Neural networks in nonlinear aircraft flight control

Abstract

This paper describes an approach for incorporating a neural network with real-time learning capability in a flight control architecture. The architecture is also applicable, in general, for the control of processes described by nonlinear differential equations of motion in which there exists a control for each degree of freedom. The main features are that the defining equations of motion for the process to be controlled are poorly known with respect to their functional forms, and that the functional forms, themselves, may undergo sudden and unexpected variation. It is well known that such systems are difficult to control, particularly when the effect of the control action enters nonlinearly. Numerical results based on 6DOF simulations of a high performance aircraft are presented to illustrate the potential benefits of incorporating neural networks as a part of a flight control system architecture.