On the application of fuzzy predictive control based on multiple models strategy to a tubular heat exchanger system

Abstract

The purpose of the paper presented here is to control the fluid temperature that flows in the inner tube of a tubular heat exchanger system by means of the fluid flow pressure. This system in its present form has a specified range of the coefficients’ variation, while the temperature of the outlet fluid could generally be controlled by either the temperature or the flow of the inlet fluid flowing in the shell tube. The control realization for the system presented is often complicated, because the variation of the system coefficients and the reference signal must be thoroughly covered by the control action. In such a case, the system behaviour must first be represented by the multiple explicit models and then the appropriate control approach needs to be realized based on new techniques. A novel multiple models control strategy using both fuzzy-based predictive control (FPC) and overall fuzzy-based predictive model (OFPM) has been proposed in this paper. Concerning the strategy, the system must be modelled through the multiple OFPMs, while the corresponding FPCs need to be designed based on the model results. Hereinafter, the best OFPM of the system is accurately identified by an intelligent decision mechanism (IDM), as long as the system coefficients are abruptly varied, at each instant of time. Subsequently, the best FPC is chosen by the IDM and therefore its control action is applied to the system. In order to demonstrate the effectiveness of the proposed strategy, simulations are carried out and the corresponding results are compared with those obtained using the well-known single model linear generalized predictive controller (SMLGPC), where the observer polynomial so-called T- polynomial is used to cope with the better disturbance rejection properties. By analysing the proposed control strategy performance in comparison with the SMLGPC, when the system coefficients and the desired set point are abruptly changed, it is easily observed that the new acquired performance is as good as the SMLGPC, where it is well known as a powerful control approach in the linear model-based predictive control family.