Distinguishability Analysis for Multiple Mass Models of Servo Systems with Commissioning Sensors

verfasst von

Mathias Tantau, Lars Perner, Mark Wielitzka

Abstract

Physically motivated models of electromechanical
motion systems enable model-based control theory and facilitate
system interpretation. Unfortunately, the effort of modelling
restricts the usage of model-based methods in many applications.
Some approaches to automatically generate models from
measurements choose the best model based on minimizing the
residual. These model selection attempts are limited due to
ambiguities in reconstructing the internal structure from the
input-output behaviour because usually motion systems have
only one actuator and one sensor. Often, it is unknown if the
resulting model is unique or if other models with different
structure would fit equally well. The set of candidate models
should be designed to contain only distinguishable models but
ambiguities are often unknown to the experimenter. In this
paper distinguishability is investigated systematically for a class
of multiple mass models representing servo positioning systems.
In the analysis a new criterion for indistinguishability is used.
The benefit of additional, structural sensors on distinguishability
of models is demonstrated which suggests to mount them
temporarily for the commissioning phase in order to facilitate
the model selection. It turns out that the best results can be
achieved if synergies among sensor signals are utilized.

Organisationseinheit(en)

Institut für Mechatronische Systeme

Externe Organisation(en)

Lenze SE

Typ

Aufsatz in Konferenzband

Seiten

2479-2486

Anzahl der Seiten

8

Publikationsdatum

2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerung und Optimierung, Artificial intelligence, Entscheidungswissenschaften (sonstige), Steuerungs- und Systemtechnik, Maschinenbau, Computational Mathematics

Elektronische Version(en)

https://doi.org/10.15488/10570 (Zugang: Offen)
https://doi.org/10.23919/ECC54610.2021.9654884 (Zugang: Geschlossen)