Fuzzy controller – Choosing an appropriate and smallest rule set

Abstract — Design of fuzzy controller depends mainly on rule base and to derive the desired and small fuzzy rule base is still based on trial and error. This paper concentrates on choosing the rule base and gives some useful results. Firstly, it has been shown that increasing the rules and number of membership functions beyond a certain limit is useless as it increases the complexity of FLC and has almost no effect on output response on the system. Secondly, the authors investigate how fuzzy subtractive clustering approach, modelled from a set of input/output data; can be applied to the area of control theory for linear and nonlinear dynamic systems. The objective of using this approach is to design a fuzzy logic controller with less number of rules leading to a smaller amount of computational time. Although the number of rules is automatically determined by this method, an user-specified parameter ra (the radius of influence of cluster center) strongly affects the number of rules that will be generated. Thirdly, a suggestion to choose the value of ra is also given. To support the work, well-known examples of linear and nonlinear systems are simulated under the Matlab/Simulink environment. Both noisy and non-noisy data have been considered. Simulation shows the effectiveness of the proposed fuzzy logic controller as compared to existing fuzz