Identification of self-tuning fuzzy PI type controllers with reduced rule set

Abstract

A common way of developing fuzzy controllers is by determining the rule base and some appropriate fuzzy sets over the controller's input and output ranges. A simple and efficient approach, namely, fuzzy subtractive clustering is used to identify the rule base needed to realize a self-tuning fuzzy PI-type controller. This technique provides a mechanism to obtain the reduced rule set covering, the whole input/output space as well as membership functions for each input variable. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 5 rules maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with self-tuning fuzzy PI type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE). In addition the responses due to step set-point change and load disturbance are studied and in each case the proposed scheme shows an identical performance with less number of rules.