Identification and Control Design of Fuzzy Takagi-Sugeno Model for Pressure Process Rig

Abstract

The design of an intelligent controller based on fuzzy TS model for a pressure process rig is presented. The proposed controller consists of a fuzzy TS model, a feedback fuzzy TS model, and a low pass filter combined in an internal model control structure. The identification of the fuzzy TS model uses fuzzy clustering technique to mimic the nonlinearity characteristic of the process. Instead of least-squares algorithm, the instrumental variable method is used to estimate the consequent parameters of the fuzzy TS model in order to avoid inconsistency problem. The identified model is validated with the performance indicators variance-accounted-for and root mean square. By using the technique of inverse fuzzy model analytically, the feedback fuzzy controller is designed based on the identified fuzzy TS model. The performance of the proposed controller is verified through experiments at various operating points.