Abstract:
This paper presents a comprehensive evaluation of machine learning approaches for real-time operational/ flight parameter estimation in large electric vertical takeoff and landing (eVTOL) vehicles, addressing the challenges of time-varying payloads and atmospheric disturbances in Advanced Air Mobility (AAM) missions. Artificial Neural Networks (ANN), Gaussian Process Regression (GPR), and Support Vector Machines (SVM), are compared for their ability to estimate gross weight (GW), longitudinal center of gravity position (CGx), and airspeed (Ux) using readily available flight control inputs and aircraft attitudes. The models are tested on clean data, turbulence-affected data, and reduced training data to assess performance trade-offs between computational cost and prediction accuracy. Results demonstrate that GPR consistently achieves the highest accuracy across all prediction tasks with maximum errors below 0.3% of nominal values, though at significantly higher computational cost compared to ANN and SVM. Under turbulent conditions, ANN and GPR exhibit notable reductions in accuracy, resulting in all three models (ANN, GPR, and SVM) achieving similar levels of prediction performance. Data reduction analysis reveals that using the Multipoint Maximal Variance Retention (MMVR) algorithm allows an 85–90% reduction in training data while keeping errors below 3.5%, striking an optimal balance between accuracy and efficiency. These analyses demonstrate the feasibility of ML-based operational/flight parameter estimation for AAM operations where direct measurement systems are impractical or cost-prohibitive.