Control-Relevant Model Selection for Servo Control Systems

verfasst von
Mathias Tantau, Torben Jonsky, Zygimantas Ziaukas, Hans-Georg Jacob
Abstract

Several techniques related to control design rely on parametric system models. In the industry of servo control commissioning these techniques are not well established, mostly because success hinges on the selection of a suitable model. Automatic model selection in view of control design requires a control-relevant criterion for identification and nomination of the best model. If in addition a bright-grey box model is required, the dominant physical effects of the system under study should be included in the model automatically. In this paper the $\nu$-gap metric is compared with a robust control-relevant identification criterion with known controller in view of control relevance and feasibility of the identification. A focus is laid on servo control design and experiments are performed on a storage and retrieval system with off-the-shelf industrial components. It is found that the theoretical properties of both criteria are not as different as one might expect. Practically, both criteria are not easy to use but the identification with known controller emphasises certain frequencies more dominantly than the $\nu$-gap metric making it even more difficult to obtain universal plant models under realistic conditions.

Organisationseinheit(en)
Institut für Mechatronische Systeme
Externe Organisation(en)
Lenze SE
Typ
Aufsatz in Konferenzband
Anzahl der Seiten
8
Publikationsdatum
2022
Publikationsstatus
Angenommen/Im Druck
Peer-reviewed
Ja