A Fixed-Point Iteration Scheme for Sensitivity-Based Distributed Optimal Control

This article presents a sensitivity-based algorithm for distributed optimal control problems (OCP) of multi-agent systems with nonlinear dynamics and state/input couplings, as they arise, for instance, in distributed model predictive control. The algorithm relies on first-order sensitivities to cooperatively solve the distributed OCP in parallel. The solutions to the resulting local OCPs are computed with a fixed-point scheme and communicated within one communication step per algorithm iteration to the neighbors. Convergence results are presented under the inexact minimization of the local OCP. The algorithm is evaluated in numerical simulations for an example system.