Advertisement

Neuro-Control Techniques

  • Sigeru Omatu
  • Marzuki Khalid
  • Rubiyah Yusof
Chapter
  • 107 Downloads
Part of the Advances in Industrial Control book series (AIC)

Abstract

In many real-world applications, there are many nonlinearities, unmodeled dynamics, unmeasurable noise, multiloop, etc., which pose problems to engineers in trying to implement control strategies. During the past two decades development of new control strategies has been largely based on modern and classical control theories. Modern control theory such as adaptive and optimal control techniques and classical control theory have been based mainly on linearization of systems [1]–[5].

Keywords

Neural Network Hide Layer Plant Output Cerebellar Model Articulation Controller Multilayered Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Åström, K.J. and B. Witternmark, “Adaptive Control,” Addison-Wesley, New York, 1989.Google Scholar
  2. [2]
    Box, G.E. and G.H. Jenkins, “Time Series Analysis: Forecasting and Control”, Holden-Day, San Francisco, 1976.zbMATHGoogle Scholar
  3. [3]
    Kosko, B., “Neural Networks for Signal Processing”, Prentice-Hall, Englewood Cliffs, New Jersey, 1992Google Scholar
  4. [4]
    Nozaka, Y., “Trend of new control theory application in industrial process control (A survey)”, Proc. of 12th IFAC World Congress, Sydney, Vol. VI, pp. 51–56, 1993.Google Scholar
  5. [5]
    Åström, K.J., “Towards intelligent control”, IEEE Control Systems Magazine, Vol. 9, pp. 60–69, 1989.CrossRefGoogle Scholar
  6. [6]
    White, D.A. and D.A. Sofge, “Handbook of Intelligent Control”, Van Nostrand Reinhold, New York, 1992.Google Scholar
  7. [7]
    Wiener, N., “Cybernetics or Control and Communication in the Animal and the Machine”, MIT Press, Cambridge, Massachusetts, 1948.Google Scholar
  8. [8]
    Cybenko, G., “Approximation by superpositions of a sigmoidal function”, Mathematics Control, Signal & System, Vol. 2, pp. 303–314, 1989.MathSciNetzbMATHCrossRefGoogle Scholar
  9. [9]
    Funahashi, K.I., “On the approximate realization of continuous mappings by neural networks”, Neural Networks, Vol. 2, pp. 183–192, 1989.CrossRefGoogle Scholar
  10. [10]
    Hornik, K., M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators”, Neural Networks, Vol. 2, pp. 359–366, 1989.CrossRefGoogle Scholar
  11. [11]
    Saerens, M. and A. Soquet, “A neural controller based on backpropagation algorithm”, Proc. of First IEE Int. Conf on Artificial Neural Networks, London, pp. 211–215, 1989.Google Scholar
  12. [12]
    Ogata, K., “Discre-time Control Systems”, Prentice-Hall, Englewood Cliffs, New Jersey, 1987.Google Scholar
  13. [13]
    liguni, Y., H. Sakai, and H. Tokumaru, A non-linear regulator design in the presence of system uncertainties using multilayered neural networks,IEEE Trans. on Neural Networks, Vol. 2, pp. 410–417, 1991.Google Scholar
  14. [14]
    Narendra, K.S. and K. Parthasarathy, “Identification and control of dynamical systems using neural networks”, IEEE Trans. on Neural Networks, Vol. 1, pp. 4–27, 1990.CrossRefGoogle Scholar
  15. [15]
    Neuralogix NLX420 Datasheet, American Neuralogix Inc., 1992.Google Scholar
  16. [16]
    Levin, E., R. Gewirtzman, and G.F. Inbar, “Neural network architecture for adaptive system modelling and control”, Proc. of Int. Neuro-Control Techniques 167 Joint Conf. on Neural Networks, Washington D.C., Vol. II, pp. 311–316, 1989.Google Scholar
  17. [17]
    Kosko, B., “Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence”, Prentice-Hall, Englewood Cliffs, New Jersey, 1991.Google Scholar
  18. [18]
    Miller, W.T., R.S. Sutton, and P.J. Werbos, “Neural Networks for Control”, MIT Press, Cambridge, MA, 1990.Google Scholar
  19. [19]
    Barto, A.G., “Connectionist learning for control”, in Neural Networks for Control, MIT Press, Cambridge, Massachusetts, pp. 5–58, 1990.Google Scholar
  20. [20]
    Werbos, P.J., “Overview of designs and capabilities”, in Neural Networks for Control, MIT Press, Cambridge, MA, pp. 59–65, 1990.Google Scholar
