Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems

No. 5: AAAI-22 Technical Tracks 5

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 36

AAAI Technical Track on Intelligent Robotics

Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity. Stability is a crucial property for safety-critical dynamical systems, while stabilization of partially observed systems, in many cases, requires controllers to retain and process long-term memories of the past. We consider the important class of recurrent neural networks (RNN) as dynamic controllers for nonlinear uncertain partially-observed systems, and derive convex stability conditions based on integral quadratic constraints, S-lemma and sequential convexification. To ensure stability during the learning and control process, we propose a projected policy gradient method that iteratively enforces the stability conditions in the reparametrized space taking advantage of mild additional information on system dynamics. Numerical experiments show that our method learns stabilizing controllers with fewer samples and achieves higher final performance compared with policy gradient.

10.1609/aaai.v36i5.20476