Cover
Vol. 12 No. 1 (2016)

Published: June 30, 2016

Pages: 54-70

Original Article

Fuzzy-Neural Petri Net Distributed Control System Using Hybrid Wireless Sensor Network and CAN Fieldbus

Ali A. Abed 1 Abduladhem A. Ali Nauman Aslam Computer Science & Digital Techniques, Northumbria Univ. nauman.aslam@northumbria.ac.uk Ali F. Marhoon 2
1Computer Engineering, Basra University,
2Electrical Engineering, Basra University,
History
  • Received: April 30, 2016
  • Available Online: June 30, 2016
DOI

https://doi.org/10.37917/ijeee.12.1.6

Abstract

The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless system involves the building of a wireless sensor network (WSN) for data acquisition and controller area network (CAN) protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID- like Fuzzy Neural Petri Net (FNPN) system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN) controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.

Keywords

References

  1. Ivan C. and Thomas F., Using distributed control system (DCS) for distillation column control in an undergraduate unit operations laboratory, 2009 American Control Conference, Hyatt Regency Riverfront, St. Louis, MO, USA, 2009.
  2. Santos M.R., de Olivera B.G.M, de Melo M.T., de Olivera A.J.B., Santos E.A.B., Freitas R.D., Serra R., Santos M.M.L., A smart antenna system for remote supervision, Journal of Applied Electromagnetics and Mechanics 45 (2014) 293-299.
  3. Xiangheng L., and Andrea G., Wireless network design for distributed control, Stanford University, USA, Supported by ABB through the Stanford Networking Research Center, 2004.
  4. Feng X., Yu-Chu Tian, Yanjun Li and Youxian Sun, Wireless sensor/actuator network design for mobile control applications, Sensors 2007, 7, 2157-2173.
  5. Almeida, L. ; Tovar, E. ; Fonseca, J.A.G. ; Vasques, F., Schedulability analysis of realtime traffic in WorldFIP networks: an 1165 – 1174.
  6. Felipe G., Gonzalez-Castano F.J., Franck, L ., Extending vehicular CAN field buses with delay tolerant networks, IEEE Trans. On 2008.
  7. Qingfeng L., et al., Evaluation of delays by foundation field bus H1 networks, Transaction on No.10, October 2009.
  8. Ajay J., et al , Wireless Sensor Network (WSN): Architectural Design issues and Challenges, (IJCSE) International Journal on Computer Science and Engineering, Vol. 02, No. 09, 2010, 3089-3094.
  9. Tik L., et al., Monitoring of an aeroponic Vol. 9, No.3, 2009.
  10. Shih-Lun C., et al , Wireless Body Sensor Network with Adaptive Low-Power Design for Biometrics and Healthcare Applications,
  11. Sinopoli B., et al., Distributed control applications within sensor networks, proceeding of the IEEE 2003, Vol.91, Issue 8, P. 1235-1246.
  12. Li S., Wireless sensor actuator network for light monitoring and control application, proceeding 3 rd Consumer Communications and Networking Conference, Vol.2, 2006, P.974-978.
  13. Andrzej P., et al., Simulation of greenhouse climate monitoring and control with wireless sensor network and event-based control, Sensors, 2009, 9 (1), 232-252.
  14. Hazem M., Wireless networked control system coordination agent, M.Sc. Thesis, University of New Brunswick, Canada, 2010.
  15. Lin C. et al., Temperature control with a neural fuzzy network, Transactions on Systems, Man., and Cyber. Part C: Applications and Reviews, Vol. 29, No.3, 1999, P.440-451.
  16. Mastorocostas P., and Theocharis J. A recurrent fuzzy-neural model for dynamic system identification, IEEE Transactions on Syst., Man., and Cyber, Part B: Cybernetics, Vol. 32, No.2, 2002.
  17. Jingwei X., Fuzzy PID control through optimization: A new method for PID control, PhD Thesis, Marquette University, Milwaukee, Wisconsin, 2006.
  18. Rong J., and Chia M., Design of dynamic Petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot, Transactions on Industrial Electronics, Vol. 56, No. 7, 2009.
  19. Shahram M-B, A committee machine based soft sensor as an alternative to multiphase flow meter for oil flow rate prediction of the wells, Journal of Intelligent & Fuzzy Systems 26 (2014) 2719-2729.
  20. Suphan G., et al., Distributed control of network devices through remote terminal, KINTEX, Gyeonggi-Do, Korea, 2005.
  21. Amir F., and Li M. Tank gauging control system monitoring based on innovative DCS, July 2010.
  22. Amir F., High speed redundant multinetwork DCS-based in innovative tank
  23. Enhanced 8-bit microcontroller with CAN controller and flash memory, Atmel Corporation, 2008.
  24. Victoria J. Hodge, Simon O’Keefe, Michael Weeks, and Anthony Moulds, Wireless Sensor Networks for Condition Monitoring Transactions on Intelligent Transportation Systems, Vol.16, No.3, PP. 1088-1106, 2015
  25. Philip L., David G., TinyOS programming, Cambridge University Press, UK, 2009.
  26. Crossbow, Xserve user's manual, Revision E, PN: 7430-0111-01, 2007.
  27. Stuart B., Real-time computer control: An