On-Line Learning Nonlinear Direct Neurocontrollers for Restructurable Control Systems

Abstract

This paper describes an innovative approach to the problem of the on-line determination of a control law in order to achieve a dynamic reconfiguration of an aircraft that has sustained extensive damage to a vital control surface. The approach consists of the use of on-line learning neural network controllers that have the capability of bringing an aircraft, whose dynamics can become unstable after a substantial damage, back to an equilibrium condition. This goal has been achieved through the use of a specific training algorithm, the extended back-propagation algorithm (EBPA), and proper selection of the architectures for the neural network controllers. The EBPA has recently shown remarkable improvements over the back-propagation algorithm in terms of convergence time and local minimum problems. The methodology is illustrated through a nonlinear dynamic simulation of a typical combat maneuver for a high-performance aircraft.