Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:763-776, 2022.
In this work, we propose a data-driven approach to synthesize safety controllers for continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed framework is based on notions of so-called control barrier certificates, constructed from data while providing a guaranteed confidence of 1 on the safety of unknown systems. Under a certain rank condition, we synthesize polynomial state-feedback controllers to ensure the safety of the unknown system only via a single trajectory collected from it. We demonstrate the effectiveness of our proposed results by applying them to a nonlinear polynomial-type system with unknown dynamics.