PREDICTIVE CONTROL OF A HEATEXCHANGER BASED ON LOCAL FUZZY MODELS AND NEURAL NETWORKS

This paper deals with identification and control of a highly nonlinear real world application. The performance and applicability of the proposed methods are demonstrated for an industrial heat exchanger. The main difficulties for identification and control of this plant arise from the strongly nonlinear center. First, a neural network based predictive controller using Multi Layer Perceptron (MLP) is designed to govern the dynamics of a heat exchanger pilot plant. The performance of the proposed controller is compared with that of Local Linear Model Tree (LOLIMOT) through simulation studies.