Synthesis of Stabilizing Recurrent Equilibrium Network Controllers

Neelay Junnarkar, He Yin, Fangda Gu, Murat Arcak, Peter J. Seiler

Abstract: We propose a parameterization of a nonlinear dynamic controller based on the recurrent equilibrium network, a generalization of the recurrent neural network. We derive constraints on the parameterization under which the controller guarantees exponential stability of a partially observed dynamical system with sector bounded nonlinearities. Finally, we present a method to synthesize this controller using projected policy gradient methods to maximize a reward function with arbitrary structure. The projection step involves the solution of convex optimization problems. We demonstrate the proposed method with simulated examples of controlling a nonlinear inverted pendulum.