Abstract
Despite the existence of formal guarantees for learning-based control approaches, the relationship between data and control performance is still poorly understood. In this paper, we propose a Lyapunov-based measure for quantifying the impact of data on the certifiable control performance. By modeling unknown system dynamics through Gaussian processes, we can determine the interrelation between model uncertainty and satisfaction of stability conditions. This allows us to directly asses the impact of data on the provable stationary control performance, and thereby the value of the data for the closed-loop system performance. Our approach is applicable to a wide variety of unknown nonlinear systems that are to be controlled by a generic learning-based control law, and the results obtained in numerical simulations indicate the efficacy of the proposed measure.
| Original language | English |
|---|---|
| Pages (from-to) | 623-635 |
| Number of pages | 13 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 144 |
| State | Published - 2021 |
| Event | 3rd Annual Conference on Learning for Dynamics and Control, L4DC 2021 - Virtual, Online, Switzerland Duration: 7 Jun 2021 → 8 Jun 2021 |
Keywords
- Gaussian processes
- data-driven control
- data-efficient learning
- safe learning-based control