Nonlinear Hierarchical MPC With Application to Aircraft Fuel Thermal Management Systems

Abstract

A nonlinear hierarchical model predictive control (MPC) framework is proposed and applied to maximize the thermal endurance of aircraft. Effectively controlling the fuel temperatures in a nonlinear multitimescale aircraft fuel thermal management system (FTMS) requires controllers capable of long-term planning and fast update rates. In this article, a two-level hierarchical MPC controller is formulated using successive linearization (SL) that directly accounts for the multitimescale and nonlinear system dynamics to achieve accurate predictive capabilities and computational efficiency. Detailed simulation results show that the proposed hierarchical structure can increase aircraft thermal endurance by at least 21% compared to a centralized approach while significantly reducing the computational cost. The results also show that SL provides a valuable framework for efficiently accounting for nonlinear system dynamics within both levels of the hierarchical MPC formulation.