# Fuzzy Control System and Its Applications

A Fuzzy Control system is an arrangement of physical components that are 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 design 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

## 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

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

• 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

## 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 a 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 a fuzzy rule base by assigning a 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 a 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