Fuzzy Model Predictive Control

Abstract

In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for a highly nonlinear process. In this approach, the process is described by fuzzy convolution model that consists of a number of quasi-linear fuzzy implications (FI). In controller design, prediction errors and control energy are minimized through a two-layered iterative optimization process. Results from validation of the control system are presented. A client/server Fuzzy Model Predictive Control (FMPC) architecture for on-line implementation is proposed. The Server Application is a multi-layer application. At the higher level there are implemented upper FMPC and the two communication classes (Input/Output DDE channels), at the lower level there are implemented the controllers for the subsystems corresponding to the low-level FMPC.