Authors: Mathias Tantau; Christian Helmke; Lars Perner; Mark Wielitzka
Addresses: Institute of Mechatronic Systems, Leibniz University Hannover, An der Universität 1, 30823, Garbsen, Germany ' Institute of Mechatronic Systems, Leibniz University Hannover, An der Universität 1, 30823, Garbsen, Germany ' R&D Automation Systems – Motion Control, Lenze SE, Hans-Lenze-Straße 1, 31855 Aerzen, Germany ' Institute of Mechatronic Systems, Leibniz University Hannover, An der Universität 1, 30823, Garbsen, Germany
Abstract: Physically motivated models of electromechanical motion systems are required in several applications related to control design and auto-tracking, model-based fault detection, feed-forward, and simply interpretation. However, attempts to create such models automatically via structure and parameter identification struggle with ambiguities regarding the correct internal structure of the model. Designing a reasonable set of candidate models is difficult, because it is not known which models are distinguishable and which are not. This paper gives a simple to use necessary condition for indistinguishability of multiple mass models as they are used to model the control-relevant features of motion systems. In an automated way models are generated that can be created by considering elasticities at different positions in the mechanical structures. The condition is applied to these models for the case of three masses. In three examples it is shown that the criterion simplifies the subsequent structure and parameter identification considerably by reducing the number of possible models. For higher numbers of masses, however, it would become intractable.
Keywords: indistinguishability analysis; multiple mass resonators; multiple mass models; electric drive trains; electromechanical motion systems; servo control systems; structure and parameter identification; model selection; model structure optimisation; transfer function type; poles and zeros; frequency domain; FRF; frequency response function.
International Journal of Modelling, Identification and Control, 2020 Vol.36 No.3, pp.175 - 187
Received: 08 May 2020
Accepted: 25 Sep 2020
Published online: 01 Aug 2021 *