Fuzzy Logic Control Tutorial


What is Fuzzy Logic?


Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth -- truth values between "completely true" and "completely false". It was introduced by Dr. Lotfi Zadeh of UC/Berkeley in the 1960's as a means to model the uncertainty of natural language.

Fuzzy logic is a multivalued logic that allows for degrees (e.g., normal versus slow or fast) of set membership—a more practical way to deal with the issues you face in the real world. Unlike binary (yes or no) information, fuzzy logic emulates your ability to reason and make use of approximate data to find precise solutions.

Among fuzzy logic’s benefits are fault tolerance and the ability to provide accurate responses to ambiguous data. According to David Brubaker, president of the fuzzy-logic and embedded-systems consulting firm Huntington Group (Menlo Park, CA), products designed with fuzzy logic have simpler controls, are easier to build and test, and provide smoother control than those using conventional systems.

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Where is fuzzy logic used?

Fuzzy logic is used directly in very few applications. The Sony PalmTop apparently uses a fuzzy logic decision tree algorithm to perform handwritten (well, computer lightpen) Kanji character recognition.

  • 1965 Seminal Paper “Fuzzy Logic” by Prof. Lotfi Zadeh, Faculty in Electrical Engineering, U.C. Berkeley, Sets the Foundation of the “Fuzzy Set Theory”
  • 1970 First Application of Fuzzy Logic in Control Engineering (Europe)
  • 1975 Introduction of Fuzzy Logic in Japan
  • 1980 Empirical Verification of Fuzzy Logic in Europe
  • 1985 Broad Application of Fuzzy Logic in Japan
  • 1990 Broad Application of Fuzzy Logic in Europe
  • 1995 Broad Application of Fuzzy Logic in the U.S.
  • 2000 Fuzzy Logic Becomes a Standard Technology and Is Also Applied in Data and Sensor Signal Analysis. Application of Fuzzy Logic in Business and Finance.

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Why Fuzzy Logic?

You can set up a fuzzy system for the same purpose you set up any other computing system—to map inputs to outputs. Basically, it consists of three stages: fuzzification, rule evaluation, and defuzzification.

Fuzzification is a process that combines actual values (e.g., barometric pressure) with stored membership-function data to produce fuzzy input values. Rule evaluation, or fuzzy inferencing, is a way of producing numeric responses from linguistic rules based on system input values. In the last stage—defuzzification—a fuzzy system combines all its outputs and obtains a representative number.

To see if this number solves the original problem and gives you an accurate answer in all cases, Fred Watkins, president of HyperLogic, a firm that produces fuzzy-logic development tools, says it’s necessary to come up with a performance measure (theoretically, an ideal correct response). You can then run the engine in a variety of contexts. If the number doesn’t turn out to be a good solution, you tune the system parameters until you reach a satisfactory conclusion. Even as the rules of a fuzzy engine become more complex, says Watkins, the general concepts remain the same.

According to Emdad Khan, manager of fuzzy and neural networks for the Embedded Systems Division of National Semiconductor, you can construct a PC-based fuzzy-logic system (e.g., to use m a simple management project) using software alone. However, general-purpose or dedicated microprocessors are available for more complicated applications (see ure).

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What is Fuzzy Logic Control?

  • Just as fuzzy logic can be described as ’computing with words rather than numbers’, fuzzy control can be described as ’control with sentences rather than equations’.

  • It is more natural to use sentences, or rules, in for instance operator controlled plants, with the control strategy written in terms of if-then clauses.

  • If the controller furthermore adjusts the control strategy without human intervention it is adaptive.

  • Fuzzy control is a practical alternative for a variety of challenging control applications since it provides a convenient method for constructing nonlinear controllers via the use of heuristic information.

  • Such heuristic information may come from an operator who has acted as a “human-in-the-loop” controller for a process.

  • In the fuzzy control design methodology, we ask this operator to write down a set of rules on how to control the process, then we incorporate these into a fuzzy controller that emulates the decision-making process of the human.

  • In other cases, the heuristic information may come from a control engineer who has performed extensive mathematical modeling, analysis, and development of control algorithms for a particular process. Again, such expertise is loaded into the fuzzy controller to automate the reasoning processes and actions of the expert.

  • Regardless of where the heuristic control knowledge comes from, fuzzy control provides a user-friendly formalism for representing and implementing the ideas we have about how to achieve high-performance control.


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Fuzzy Logic to C Converter!

This is a newly software on the Market which converts Matlab Fuzzy Logic Files (.Fis - made from the fuzzy logic tool) to Ansi C. It accepts different system like, SISO (Single input Single output system), MISO (Multiple input Single output system) and MIMO (Multiple input Multiple output system).



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