Controller design for robust invariance from noisy data
Not on the map. Why?
Abstract (arXiv)
For an unknown linear system, starting from noisy open-loop input-state data collected during a finite-length experiment, we directly design a linear feedback controller that guarantees robust invariance of a given polyhedral set of the state in the presence of disturbances. The main result is a necessary and sufficient condition for the existence of such a controller, and amounts to the solution of a linear program. The benefits of large and rich data sets for the solution of the problem are discussed. A numerical example about a simplified platoon of two vehicles illustrates the method.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jul 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 3.
4 papers.
- 2021Robust Data-Driven Predictive Control using Reachability Analysiscited by 3
- 2025Data-Driven Motion Planning for Uncertain Nonlinear Systemscited by 1
- 2025Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systemscited by 0
- 2025SAFE--MA--RRT: Multi-Agent Motion Planning with Data-Driven Safety Certificatescited by 0
- 2025SAFE--MA--RRT: Multi-Agent Motion Planning with Data-Driven Safety Certificates4 Sep 2025
- 2025Data-Driven Motion Planning for Uncertain Nonlinear Systems31 Jul 2025
- 2025Risk-Aware Safe Reinforcement Learning for Control of Stochastic Linear Systems14 May 2025
- 2021Robust Data-Driven Predictive Control using Reachability Analysis25 Mar 2021