Enabling real-time identification and detection of changing pilot and controlled element (CE) dynamics in manual control tasks is essential for the development of real-time adaptive support systems (i.e., a “R2D2”) that 1) monitor closed loop dynamics and 2) detect irregularities therein. This study developed a method for identifying CE dynamics in compensatory target-tracking and disturbance-rejection tasks with time-varying pilot and CE dynamics. The CE dynamics were modelled using autoregressive exogenous (ARX) models, with the model parameters estimated using Ordinary Least Squares (OLS) and Recursive Least Squares (RLS). The effects of disturbance power, pilot remnant intensity, and RLS memory horizon on estimation bias and quality fit are studied using Monte Carlo simulations with 100 realizations. The simulation results are verified against the calculated least-squares best-fit model. A memory horizon of 512 samples (5.12 s at 100 Hz) was found to be optimal for all simulated configurations. The results show that for pure target-tracking tasks, a properly selected ARX model structure and an appropriately chosen RLS memory horizon allow for perfect model identification. In contrast, for pure disturbance-rejection, the identification results show the opposite. Increasing pilot remnant intensity was found to have a positive effect on both the mean parameter biases and quality of fit. With this work, an in-simulation functional approach for CE dynamics identification is established, together with a procedure for obtaining the least-squares best possible model fit for the CE ARX model, thereby providing a reference for the development and verification of real-time CE identification methods that, alongside real-time pilot dynamics identification methods, are necessary for the development of the monitoring functionality envisioned for R2D2-like systems.

Training in realistic conditions is crucial for fighter pilots. During this training, a red air team is used to represent adversary threats. Currently, the red air team is made up of friendly aircraft that mimic the tactics of the expected adversaries. However, this method has its limitations, such as that these friendly aircraft do not correctly mimic the performance and detectable emissions of the real adversary aircraft. Furthermore, using real combat aircraft has other downsides. They require active fighters and pilots that require expensive training, and using real aircraft means that these expensive combat aircraft need to spend a lot of their service life filling the role of red air instead of flying real missions. As red air flying hours are not considered to be useful training for the pilots flying them, there is no need for using combat-ready aircraft that can carry real armament, nor for a pilot in the cockpit. Using real combat aircraft has other extensive costs attached to it and is unsustainable looking at its real intended purpose. Just to have a real combat aircraft in the red air fleet requires acquisition of the aircraft, taking it away from active service that it was designed for. It needs a (ground)crew to operate it. It also needs lots of maintenance, requiring mechanics, engineers, tools, hardware, and much more. All of this and the combat aircraft is not used for its designed capabilities in flag missions when it is part of the red team. Therefore, there is a desire for a UAV that can match the performance of the real adversaries, is less expensive to operate, and is more sustainable than the current alternatives to fill the role of red air...