Further Development of an Approach to Aircraft Handling Qualities Prediction

For the decades that followed the publishing of the Cooper-Harper report that formalized a standard and universally recognized handling qualities pilot rating scale, researchers have sought to correlate pilot compensation—as well as physical and mental workload—with the assigned rating. A quantitative correlation remains elusive. In recent years, new physiological measurement devices have been developed that together with software processing tools can provide accurate measures of psychophysiological measures including cognitive workload, distraction, and high/low engagement based on electroencephalogram (EEG) and electrocardiogram (ECG) measures (i.e., brain waves and heart rate variability). The pilot compensation referred to in the Cooper-Harper scale is also a function of task performance measures that reflect aircraft characteristics and inceptor activity that reflects upon physical workload. Using a new piloted simulation test database generated in Manned Flight Simulator's containerized rotary-wing simulator with ten experienced test pilots, a machine learning-based software algorithm that integrates a disparate mix of pilot-vehicle system, physiological, and task performance measures was used to further develop an approach to predict handling qualities levels and ratings.