Easy Way to Understand Basic Elements of Fuzzy Logic System

Basically, Fuzzy Logic concept consists of 3 stages i.e.:

1.       Fuzzification

2.       Fuzzy Inference

3.       Defuzzification 

Fuzzification is a process to convert numerical values (real values) into Linguistic values (Fuzzy values). Defuzzification is vice versa of Fuzzification that converts Linguistic values (Fuzzy value) into numerical values (real values), and Fuzzy Inference is located between Fuzzification and Defuzzification. Fuzzy Inference is the brain of Fuzzy system that connects Fuzzification to Defuzzification. Fuzzy Inference usually uses IF-THEN rules to connect Fuzzification to Defuzzification. More detail explanation on Fuzzification, Fuzzy Inference, and Defuzzification will be discussed later.

In order to understand Fuzzy Logic concept easier, a controller application using Fuzzy Logic algorithm is usually used. Figure beside is an application of Fuzzy Logic to control a container crane from trailer to ship. Overlapping of membership functions in Fuzzy Logic will give a smooth control system to put a container to the ship properly with very small error correction.

To control a container crane using Fuzzy Logic, firstly we must define input and output variables of the container crane system. Input variables are all external variables that influence the moving position of the container from the trailer to the ship. In this case study of the container crane controller, there are 2 input variables that give effect to the system i.e. Angle and Distance. Angle variable is angle of container position to the crane head, and Distance variable is distance between the trailer to the ship. Numerical values (real values) of these input variables will be converted to be Linguistic values (Fuzzy values) in the Fuzzification process as shown in the following figure.

 

In addition, output variable is an internal variable that causes the moving of motor inside the crane. In this container crane system, Power is the output variable that will cause the motor of the crane moving. As the input variable Distance is far, it causes the crane needs more power to move the motor. 

In the next posting we will discuss on Fuzzy Inference. As the brain of Fuzzy Logic controller, it will be better if Fuzzy Inference is designed by expert people that already known the container crane system very well if its controlled by conventional controller. Fuzzy Inference system will choose the best decision of output variable (Power) to control the system based on their input variables.

Source of  figures:

Industrial Application of Fuzzy Logic Control (slide presentations), Inform Software Corporation, 20001  Midwest Rd., Oak Brook, IL 60521, U. S. A.