  21. [21]
    Hunt, K.J., D. Sbarbaro, R. Zbikowski, and P.J. Gawthrop, “Neural networks for control systems–a survey”, Automatica, Vol. 28, pp. 1083–1112, 1992.MathSciNetzbMATHCrossRefGoogle Scholar
  22. [22]
    Widrow, B. and F.W. Smith, “Pattern-recognizing control systems”, Proc. of Computer and Information Sciences, Washington D.C., Spartan, Washington, 1964.Google Scholar
  23. [23]
    Albus, J.S., “A new approach in manipulator control: the cerebellar model articulation controller (CMAC)”, Journal of Dynamic Systems, Measurement and Control, pp. 220–227, 1975.Google Scholar
  24. [24]
    Albus, J.S., “Data storage in the cerebellar model articulation controller”, Journal of Dynamic Systems, Measurement and Control, pp. 228–233, 1975.Google Scholar
  25. [25]
    Miller III, W.T., F.H. Glanz, and L.G. Kraft, “CMAC: An associative neural network alternative to backpropagation”, Proc. of IEEE, Vol. 78, pp. 1561–1567, 1990.CrossRefGoogle Scholar
  26. [26]
    Barto, A.G., R.S. Sutton, and C.W. Anderson, “Neuronlike adaptive elements that can solve difficult learning control problems”, IEEE Trans. on Systems, Man, and Cybernetics, Vol. SMC-13, pp. 834–846, 1983.Google Scholar
  27. [27]
    Gaines, B.R. and J.H. Andreae, “A learning machine in the context of the general control problem”, Proc. of 3rd IFAC Congress, IME, London, pp. 14B. 1–14B. 8, 1966.Google Scholar
  28. [28]
    Witten, I.H., “An adaptive optimal controller for discrete-time Markov environments”, Information and Control, Vol. 34, pp. 286–95, 1977.MathSciNetzbMATHCrossRefGoogle Scholar
  29. [29]
    Werbos, P.J., “Advanced forecasting methods for global crisis warning and models of intelligence”, General System Yearbook, Vol. 22, pp. 25–38, 1977.Google Scholar
  30. [30]
    Holland, J.H., “Escaping brittleness: The possiblity of general-purpose learning algorithm algorithms applied to rule-based systems”, R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, Eds., Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan Kaufmann, San Mateo, CA, 1986.Google Scholar
  31. [31]
    Anderson, C.W., “Learning and problem solving with multilayer connectionist systems”, Ph.D. Thesis, University of Mass., Amherst, 1986.Google Scholar
  32. [32]
    Anderson, C.W., “Strategy learning with multilayer connectionist representations”, Tech. Rep. TR87–509. 3, GTE Laboratories Inc., Waltham, MA, 1987.Google Scholar
  33. [33]
    Widrow, B. and S. D. Stearns,“Adaptive Signal Processing”, Prentice-Hall, Englewood Cliffs, New Jersey, 1985.zbMATHGoogle Scholar
  34. [34]
    Kuperstein, M., “Neural model of adaptive hand-eye co-ordination for single postures”, Science, Vol. 239, pp. 1308–1311, 1988.CrossRefGoogle Scholar
  35. [35]
    Kuperstein, M., and J. Rubinstein, “Implementation of an adaptive neural controller for sensory-motor co-ordination”, IEEE Control Systems Magazine, Vol. 9, pp. 25–30, 1989.CrossRefGoogle Scholar
  36. [36]
    Srinivasan, V., A.G. Barto, and B.E. Ydstie,“Pattern recognition and feedback via parallel distributed processing”, Annual Meeting of the American Institute of Chemical Engineers, Washington, D.C., 1988.Google Scholar
  37. [37]
    Sanner, R.M. and D.L. Akin, “Neuromorphic pitch attitude regulation of an underwater telerobot”, IEEE Control Systems Magazine, Vol. 10, pp. 62–68, 1990.CrossRefGoogle Scholar
  38. [38]
    Psaltis, D., A. Sideris, and A. Yamamura, “A Multilayered neural network controller”, IEEE Control Systems Magazine, Vol. 8, pp. 1721, 1988.CrossRefGoogle Scholar
  39. [39]
    Saerens, M. and A. Soquet, “A neural controller based on backpropagation algorithm”, Proc. of First IEE Int. Conf on Artificial Neural Networks, London, pp. 211–215, 1989.Google Scholar
  40. [40]
    Kawato, M., K. Furukawa, and R. Suzuki, “A hierarchical neural network model for control and learning of voluntary movement”, Biological Cybernetics, Vol. 57, pp. 169–185, 1987.zbMATHCrossRefGoogle Scholar
