Tactile Limit Avoidance Cueing Using Adaptive Dynamic Trim

This paper describes the implementation of an adaptive dynamic trim limit avoidance method, applied to main rotor blade stall. The system predicts the blade stall limit without a priori training of its neural network and solved for the critical control position where a tactile softstop is placed as a limit avoidance cue. The method is evaluated in piloted simulations, at the Georgia Tech Carefree Maneuver Lab, within the Real-Time Interactive Prototype Technology Integration Development Environment (RIPTIDE) developed at the Army/NASA Rotorcraft Center. The method is compared to the static neural network prediction method used in the HELMEE study for the same limit. While tactile cueing results in increased agility due to reduced pilot workload, the adaptive dynamic trim based approach is shown to be more effective in maintaining the limit parameter response within the prescribed limits.