Fuzzy logic controllers

FUZZY LOGIC CONTROLLERS SUMMARY Keywords : Fuzzy logic, Fuzzy logic controller, Fuzzy PID with one input, Fuzzy PID with two inputs, Look-up table, Resetting parameter, Generalized Fuzzy PI controller When designing a control system, an initial step is to obtain a mathematical model for the plant and controller. This model represent the formulation of prior information into an analytic structure, but many real world systems have unknown parameters or highly complexs and nonlinear characteristics. Attempts to overcome these difficulties have led to research into very complexs controllers, which may cause difficulties when applied. Fuzzy logic control appears very useful when the processes are too complex for analysis by conventional quantitative techniques. Experiences show that the fuzzy logic control yields results superior to those obtained by conventional control algorithms in the complex situation where the system or parameters are difficult to obtain. Other advantages of fuzzy control are; 1- it can work with less precise inputs; 2- it doesn't need fast processors; 3- it needs less data storage in the form of membership functions and rules than conventional look-up table for nonlinear controllers; and 4- it is more robust than other nonlinear controllers. Fuzzy logic is much closer in spirit to human thinking and natural language than the traditional logical systems. Viewed in this perspective, the essential part of the FLC (Fuzzy Logic Controller) is a set of linguistic control rules related by the dual concepts of fuzzy implication and compositional rule of inference. Then, the FLC provides an algorithm which can convert the linguistic control strategy based on expert knowledge into an automatic control strategy. The components of conventional and fuzzy systems are quite alike, differing mainly in that fuzzy systems contain "fuzzifiers" which convert inputs into their fuzzy representations, and "defuzzifiers" which convert the output of the fuzzy process logic into "crisp" (numerically precise) solution variables. In a fuzzy system, the values of fuzzified input execute all the rules in the knowledge repository that have the fuzzified input as part of their premise. This process generates a new fuzzy set representing each output or solution variable Defuzzification creates a value for the output variable from that new fuzzy set. XI