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NEURO - FUZZY CONTROL A CASE STUDY

NEURO - FUZZY CONTROL A CASE STUDY. DR. T. THYAGARAJAN PROFESSOR & HEAD DEPT. OF INST. ENGG. ANNA UNIVERSITY, MIT CAMPUS thyagu_vel@yahoo.co.in. CONTENTS. What is FLC? Where FLC? Components of FLC Applications Advantages Case study Disadvantages NFC design

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NEURO - FUZZY CONTROL A CASE STUDY

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  1. NEURO - FUZZY CONTROL A CASE STUDY DR. T. THYAGARAJAN PROFESSOR & HEAD DEPT. OF INST. ENGG. ANNA UNIVERSITY, MIT CAMPUS thyagu_vel@yahoo.co.in

  2. CONTENTS • What is FLC? • Where FLC? • Components of FLC • Applications • Advantages • Case study • Disadvantages • NFC design • Closed loop studies • Comparison of performance indices • Conclusion • Future Scope

  3. WHAT IS FLC? • FLC emulates the human mind for monitoring the process parameters and takes decisions regarding the control action • FLC converts a linguistic control scheme using expert knowledge base into an automatic control stratergy

  4. WHERE FLC? • Where one or more variables are continuous • Where mathematical model of the process does not exist (or) too complex to evaluate the model • Where high ambient noise level has to be dealt with • Where inexpensive sensor / low precision microcontroller are to be used • Where the expert knowledge about the system behaviour is available

  5. ADVANTAGES OF FLC • Detailed mathematical model is not necessary • Ideal for complex/nonlinear systems • Compatible with existing control system • Provides robust control • Hardware implementation is possible • Demonstrates smooth control action even with small number of rules

  6. APPLICATIONS • REFRIGERATOR • AIR CONDITIONER • WASHING MACHINE • VIDEO CAMERA • HOT WATER HEATER • LIFTS/ELEVATOR • ELECTRIC TRAIN • PROCESS/SYSTEM CONTROL

  7. COMPONENTS OF FLC • Fuzzification • Knowledge base • Decision making logic (or) inference engine • Defuzzification

  8. FLC BLOCKDIAGRAM

  9. Fuzzification • Measure the input variables (error, change in error/integral error) • Convert the input variables into suitable linguistics values (VS = Very Small, S = Small, M = Medium, L =Large, VL = Very Large etc) Convert the input variables into corresponding universe of discourse using membership function)

  10. KNOWLEDGE BASE • (a) Data Base • (b) Rule Base • Data base is used to define linguistic control variables • IF <fuzzy proportion > THEN <fuzzy proportion > • ‘IF’ part is called ‘antecedent ‘(e,ce,ie) • ‘THEN’ part is called ‘consequent’ (mv) • The combination is called ‘premise’

  11. DECISION MAKING LOGIC (OR)INFERENCE ENGINE • Capability of simulating human decision making process • Infers a system of rules through fuzzy operators namely ‘AND’ and ‘OR’ • Generates a single truth value using Max-Min criteria

  12. DEFUZZIFICATION • Yields a crisp, non-fuzzy control action. • (i) Max-Criteria • (ii) Mean of the maximum • (iii) Centre of area method Z0 = j . X j • -------- i N is the number of quantization levels j is the max. value of membership corresponds to ith quantization level X j is the support value at which membership function reaches maximum value

  13. FLC- CASE STUDY

  14. FLOW CHART

  15. AIR HEATING SYSTEM

  16. DESIGN OF OPTIMAL PID CONTROLLER • MODEL OF THE AHS AS FOPDT • USE Z-N TUNING RULE TO FIND THE INITIAL PID CONTROLLER SETTINGS • BY TRIAL-AND-ERROR TUNE THE PID CONTROLLER FOR OPTIMAL SETTINGS • FIND e(t), ie(t) or ce(t) and m(t) and use them as knowledge base

  17. Design details of PID • AHS FOPDT MODEL = (0.2 X e -16s)/(1 +220 s) Optimal PID controller settings: Kc =67.56 Ti =31.4 Td =7.85

  18. e(t)

  19. ie(t)

  20. m(t)

  21. Membership function

  22. Rule base matrix

  23. DESIGN OF TRADITIONAL FLC • Input variables = e(t) and ie(t) • Quantization levels = 5 • For e(t): MN,N,Z,P and MP • For ie(t): VS,SM,L and VL • For u(t): VS,SM,L and VL • Membership function: Triangular • Truth value generation: Max-min Criteria • Defuzzification: Centre of area method

  24. Closed loop response with PID

  25. Closed loop response with FLC

  26. FUTURE SCOPE • Hybrid control strategies can be designed using FLC, ANN and GA • ANN can be used to generate the membership values • GA can be used to tune the FLC • Adaptive FLC • Neuro fuzzy control

  27. Neuro-fuzzy control- case study • In the conventional fuzzification, finding the corresponding universe of discourse value for every quantization level needs repeated computation. • In the case of NFC, the conventional fuzzification is replaced by ANN technique • Two ANN models, one for e(t) and other for ie(t) are formulated • These ANN models are used to carryout fuzzification

  28. Finding the no. of hidden layer neurons for e(t)

  29. Finding the no. of iterations for e(t)

  30. Optimal ANN architecture for e(t)

  31. ANN parameters for e(t) • Input neurons: 2 • Hidden neurons: 1 5 • Output neurons: 5 • Bias: 1 • Learning rate: 0.7 • Momentum factor: 0.3 • Iterations: 31,600

  32. ANN based membership function for e(t)

  33. Finding the no. of hidden layer neurons for ie(t)

  34. Finding the no. of iterations for ie(t)

  35. Optimal ANN architecture for ie(t)

  36. ANN parameters for ie(t) • Input neurons: 2 • Hidden neurons: 1 2 • Output neurons: 5 • Bias: 1 • Learning rate: 0.7 • Momentum factor: 0.3 • Iterations: 25,000

  37. ANN based membership function for ie(t)

  38. Closed loop performance using NFC

  39. QUANTITATIVE COMPARISION

  40. Conclusion • ANN based fuzzification avoids the repeated computations carried out in the conventional fuzzification. • Robust ANN models for e(t) and ie(t) can be formulated with minimum number of input-output data pair • The iterations required for convergence are also less

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