Dual Fuzzy Neural Network Control in Civil Aviation Intelligent Landing System

摘要

An intelligent landing system design using dual fuzzy neural network is proposed for research aircraft similar in configuration to civil aviation aircraft. The control law to track the pitch rate command is developed based on system theory. The controller architecture uses two fuzzy neural networks, which is capable of implementing fuzzy inference in general and neural network mechanism in particular. Neural network 1 with linear filters and back propagation through time learning algorithm is used to approximate the control law as system control part. The bounded signal requirement to develop the neural controller is circumvented using an off-line finite time training scheme in neural network 2 as system learning part, which provides the necessary stability and tracking performances. On-line learning scheme is implemented to compensate for uncertainties due to variation in aerodynamic coefficients, control surface failures and also variations in center of gravity position. The performance of the proposed control scheme is validated at different flight conditions. The disturbance rejection capability of the neural controller is analyzed in the presence of the realistic gust and sensor noises.