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.

本文提出了有效的技术来综合安全集合和控制律，通过基于凸优化问题的近似来提供改进的可伸缩性质，其中一种方法仅需要近似的线性系统模型和Lipschitz连续的未知非线性动力学，展示了数字示例，包括一个自主车队。

基于数据的安全证书的可伸缩合成及其在基于学习的控制中的应用