Details
| Original language | English |
|---|---|
| Pages (from-to) | 111-125 |
| Number of pages | 15 |
| Journal | International Journal of Mechatronics and Automation |
| Volume | 8 |
| Issue number | 3 |
| Publication status | Published - 21 Oct 2021 |
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
- Distinguishability analysis, Electromechanical motion systems, Equivalence of structures, Frequency domain, Markov parameter approach, Model selection, Multiple mass resonators, Servo control system, Structure and parameter identification, Transfer function approach
ASJC Scopus subject areas
- Mathematics(all)
- Computational Mathematics
- Computer Science(all)
- Artificial Intelligence
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
- Engineering(all)
- Computational Mechanics
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In: International Journal of Mechatronics and Automation, Vol. 8, No. 3, 21.10.2021, p. 111-125.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Model Selection for Servo Control Systems
AU - Tantau, Mathias
AU - Perner, Lars
AU - Wielitzka, Mark
N1 - Funding Information: This work was sponsored by the German Forschungsvereinigung Antriebstechnik e.V. (FVA) and the AiF Arbeitsgemeinschaft industrieller Forschungsvereinigungen ‘Otto von Guericke’, e.V.
PY - 2021/10/21
Y1 - 2021/10/21
N2 - 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.
AB - 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.
KW - Distinguishability analysis
KW - Electromechanical motion systems
KW - Equivalence of structures
KW - Frequency domain
KW - Markov parameter approach
KW - Model selection
KW - Multiple mass resonators
KW - Servo control system
KW - Structure and parameter identification
KW - Transfer function approach
UR - http://www.scopus.com/inward/record.url?scp=85118306847&partnerID=8YFLogxK
U2 - 10.15488/11498
DO - 10.15488/11498
M3 - Article
VL - 8
SP - 111
EP - 125
JO - International Journal of Mechatronics and Automation
JF - International Journal of Mechatronics and Automation
SN - 2045-1059
IS - 3
ER -