A Simple Discretization Scheme for Gain Matrix Conditioning

Autor:	O'Connor, Daniel L., Siang, Lim C., Elnawawi, Shams
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	In industrial model predictive controllers (MPCs), models generated from regression-based system identification methods typically contain small or even physically non-existent degrees of freedom. Control issues can arise when the steady-state optimizer uses these small degrees of freedom to calculate targets for plant operation due to matrix ill-conditioning. Mathematical techniques like Relative Gain Array (RGA) and Singular Value Decomposition (SVD) are helpful for analyzing controller gain interactions and identifying conditioning issues, which can be corrected relatively easily in small models. However, these techniques are difficult and tedious to apply for larger, more complex models. This paper describes a novel, non-iterative, RGA-based, binning technique for discretizing the gain matrix and quickly solving 2x2 conditioning issues for any model size, while guaranteeing gain adjustments below a certain threshold. Higher order interactions are also discussed. Comment: Revised manuscript accepted in AdCONIP 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.13291 Zobrazit plný text záznamu View this record from Arxiv