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Multivariable and Closed-Loop Identification for Model Predictive Control

In this work we will study multivariable and closed-loop identification of large scale industrial processes for use in model predictive control (MPC). The advantages of closed-loop identification will be discussed and related problems of identification are outlined. Then, asymptotic method (ASYM) of identification is introduced. The four problems, test signal design for control, model order/structure selection, parameter estimation and model validation, are solved in a systematic manner. The method provides accurate input/output model and unmeasured disturbance model, model errors are quantified by an upper error bound matrix that can be used for model validation and test redesign. To demonstrate the use of the method, the identification of a deethanizer for use in MPC will be presented.

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Details:
Technical Paper (521 KB , PDF)
Author: Yucai Zhu. Firmin Butoyi

Description:
A discussion on multivariable and closed-loop identification of large scale industrial processes for use in model predictive control (MPC).