The interaction between the helicopter vibrations and the pilot involuntary control input can lead to the emergence of adverse, possibly even unstable, feedback loops. These phenomena are called Pilot-Assisted Oscillations (PAO). One of the most important is the "Collective Bounce", caused by vertical vibrations of the cockpit inducing an unwanted collective control input. On the rotorcraft side, the main rotor coning mode excitation has been shown to produce a phase margin reduction in the collective pitch-heave loop transfer function. On the pilot's side, biometrics such as stature, weight, age and sex are known to play a major role, but relatively limited effort has been placed in exploring the effects of their variability. This work represents a first attempt at filling the gap. A pseudo-random population of pilots, exhibiting different biometrics, is generated and the corresponding multibody biomechanical models are derived. The population is then simulated in a feedback loop with the rotorcraft dynamics and allowed to evolve, through a genetic (de-)optimization algorithm, towards the individuals most likely to be prone to instability. The result of the (de-)optimization process is the identification of the worst possible pilot biometrics with regard to collective bounce proneness on the modeled rotorcraft.
Helicopter Collective Bounce Proneness: Which are the Good, the Bad (and the Ugly!) Pilot Biometrics?
ZANONI, ANDREA;MUSCARELLO, VINCENZO
2017-01-01
Abstract
The interaction between the helicopter vibrations and the pilot involuntary control input can lead to the emergence of adverse, possibly even unstable, feedback loops. These phenomena are called Pilot-Assisted Oscillations (PAO). One of the most important is the "Collective Bounce", caused by vertical vibrations of the cockpit inducing an unwanted collective control input. On the rotorcraft side, the main rotor coning mode excitation has been shown to produce a phase margin reduction in the collective pitch-heave loop transfer function. On the pilot's side, biometrics such as stature, weight, age and sex are known to play a major role, but relatively limited effort has been placed in exploring the effects of their variability. This work represents a first attempt at filling the gap. A pseudo-random population of pilots, exhibiting different biometrics, is generated and the corresponding multibody biomechanical models are derived. The population is then simulated in a feedback loop with the rotorcraft dynamics and allowed to evolve, through a genetic (de-)optimization algorithm, towards the individuals most likely to be prone to instability. The result of the (de-)optimization process is the identification of the worst possible pilot biometrics with regard to collective bounce proneness on the modeled rotorcraft.File | Dimensione | Formato | |
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