Sheet and film process control is often structured such that separate controllers and actuators are dedicated to either the temporal (i.e, machine direction) variations or the spatial (i.e., cross direction) variations. The dedicated machine direction (MD) and cross direction (CD) controllers require separate measurements of the MD and CD sheet property profiles, respectively. The current industrial standard involves a traversing sensor that acquires a signal containing both MD and CD property variations. The challenge then becomes how does one extract separate MD and CD profiles from the mixed signal. Numerous techniques have been proposed, but ultimately the traditional exponential filtering method continues to be the industrial standard. A more recent technique, compressive sensing, appears promising but previous developments do not address the industrial constraints. In the first part of this thesis the compressive sensing technique is developed further, specifically with regards to feasibility of implementation. A comparative analysis is performed to determine the benefits and drawbacks of the proposed method. Model-based control has gained widespread acceptance in a variety of industrial processes. To ensure adequate performance, these model-based controllers require a model that accurately represents the true process. However, the true process is changing over time as a result of the various operating conditions and physical characteristics of the process. In part two of this thesis an integrated adaptive control strategy is introduced for the multi-input multi-output MD process of a paper machine. This integrated framework consists of process monitoring, input design and system identification techniques developed in collaboration with multiple colleagues. The goal of this work is to unify these efforts and exhibit the integrated functionality on an industrial paper machine simulator.
Recommended citation: Rippon, L. D. (2017). “Sheet profile estimation and machine direction adaptive control.” University of British Columbia, Vancouver. MASc dissertation.