Open AccessOffset free data driven control: application to a process control trainer
Author(s) -
Salvador Jose R.,
Rodriguez Ramirez Daniel,
Alamo Teodoro,
Muñoz de la Peña David
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.0376
Subject(s) - offset (computer science) , control theory (sociology) , computer science , trainer , tracking error , affine transformation , process (computing) , data driven , control engineering , control (management) , artificial intelligence , mathematics , engineering , programming language , operating system , pure mathematics
This work presents a data driven control strategy able to track a set point without steady‐state error. The control sequence is computed as an affine combination of past control signals, which belong to a set of trajectories stored in a process historian database. This affine combination is computed so that the variance of the tracking error is minimised. It is shown that offset free control, that is zero mean tracking error, is achieved under the assumption that the state is measurable, the underlying dynamics are linear and the trajectories of the database share the same error dynamics and are in turn offset free. The proposed strategy learns the underlying controller stored in the database while maintaining its offset free tracking capability in spite of differences in the reference, disturbances and operating conditions. No training phase is required and newly obtained process data can be easily taken into account. The proposed strategy, related to direct weight optimisation learning techniques, is tested on a process control trainer.