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Fuzzy Engineering
There is more to fuzzy logic than some interesting math, it has some impressive applications in engineering. The main application of fuzzy logic in engineering is in the area of control systems. The definition of a control system, given by Richard Dorf in Modern Control Systems is: "An interconnection of components forming a system configuration that will provide a desired response." This means that a control system needs to know the desired response (input) and it needs to process this input and attempt to acheive it. The general control system can then be summarized with the following diagram:
The process is the system that is being controlled and cannot typically be changed. The controller then, must take the input and also take measurements from the process and use this information to generate the appropriate input to the process. A basic example of a controller would be a summing point that will provide the difference between input and output to the process, whereas a more advanced controller would be a PID controller. A fuzzy logic based controller will use fuzzy membership functions and inference rules to determine the appropriate process input. Designing a fuzzy controller is a more intuitive approach to controller design since it uses a comprehendable linguistic rule base.
A fuzzy controller can be broken down into three main processes. The first of these is the fuzzification, this uses defined membership functions to process the inputs and to fuzzify them. These fuzzified inputs are then used in the second part, the rule-based inference system. This system uses previously defined linguistic rules to generate a fuzzy response. The fuzzy response is then defuzzified in the final process: defuzzification. This process will provide a real number as an output.
Designing a fuzzy controller can be done with several different computer based tools, the tool we will be using is the Fuzzy Logic Toolbox in MATLAB with Simulink. This toolbox provides a GUI for defining membership functions and inference rules and can be integrated with Simulink. The tutorials below will walk you through the process of designing a fuzzy controller using the Fuzzy Logic Toolbox. These tutorials assume that you are familiar with MATLAB and Simulink. If not, click here for a MATLAB Tutorial or click here for a Simulink Tutorial.
Select the desired Tutorial. The Beginners tuturial shows you how to get create a fuzzy controller and how a fuzzy controller behaves. The Advanced Tutorial applies a fuzzy controller to an process.