| Autor: |
Taylor, Andrew J., Dorobantu, Victor D., Krishnamoorthy, Meera, Le, Hoang M., Yue, Yisong, Ames, Aaron D. |
| Rok vydání: |
2019 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/CDC40024.2019.9029226 |
| Popis: |
The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis. |
| Databáze: |
arXiv |
| Externí odkaz: |
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