Theory and flight test validation of long term learning adaptive flight controller

Most current recursive adaptive methodologies employing Neural Networks rely only on the instantaneous states for the purpose of adaptation. For high dimensional problems commonly encountered in control of high performance aerospace systems these methods are susceptible to adapting only on the current region of state space. Since these methods do not exhibit long term learning and global adaptation, little performance gain can be expected when a system returns to a previously encountered region of the state space. In this paper we propose a novel approach to adaptive control which uses the current or the online information as well as stored or background information concurrently for adaptation. We show that using a combined online and background learning approach it is possible to incorporate long term learning in the adaptive flight controller, which enhances performance of the controller when it encounters a maneuver that has been performed in the past. The theory and implementation of such an approach, with a summary of simulation results, are described. The algorithm has been successfully tested in flight and analysis of the data is currently ongoing. Flight test results will be presented at the time of the conference.