Title: MPC Without the Computational Pain: The Benefits of SLS and Layering in Distributed Control

Abstract: The System Level Synthesis (SLS) approach facilitates distributed control of large cyberphysical networks in an easy-to-understand, computationally scalable way. We present a case study motivated by the power grid, with communication constraints, actuator saturation, disturbances, and changing setpoints. This simple but challenging case study necessitates the use of model predictive control (MPC); however, MPC incurs significant online computational cost and often scales poorly to large systems. We overcome these challenges by combining various SLS-based techniques, including SLS-based MPC, in a layered controller. This controller achieves performance that is within 3% of the centralized MPC performance, requires only 5% of the online computational resources of distributed MPC, and scales to systems of arbitrary size. For the unfamiliar reader, we also present a review of the SLS approach and its associated extensions in nonlinear control, MPC, adaptive control, and learning for control.