Parameter identification of Takagi-Sugeno Fuzzy model of surge tank system
Muhammad Saleheen Aftab, Muhammad Bilal Kadri · 2013
This paper presents Takagi-Sugeno (TS) fuzzy modeling of a dynamic system. TS fuzzy model of a highly nonlinear system of Surge Tank has been proposed which has fixed structure that is linear in parameters. The rule base for the fuzzy model is obtained from the input-output data set, extracted from the measurements performed on the actual system. Number of rules has been determined utilizing the fuzzy clustering approach that incorporates fuzzy c-means (FCM) algorithm. After the formation of the rules, parameters of the Takagi-Sugeno Systems have been identified. Two different approaches are presented for the training of the fuzzy system: Batch Least Squares (BLS) algorithm, and the Gradient method. The trained fuzzy systems from the two approaches are tested with different forms of inputs and their performance is analyzed and validated by comparing with the real system.