Gain Conditioning for Linear Model Predictive Control
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Authors
Sanborn, Stephen
Date
Type
thesis
Language
eng
Keyword
Gain conditioning , mpc , Linear model predictive control
Alternative Title
Abstract
Linear model predictive control (MPC) is used in a wide variety of process industries. Linear MPCs calculate adjustments in multiple manipulated variables (MVs) to ensure that operating constraints are obeyed and that controlled variables (CVs) are maintained near their setpoints. Industrial practitioners who develop MPC applications are interested in preventing erratic behavior of MPCs, which can be caused by imperfect models with ill-conditioned gain matrices. Currently, control practitioners use time-consuming methods based on relative gain arrays and singular-value thresholding to condition their gain matrices to prevent degraded controller performance. These techniques tend to require many iterations to successfully condition a large gain matrix. In the proposed approach I extend an orthogonalization-based parameter-ranking algorithm, which was originally developed to aid parameter estimation in fundamental models, so it can be used to identify and address gain conditioning problems. The proposed method ranks MVs from most influential to least influential while accounting for correlated steady-state influences of MVs on CVs. Problematic MVs are identified, and a linear optimization algorithm is used to find optimal gain adjustments to condition the gain matrix. To test the effectiveness of the proposed methodology, I use industrial case studies based on fluidized catalytic cracking models.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
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Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.