Adaptive Limit and Control Margin Prediction and Limit Avoidance

This paper describes the use of a novel adaptive limit margin detection and avoidance technique for predicting dynamic trim values of limit parameters and corresponding control margins for limit avoidance in a rotorcraft. Adaptive neural networks are employed for on-line learning and modelling error compensation. The new approach utilizes an observer type adaptive neural network loop for an estimation of the correct aircraft model. The identified aircraft model is then used to predict the dynamic trim response behavior of the limit parameters and the corresponding control margins. Only standard sensor measurements are used for adaptation. The effectiveness of the proposed adaptive limit detection technique is evaluated through a series of simulations using the nonlinear Generic Tiltrotor Simulation (GTRSIM) program.