A model reference control structure using a fuzzy neural network. Fuzzy Sets Syst 73:291–312

    Article  MathSciNet  MATH  Google Scholar 

  12. Lin CT, Lee CSG (1996) Neural fuzzy systems: a neural-fuzzy synergism to intelligent systems. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  13. Ku CC, Lee KY (1995) Diagonal recurrent neural networks for dynamic systems control. IEEE Trans Neural Netw 6:144–156

    Article  Google Scholar 

  14. Lee CH, Teng CC (2000) Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans Fuzzy Syst 8:349–366

    Article  Google Scholar 

  15. Lin FJ, Wai RJ (2001) Hybrid control using recurrent fuzzy neural network for linear-induction motor servo drive. IEEE Trans Fuzzy Syst 9:102–115

    Article  Google Scholar 

  16. Aliev RA, Guirimov BG, Fazlollahi B, Aliev RR (2009) Evolutionary algorithm-based learning of fuzzy neural networks. Part 2: recurrent fuzzy neural networks. Fuzzy Sets Syst 160:2553–2566

    Article  MathSciNet  MATH  Google Scholar 

  17. Lin CM, Hsu CF (2002) Recurrent neural network adaptive control of wing-rock motion. J Guid Control Dyn 25:1163–1165

    Article  Google Scholar 

  18. Mon YJ, Lin CM, Leng CH (2008) Recurrent fuzzy neural network control for MIMO nonlinear systems. Intell Autom Soft Comput 14(4):395–415

    Google Scholar 

  19. Mon YJ, Lin CM (2012) Supervisory recurrent fuzzy neural network guidance law design for autonomous underwater vehicle. Int J Fuzzy Syst 14(1):54–64

    Google Scholar 

  20. Choi HH (2009) Output feedback stabilization of uncertain fuzzy systems using variable structure system approach. Fuzzy Sets Syst 160:2812–2823

    Article  MATH  Google Scholar 

  21. Slotine JJE, Li W (1991) Applied nonlinear control. Prentice-Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  22. Lin CM, Chen CH (2006) Adaptive RCMAC sliding mode control for uncertain nonlinear systems. Neural Comput Appl 15:253–267

    Article  Google Scholar 

  23. Da F (2008) Fuzzy neural network sliding mode control for long delay time systems based on fuzzy prediction. Neural Comput Appl 17:531–539

    Article  Google Scholar 

  24. Spooner JT, Passino KM (1996) Stable adaptive control using fuzzy systems and neural networks. IEEE Trans Fuzzy Syst 4(3):339–359

    Article  Google Scholar 

Download references

Acknowledgment

This paper is partially funded by teacher’s research project of Taoyuan Innovation Institute of Technology.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Taoyuan Innovation Institute of Technology, Chung-Li, TaoYuan, 320, Taiwan

    Yi-Jen Mon

  2. Department of Electrical Engineering, Yuan Ze University, Chung-Li, TaoYuan, 320, Taiwan

    Chih-Min Lin

Authors
  1. Yi-Jen Mon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chih-Min Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi-Jen Mon.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mon, YJ., Lin, CM. Supervisory recurrent fuzzy neural network control for vehicle collision avoidance system design. Neural Comput & Applic 21, 2163–2169 (2012). https://doi.org/10.1007/s00521-012-1098-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1098-8

Keywords

Search

Navigation