A stochastic approach to optimal soaring problem and robust adaptive LQG control

Abstract

The soaring performance of a glider Unmanned Aerial Vehicle (UAV) depends a great deal on the thermal characteristics including thermal strength and location. Onboard sensor measurements are typically corrupted by noise whereas gusts and turbulence effects can cause significant performance degradation during inter-thermal glide. In this paper we provide a stochastic approach to the optimal soaring problem. We quantify the performance losses due to incorrect thermal data, consider the effect of stochastic gust disturbances, and simulate the deterioration in the system response in the presence of additional sensor inaccuracies. Although the recovery of losses due to incorrect thermal data is possible only if data resources being used could be enhanced, an adaptive tracking control implied by a Linear Quadratic Regulator (LQR) design inherently provides the optimal control when the aircraft is subject to gust represented by a Gaussian white noise. The LQR design, however, needs to be improved if in addition stochastic noise is posed at each sensor measurement channel. By including an adaptive Kalman-Buey filter and modifying the adaptive law and our optimum trajectory generation algorithm inputs accordingly, we obtain an adaptive Linear Quadratic Gaussian (LQG) control design. As a result, the aircraft response meets the performance requirements in the presence of stochastic process and measurement noise. Copyright © 2007 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.