Abstract
Robust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key observation is that model accuracy and model complexity should depend on the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems. Simulations confirm that the model order depends on the control objectives. Overall, the framework enables systematic identification of model sets for robust control.
| Original language | English |
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| Title of host publication | Preprints 21st IFAC World Congress 2020 |
| Number of pages | 1 |
| Publication status | Published - 2020 |
| Event | 21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020 Conference number: 21 https://www.ifac2020.org/ |
Conference
| Conference | 21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) |
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| Abbreviated title | IFAC 2020 |
| Country/Territory | Germany |
| City | Berlin |
| Period | 12/07/20 → 17/07/20 |
| Internet address |
Keywords
- Identification for Control
- Robust control
- Motion control
- Mechatronic systems
- Frequency domain identification
- Identification and control methods
- Order selection