| Title |
Offset free data driven control: application to a process control trainer |
| Authors |
SALVADOR ORTIZ, JOSÉ RAMÓN, Rodriguez Ramirez, Daniel , Alamo, Teodoro , Munoz de la Pena, David |
| External publication |
Si |
| Means |
IET Contr. Theory Appl. |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
1 |
| SJR Quartile |
1 |
| JCR Impact |
3.34300 |
| SJR Impact |
1.35800 |
| Publication date |
17/12/2019 |
| ISI |
000500280200012 |
| DOI |
10.1049/iet-cta.2019.0376 |
| Abstract |
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. |
| Keywords |
predictive control; process control; control system synthesis; learning (artificial intelligence); optimisation; newly obtained process data; offset free tracking capability; underlying controller; er |
| Universidad Loyola members |
|
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