Abstract
In this note, we explore a middle ground between data-driven model reduction and data-driven control. In particular, we use snapshots collected from the system to build reduced models that can be expressed in terms of data. We illustrate how the derived family of reduced models can be used for data-driven control of the original system under suitable conditions. Finding a control law that stabilizes certain solutions of the original system as well as the one that reaches any desired state in final time are studied in detail.
| Original language | English |
|---|---|
| Pages (from-to) | 833-838 |
| Number of pages | 6 |
| Journal | IEEE Control Systems Letters |
| Volume | 4 |
| Issue number | 4 |
| Early online date | 12-May-2020 |
| DOIs | |
| Publication status | Published - Oct-2020 |
| Event | 59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of Duration: 14-Dec-2020 → 18-Dec-2020 |
Keywords
- Data models
- Reduced order systems
- Linear systems
- Complexity theory
- Tools
- Analytical models
- Data-driven model reduction
- data-driven control
- linear systems
- SYSTEMS