Abstract:
This study investigates the use of machine learning (ML) models to estimate the gross weight (GW), the longitudinal position of the center of gravity (CGx), and 1/rev cyclic flapping angles (β1c and β1s) of a compound helicopter with three redundant controls - main rotor RPM, collective propeller thrust, and stabilator angle. Neural Network (NN), Gaussian Process for Regression (GPR), and Support Vector Machine (SVM) algorithms are employed to develop estimation models using supervised training. The airspeed, redundant controls, main rotor controls, aircraft attitudes, and main rotor torque are selected as input variables (predictors) to the models due to their accessibility through the aircraft Health and Usage Monitoring System (HUMS). The dataset is split into low-speed and high-speed regimes to compare the prediction accuracy and training cost of separate regime models against a combined full-regime model. Separate airspeed regime GPR models showed superior performance in GW estimation, with higher accuracy and cost-effectiveness compared to a single full-regime model. For CG estimation, GPR again outperformed NN and SVM, although the maximum outlier errors increase significantly if a 95% confidence interval is considered. Finally, for 1/rev cyclic flapping angle predictions, SVM estimations, though not superior to GPR or NN, were acceptable and had a significantly lower computational cost. The study also examined the importance of predictors, highlighting that, on average, certain predictors like rotor RPM and rotor torque are less influential, but their removal degraded performance and had no cost benefit.