Abstract
Input saturation is an ubiquitous nonlinearity in control systems and arises from the fact that all actuators are subject to a maximum power, thereby resulting in a hard limitation on the allowable magnitude of the input effort. In the scientific literature, anti-windup augmentation has been proposed to recover the desired linear closed-loop dynamics during transients, but the effectiveness of such a compensation is strongly linked to the accuracy of the mathematical model of the plant. In this work, it is shown that a feedback controller with embedded anti-windup compensator can be directly identified from data, by suitably extending the existing data-driven design theory. The effectiveness of the resulting method is illustrated on a benchmark simulation example.
| Original language | English |
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| Title of host publication | Proceedings of the 2nd Conference on Learning for Dynamics and Control |
| Publisher | PMLR |
| Pages | 46-54 |
| Number of pages | 9 |
| Volume | 120 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 2nd Annual Conference on Learning for DynamIcs & Control, L4DC 2020 - UC Berkeley, Berkeley, United States Duration: 11 Jun 2020 → 12 Jun 2020 Conference number: 2 https://l4dc.org |
Publication series
| Name | Proceedings of Machine Learning Research |
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| Publisher | PMLR |
| Volume | 120 |
| ISSN (Electronic) | 2640-3498 |
Conference
| Conference | 2nd Annual Conference on Learning for DynamIcs & Control, L4DC 2020 |
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| Abbreviated title | L4DC |
| Country/Territory | United States |
| City | Berkeley |
| Period | 11/06/20 → 12/06/20 |
| Internet address |
Keywords
- Data-driven control
- Anti-windup
- Saturated systems
- Virtual reference feedback tuning
- direct data-driven control
- anti-windup
- input saturation