Enhancing performance of generalized minimum variance control via dynamic data reconciliation

摘要

The design, tuning, and implementation of controllers are crucial for the solutions to control problems. Generalized minimum variance control (GMVC) has attractive properties and it is widely used for controller performance enhancement. The measured signals of process output variables, which are used as feedback signals, are generally subject to measurement noise. However, the GMVC theory assumes the feedback signals are the process outputs, which rarely consider the unavoidable measurement noise. By additionally considering the measurement noise, the control performance of GMVC with the measurement noise is analyzed in this paper. The dynamic data reconciliation (DDR) method, which uses the information of both the process model and the measurement data to reconcile the measured signals, is introduced. It is combined with GMVC to reduce the effect of the measurement noise on the results of GMVC. The effectiveness of GMVC combined with DDR is illustrated in two case studies, where the proposed method is compared with the original GMVC and the GMVC with the conventional digital filter. The results in both SISO and MIMO control systems show that the proposed GMVC combined with DDR can reduce the effect of the measurement noise and achieve better control performance. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.