**Rule Decision Making and Outcome Determination**

The easiest way to formulate the rules for a fuzzy logic controller is to first write them as IF…THEN statements that describe how the inputs affect the outcome.

Some fuzzy controllers are capable of handling two outputs at the same time, thus allowing two rules to be combined. For example, the rules:

Some fuzzy controllers are capable of handling two outputs at the same time, thus allowing two rules to be combined. For example, the rules:

IF A = PS AND B = NS THEN C = ZR

IF A = PS AND B = NS THEN D = NS

IF A = PS AND B = NS THEN D = NS

can be combined into one rule:

IF A = PS AND B = NS THEN C = ZR and D = NS

This rule gives two outcomes, thus invoking two defuzzification processes, one for each controlling output. It is easiest, however, to create each rule individually (with only one outcome) and then combine them later.

A fuzzy logic controller may or may not provide a choice of output membership function shapes (L, P, S, or Z). Moreover, it may or may not provide a choice about whether the functions are continuous or noncontinuous. However, before defuzzification occurs, all fuzzy controllers add the outcomes based on the appropriate rule logic. If the rule contains a logical AND function, the controller will select the lowest output value; if the rule contains an OR function, the controller will select the highest output value.

If an application requires a highly accurate or smooth output, the rules should be designed so that an input condition triggers two or more rules. To do this, either the input membership functions must overlap or two input conditions must influence the same output.

A fuzzy logic controller may or may not provide a choice of output membership function shapes (L, P, S, or Z). Moreover, it may or may not provide a choice about whether the functions are continuous or noncontinuous. However, before defuzzification occurs, all fuzzy controllers add the outcomes based on the appropriate rule logic. If the rule contains a logical AND function, the controller will select the lowest output value; if the rule contains an OR function, the controller will select the highest output value.

If an application requires a highly accurate or smooth output, the rules should be designed so that an input condition triggers two or more rules. To do this, either the input membership functions must overlap or two input conditions must influence the same output.

**Defuzzification**

During the implementation of a fuzzy logic system, the system designer may be required to choose a defuzzification method, especially if the output membership function is noncontinuous.

Defuzzification methods include the center of gravity (centroid), the left-most maximum, and the right-most maximum. If the selected defuzzification method is the center of gravity approach, the triggering rules must be arranged so that at least one rule is triggered at all times.

Thus, there must always be an output from a rule. The controller will generate an error if there is no output due to a gap in input condition coverage (see Figure 14).

Defuzzification methods include the center of gravity (centroid), the left-most maximum, and the right-most maximum. If the selected defuzzification method is the center of gravity approach, the triggering rules must be arranged so that at least one rule is triggered at all times.

Thus, there must always be an output from a rule. The controller will generate an error if there is no output due to a gap in input condition coverage (see Figure 14).

**CONCLUSION**Fuzzy logic processing is a three-step procedure consisting of fuzzification, fuzzy processing, and defuzzification. Using this three-step process, a fuzzy controller can take vague nondiscrete input

data and convert it into a specific output. This conversion process depends on the membership functions and rules established by the system designer during system implementation. When used correctly, fuzzy logic controllers can improve the performance of PLC systems that control both closed-loop and open-loop systems. They can also lead to the automation of tasks that previously required human intervention. Together, PLCs and fuzzy logic technology form a powerful tool for enhancing complex system automation.

data and convert it into a specific output. This conversion process depends on the membership functions and rules established by the system designer during system implementation. When used correctly, fuzzy logic controllers can improve the performance of PLC systems that control both closed-loop and open-loop systems. They can also lead to the automation of tasks that previously required human intervention. Together, PLCs and fuzzy logic technology form a powerful tool for enhancing complex system automation.

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