Title: Model selection for servo control systems

Authors: Mathias Tantau; Lars Perner; Mark Wielitzka

Addresses: Institute of Mechatronic Systems, Leibniz University Hannover, An der Universität 1, 30823, Garbsen, Germany ' Lenze Automation GmbH, Am Alten Bahnhof 11, 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. However, the effort of modelling is high and automatic modelling would be appealing. The intuitive approach to select the model with the best fit has the shortcoming that the chosen model may be one with high complexity in which some of the parameters are not identiifable or uncertain. Also, ambiguities in selecting the model structure would lead to false conclusions. This paper proposes a strategy for frequency domain model selection ensuring practical identifiability. Also, the paper describes distinguishability analysis of candidate models utilising transfer function coecients and Markov parameters. Model selection and distinguishability analysis are applied to a class of models as they are commonly used to describe servo control systems. It is shown in experiments on an industrial stacker crane that model selection works with little user interaction, except from defining normalised hyperparameters.

Keywords: model selection; structure and parameter identification; frequency domain; distinguishability analysis; equivalence of structures; multiple mass resonators; servo control system; electromechanical motion systems; transfer function approach; Markov parameter approach.

DOI: 10.1504/IJMA.2021.118426

International Journal of Mechatronics and Automation, 2021 Vol.8 No.3, pp.111 - 125

Received: 13 Oct 2020
Accepted: 09 Nov 2020
Published online: 25 Oct 2021 *

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