A novel online model-based wind estimation approach for quadrotor micro air vehicles using low cost MEMS IMUs

This work extends the drag-force enhanced quadrotor model by denoting the free stream air velocity as the difference between the ground speed and the wind speed. It is demonstrated that a relatively simple nonlinear observer is capable of estimating the local wind components, provided accelerometer and GPS-velocity measurements are available. We perform a wind tunnel experiment at various wind speeds using a quadrotor vehicle with a low-cost Inertial Measurement Unit (IMU) and a motion tracking system to provide accurate ground speed measurements. It is shown that the onboard Extended Kalman Filter (EKF) accurately estimates the wind components.

[1]  Sami Haddadin,et al.  Simultaneous estimation of aerodynamic and contact forces in flying robots: Applications to metric wind estimation and collision detection , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Robert E. Mahony,et al.  Aerodynamic power control for multirotor aerial vehicles , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Timothy W. McLain,et al.  Quadrotors and Accelerometers: State Estimation with an Improved Dynamic Model , 2014, IEEE Control Systems.

[4]  Houria Siguerdidjane,et al.  On the guidance of a UAV under unknown wind disturbances , 2014, 2014 IEEE Conference on Control Applications (CCA).

[5]  Rogelio Lozano,et al.  Trajectory Control of a Quadrotor Subject to 2D Wind Disturbances , 2013, J. Intell. Robotic Syst..

[6]  Tarek Hamel,et al.  Nonlinear control of VTOL UAVs incorporating flapping dynamics , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Claire J. Tomlin,et al.  Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment , 2007 .

[8]  Philippe Martin,et al.  The true role of accelerometer feedback in quadrotor control , 2010, 2010 IEEE International Conference on Robotics and Automation.

[9]  J. Neidhoefer,et al.  Wind Field Estimation for Small Unmanned Aerial Vehicles , 2010 .

[10]  Steven L. Waslander,et al.  Wind Disturbance Estimation and Rejection for Quadrotor Position Control , 2009 .

[11]  Timothy Bretl,et al.  An improved model-based observer for inertial navigation for quadrotors with low cost IMUs , 2016 .

[12]  Yasmina Bestaoui,et al.  Quad-rotor MAV trajectory planning in wind fields , 2013, 2013 IEEE International Conference on Robotics and Automation.

[13]  Gamini Dissanayake,et al.  Improved State Estimation in Quadrotor MAVs: A Novel Drift-Free Velocity Estimator , 2015, IEEE Robotics & Automation Magazine.

[14]  Robert E. Mahony,et al.  Estimating body-fixed frame velocity and attitude from inertial measurements for a quadrotor vehicle , 2014, 2014 IEEE Conference on Control Applications (CCA).

[15]  Gamini Dissanayake,et al.  Model-aided state estimation for quadrotor micro air vehicles amidst wind disturbances , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  A. Krener,et al.  Nonlinear controllability and observability , 1977 .

[17]  Teodor Tomic.  Evaluation of acceleration-based disturbance observation for multicopter control , 2014, 2014 European Control Conference (ECC).