Targeted excitation and re-identification methods for multivariate process and model predictive control

Abstract

A process controlled using model predictive control is required to be re-identified when significant plant-model mismatch (PMM) occurs. During data acquisition for re-identification, the process is excited to enable accurate re-identification. However, the process excitation worsens the control performance. To prevent this problem, a new model-update framework that consists of targeted excitation (TE) and targeted re-identification (TR) is proposed. In TE, only the manipulated variables corresponding to problematic transfer functions that have significant PMM are excited during data acquisition. On the other hand, the other manipulated variables are optimized to suppress the variations of the controlled variables. After data is acquired using TE, the TR method re-identifies only the problematic transfer functions by using the other transfer-function models without large PMM. The validity of the proposed framework is examined by theoretical analysis and numerical case studies. In the theoretical analysis, the stability during data acquisition using TE and the asymptotic bias of the parameters re-identified using TR were considered. In the numerical case studies, the applicability of the proposed framework to several processes including a fluid catalytic cracking (FCC) process was examined. As a result, it was shown that, for all the processes, the proposed framework can improve both the control performance during data acquisition and the model accuracy after re-identification, compared to an existing method that excites all the inputs during data acquisition.