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- Title
Error-triggered on-line model identification for model-based feedback control.
- Authors
Alanqar, Anas; Durand, Helen; Christofides, Panagiotis D.
- Abstract
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
- Subjects
ONLINE monitoring systems; FEEDBACK control systems; CLOSED loop systems; LYAPUNOV exponents; NONLINEAR dynamical systems
- Publication
AIChE Journal, 2017, Vol 63, Issue 3, p949
- ISSN
0001-1541
- Publication type
Article
- DOI
10.1002/aic.15430