Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019) |
| Editors | Oleg Gusikhin, Kurosh Madani, Janan Zaytoon |
| Place of Publication | Prag |
| Pages | 368-376 |
| Number of pages | 9 |
| ISBN (electronic) | 9789897583803 |
| Publication status | Published - 2019 |
| Event | 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019 - Prague, Czech Republic Duration: 29 Jul 2019 → 31 Jul 2019 |
Publication series
| Name | ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics |
|---|---|
| Volume | 1 |
Abstract
The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness.
Keywords
- Backlash, Genetic programming, Modelling, Multiple-mass resonators, Phenomenological models, Simultaneous identification of structure and parameters
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Engineering(all)
- Control and Systems Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019). ed. / Oleg Gusikhin; Kurosh Madani; Janan Zaytoon. Prag, 2019. p. 368-376 (ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics; Vol. 1).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Structure and parameter identification of process models with hard non-linearities for industrial drive trains by means of degenerate genetic programming
AU - Tantau, Mathias
AU - Perner, Lars
AU - Wielitzka, Mark
AU - Ortmaier, Tobias
N1 - Funding information: This work was sponsored by the German Forschungsvereinigung Antriebstechnik e.V. (FVA).
PY - 2019
Y1 - 2019
N2 - The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness.
AB - The derivation of bright-grey box models for electric drives with coupled mechanics, such as stacker cranes, robots and linear gantries is an important step in control design but often too time-consuming for the ordinary commissioning process. It requires structure and parameter identification in repeated trial and error loops. In this paper an automated genetic programming solution is proposed that can cope with various features, including highly non-linear mechanics (friction, backlash). The generated state space representation can readily be used for stability analysis, state control, Kalman filtering, etc. This, however, requires several special rules in the genetic programming procedure and an automated integration of features into the defining state space form. Simulations are carried out with industrial data to investigate the performance and robustness.
KW - Backlash
KW - Genetic programming
KW - Modelling
KW - Multiple-mass resonators
KW - Phenomenological models
KW - Simultaneous identification of structure and parameters
UR - http://www.scopus.com/inward/record.url?scp=85073123317&partnerID=8YFLogxK
U2 - 10.5220/0007949003680376
DO - 10.5220/0007949003680376
M3 - Conference contribution
AN - SCOPUS:85073123317
T3 - ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics
SP - 368
EP - 376
BT - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019)
A2 - Gusikhin, Oleg
A2 - Madani, Kurosh
A2 - Zaytoon, Janan
CY - Prag
T2 - 16th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2019
Y2 - 29 July 2019 through 31 July 2019
ER -