Recurrent neural network controllers synthesis with stability guarantees for partially observed systems

Neural network controllers are attractive for control tasks, yet safety-critical systems demand stability, especially under partial observability where long-term memory is needed. We consider RNNs as dynamic controllers for nonlinear uncertain partially observed systems and derive convex stability conditions via integral quadratic constraints, S-lemma, and sequential convexification. To enforce stability during learning and control, we propose a projected policy gradient in a reparameterized space using mild additional system information. Experiments show stabilizing controllers learned with fewer samples and higher final performance than policy gradient baselines.