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Orthogonalization-Based Gain Conditioning for Linear Model Predictive Control
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AI
DOI：10.1021/acs.iecr.2c02700.png)
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摘要
En 中文
Industrial practitioners who develop linear model predictive control (MPC) applications want to prevent undesirable controller behavior caused by ill-conditioned gain matrices and model mismatch. Control practitioners currently use time-consuming iterative methods based on relative gain arrays and singular value decomposition to condition their gain matrices to prevent degraded controller performance. Here, we propose a more straightforward approach, which extends an orthogonalization-based parameter ranking algorithm originally developed to aid parameter estimation in fundamental models. The proposed method ranks manipulated variables (MVs) from most influential to least influential while accounting for correlated steady-state influences of MVs on controlled variables. Problematic MVs are identified, and a constrained linear optimization algorithm is used to find optimal adjustments to condition the gain matrix. The effectiveness of the proposed methodology is verified using a new industrial case study based on fluidized catalytic cracking.
Keyword:
LP-MPC
ESTIMABILITY
INDUSTRIAL
期刊
I
IF:
3.9
论文数:
4.0W
被引数:
9.6W
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