FUZZY LOGIC CONTROL COMPONENTS
A fuzzy logic controller’s control process consists of three main components, or actions, that must be completed sequentially to determine the appropriate output value. These components are:
• fuzzification
• fuzzy processing
• defuzzification
As shown previously in Figure 2, when a fuzzy controller receives input data, it translates it into a fuzzy form. This process is called fuzzification. The controller then performs fuzzy processing, which involves the evaluation of the input information according to IF…THEN rules created by the user during the fuzzy control system’s design stage. Once the fuzzy controller finishes the ruleprocessing stage and arrives at an outcome conclusion, it begins the defuzzification process. In this final step, the fuzzy controller converts the output conclusions into “real” output data (e.g., analog counts) and sends this data to the process via an output module interface. If the fuzzy logic controller is located in the PLC rack and does not have a direct or built-in I/O interface with the process, then it will send the defuzzification output to the PLC memory location that maps the process’s output interface module.
• fuzzification
• fuzzy processing
• defuzzification
As shown previously in Figure 2, when a fuzzy controller receives input data, it translates it into a fuzzy form. This process is called fuzzification. The controller then performs fuzzy processing, which involves the evaluation of the input information according to IF…THEN rules created by the user during the fuzzy control system’s design stage. Once the fuzzy controller finishes the ruleprocessing stage and arrives at an outcome conclusion, it begins the defuzzification process. In this final step, the fuzzy controller converts the output conclusions into “real” output data (e.g., analog counts) and sends this data to the process via an output module interface. If the fuzzy logic controller is located in the PLC rack and does not have a direct or built-in I/O interface with the process, then it will send the defuzzification output to the PLC memory location that maps the process’s output interface module.
Fuzzification
The fuzzification process is the interpretation of input data by the fuzzy controller. Fuzzification consists of two main components:
• membership functions
• labels
Membership Functions. During fuzzification, a fuzzy logic controller receives input data, also known as the fuzzy variable, and analyzes it according to user-defined charts called membership functions (see Figure 4). Membership functions group input data into sets, such as temperatures that are too cold, motor speeds that are acceptable, etc. The controller assigns the input data a grade from 0 to 1 based on how well it fits into each membership function (e.g., 0.45 too cold, 0.7 acceptable speed). Membership functions can have many shapes, depending on the data set, but the most common are the S, Z, L, and P shapes shown in Figure 5. Membership functions consist of connected line segments defined by the lines’ end points. Each membership function can have up to three line segments with a maximum of four end points. The grade at each end point must have a value of 0 or 1. A membership function’s shape does not have to be symmetrical; however, it must comply with the previously discussed specifications.
The fuzzification process is the interpretation of input data by the fuzzy controller. Fuzzification consists of two main components:
• membership functions
• labels
Membership Functions. During fuzzification, a fuzzy logic controller receives input data, also known as the fuzzy variable, and analyzes it according to user-defined charts called membership functions (see Figure 4). Membership functions group input data into sets, such as temperatures that are too cold, motor speeds that are acceptable, etc. The controller assigns the input data a grade from 0 to 1 based on how well it fits into each membership function (e.g., 0.45 too cold, 0.7 acceptable speed). Membership functions can have many shapes, depending on the data set, but the most common are the S, Z, L, and P shapes shown in Figure 5. Membership functions consist of connected line segments defined by the lines’ end points. Each membership function can have up to three line segments with a maximum of four end points. The grade at each end point must have a value of 0 or 1. A membership function’s shape does not have to be symmetrical; however, it must comply with the previously discussed specifications.
Labels. Each fuzzy controller input can have several membership functions, with seven being the
norm, that define its conditions. Each membership function is defined by a name called a label. For example, an input variable such as temperature might have five membership functions labeled as cold, cool, normal, warm, and hot. Generically, the seven membership functions have the following labels, which span from the data range’s minimum point (negative large) to its maximum point (positive large):
• NL (negative large)
• NM (negative medium)
• NS (negative small)
• ZR (zero)
• PS (positive small)
• PM (positive medium)
• PL (positive large)
Figure 6 shows an example of an input variable with seven L-shaped membership functions using all of the possible labels. A group of membership functions forms a fuzzy set. Although most fuzzy sets have an odd number of labels, a set can also have an even number of labels. For example, a fuzzy set may have four or six labels in any shape, depending on how the inputs are defined in relationship to the membership function.
to be continued...................
to be continued...................
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