Augmentation of an Intelligent Flight Control System for a Simulated C-17 Aircraft

Abstract

This paper examines a neural-adaptive flight control system augmented with linear programming theory and adaptive critic techniques for a simulated C-17 aircraft. The baseline Intelligent Flight Control (IFC) system is composed of a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and make the controller easy to apply when implemented on different aircraft. In this study, IFC has been augmented with linear programming (LP) theory and adaptive critic technologies. LP is used to optimally allocate requested control deflections and the adaptive critic modifies the parameters of the aircraft reference model for consistent handling qualities. Full-motion piloted simulation studies were performed on a Boeing C-17. Subjects included NASA and Air Force pilots. Results, including subjective pilot ratings and time response characteristics of the system, demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.