Rotorcraft Vibration Adjustment Using Neural Network

The invention transitions from a single-objective vibration minimization approach to a multi-dimensional optimization framework that simultaneously considers both measured vibration levels and physiological perception criteria. This is achieved by introducing a second objective function that models occupant comfort based on vibration characteristics, thereby adding a new dimension to the optimization problem that accounts for human sensory response beyond raw measurement data.

The invention implements a feedback mechanism where physiological perception models provide information back to the adjustment process. By incorporating models that predict how occupants perceive vibration based on measured characteristics, the system creates a closed-loop feedback system that continuously refines adjustment parameters to satisfy both measurement objectives and comfort requirements.

The invention changes the parameters of the optimization problem by introducing physiological perception models as additional objective functions. Instead of solely optimizing based on measured vibration magnitudes, the system now optimizes based on a composite set of parameters that includes both physical vibration measurements and modeled physiological responses, thereby transforming the optimization criteria to simultaneously achieve efficiency and reliability.

The invention creates a composite objective function that combines multiple types of information: measured vibration data and physiological perception models. This composite approach integrates different types of data and modeling results into a unified optimization framework, allowing the system to achieve both vibration reduction efficiency and occupant comfort by synthesizing multiple information sources into a single decision-making structure.

The invention applies preliminary action by pre-training neural network models with physiological perception data before actual rotor adjustment operations. By preparing these predictive models in advance with comprehensive training data that includes physiological response characteristics, the system enables rapid adjustments during operation while maintaining high accuracy in predicting occupant comfort, as the models are already calibrated to recognize comfort-relevant patterns.

The invention substitutes traditional trial-and-error mechanical adjustment methods with neural network-based predictive modeling. Instead of physically adjusting rotor parameters through iterative testing, the system uses trained neural networks to predict optimal adjustment parameters based on input vibration data and physiological models, thereby replacing time-consuming mechanical iteration with rapid computational prediction that maintains or improves accuracy.