Nonlinear Hierarchical MPC for Maximizing Aircraft Thermal Endurance

Abstract

In modern high-performance aircraft, the Fuel Thermal Management System (FTMS) plays a critical role in the overall thermal energy management of the aircraft. Actuator and state constraints in the FTMS limit the thermal endurance and capabilities of the aircraft. Thus, an effective control strategy must plan and execute optimized transient fuel mass and temperature trajectories subject to these constraints over the entire course of operation. For the control of linear systems, hierarchical Model Predictive Control (MPC) has shown to be an effective approach to coordinating both short- and long-term system operation with reduced computational complexity. However, for controlling nonlinear systems, common approaches to system linearization may no longer be effective due to the long prediction horizons of upper-level controllers. This paper explores the limitations of using linear models for hierarchical MPC of the nonlinear FTMS found in aircraft. Numerical simulation results show that linearized models work well for lower-level controllers with short prediction horizons but lead to significant reductions in aircraft thermal endurance when used for upper-level controllers with long prediction horizons. Therefore, a mixed-linearity hierarchical MPC formulation is presented with a nonlinear upper-level controller and a linear lower-level controller to achieve both high performance and high computational efficiency.

Nonlinear Hierarchical MPC With Application to Aircraft Fuel Thermal Management Systems

“…The linearized dynamics (4b) make Problem 2 an LP, which can be solved much more efficiently than the NLP from Problem 1. However, as shown in [1], an LTI model of the FTMS may generate very large prediction errors in temperature. For the three-state model considered in [1], temperature prediction errors on the order of 70 K were reported over a long prediction horizon.…”

“…However, as shown in [1], an LTI model of the FTMS may generate very large prediction errors in temperature. For the three-state model considered in [1], temperature prediction errors on the order of 70 K were reported over a long prediction horizon. Even if, in some cases, the prediction errors may cause the LMPC to act conservatively such that the resulting thermal endurance is satisfactory, in other cases, the errors may lead the controller to achieve very poor and unacceptable performance.…”

“…Previous research has shown that FTMS operating decisions at the beginning of a mission can significantly affect the thermal endurance of the aircraft [1], [2], and therefore, an effective FTMS controller requires a long planning horizon. However, long-term planning may not be effective if the prediction model does not accurately capture the nonlinear dynamics of the system.…”

“…However, long-term planning may not be effective if the prediction model does not accurately capture the nonlinear dynamics of the system. As shown in [1], the use of a linearized model over a long prediction horizon can result in large prediction errors and reduced thermal endurance. Moreover, when considering the dynamics of fast states or high-frequency disturbances, the controller must simultaneously have a long planning horizon and a fast control update rate.…”

“…This work seeks to fill this gap by proposing the use of NMPCat both levels and using an SL algorithm to solve the related NLPs efficiently. This work is an extension of [1], where the lower level controller considered linearized dynamics. In addition, a more complete model of the FTMS is used, which includes additional states and an actuator.…”