Fuzzy rule-adaptive model predictive control for a multivariable heating system

This paper presents design of a rule-adaptive fuzzy model predictive control (MPC) algorithm for controlling temperatures of a multivariable soil-heating process system. The system uses Takagi-Sugeno (TS) type fuzzy model structure. The control objective is to track a desired temperature profile at three locations in the soil sample using three heat sources located at the outer surface of the soil cell. The system recognizes the active fuzzy rules which are recursively adapted for handling the time-variant behavior of the process. For the simulations the soil-heating system is modeled using a general-purpose ABAQUS finite element (FE) program. The dynamic control program is linked to the FE model using a user-defined subroutine. In order to show the effectiveness, the performance of the proposed scheme is compared against the non-adaptive fuzzy model based MPC scheme. A classical non-adaptive MPC is also developed to confirm the superiority of the fuzzy model based MPC controllers