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This paper introduces a method for conducting experimental hardware-in-the-loop (xHIL), in which behavioral-level models are coupled with an advanced power emulator (APE) to emulate an electrical load on a power generation system. The emulator is commanded by behavioral-level models running on an advanced real-time simulator that has the capability to leverage Central Processing Units (CPUs) and field programmable gate arrays (FPGA) to meet strict real-time execution requirements. The paper will be broken down into four topics: 1) the development of a solution to target behavioral-level models to an advanced, real-time simulation device, 2) the development of a high-bandwidth, high-power emulation capability, 3) the integration of the real-time simulation device and the APE, and 4) the application of the emulation system (simulator and emulator) in an xHIL experiment.

Experimental Hardware-in-the-loop (xHIL) testing utilizing signal and/or power emulation imposes a hard real-time requirement on models of emulated subsystems, directly limiting their fidelity to what can be achieved in real-time on the available computational resources. Most real-time simulators are CPU-based, for which the overhead of an instruction-set architecture imposes a lower limit on the simulation step size, resulting in limited model bandwidth. For power-electronic systems with high-frequency switching, this limit often necessitates using average-value models, significantly reducing fidelity, in order to meet the real-time requirement. An alternative approach emerging recently is to use FPGAs as the computational platform, which, although offering orders-of-magnitudes faster execution due to their parallel architecture, they are more difficult to program and their limited fabric space bounds the size of models that can be simulated.

Of all aircraft power and thermal loads, flight controls can be the most challenging to quantify because they are highly variable. Unlike constant or impulsive loads, actuator power demands more closely resemble random processes. Some inherent nonlinearities complicate this even further. Actuation power consumption and waste heat generation are both sensitive to input history. But control activity varies considerably with mission segment, turbulence and vehicle state. Flight control is a major power consumer at times, so quantifying power demand and waste heat is important for sizing power and thermal management system components. However, many designers sidestep the stochastic aspects of the problem initially, leading to overly conservative system sizing. The overdesign becomes apparent only after detailed flight simulations become available. These considerations are particularly relevant in trade studies comparing electric versus hydraulic actuation.