Skip to main content
SpringerLink
Log in

Traditional Control Schemes

  • Chapter
Neuro-Control and its Applications

Part of the book series: Advances in Industrial Control ((AIC))

  • 142 Accesses

Abstract

In this chapter we are going to discuss several other methods of control. These methods include PID (proportional-plus-integral-plus derivative) control, self-tuning control, self-tuning PID control, generalized predictive control, and also fuzzy logic control. One of the earliest controllers that were used for control were the PI and PID controllers. PI and PID controllers have been proven to be remarkably effective in regulating a wide range of processes. The use of PI and PID controllers does not require an exact process model and hence, they are effective on industrial processes whose models are considerably difficult to derive. The PI and PID controllers are based on classical control theory and much easier to understand. Field engineers and process operators are able to relate the parameter settings and control system actions.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ziegler, J.G. and N.B. Nichols, “Optimum settings for automatic controllers”, Trans. of ASME, Vol. 15, pp. 827–834, 1942.

    Google Scholar 

  2. Takahashi, Y. and C.S. Chan, “Parametereinstellung bei linearen DDC-algorithmen”, Regelungstechnik und Prozess-Datenverarbeitung, Vol. 19, pp. 237–244, 1971.

    MATH  Google Scholar 

  3. Nishikawa, Y., N. Sanomiya, T. Ohta, and H. Tanaka, “A method for auto-tuning of PID control parameters”, Automatica, Vol. 20, pp. 321–332, 1984.

    Article  MATH  Google Scholar 

  4. Aström, K.J., and T. Hagglung, “Automatic tuning of simple regulators with specifications on phase and amplitude margins”, Automatica, Vol. 20, pp. 645–651, 1984.

    Article  MATH  Google Scholar 

  5. Kalman, R.E., “Design of self-optimizing control system”, Trans. of ASME, Vol. 80, pp. 468–478, 1958.

    Google Scholar 

  6. Âström, K.J. and B. Wittenmark, “On self-tuning regulators”, Automatica, Vol. 9, pp. 185–199, 1973.

    Article  MATH  Google Scholar 

  7. Wittenmark, B. “Self-tuning PID controllers based on pole placement”, Lund Inst. of Technical Report, TFRT-7179, 1979.

    Google Scholar 

  8. Savelli, J.C., K. Warwick, and J.H. Wescott, “Implementation of an adaptive PID self-tuning controller”, Proc. of Melcon, Athens, Vol. 2, 1983.

    Google Scholar 

  9. Gawthrop, P.J., “Self-tuning PID controllers: Algorithm and implementation”, IEEE Trans. on Automatic Control, Vol. 31, pp. 201–209, 1986.

    Article  MATH  Google Scholar 

  10. Banyasz, C. and L. Keviczky, “Direct methods for self-tuning PID regulators”, Proc. of the 6th IFAC Symposium on Identification & System Parameter Estimation (Bekey, G.A. and G.N. Saridis, Eds.), Washington, D.C., pp. 1395–1400, 1982.

    Google Scholar 

  11. Ortega, R. and R. Kelly,“PID self-tuners: Some theoretical and practical aspects”, IEEE Trans. on Industrial Electronics, Vol. IE-31, pp. 312–332, 1984.

    Google Scholar 

  12. Radke, F. and R. Isermann,“A parameter-adaptive PID-controller with stepwise parameter optimization”, Automatica, Vol. 23, pp. 449–457, 1987.

    Article  MATH  Google Scholar 

  13. Jones, A.H. and B. Porter, “Design of adaptive digital set-point tracking PID controllers incorporting recursive step-response matrix identifiers for multivariable plants”, IEEE Trans. on Automatic Control, Vol. AC-32, pp. 459–463, 1987.

    Google Scholar 

  14. Proudfoot, C.G., P.J. Gawthrop, and O.L.R. Jacobs, “Self-tuning PI control of a pH neutralisation process”, Proc. of IEE, Pt-D, Vol. 130, pp. 267–272, 1983.

