Adaptive parameter estimator based on T–S fuzzy models and its applications to indirect adaptive fuzzy control design

Abstract

In this paper, a new on-line parameter estimation methodology for the general continuous time Takagi–Sugeno (T–S) fuzzy model whose parameters are poorly known or uncertain is presented. An estimator with an appropriate adaptive law for updating the parameters is designed and analyzed based on the Lyapunov theory. The adaptive law is designed so that the estimation model follows the parameterized plant model. By the proposed estimator, the parameters of the T–S fuzzy model can be estimated by observing the behavior of the system and it can be a basis for indirect adaptive fuzzy control. In order to show the applicability of the proposed estimator, indirect adaptive fuzzy control design examples with the proposed estimator are presented.