TY - GEN

T1 - Control-Relevant Model Selection for Servo Control Systems

AU - Tantau, Mathias

AU - Jonsky, Torben

AU - Ziaukas, Zygimantas

AU - Jacob, Hans-Georg

N1 - Funding Information: *This work carried out as part of the research project "Automated Control Design based on (partly) automatically generated, Control-optimal Models" (FVA 665 IV), sponsored by the German Forschungsvereinigung Antriebstechnik e.V. (FVA) 1Institute of Mechatronic Systems, Leibniz University Hannover, An der Universität 1, 30823 Garbsen, Germany mathias.tantau@imes.uni-hannover.de 2Lenze SE, Hans-Lenze-Str. 1, 31855 Aerzen, Germany torben.jonsky@lenze.com

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85134290048&partnerID=8YFLogxK

U2 - 10.1109/CoDIT55151.2022.9804063

DO - 10.1109/CoDIT55151.2022.9804063

M3 - Conference contribution

SP - 86

EP - 93

BT - 8th International Conference on Control, Decision and Information Technologies

ER -