Constrained parameter estimation in fuzzy modeling

This paper presents an algorithm for incorporating of a priori knowledge into data-driven identification for dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modeled process such as its stability minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using input-output data, optimal parameter values are then found by means of quadratic programming. The proposed approach was successfully applied to the identification of a laboratory liquid level process.