Dynamic neuro-fuzzy control design for civil aviation aircraft in intelligent landing system

This paper presents the aircraft intelligent landing system with dynamic neuro-fuzzy controller that enhances the fault tolerant capabilities of a high performance civil aviation aircraft during the landing phase when subjected to severe winds and failures such as stuck control surfaces. The controller proposed in this paper is comprised of a fuzzy logic controller (FLC) in the feedback configuration and two dynamic neural networks in the forward path. A dynamic control network (DCN) is used to control the intelligent landing system, and a dynamic learning network (DLN) is employed to learn the weighting factor of the fuzzy logic. It is envisaged that the integration of fuzzy logic and neural network based-controller will encompass the merits of both technologies, and thus provide a robust controller for the intelligent landing system. The fuzzy logic controller, based on fuzzy set theory, provides a means for converting a linguistic control strategy into control action and offering a high level of computation. This design is carried out for no failure conditions but with the aircraft being subjected to winds. Simulation studies indicate that the designed conventional controller has only a limited failure handling ability. However, neural controller augmentation considerably improves the ability to handle large faults and meet the strict touchdown dispersion requirements, thus enlarging the fault-tolerance envelope.