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UBC Theses and Dissertations

Performance assessment and online input design for closed-loop identification of machine directional properties on paper machines Yousefi, Mahdi

Abstract

Model-based controllers based on incorrect estimates of the true plant behaviour can be expected to perform badly. Due to the fact that machine directional proper- ties in paper machines can be controlled by model predictive control, it is important for us to use a valid model of the process in the controller to keep controller per- formance high. Performance is measured to detect model-plant mismatch using a minimum variance index and a closely related user-specified criterion. In this the- sis, we define a sensitivity measure that relates system performance to model-plant mismatch, and use it to explore this sensitivity for three realistic types of paramet- ric modelling errors. This analysis shows the power of the indices to detect model plant mismatch. In addition, the effect of model-plant mismatch on the closed loop behaviour is discussed. To compensate controller performance in the case of model-plant mismatch, the process needs to be re-identified to update the process model. This thesis presents a new approach to input design for closed loop identification. The idea is to maximize the trace of the Fisher information matrix associated with the plant model in a moving horizon framework, while enforcing explicit constraints on both inputs and outputs. The result is the richest possible excitation signal for which the operation of a running closed-loop system remains within acceptable bounds. The method can be combined with a fixed model variable regressor technique to esti- mate time delays. The suggested technique is implemented and used to monitor performance of machine-directional processes in an industrial paper machine and identify the pro- cess if any degradation in controller performance because of model-plant mismatch is detected.

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Attribution-NonCommercial-NoDerivs 2.5 Canada