Identification of local model networks parameters using fuzzy clustering

Abstract

In this work the use of fuzzy clustering for identification of parameters of the local model network (LMN) from input-output data is studied. The main idea is based on development of the local linear models for the whole operating range of the controlled process. The local models are identified from measured data using clustering and local least squares method. The nonlinear plant is then approximated by a set of locally valid sub-models, which are smoothly connected using the validity function. The parameters for the GPC controller are computed at each sampling interval from the linearization of LMN. The proposed identification and control method is illustrated by the simulation study on the MIMO liquid process. © 2010 IFAC.