Premium
Error‐triggered on‐line model identification for model‐based feedback control
Author(s) -
Alanqar Anas,
Durand Helen,
Christofides Panagiotis D.
Publication year - 2017
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15430
Subject(s) - control theory (sociology) , process (computing) , identification (biology) , linear model , computer science , system identification , nonlinear system , controller (irrigation) , process modeling , process control , model predictive control , errors in variables models , control engineering , lyapunov function , state space representation , line (geometry) , state space , feedback loop , work in process , control (management) , engineering , data modeling , algorithm , mathematics , artificial intelligence , machine learning , operations management , database , biology , operating system , geometry , quantum mechanics , agronomy , statistics , botany , physics , computer security
In industry, it may be difficult in many applications to obtain a first‐principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state‐space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error‐triggered on‐line model identification approach is introduced for closed‐loop systems under model‐based feedback control strategies. The linear models are re‐identified on‐line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re‐identification on‐line when necessary. The proposed approach is demonstrated through two chemical process examples using a model‐based feedback control strategy termed Lyapunov‐based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error‐triggered on‐line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed‐loop stability of the process under LEMPC. © 2016 American Institute of Chemical Engineers AIChE J , 63: 949–966, 2017