An online pilot manual control behavior identification method, based on recursive low-order time-series model estimation, is presented and validated using experimental data. Eight participants performed compensatory tracking tasks with time-varying vehicle dynamics, where, at an unpredictable moment during a run, a sudden degradation in dynamics could occur. They were instructed to push a button when they detected a change in dynamics. Two methods to automatically detect the moment when pilot adaptation occurs from online estimated parameter traces are discussed. Results show that pilots are more accurate in detecting changes than either algorithm. But when the algorithms are correct, they are often quicker to detect pilot adaptation than pilots themselves. The presented techniques have potential but need improvements.

Time-varying pilot control identification is essential for better understanding of how pilots respond when faced with sudden changes in the dynamics of the vehicle they control, such as when automatic control and stabilization systems disengage or undergo a mode transition. This paper presents the results of a human-in-the-loop experiment performed at TU Delft to test a promising online pilot identification method, based on recursive low-order ARX identification, developed in earlier work. In the experiment, eight skilled participants performed tracking tasks with time-varying vehicle dynamics, where at an unpredictable moment during each tracking run a sudden degradation in vehicle stability was induced. In addition to controlling the time-varying vehicle, participants were asked to indicate when they detected the change in the vehicle dynamics with a button push. This paper compares the effectiveness of two different approaches to detect the moment when pilot adaptation occurs from online identified pilot parameter traces. Overall, the results indicate that the lag in this detection of identified pilot adaptation is equivalent to the subjective detection times, or less. This implies that these online techniques have clear potential for ensuring timely and effective changes in adaptive pilot support systems.