    Article  MATH  Google Scholar 

  15. Yamamoto, T., S. Omatu, and T. Hotta, “A construction of self-tuning PID control algorithm and its application”, Proc. of SICE, Japan, pp. 1143–1146, 1983.

    Google Scholar 

  16. Rad, A.B. and P.J. Gawthrop, “Explicit PID self-tuning control for systems with unknown time-delay”, Proc. of IFAC Int. Symposium on Intelligent Tuning and Adaptive Control, Singapore, 1991.

    Google Scholar 

  17. Cameron, F. and D.E. Seborg, “A Self-tuning controller with a PID structure”, Int. Journal of Control, Vol. 30, pp. 401–417, 1983.

    Article  Google Scholar 

  18. Clarke, D.W. and P.J. Gawthrop,“Self-tuning controller”, Proc. of IEE, Pt-D, Vol. 122, pp. 929–934, 1975.

    Google Scholar 

  19. Clarke, D.W. and P.J. Gawthrop, “Self-tuning control”, Proc. of IEE, Pt-D, Vol. 126, pp. 633–640, 1979.

    Google Scholar 

  20. Johansson, R., “Multivariable adaptive control”, Dept. of Automatic Control, Lund Inst. of Tech., Report LUFTD2/TRFT-1024/1–207, 1983.

    Google Scholar 

  21. Richalet, J.A., J.L.T. Rault, and J. Papon,“Model predictive heuristic control: applications to industrial processes”, Automatica, Vol. 14, pp. 413–428, 1976.

    Article  Google Scholar 

  22. Cutler, C.R. and B.C. Ramaker,“Dynamic matrix control - A computer control algorithm”, JACC, Paper WP5-B, San Francisco, 1986.

    Google Scholar 

  23. De Keyser, R.M.C. and A.V. Cauwenberghe,“A self-tuning multi step predictor application”, Proc. 6th IFAC Symposium on Identification and System Parameter Estimation, Washington, DC, pp. 1558–1563, 1979.

    Google Scholar 

  24. Ydstie, B., “Extended horizon adaptive control”, Proc. of 9th IFAC World Congress, Budapest, Hungary, 1984.

    Google Scholar 

  25. Peterka, V., “Predictor-based self-tuning control”, Automatica, Vol. 20, pp. 39–50, 1984.

    Article  MATH  Google Scholar 

  26. Clarke, D.W., C. Mohtadi, and P.S. Tuffs, “Generalized predictive control - Part I: The basic algorithm”, Automatica, Vol. 23, pp. 137148, 1987.

    Google Scholar 

  27. Clarke, D.W., C. Mohtadi, and P.S. Tuffs, “Generalized predictive control–Part II: Extensions and interpretations”, Automatica, Vol. 23, pp. 149–160, 1987b.

    Article  MATH  Google Scholar 

  28. Clarke, D.W. and C. Mohtadi, “Properties of generalized predictive control”, Automatica, Vol. 25, pp. 859–875, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  29. De Keyser, R.M.C., PH.G.A. Van De Velde, and F.A.G. Dumartier, “A comparative study of self-adaptive long range predictive control methods”, Automatica, Vol. 24, pp. 49–163, 1988.

    Google Scholar 

  30. Zadeh, L.A., “Fuzzy sets”, Information and Control, Vol. 8, pp. 338–352, 1965a.

    Article  MathSciNet  MATH  Google Scholar 

  31. Söderström, T. and P. Stöica, “System Identification ”, Prentice-Hall, London, 1989.

    Google Scholar 

  32. Aström, K.J. and B. Wittenmark, “Adaptive Control. ”, Addison- Wesley, New York, 1989.

    Google Scholar 

  33. Bierman, G.J., “Factorization Methods for Discrete Sequential Estimation ”, Academic Press, New York, 1977.

    Google Scholar 

  34. Gawthrop, P.J., “Some interpretations of the self-tuning controller”, Proc. of IEE, Pt-D, Vol. 124, pp. 889–894, 1977.

    Google Scholar 

  35. Clarke, D.W., “Introduction to self-tuning controllers”, In Harris and Billings (Eds.), Peter Pegrinus, (UK), pp. 36–71, 1981.

