Learning Robust Control Lyapunov Functions through Lipschitz Neural Networks
The work introduces a method to learn robust control Lyapunov functions and stabilizing controllers for nonlinear systems using Lipschitz Neural Networks (LNNs). It achieves this by learning both components jointly and then using explicit bounds on the network's higher-order derivatives to verify the Lyapunov conditions efficiently. This results in a faster, GPU-friendly verification process compared to prior methods.