Direct Adaptive Neural Flight Controller for F-8 Fighter Aircraft

Abstract

A novel neural network approach based on model-following direct adaptive control system design is proposed to improve damping and also to follow pilot commands accurately. The control law is derived using system theory and an implicit function theorem. A neural network with a linear filter is used to approximate the control law. The neural controller is trained using offline (finite interval of time) and online learning strategies. The neural controller was trained offline using the error signal between the aircraft response and pilot command (reference model). The offline-trained neural controller provides the necessary damping and tracking performances. The neural controller is adapted online for variations in aerodynamic coefficients or control surface deficiencies caused by control surface damage. A discrete time linear dynamic longitudinal model of a high-performance aircraft (F-8) is considered to demonstrate the effectiveness of the proposed control scheme. The performance results of the proposed control scheme are compared with the recently developed dynamic inversion technique and fully tuned radial basis function network. The neural controller performance is also evaluated for steady climb-and-hold maneuver in a nonlinear six-degree-of-freedom aircraft model.