    Google Scholar 

  36. Clarke, D.W., A.J.F. Hodgson, and P.S. Tuffs, “Offset problem and K-incremental predictors in self-tuning control”, Proc. of IEE, Pt-D, Vol. 130, pp. 217–225, 1983.

    MATH  Google Scholar 

  37. Tuff, P.S. and D.W. Clarke,“Self tuning control of offset-A unified approach”. Proc. of IEE, Pt-D, Vol. 132, pp. 100–110, 1985.

    Google Scholar 

  38. Clarke, D.W., “Self-tuning control of non minimum-phase systems” Automatica, Vol. 20, pp. 501–517, 1984.

    Article  MATH  Google Scholar 

  39. Clarke, D.W., P.P. Kanjilal, and C. Mohtadi, “A generalised LQG approach to self-tuning control, part 1: Aspects of design” Int. Journal of Control, Vol. 45, pp. 1509–1523, 1985.

    Article  Google Scholar 

  40. Kailath, T., “Linear Systems”, Prentice-Hall, Englewood Cliffs, New Jersey, 1980.

    Google Scholar 

  41. Gantmacher, F.R., “The Theory of Matrices”, Chelsea Publishing Company, New York, 1977.

    Google Scholar 

  42. Maciejowski, J.M., “Multivariable Feedback Design”, Addison- Wesley, London, 1989.

    Google Scholar 

  43. Isermann, R., “Digital Control Systems: Vol. I and II”, Springer- Verlag, Berlin, 1989.

    Google Scholar 

  44. Kokotovic, P.V., R.E.Jr. O’Malley, and P. Sannuti, “Singular perturbations and order reduction in control theory–An overview”, Automatica, Vol 12, pp. 123–132, 1976.

    Article  MathSciNet  MATH  Google Scholar 

  45. Mishra, R.N. and D.A. Wilson, “A New Algorithm for Optimal Reduction of Multivariable Systems”, Int. Journal of Control, Vol. 31, pp. 443–466, 1980.

    Article  MathSciNet  MATH  Google Scholar 

  46. Borrison, U., “Self tuning for a class of multivariable systems”, Automatica, Vol. 15, pp. 209–215, 1979.

    Article  Google Scholar 

  47. Aström, K.J., U. Borrison, L. Ljung, and B. Wittenmark, “Theory and applications of self-tuning regulators”, Automatica, Vol. 13, pp 457–476, 1977.

    Article  MATH  Google Scholar 

  48. Bitmead, R.R., M. Gevers, and V. Wertz, “Adaptive Optimal Control”, Prentice-Hall, Australia Pty Ltd., 1990.

    Google Scholar 

  49. Zadeh, L.A., “Fuzzy sets and systems”, Proc. of Symp. on System Theory, Polytechnic Institute of Brooklyn, pp. 29–37, 1965.

    Google Scholar 

  50. Zadeh, L.A., “Outline to a new approach to the analysis complex systems and decision processes”, IEEE Trans. on Systems, Man, and Cybernetics. Vol. 3, pp. 28–44, 1973.

    Article  MathSciNet  MATH  Google Scholar 

  51. Assilian, S. and E.H. Mamdani, “An experiment in linguistic synthesis with a fuzzy logic controller”, Int. Journal of Man-Machine Studies, Vol. 7, pp. 1–13, 1974.

    Google Scholar 

  52. Mamdani, E.H., “Applications of fuzzy algorithms for a simple dynamic plant”, Proc. of IEE., Vol. 121, pp. 1585–1588, 1974.

    Google Scholar 

  53. Mamdani, E.H, “Advances in the linguistic synthesis of fuzzy controllers”, IEEE Trans. on Computer, Vol. C-26, pp. 1182–1191, 1977.

    Google Scholar 

  54. King, P.J. and E.H. Mamdani, “The application of fuzzy control systems to industrial processes”, Automatica, Vol. 13, pp. 235–242, 1977.

    Article  Google Scholar 

  55. Mamdani, E.H., “Application of fuzzy set theory to control systems: a survey”, Fuzzy Automata and Decision Proceses (Gupta, M.M., G.N. Sardis, and B.R. Gaines, Eds. ), North-Holland, 1977.

