PID based control performance assessment for rolling mills: A multiscale PCA approach

Abstract

This work is focused on exploring the use of Multiscale Principal Component Analysis (MSPCA) for performance assessment and diagnosis of PID controllers in steel rolling processes. An optimal PID controller is used for performance benchmark. Diagnosis is implemented using an angle-classifier PCA-based approach. Theoretical framework of MSPCA, the performance index and the diagnosis procedure are developed. A procedure for implementing the proposed algorithm is presented. Simulations on the rolling mill roll force demonstrate reliable assessment and diagnosis results and practical use of the proposed method.

Control Performance Monitoring of State-Dependent Nonlinear Processes

“…Real oxyfuel furnace data provided by Swerea MEFOS (One of the partners of the Cognitive Control Project completed in our previous work (Recalde, Katebi, and Tauro, 2013;Recalde, Katebi, and Yue, 2014) was employed in this CPM study. The furnace is used to reheat the slabs coming from the rolling mill and consists of 2 parts being The temperature setpoints (T sp ) for these zones are also added for illustration and comparison.…”

Sequential Control Performance Diagnosis of Steel Processes

“…Controller pre-assessment is a specific diagnosis method of poor performing controllers, implemented before CPA (Recalde et al, 2013). An output performance index is formulated as the ratio of the output covariance matrix obtained using a MPC benchmark to the plant output covariance matrix:…”