Scalable synthesis of safety certificates from data with application to learning-based control
This work is significant for researchers and practitioners in control theory and robotics, as it provides a more scalable method for ensuring safety in learning-based control systems, which are increasingly being deployed in safety-critical applications.
This paper tackles the problem of synthesizing safety certificates for learning-based control systems, which traditionally struggle with the trade-off between performance and safety. The authors propose efficient techniques using convex optimization to approximate safe sets and control laws, demonstrating their approach with numerical examples including an autonomous vehicle convoy.
The control of complex systems faces a trade-off between high performance and safety guarantees, which in particular restricts the application of learning-based methods to safety-critical systems. A recently proposed framework to address this issue is the use of a safety controller, which guarantees to keep the system within a safe region of the state space. This paper introduces efficient techniques for the synthesis of a safe set and control law, which offer improved scalability properties by relying on approximations based on convex optimization problems. The first proposed method requires only an approximate linear system model and Lipschitz continuity of the unknown nonlinear dynamics. The second method extends the results by showing how a Gaussian process prior on the unknown system dynamics can be used in order to reduce conservatism of the resulting safe set. We demonstrate the results with numerical examples, including an autonomous convoy of vehicles.