State Estimation for Kite Power Systems with Delayed Sensor Measurements

Abstract We present a novel estimation approach for airborne wind energy systems with ground-based control and energy generation. The estimator fuses measurements from an inertial measurement unit attached to a tethered wing and position measurements from a camera as well as line angle sensors in an unscented Kalman filter. We have developed a novel kinematic description for tethered wings to specifically address tether dynamics. The presented approach simultaneously estimates feedback variables for a flight controller as well as model parameters, such as a time-varying delay. We demonstrate the performance of the estimator for experimental flight data and compare it to a state-of-the-art estimator based on inertial measurements.

[1]  Rocco Vertechy,et al.  Airborne Wind Energy Systems: A review of the technologies , 2015 .

[2]  Tony A. Wood,et al.  Visual Motion Tracking and Sensor Fusion for Ground-Based Kite Power Systems , 2017 .

[3]  Tony A. Wood,et al.  Visual Motion Tracking and Sensor Fusion for Kite Power Systems , 2018 .

[4]  Michael Erhard,et al.  Sensors and navigation algorithms for flight control of tethered kites , 2013, 2013 European Control Conference (ECC).

[5]  Lorenzo Fagiano,et al.  Automatic Crosswind Flight of Tethered Wings for Airborne Wind Energy: Modeling, Control Design, and Experimental Results , 2013, IEEE Transactions on Control Systems Technology.

[6]  Roland Schmehl,et al.  Applied Tracking Control for Kite Power Systems , 2014 .

[7]  Aldo U. Zgraggen,et al.  Model-based flight path planning and tracking for tethered wings , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[8]  Lorenzo Fagiano,et al.  Real-Time Optimization and Adaptation of the Crosswind Flight of Tethered Wings for Airborne Wind Energy , 2013, IEEE Transactions on Control Systems Technology.

[9]  Michael Erhard,et al.  Control of Towing Kites for Seagoing Vessels , 2012, IEEE Transactions on Control Systems Technology.

[10]  Tony A. Wood,et al.  Range-inertial estimation for airborne wind energy , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[11]  Anastasios I. Mourikis,et al.  Online temporal calibration for camera–IMU systems: Theory and algorithms , 2014, Int. J. Robotics Res..

[12]  Jonathan Kelly On temporal and spatial calibration for high accuracy visual-inertial motion estimation , 2011 .

[13]  Mario Zanon,et al.  Orbit control for a power generating airfoil based on nonlinear MPC , 2012, 2012 American Control Conference (ACC).

[14]  Lorenzo Fagiano,et al.  On Sensor Fusion for Airborne Wind Energy Systems , 2012, IEEE Transactions on Control Systems Technology.

[15]  Aldo U. Zgraggen,et al.  Model-based identification and control of the velocity vector orientation for autonomous kites , 2015, 2015 American Control Conference (ACC).

[16]  Tony A. Wood,et al.  Pumping Cycle Kite Power with Twings , 2018 .

[17]  Aldo U. Zgraggen Automatic Power Cycles for Airborne Wind Energy Generators , 2014 .