medigraphic.com
SPANISH

Revista Mexicana de Ingeniería Biomédica

  • Contents
  • View Archive
  • Information
    • General Information        
    • Directory
  • Publish
    • Instructions for authors        
  • medigraphic.com
    • Home
    • Journals index            
    • Register / Login
  • Mi perfil

2014, Number 2

<< Back Next >>

Rev Mex Ing Biomed 2014; 35 (2)

Novel Fuzzy Logic Controller based on Time Delay Inputs for a Conventional Electric Wheelchair

Rojas M, Ponce P, Molina A
Full text How to cite this article

Language: English
References: 24
Page: 125-142
PDF size: 2803.70 Kb.


Key words:

fuzzy logic, dynamic, controller, wheelchair, ultrasonic sensors.

ABSTRACT

This work proposes a Dynamic fuzzy logic Controller for the navigation problem of an electric wheelchair. The controller uses present data from three ultrasonic sensors as the main source of information from the environment. However other inputs, named as “dynamic time delay”, are obtained from past samples of those static data and are used to design the rule base. Although fuzzy logic controllers with static inputs could solve basic navigation problems, the proposed structure with dynamic inputs gets an excellent performance for more complex navigation problems. There were designed static and dynamic navigation strategies, which were first deployed in software just to evaluate their behavior. They were tested in a maze and their trajectories were compared to select the best. For improving its response, the dynamic fuzzy logic strategy was deployed in hardware. The paper presents a comparison between the software and hardware applications to illustrate the possibility of implementing the proposed methodology in different platforms. The dynamic fuzzy logic controller led the electric wheelchair without colliding against walls, and is a high performance navigation system. Moreover, this controller could solve the sensor limitations.


REFERENCES

  1. World Health Organization, “World report on disability”, The World Bank, 2011.

  2. D. Ding and R. A. Cooper, “Electric- Powered Wheelchairs, A Review of Current Technology and Insight into Future Direction”, IEEE Control System Magazine, vol. 25, no. 2, pp. 22-34, 2005.

  3. C. Urdiales, Collaborative Assistive Robot for Mobility Enhancement (CARMEN), Malaga: Springer , 2013.

  4. R. C. Simpson, “Smart Wheelchairs: A literature review”, Journal of Rehabilitation Research & Development, vol. 42, no. 4, pp. 423-436, 2005.

  5. P. Boucher, “Design and validation of an intelligent wheelchair towards a clinically-functional outcome”, Journal of Neuroengineering and Rehabilitation, vol. 10, 2013.

  6. S. P. Levine, “The NavChair Assistive Wheelchair”, IEEE Transactions on Rehabilitation Engineering, vol. 7, no. 4, 1999.

  7. L. Conde, G. Pires and U. Nunes, “A behavior based fuzzy control architecture for path tracking and obstacle avoidance”, in Proceedings of the 5th Portuguese Conference on Automatic Control, 2002.

  8. J. J. Slotine and L. W., Applied Nonlinear Control, New Jersey: Prentice Hall, 1991.

  9. L. Zadeh, "Fuzzy sets *," Information and Control, vol. 8, no. 3, p. 338-353, 1965.

  10. H. A. Hagras, “A Hierarchical Type-2 fuzzy logic Control Architecture for Autonomous Mobile Robots”, IEEE Transactions on Fuzzy Systems, vol. 12, no 4, 2004.

  11. S.-L. El-Teleity, “Fuzzy logic control of an autonomous mobile robot”, de Methods and Models in Automation and Robotics (MMAR), 2011 16th International Conference on, Miedzyzdroje, 2011.

  12. K. D. a. S. T. G.P. Moustris, “Feedback Equivalence and Control of Mobile Robots Through a Scalable FPGA Architecture”, de Recent Advances in Mobile Robotics, InTech, 2011, pp. 401-426.

  13. M. Njah and M. Jallouli, “Wheelchair Obstacle Avoidance Based on Fuzzy Controller and Ultrasonic Sensors”, in International Conference on Computer Applications Technology (ICCAT), Sousse, 2013.

  14. G. Liu, “Fuzzy Controller for Obstacle Avoidance in Electric Wheelchair with Ultrasonic Sensors”, in International Symposium on Computer Science and Society, Kota Kinabalu, 2011.

  15. G. Pires and U. Nunes, “A Wheelchair Steered through Voice Commands and Assisted by a Reactive Fuzzy-Logic Controller. Journal of Intelligent and Robotic Systems”, Journal of Intelligent and Robotic Systems, vol. 34, no. 3, pp. 301-314, 2002

  16. H. R. Moslehi, “Design and Development of fuzzy logic Operated Smart Motorized Wheelchair”, in 24th Canadian Conference on Electrical and Computer Engineering (CCECE), Niagara Falls, Canada., 2011.

  17. I. Spacapan, J. Kocijan and T. Bajd, “Simulation of fuzzy-logic-based intelligent wheelchair control system”. Journal of Intelligent & Robotic Systems, vol. 39, no. 2, pp. 227-241, 2004.

  18. V. Tyagi, N. Gupta and P. Tyagi., “Smart wheelchair using fuzzy inference system”, in Global Humanitarian Technology Conference: South Asia Satellite (GHTCSAS), 2013.

  19. M. Ren and K. H.A., “A fuzzy logic map matching for wheelchair navigation”, GPS solutions, vol. 16, no. 3, pp. 273-282, 2012.

  20. M. Poplawski and M. Bialko., “Implementation of parallel fuzzy logic controller in FPGA circuit for guiding electric wheelchair”, in Conference on Human System Interactions, 2008.

  21. P. Marek and M. Bialko, “Implementation of fuzzy logic Controller in FPGA Circuit for Guiding Electric Wheelchair”, in 11th International Conference, ICAISC 2012, Zakopane, Poland, 2012.

  22. K. Parnell and R. Bryner, “Comparing and Contrasting FPGA and Microprocessor System Design and Development”, 21 July 2004. [Online]. Available: http://www.xilinx.com/.

  23. J. Songmin and e. al., “Multimodal intelligent wheelchair control based on fuzzy algorithm”, in International Conference on Information and Automation (ICIA), 2012.

  24. Parallax Inc., “Product documentation for the PING))) Ultrasonic Distance Sensor”, 11 9 2009. [En línea]. Available: http://www.parallax.com/sites/default/ files/downloads/28015-PING-Documentation- v1.6.pdf.




2020     |     www.medigraphic.com

Mi perfil

C?MO CITAR (Vancouver)

Rev Mex Ing Biomed. 2014;35