Data-driven design of safe control for polynomial systems
Not on the map. Why?
Abstract (arXiv)
We consider the problem of designing an invariant set using only a finite set of input-state data collected from an unknown polynomial system in continuous time. We consider noisy data, i.e., corrupted by an unknown-but-bounded disturbance. We derive a data-dependent sum-of-squares program that enforces invariance of a set and also optimizes the size of the invariant set while keeping it within a set of user-defined safety constraints; the solution of this program directly provides a polynomial invariant set and a state-feedback controller. We numerically test the design on a system of two platooning cars.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 23 Dec 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1.
1 paper.
- 2023Safely Learning Dynamical Systemscited by 0
- 2023Safely Learning Dynamical Systems20 May 2023