    Google Scholar 

  56. Tong, R. M. “A control engineering review of fuzzy systems”, Automatica, Vol. 13, pp. 559–569, 1977.

    Article  Google Scholar 

  57. Wiener, N., “Cybernetics: on control and communication in the animal and the machine”, MIT Press, Cambridge, MA, 1948.

    Google Scholar 

  58. Togai, M. and H. Watanabe, “Expert system on a chip: an engine for real-time approximate reasoning”, IEEE Expert, Vol. 1, pp. 55–62, 1986.

    Article  Google Scholar 

  59. Yamakawa, T., “Fuzzy microprocessors–rule chip and defuzzification chip” Proc. of Int. Workshop on Fuzzy Systems Applications, Iizuka-88, Japan, pp. 51–52, 1988.

    Google Scholar 

  60. Kuo, Y.H., C.I. Kao, and J.J. Chen, “A fuzzy neural network model and its hardware implementation”, IEEE Trans. on Fuzzy Systems, Vol. 1, pp. 171–183, 1993.

    Article  Google Scholar 

  61. Dubois, D. and H. Prade, “Fuzzy Sets and Systems: Theory and Applications”, Academic Press, New York, 1980.

    Google Scholar 

  62. Klir, G. and T. Folger, “Fuzzy Sets: Uncertainty and Information ”, Prentice-Hall, Engelwood Cliffs, New Jersey, 1988.

    Google Scholar 

  63. Zimmermann, H., “Fuzzy Set Theory and its Applications”, 2nd. Ed., Kluwer, Boston, 1990.

    Google Scholar 

  64. Kosko, B., “Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence ”, Prentice-Hall, Englewood Cliffs, New Jersey, 1991.

    Google Scholar 

  65. Pedrycz, W., “Fuzzy Control and Fuzzy Systems”, 2nd Ed., Wiley, Somerset, 1993.

    Google Scholar 

  66. Pacini, P.J. and B. Kosko “Comparison of fuzzy and Kalman-filter target-tracking control systems”, in Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall, Englewood Cliffs, New Jersey, pp. 379–406, 1991.

    Google Scholar 

  67. Kong, S.G. and B. Kosko, “Adaptive fuzzy systems for backing up a truck-and-trailer”, IEEE Trans. on Neural Networks, Vol. 3, pp. 211–223, 1992.

    Article  Google Scholar 

  68. Langari, R. and H.R. Berenji “Fuzzy logic in control engineering”, in Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches. ( White, D.A. and D.A. Sofge, Eds.), Van Nostrand Reinhold, New York, 1992.

    Google Scholar 

  69. Procyk, T.J. and E.H. Mamdani, “A linguistic self-organizing process controller”, Automatica, Vol. 15, pp. 15–30, 1979.

    Article  MATH  Google Scholar 

  70. Daley, S. and K.F. Gill, “A design study of a self-organizing fuzzy logic controller”, Proc. of I. Mech. E, Vol. 200, (CI), pp. 59–69, 1986.

    Article  Google Scholar 

  71. Linkens, D.A. and M.F. Abbod,“Self-organizing fuzzy logic control and the selection of its scaling factors”, Trans. on Instrumentation, Measurement and Control, Vol. 14, pp. 114–125, 1992.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Systems Sciences, College of Engineering, Osaka Prefecture University, Sakai, Osaka 593, Japan

    Sigeru Omatu PhD (Professor)

  2. Business and Advanced Technology Centre, Universiti Teknologi Malaysia, Jalan Semarak, 54100, Kuala Lumpur, Malaysia

    Marzuki Khalid PhD (Associate Professor) & Rubiyah Yusof PhD (Lecturer)

Authors
  1. Sigeru Omatu PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marzuki Khalid PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rubiyah Yusof PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag London Limited

About this chapter

Cite this chapter

Omatu, S., Khalid, M., Yusof, R. (1996). Traditional Control Schemes. In: Neuro-Control and its Applications. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-3058-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-3058-1_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-3060-4

  • Online ISBN: 978-1-4471-3058-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation