Fuzzy identification based on improved T-S fuzzy model and its application in evaporator

In this paper, a nonlinear model identification method is applied to a thermal plant. The aim of this work is to develop a moderately complex model with interpretable structure for a complex evaporator, which is the main component of Organic Rankine Cycle System. Based on the subtractive clustering algorithm, the T-S (Takagi-Sugeno) model is derived. The clustering centers can be obtained automatically through the input-output data. Then the cluster centers and cluster radiuses are further modified by the data and consequent parameters are identified by least-square algorithm. The validity of identification algorithm is tested and verified. The simulations show that the identification results are satisfactory.