Introduction to Fuzzy Logic

Fuzzy Logic Systems Architecture

  1. Rule Base: It contains all the rules and “if-then” conditions offered by experts to control decision-making. The most recent update in fuzzy logic provides a number of methods for the design and tuning. Moreover, the update has significantly reduced the number of sets of rules.
  2. Fuzzification: Fuzzification is the second in this series and it helps to convert inputs. It helps in converting crisp numbers to fuzzy sets. Crisp inputs are measured by sensors and passed into a control system for processing. The module is used to transform inputs of the system and also helps in splitting the input signals into five steps:
    — LP: x is a large positive.
    — MP: x is a medium positive.
    — S: x is small.
    — MN: x is medium negative.
    — LN: x is large negative.
  3. Inference Engine: The third one helps in determining the degree of match between fuzzy inputs and fuzzy rules. Based on that percentage it decides which rule is needed to be implemented. After it, to develop the control actions, applied rules are combined. Generally, the process helps in simulating the human reasoning process and that’s by making fuzzy inference on the inputs and “if-then” rules.
  4. Defuzzification: In this module, the transformation of a fuzzy set into crisp value takes place. There are a number of techniques available to do so, and it’s up to the programmer to select the best from the available ones.
  5. Linguistic Variables: Linguistic variables are basically the input and output variables of the system. The values of these variables are mostly words and sentences from the natural languages and no numeric value. Linguistic variables can be decomposed into a set of linguistic terms.

Example: Consider an air conditioner

Membership Functions

Fuzzy logic Controller

Use of Fuzzy Logic?

  • Consumer products and control machines
  • Dealing with uncertainty in engineering
  • Giving at least acceptable reasoning if not accurate reasoning.
  • Very flexible and easy to implement
  • Helps in mimic of the logic of human thoughts
  • Allows a person to build non-linear function of arbitrary complexity
  • Build with complete guidance of experts
  • In fuzzy logic, interference is a process of propagating elastic constraints
  • Highly suitable method for uncertain reasoning

When should fuzzy logic not be used?

  • If it’s not easy for a person to map input space to an output space.
  • Fuzzy logic can’t be applied in situations involving common sense.
  • If controllers can do the job perfectly without the use of fuzzy logic.

How is Fuzzy Logic different from conventional control methods?

Fuzzy Logic Applications

a. Automotive Systems

  • Automatic Gearboxes
  • Four-Wheel Steering
  • Vehicle environment control

b. Consumer Electronic Goods

  • Hi-Fi Systems
  • Photocopiers
  • Still and Video Cameras
  • Television

c. Domestic Goods

  • Microwave Ovens
  • Refrigerators
  • Toasters
  • Vacuum Cleaners
  • Washing Machines

d. Environment Control

  • Air Conditioners/Dryers/Heaters
  • Humidifiers

Advantages of Fuzzy Logic System

  • The Fuzzy logic system is very easy and understandable.
  • The Fuzzy logic system is capable of providing the most effective solution to complex issues.
  • The system can be modified easily to improve or alter the performance.
  • The system helps in dealing engineering uncertainties.
  • It is widely used for commercial and practical purposes.
  • Fuzzy logic systems can be programmed in a situation when feedback sensors stop working.
  • Economical sensor can be used which will help to keep overall system cost low.
  • Robust setup as no precise inputs required.
  • Fuzzy logic can be programmed in a situation where feedback sensor stops working.

Disadvantages of Fuzzy Logic Systems

  • In fuzzy logic setting, exact rules and membership functions are difficult tasks.
  • Fuzzy logic is not always correct, so the results are based on assumptions and may not be widely accepted.
  • In some cases, fuzzy logic is confused with probability theory and terms.
  • Extensive testing with hardware is required for validation and verification of fuzzy knowledge based systems.
  • Fuzzy logic doesn’t have the capability of machine learning and neural network type pattern recognition.

Future Scope

References

  1. R.-E. Precup and H. Hellendoorn, “A survey on industrial applications of fuzzy control,” Computers in Industry, vol. 62, no. 3, pp. 213–226, Apr. 2011, doi: 10.1016/j.compind.2010.10.001
  2. K. Tanaka and M. Sugeno, “Stability analysis and design of fuzzy control systems,” Fuzzy Sets and Systems, vol. 45, no. 2, pp. 135–156, Jan. 1992, doi: 10.1016/0165–0114(92)90113-I.
  3. E. H. Mamdani, “Advances in Linguistic-Synthesis of Fuzzy Controllers,” International Journal of Man-Machine Studies, vol. 8, no. 6, pp. 669–678, 1976, doi: 10.1016/S0020–7373(76)80028–4.
  4. C.-C. Lee, “Fuzzy logic in control systems: fuzzy logic controller. I,” IEEE Transactions on Systems, Man and Cybernetics, vol. 20, no. 2, pp. 404–418, Apr. 1990, doi: 10.1109/21.52551.
  5. T. J. Ross, Fuzzy logic with engineering applications. Hoboken, NJ: John Wiley, 2004.

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