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.

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.

Abstract: A system and method modifies a dynamic model of a process in a plant for an advanced process control controller wherein the model includes sub models. Performance of the controller is monitored and performance degradation is quantified as the process changes. It is then determined whether a selected number of sub models need updating or the entire model dynamics need updating as a function of the quantified controller performance degradation If a selected number of sub models need updating, an excitation signal is initiated for such sub models to identify new sub models. If the entire model dynamics need updating, a complete perturbation signal is initiated and triggers exhaustive closed-loop identification of entire model. The newly identified model or sub models is incorporated in the controller.