Decomposition of nonlinear dynamics using multiple model approach and gap metric analysis

Abstract

Multiple models for various stages of a non-linear process control are developed by clustering perturbation data obtained from the nonlinear process so as to permit multiple local data regions to be identified as a function of substantial similarity between the data, wherein the data of first data set represent the non-linear process. A discrete model corresponding to each of the local data regions is generated. The number of the discrete models may be reduced as a function of prediction error between actual outputs of the process and predicted outputs of the models and as a function of a gap metric based on closed loop similarity and frequency response similarity between the models.