Long Term Learning Adaptive Neural Network Estimator Based Limit Detection

Dynamic adaptive models are commonly used to estimate allowable control travel and the proximity to a limiting flight condition in the design of advanced envelope protection algorithms for fly by wire aircraft. In this paper linear models are compensated with adaptive neural networks to build adaptive models of relevant aircraft dynamics. A stack of data collected during flight is used to update the network weights online. The data stack is made up of instantaneously measured data and recorded data during simulations. It is observed that by using recorded data in a stack can cancel out new modeling errors in a short time and results with better predictions of approaching limits compared to using instantaneous data only.