Learning Robust Safety Controllers for Uncertain Input-Affine Polynomial Systems
This paper offers a direct data-driven approach for learning robust control barrier certificates (R-CBCs) and robust safety controllers (R-SCs) for discrete-time input-affine polynomial systems with unknown dynamics under unknown-but-bounded disturbances. The proposed method relies on data from input-state observations collected over a finite-time horizon while satisfying a specific rank condition to ensure the system is persistently excited. Our data-driven scheme enables the synthesis of R-CBCs and R-SCs directly from observed data, bypassing the need for explicit modeling of the system's dynamics and thus ensuring robust system safety against disturbances within a finite time horizon. Our proposed approach is formulated as a sum-of-squares (SOS) optimization problem, providing a structured design framework. Two case studies showcase our method's capability to provide robust safety guarantees for unknown input-affine polynomial systems under bounded disturbances, demonstrating its practical effectiveness.
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