Application of a self tuner using fuzzy control technique

ABSTRACT

A self-tuning expert fuzzy controller has been developed and applied in real time to a process control problem. As in other expert systems, the knowledge base consists of rules describing the control law in terms of the process error and the resulting control action. Conditions and conclusions of each rule are fuzzy variables which are described through their membership curves. The inference engine used is the backward chaining process of the Prolog language. To implement the self-tuning property, the membership curve of the controller output has been changed according to an error based performance index. A control supervisor makes this tuning decision as a function of past or predicted future set-point errors of the control system. To verify the viability of this fuzzy controller, it has been applied to control the speed of a DC motor operating under different loading conditions. The paper also discusses the stability problems associated with this control scheme.

REFERENCES

• 1: Porter, B., Jones, A.H., and McKeowan, C.B.' "Real Time Expert Tuners for PI Controllers", lEE Proc., Vol. 134, Pt. D. No. 4, July 1987.
• 2: Hirta, K., Arai, Y., Pedrycz, W.' "Robot Control Based on Membership and Vagueness", Approximate Reasoning in Expert Systems, M.M. Gupta, A. Kandel, W. Bandler, J.B. Kiszka. (editors) Elsevier Science Publications (North-Holland), 1985.
• 3: Zadah, L.A.' "Making Computers Think Like People", IEEE Spectrum, pp. 26-32, August 1984.
• 4: Rutherford, D., Carter, G.A. "A Heuristic Adaptive Controller for a Sinter Plant", Proc. 2nd iFAC Symp. Automation in Mining, Mineral and Metal Processing, Johannesburg, 1976.
• 5: Lea, N.R., "Application of Fuzzy Sets to Data Monitoring and Process Control", ISA Proceedings. Paper #88-1610, 1988.
• 6: Tong, R.M., "A Control Engineering Review of Fuzzy Systems', Automatica, 1977, Vol. 16, pp. 559- 569.
• 7: Ray, R.S., Majumder, D.D., "Application of Circle Criteria for Stability Analysis of Linear SISD and MIMD Systems~, IEEE Trans. Vol. SMC-14, March/April 1984.
• 8: Bare, W.H., Mulho!land, R.J., Sofer, S.S.' "Desigh of a Self Tuning Expert Controller for a Gasoline Refinery Catalytic Reformer", 1988 American Control Conference Paper WA8 pp. 247-253.
• 9: Daley, S., Gill, K.F.' A Design Study of Self Organizing Fuzzy Logic Controller", Proc Instn. Mech. Engrs. Part C 1986,200 (C1), 59-69.
• 10: Daley, S., Gill, K.F.' "Attitude Control of a Spacecraft Using an Extended Self Organizing Fuzzy Logic Controller~, Proc. inst. Mech. Engrgs. Vol. 201, No. C 1, 1987.
• 11: Kickert, W.J.M., Van Lauta Lemke H.R.' "Appliction of a Fuzzy Controller in a Warm Water Plant", Automatica, Vol. 12, pp. 301-308, 1976.
• 12: Zadeh, K.L.' "The Concept of Linguistic Variable and Its Application to Approximate Reasoning", Information Sciences, Vol. 8, pp. 199-249., Vol. 8 pp. 301-375., Vol. 9 43-80, 1975.
• 13: Peter A. Cook, Nonlinear dynamical systems, Prentice Hall International (UK) Ltd., Hertfordshire, UK, 1986
• 14: Sripada ,N.R, Fisher, D.G.,Mc~rris,A.J "Fuzzy control" Proc. of IEEE Vol. 134 Pt.D No.4 1987.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving

          Nouns: Prolog

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Deduction (e.g., natural, rule-based); Uncertainty, "fuzzy," and probabilistic reasoning

  J. Computer Applications
  J.7 COMPUTERS IN OTHER SYSTEMS
      Subjects: Process control

General Terms:
Experimentation, Languages, Performance, Theory