NEURAL-NETWORK-BASED MODELING OF ROTORCRAFT VIBRATION FOR REAL-TIME APPLICATIONS

Summary The overall objective of this ongoing effort is to provide the capability to model and simulate rotorcraft aeromechanics behaviors in real-tune. This would be accomplished by the addition of an aeromechanics element to an existing high-fidelity, real-time helicopter flight simulation. As a first step, the peak vertical vibration at the pilot floor location was considered in this neural-network-based study. The flight conditions considered were level flights, rolls, pushovers, pull-ups, autorotations, and landing flares. The NASA/Army UH-60A Airloads Program flight test database was the source of raw data. The present neural network training databases were created in a physically consistent manner. Two modeling approaches, with different physical assumptions, were considered. The first approach involved a "maneuver load factor" that was derived using the roll-angle and the pitch-rate. The second approach involved the three pilot control stick positions. The resulting, trained back-propagation neural networks were small, implying rapid execution. The present neural-network-based approach involving the peak pilot vibration was utilized in a quasi-static manner to simulate an extreme, time-varying pull-up maneuver. For the above pull-up maneuver, the maneuver load factor approach was better for real-time simulation, i.e., produced greater fidelity, as compared to the control stick positions approach. Thus, neural networks show promise for use in high-fidelity, realtime modeling of rotorcraft vibration.