Increasing air traffic density as well as new strict environmental regulations make necessary to develop more advanced guidance systems for civil transportation aircraft since complex manoeuvres may appear necessary to meet the corresponding safety, efficiency and environmental constraints. In this paper, a flatness-based guidance control scheme is proposed to fulfil this objective. From the point of view of flatness theory, flight guidance dynamics are shown to be implicit differentially flat with respect to the inertial position of an aircraft. To overcome the numerical difficulty implied by the implicit character of this flatness property, artificial neural networks are introduced to capture the differential relationship between flat outputs and guidance directives which can be submitted to the autopilot so that the aircraft is able to perform complex trajectories. Additional adaptive capability appears necessary to compensate for model approximations, disturbances and neural network limitations.
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