Intelligent landing control system for civil aviation aircraft with dual fuzzy neural network

This paper presents the intelligent landing control system that overcome wind disturbance problem of a civil aviation aircraft during the landing phase when subjected to severe winds and failures such as stuck control surfaces. The controller architecture uses a dual fuzzy neural network (DFNN) controller, which is capable of implementing fuzzy inference in general and neural network mechanism in particular. A systematic method for mapping an existing rule base into a set of dual fuzzy neural network weights has also been presented. However, in order to utilize this method to initialize the dual fuzzy neural network weights, such a rule base obtained from domain experts or from experimental data through systematic, knowledge acquisition methods has been proposed. It uses one neural network as on-line learning and does not need a priori training. Simulations show that it improved the performance of conventional automatic landing system (ALS) and guide the aircraft to a safe landing.