GENETIC OPTIMIZATION OF FUZZY CONTROLLER

Abstract

Many non-linear, inherently unstable systems exist whose control using conventional methods is both difficult to design and unsatisfactory in implementation. Fuzzy Logic Controllers are a class of non-linear controllers that make use of human expert knowledge and an implicit imprecision to apply control to such systems. The Knowledge Base of a Fuzzy Logic Controller (FLC) encapsulates expert knowledge and consists of the Data Base (membership functions) and Rule-Base of the controller. Optimization of both of these Knowledge Base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low numbers of input variables. However, for large numbers of inputs, more formal methods of Knowledge Base optimization are required. The construction of these controllers can be quick and effective in the presence of expert knowledge; conversely, in the absence of such knowledge, their design can be slow and based on trial-and-error rather than a guided approach. Genetic Algorithms provide a way of surmounting this shortcoming. These algorithms use some of the concepts of evolutionary theory, and provide an effective way of searching a large and complex solution space to give close to optimal solutions in much faster times than random trial-and-error. They are also generally more effective at avoiding local minima than differentiation-based approaches. In this report the application of Genetic Algorithms to the design and optimization of Fuzzy Logic Controllers is demonstrated. These controllers are characterized by a set of parameters. The efficacy of this approach had been tested by comparison of the GA-FLC’s performance in controlling a liquid level control system, to that of heuristically-tuned FLC.