# Fuzzy Control System and Its Applications

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

## 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

## 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:

## Steps to Design FLC:

**Identification of variables**: Input, output and state variables must be identified of the plant**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**Obtaining membership function**: Obtain membership function for each fuzzy subset**Fuzzy Rule Base Configuration**: Formulate fuzzy rule base by assigning relationship between fuzzy input and output**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**Fuzzification**: The Fuzzification process is done in this step with the help of Fuzzifier**Identification of output**: Identify the output from each rule using fuzzy approximate reasoning and combine the fuzzy output obtained from each rule**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