Path Following for Unmanned Aerial Vehicles Using L1 Adaptive Augmentation of Commercial Autopilots

The article of record as published may be located at http://dx.doi.org/10.2514/1.42056The paper presents a three-dimensional path-following control algorithm that expands the capabilities of conventional autopilots, which are normally designed to provide only guidance loops for waypoint navigation. Implementation of this algorithm broadens the range of possible applications of small unmanned aerial vehicles. The solution proposed takes explicit advantage of the fact that normally these vehicles are equipped with autopilots stabilizing the vehicles and providing angular-rate tracking capabilities. Therefore, the overall closed-loop system exhibits naturally an inner–outer (dynamics–kinematics) control loop structure. The outer-loop path-following control law developed relies on a nonlinear control strategy derived at the kinematic level, while the inner-loop consisting of the autopilot together with an L1 adaptive augmentation loop is designed to meet strict performance requirements in the presence of unmanned aerial vehicle modeling uncertainty and environmental disturbances. A rigorous proof of stability and performance of the path-following closed-loop system, including the dynamics of the unmanned aerial vehicle with its autopilot, is given. The paper bridges the gap between theory and practice and includes results of extensive flight tests performed in Camp Roberts, California, which demonstrate the benefits of the framework adopted for the control system design.Research is supported by the United States Special Operations Command under Tactical Network Training Grant, Office of Naval Research under Contracts N00014-08-WR-20287 and N00014-05- 1-0828, U.S. Air Force Office of Scientific Resarch under Contract No. FA9550-08-1-0135, U.S. Army Research Office under Contract No. W911NF-06-1-0330, NASA under Contracts NNX08BA64 and NNX08BA65A, and European Commission under Contracts EU-FP6-IST-035223 (GREX), EU-FP7-ICT-231378 (Cognitive Cooperative Control for Autonomous Underwater Vehicles), and MRTN-CT-2006-036186 (FREESUBNET Training Network)