Distributed Stochastic Optimal Control of Nonlinear Systems Based on ADMM

This letter presents an algorithm based on the alternating direction method of multipliers (ADMM) for the distributed solution of optimal control problems of stochastic multi-agent systems with nonlinear dynamics and state/input couplings as they arise, for instance, in distributed model predictive control of uncertain systems. The solution is based on a deterministic reformulation of the original stochastic problem in which certain covariances are exchanged between agents within the ADMM iterations resulting in a scalable algorithm. The algorithm is evaluated numerically for a nonlinear example system and for energy-optimal building automation control.