Distributed Model Predictive Control for More Electric Aircraft Subsystems Operating at Multiple Time Scales

Abstract

This article considers predictive control of the jet engine and electrical power distribution system of a more electric aircraft (MEA) with asynchronous distributed controllers. The shafts of the engine spools are mechanically coupled to the electrical generators of the microgrid, linking the dynamics of the two subsystems. As the subsystems and their controllers are designed by different entities, the preservation of subsystem privacy must be an integral part of any coordination mechanism and, due to the underlying subsystems having differing time scales in their dynamics, the subsystem control updates are not guaranteed to be synchronized. To address these requirements, a distributed model predictive control (D-MPC) algorithm based on the alternating direction method of multipliers (ADMM) is proposed, which accounts for and exploits the differing control update rates of the engine and power subsystem controllers while preserving privacy. An extension to the Algorithm that seeks to minimize the required communication volume by downsampling the interactions to the rate of the engine time scale is also presented. Simulation results on a high-fidelity nonlinear system model demonstrate that the distributed controllers can outperform a decentralized controller and their performance can match that of a fully centralized controller.