Fuzzy Logic  Control system
Fuzzy logic tutorial
20201120 00:52:32
Fuzzy Logic  Control System
Fuzzy logic is applied with great success in
Why Use Fuzzy Logic in Control Systems
A control system is an arrangement of physical components designed to modify another physical system so that that system has certain desired characteristics. Here are some reasons to use fuzzy logic in control systems 

When applying traditional control, you need to know the model and the function objective formulated in precise terms. This makes its application very difficult in many cases.

By applying fuzzy logic for control, we can use human expertise and experience to design a controller.

Fuzzy control rules, essentially IFTHEN rules, can be best used in the design of a controller.
Fuzzy Logic Control (FLC) Assumptions Design
When designing a fuzzy control system, the six basic assumptions following must be done 

The plant is observable and controllable  It must be assumed that the variables of input, output and status are available for observation and control.

Existence of a body of knowledge  It must be assumed that there is a body of knowledge with linguistic rules and a set of data of 'inputoutput from which the rules can be extracted.

Existence of a solution  Assume that there is a solution.

Solutio 'Good enough ' n suffices  Control engineering should look for a “good enough” solution rather than an optimal solution .

Precision range  The fuzzy logic controller must be designed within an acceptable precision range.

Stability and Optimality Issues  Stability and Optimality issues must be open when designing the logic controller fuzzy rather than being explicitly addressed.
Architecture of fuzzy logic control
The following diagram shows the architecture of fuzzy logic control (FLC).
Major components of the FLC
The following are the main components of the FLC as shown in the figure above 

Fuzzifier  The role of the fuzzifier is to convert the vatheir net inputs in fuzzy values.

Fuzzy Knowledge Base  It stores knowledge about all fuzzy inputoutput relationships. It also has the membership function which sets the input variables of the fuzzy rule base and the output variables of the controlled factory.

Fuzzy rule base  It stores knowledge about how the domain process works.

Inference Engine  It acts as a core of any FLC. Basically, it simulates human decisions by doing rough reasoning.

Defuzzifier  The role of the defuzzifier is to convert fuzzy values to net values from the fuzzy inference engine.
FLC Design Steps
Here are the steps involved in FLC design 

Identification of variables  Here, thes input, output and status variables must be identified for the installation in question.

Fuzzy subset configuration  The universe of information is

Get aining membership function  Now get the membership function for each fuzzy subset we get at the step above.

Configuration of the fuzzy rule base  Now formulate the fuzzy rule base by assigning a relation between the fuzzy input and the fuzzy output.

Fuzzification  The fuzzification process is started in this step.

Combining fuzzy outputs  Applying approximate fuzzy reasoning, locate the fuzzy output andmerge them.

Defuzzification  Finally, start the defuzzification process to form a crisp output.
Benefits of fuzzy logic control
Now let us discuss the benefits of fuzzy logic control.

Cheaper  Developing an FLC is comparatively cheaper than developing based on a model or other controller in terms of performance.

Rugged  FLCs are more rugged than PID controllers due to their ability to cover a wide range of operations

Customizable  FLCs are customizable.

Emulate Human Thinking Deduction  Basically, FLC is designed to mimic human deductive thinking, the process people use to derive conclusions from. from what they know.

Reliability  FLC is more reliable than 'a conventional control system.

Efficiency  Fuzzy logic offers more efficiency when applied in the control system.
Disadvantages of fuzzy logic control
We will now discuss the disadvantages of fuzzy logic control.

Requires a lot of data  FLC needs a lot of data to be applied.

Useful for moderate historical data  FLC is not useful for programs much smaller or larger than historical data.

Requires great human expertise  This is a disadvantage as the accuracy of the system depends on knowledge and expertise human being.

Requires regular updating of rules  Rules should be updated over time.