A Fuzzy Control system is an arrangement of physical components that is defined to alter another physical system so that this system will exhibit certain desired characteristics

Why use Fuzzy Logic in Control Systems?

  • In traditional control systems, we need to know about the model and the objective function that is formulated in a very precise manner
  • Utilize human expertise and experience for designing controller
  • The  fuzzy control rules (If-Then rules) can be best used in designing a controller

The application of fuzzy logic control extends from individual process control to biomedical instrumentation and various security systems

Types of control systems:

  • Open loop control systems
  • Closed loop control systems

Open Loop Fuzzy Control System:

The input control action is independent of the physical system output

There is no feedback mechanism present in open fuzzy control system

Example: Washing Machine

Washing Machine
Open loop fuzzy control system
Open loop fuzzy control system

Closed Loop Fuzzy Control System:

The new output of the system will depend on the previous output of the system

The system has one or more feedback loops between its input and output

Error Signal = Input – Output

Example: Air Conditioner

Air conditioner
Closed loop fuzzy controller system
Closed loop fuzzy controller system

Advantages of FLC:

  • Cheaper
  • Robust
  • Customizable
  • Emulate human deductive thinking
  • Reliability and efficiency

Disadvantages of FLC:

  • Requires lots of data to be applied
  • Needs regular updating of the rules

Applications of FLC:

  • Traffic control
  • Aircraft flight control
  • Steam engine
  • Elevator control
  • Home Appliances
  • Robot navigation

Watch on YouTube:

fuzzy control system

Steps to Design FLC:

  1. Identification of variables: Input, output and state variables must be identified of the plant
  2. Fuzzy subset configuration: The universe of information spanned by each variable is divided into number of fuzzy subsets and each subset is assigned a linguistic variable
  3. Obtaining membership function: Obtain membership function for each fuzzy subset
  4. Fuzzy Rule Base Configuration: Formulate fuzzy rule base by assigning relationship between fuzzy input and output
  5. Normalizing and scaling factors: Appropriate scaling factors for input and output variables must be chosen to normalize variables between [0, 1] and [-1, 1] intervals
  6. Fuzzification: The Fuzzification process is done in this step with the help of Fuzzifier
  7. Identification  of output: Identify the output from each rule using fuzzy approximate reasoning and combine the fuzzy output obtained from each rule
  8. Defuzzification: Initiate Defuzzification process to form crisp output

Assumption in FLC Design:

  • The plant is observation and controllable
  • Existence of a knowledge body
  • Existence of a solution
  • “Good enough solution is enough”
  • Range of precision
  • Issues regarding stability and optimality must be open