FUZZY LOGIC

Hi friends this article deals with the Introduction of fuzzy logic. This topic is very useful for the engineering students who are studying under the department of Electronics and Instrumentation, Electronics and Communication, Electrical and Electronics, Instrumentation and Control.

CONTENTS:

• Introduction to Fuzzy Logic
• Evolution of Fuzzy Logic control
• Fuzzy Logic Principles

INTRODUCTION TO FUZZY LOGIC

• Fuzzy logic is the branch of artificial intelligence that deals with the reasoning algorithms used to emulate human thinking and decision making in machines.

• These algorithms are used in applications where process data cannot be represented in binary form. For example, the statements “the air feels cool” and “he is young” are not discrete statements.

• They do not provide concrete data about the air temperature or the person’s age (i.e., the air is at 65°F or the boy is 12 years old). Fuzzy logic interprets vague statements like these so that they make logical sense.

• In the case of the cool air, a PLC with fuzzy logic capabilities would interpret both the level of coolness and its relationship to warmth to ascertain that “cool” means somewhere between hot and cold.

• In straight binary logic, hot would be one discrete value (e.g., logic 1) and cold would be the other (e.g., logic 0), leaving no value to represent a cool temperature.

• In contrast to binary logic, fuzzy logic can be thought of as gray logic, which creates a way to express in-between data values.

• Fuzzy logic associates a grade, or level, with a data range, giving it a value of 1 at its maximum and 0 at its minimum.

• For example, Figure 1a illustrates a fuzzy logic representation of a cool air temperature range, where 70°F indicates perfectly cool air (i.e., a grade value of 1).

• Any temperature over 80°F is considered hot, and any temperature below 60°F is considered cold.
• Thus, temperatures above 80°F and below 60°F have values of 0 cool, meaning they are not cool at all. Figure 1b shows another representation of the cool temperature range, where the dotted line shows that hot and cold temperatures are not cool.

• At 65°F, the fuzzy logic algorithm considers the temperature to be 50% cool and 50% cold, indicating a level of coolness

• Below 60°F, the fuzzy logic algorithm considers the temperature to be cold.

EVOLUTION OF FUZZY LOGIC CONTROL

• Fuzzy logic has existed since ancient times, when Aristotle developed the law of the excluded middle. In this law, Aristotle pointed out that the middle ground is lost in the art of logical reasoning—statements are either true or false, never in-between.

• When PLCs were developed, their discrete logic was based on the ancient reasoning techniques. Thus, inputs and outputs could belong to only one set (i.e., ON or OFF); all other values were excluded.

• Fuzzy logic breaks the law of the excluded middle in PLCs by allowing elements to belong to more than just one set.

• In the previous cool air example, the 65°F temperature input belonged to two sets, the cool set and the cold set, with grade levels indicating how well it fit into each set.

• The origins of fuzzy logic date back to the early part of the twentieth century when Bertrand Russell discovered an ancient Greek paradox that states:

• A Cretan asserts that all Cretans lie. So, is he lying? If he lies, then he is telling the
• truth and does not lie. If he does not lie, then he tells the truth and, therefore, he lies.

• In either case—that all Cretans lie or that all Cretans do not lie—a contradiction exists, because both statements are true and false. Russell found that this same paradox applied to the set theory used in discrete logic. Statements must either be totally true or totally false, leading to areas of contradiction.

• Fuzzy logic surmounted this problem in classical logic by allowing statements to be interpreted as both true and false. Therefore, applying fuzzy logic to the Greek paradox yields a statement that is both true and false: Cretans tell the truth 50% of the time and lie 50% of the time.

• This interpretation is very similar to the idea of a glass of water being half empty or half full. In fuzzy logic, the glass is both—50% full and 50% empty. Even as the amount of water decreases, the glass still retains percentages of both conditions.

FUZZY LOGIC PRINCIPLES

Figure 2 describes the operation of a fuzzy logic control system. The input to the fuzzy system is the output of the process, which is entered into the system via input interfaces. For example, in a temperature control application, the input data would be entered into the fuzzy controller using an analog input module. This input information would then go through the fuzzy logic process, where the processor would compare the input data to a database to obtain an output. Fuzzy logic processing involves the execution of IF...THEN rules, which are based on the input conditions. An input’s grade specifies how well it fits into a particular graphic input data set. Input data may be represented in a variety of forms, including count value and percentage of error deviation. Thus, if a fuzzy logic system uses an analog input that has a count range from 0 to 4095, the graphs representing he input sets will cover a span from 0 to 4095 counts.

The output of a fuzzy controller is also defined by grades, with the grade determining the appropriate output value for the control element. For example, the output of a fuzzy system could control an air conditioner, which runs faster or slower according to the output’s grade on the output chart.