INTELLIGENT FAULT-TOLERANT CONTROL OF LINEAR DRIVES USING SOFT COMPUTING

Sunan Huang, Mingbo Xiao, and Kok K. Tan

References

  1. [1] M. Cong, X. Kong, Y. Du, and J. Liu, Wafer pre-alignersystem based on vision information processing, InformationTechnology Journal, 6(8), 2007, 1245–1251.
  2. [2] M. Masrie, A. Ahmad, and R Adnan, A novel integrated sensor system for indoor air quality measurement, Proc. 5th International Colloquium on Signal Processing and Its Applications, Kuala Lumpur, Malaysia, 2009, 406–408.
  3. [3] R. Isermann, Process fault detection based on modeling and estimation methods: A survey, Automatica, 20(4), 1984,387–404.
  4. [4] J. Wu and L. Wang, Motion control of the 2-DOF parallelmanipulator of a hybrid machine tool, Robotica, 28(6), 2010, 861–868.
  5. [5] J. Wu, J. Wang, and Z. You, An overview of dynamic parameter identification of robots, Robotics and Computer Integrated Manufacturing, 26(5), 2010, 414–419.
  6. [6] O. Moseler and R. Isermann, Application of model-based fault detection to a brushless DC motor, IEEE Transactions on Industrial Electronics, 47(5), 2000, 1015–1020.
  7. [7] S.C. Lee, H.J. Lee, and Y.H. Joo, H−/H∞ sensor fault detection and isolation in linear time-invariant systems, International Journal of Control, Automation, and Systems, 10(4), 2012, 841–848.
  8. [8] X.J. Li and G.H. Yang, Adaptive fault detection and isolation approach for actuator stuck faults in closed-loop systems, International Journal of Control, Automation, and Systems, 10(4), 2012, 830–834.
  9. [9] D.P. Lozze, J.L. Weiss, J.S. Eterno, and N.M. Barrett, An automatic redesign approach for restructruable control systems, IEEE Control Systems Magazine, 5(2), 1985, 16–20.
  10. [10] J. Jiang, Design of reconfigurable control system using Eigen structure assignment, International Journal of Control, 59(2), 1994, 395–410.
  11. [11] X. Meng and G. Yang, Fault tolerant H∞ control for a class of polynomial non-linear discrete-time systems, International Journal of Control, Automation, and Systems, 10(4), 2012, 849–854.
  12. [12] J. Wu, J. Wang, L. Wang, and T. Li, Dynamics and control of a planar 3-DOF parallel manipulator with actuation redundancy, Mechanism and Machine Theory, 44(4), 2009, 835–849.
  13. [13] L.A. Zadeh, Roles of soft computing and fuzzy logic in the conception, design and deployment of information/intelligent systems, in O. Kaynak, L.A. Zadeh, B. Turksen, and I.J. Rudas (eds.), Computational intelligence: Soft computing and fuzzy-neuro integration with applications (Berlin: Springer-Verlag, 1998), 1–9.
  14. [14] Y. Diao and K.M. Passino, Stable fault-tolerant adaptive fuzzy/neural control for a turbine engine, IEEE Transactions on Control Systems Technology, 9(3), 2001, 494–509.
  15. [15] A. Abraham, Analysis of hybrid soft and hard computingtechniques for Forex monitoring systems, Proc. 2002 IEEE Int. Conf. on Fuzzy Systems, Honolulu, HI, USA, 2002, 1616–1622.
  16. [16] X.H. Yao, J.Z. Fu, and Z.C. Chen, Intelligent fault diagnosis using rough set method and evidence theory for NC machine tools, International Journal of Computer Integrated Manufacturing, 22(5), 2009, 472–482.
  17. [17] M. Weng, Intelligent diagnosis techniques in automotive engines fault based on fuzzy support vector machine, Proc. 2010 Asia-Pacific Conf. on Wearable Computing Systems, Shenzhen, China, 2010, 40–43.
  18. [18] K.V. Kumar, S.S. Kumar, and B. Praveena, Soft computing based fault diagnosis, International Journal of Computer and Electrical Engineering, 2(4), 2010, 1793–8163.
  19. [19] S. Suzuki and A. Yanagida, Research and development for fault tolerant flight control system – Part 1, Proc. 26th International Congress of the Aeronautical Sciences, Alaska, USA, 2008, 1–8.
  20. [20] K.K. Tan and S.N. Huang, Fault monitoring and fault-tolerant control design in linear drive systems, Proc. Int. Conf. on Mechanical, Electronics and Mechatronics Engineering 2012, Bangkok, Thailand, 2012, 26–30.
  21. [21] A. Chiou and X.H. Yu, Remote sensing in decision support systems: Using fuzzy post adjustment in localisation of weed prediction, Proc. 3rd Int. Conf. on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, 2007, 533–538.
  22. [22] Z. Man, H.R. Wu, S. Liu, and X.H. Yu, A new adaptivebackpropagation algorithm based on Lyapunov stability theory for neural networks, IEEE Transactions on Neural Networks, 17(6), 2006, 1580–1591.
  23. [23] A. Gomperts, A. Ukil, and F. Zurfluh, Development andimplementation of parameterized FPGA-based general purposeneural networks for online applications, IEEE Transactions on Industrial Informatics, 7(1), 2011, 78–89.
  24. [24] D. Nguyen and B. Widrow, Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights, Proc. Int. Joint Conf. on Neural Networks (1990), San Diego, CA, USA, 1990, 21–26.

Important Links:

Go Back