  41. [41]
    Kawato, M., Y. Uno, M. Isobe, and R. Suzuki, “Hierarchical neural network model for voluntary movement with application to robotics”, IEEE Control Systems Magazine, Vol. 8, pp. 8–16, 1988.CrossRefGoogle Scholar
  42. [42]
    Jordan, M.I., “Generic constraints on underspecified target trajectories”, Proc. of Int. Joint Conf. on Neural Networks (IJCNN)’ 89, Washington, Vol. I, pp. 217–225, 1989.Google Scholar
  43. [43]
    Jordan, M.I. and D.E. Rumelhart, “Forward models: Supervised learning with a distal teacher”, Cognitive Science, Vol. 16, pp. 313355, 1990.Google Scholar
  44. [44]
    Narendra, K.S. and K. Parthasarathy, “Identification and control of dynamical systems using neural networks”, IEEE Trans. on Neural Networks, Vol. 1, pp. 4–27, 1990.CrossRefGoogle Scholar
  45. [45]
    Nguyen, D.H. and B. Widrow, “Neural networks for self-learning control systems”, IEEE Control Systems Magazine, Vol. 10, pp. 18–23, 1990.CrossRefGoogle Scholar
  46. [46]
    Chen, S., S.A. Billings, and P.M. Grant, “Nonlinear system identification using neural networks”, Int. Journal of Control, Vol. 51, pp. 1215–1228, 1990.MathSciNetCrossRefGoogle Scholar
  47. [47]
    Chen, S., S.A. Billings, C.F. Cowan, and P.M. Grant, “Practical identification of NARMAX models using radial basis function”, Int. Journal of Control, Vol. 52, pp. 1327–1350, 1990.MathSciNetzbMATHCrossRefGoogle Scholar
  48. [48]
    Bhat, N.V., Jr., P.A. Minderman, T. McAvoy, and N.S., Wang, “Modeling chemical process systems via neural computation”, IEEE Control Systems Magazine, Vol. 10, pp. 24–30, 1990.CrossRefGoogle Scholar
  49. [49]
    Chu, S.R., R. Shoureshi, and M. Tenorio, “Neural networks for system identification”, IEEE Control Systems Magazine, Vol. 10, pp. 31–35, 1990.CrossRefGoogle Scholar
  50. [50]
    Billings, S.A., H.B. Jamaludin, and S. Chen, “Properties of neural networks with applications to modeling of nonlinear dynamical systems”, Int. Journal of Control, Vol. 55, pp. 193–224, 1992.zbMATHCrossRefGoogle Scholar
  51. [51]
    Sjoberg, J. and L. Ljung, “Overtraining, regularization, and searching for minimum in neural networks”, Revised Version Report Lith-ISY-I1297, Dept. of Elec. Eng., Linkoping University, Sweden, 1992.Google Scholar
  52. [52]
    Tanomaru, J. and S. Omatu, “Process control by on-line trained neural controllers”, IEEE Trans. on Industrial Electronics, Vol. 39, pp. 511521, 1992.Google Scholar
  53. [53]
    Hopfield, J.J., “Neural networks and physical systems with emergent computational abilities”, Proc. of the National Academy of Sciences, Vol. 79, pp. 2554–2558, 1982.MathSciNetCrossRefGoogle Scholar
  54. [54]
    Lippmann, R., “An introduction to computing with neural nets” IEEE ASSP Magazine, Vol. 4, pp. 4–22, 1987.CrossRefGoogle Scholar
  55. [55]
    Gomi, H. and M. Kawato, “Neural network control for a closed loop system using feedback error learning”, Neural Networks, Vol. 6, pp. 933–946, 1993.CrossRefGoogle Scholar
  56. [56]
    Nagata, S., M. Sekiguchi, and K. Asakawa, “Mobile robot control by a structured hierarchical neural network”, IEEE Control Systems Magazine, Vol. 10, pp. 69–76, 1990.CrossRefGoogle Scholar
  57. [57]
    Kinjo, H., S. Omatu, T. Yamamoto, and S. Tamaki, “Suboptimal control for a non-linear system using neural networks”, Proc. of 1st Asian Control Conference, pp. 551–554, Tokyo, 1994.Google Scholar
  58. [58]
    Saiful A. and S. Omatu, “Neuromorphic self-tuning PID controller”, Proc. of 1993 IEEE ICNN, San Francisco, pp. 552–557, 1993.Google Scholar

Copyright information

© Springer-Verlag London Limited 1996

Authors and Affiliations

  • Sigeru Omatu
    • 1
  • Marzuki Khalid
    • 2
  • Rubiyah Yusof
    • 2
  1. 1.Department of Computer and Systems Sciences, College of EngineeringOsaka Prefecture UniversitySakai, Osaka 593Japan
  2. 2.Business and Advanced Technology CentreUniversiti Teknologi MalaysiaKuala LumpurMalaysia

Personalised recommendations