Fuzzy model-based predictive control

TL;DR: In this paper, a fuzzy Takagi-Sugeno type model of the nonlinear dynamics is produced based on its local linearisations and the recently proposed fuzzy predictive control strategy is then applied to obtain a nonlinear fuzzy predictive controller.

TL;DR: A new multiple models control strategy using the well-known linear generalized predictive control (LGPC) scheme has been proposed, in this paper, and the main idea is to represent the operating environments of the system, which have a wide range of variation with respect to time by multiple explicit linear models.

TL;DR: A new strategy for control of tubular heat exchanger system is presented using generalized predictive control (GPC) scheme and multiple models method that can verify the validity of the proposed control scheme.

TL;DR: Fuzzy model predictive control (FMPC) algorithms presented here are model-based control schemes in which the models used for prediction are Takagi-Sugeno fuzzy systems (TSFS).

TL;DR: A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement and to be a contender for general self-tuning applications.

TL;DR: The approximation capability of the Takagi-Sugeno model are investigated and the importance of the constant term in such a polynomial is highlighted showing that if it is discarded the Takaga-S Sugeno model features the same approximation power of the simpler constant-consequence systems.

TL;DR: Approaches reported on here investigate nonlinear Takagi-Sugeno (TS) fuzzy model formulations, where a linear-in-the-parameter identification problem is formulated for various combinations of measured variables and system delays.

TL;DR: Some theoretical aspects in developing fuzzy dynamic models and in applying them to large-scale process control problems are described.