Adaptive Flight Path Control of Airborne Wind Energy Systems

In this paper, we applied a system identification algorithm and an adaptive controller to a simple kite system model to simulate crosswind flight maneuvers for airborne wind energy harvesting. The purpose of the system identification algorithm was to handle uncertainties related to a fluctuating wind speed and shape deformations of the tethered membrane wing. Using a pole placement controller, we determined the required locations of the closed-loop poles and enforced them by adapting the control gains in real time. We compared the path-following performance of the proposed approach with a classical proportional-integral-derivative (PID) controller using the same system model. The capability of the system identification algorithm to recognize sudden changes in the dynamic model or the wind conditions, and the ability of the controller to stabilize the system in the presence of such changes were confirmed. Furthermore, the system identification algorithm was used to determine the parameters of a kite with variable-length tether on the basis of data that were recorded during a physical flight test of a 20 kW kite power system. The system identification algorithm was executed in real time, and significant changes were observed in the parameters of the dynamic model, which strongly affect the resulting response.

[1]  Moritz Diehl,et al.  Warping model predictive control for application in control of a real airborne wind energy system , 2018 .

[2]  Daniel Rixen,et al.  Nonlinear aeroelasticity, flight dynamics and control of a flexible membrane traction kite , 2013 .

[3]  Roland S. Burns,et al.  Advanced control engineering , 2001 .

[4]  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.

[5]  A. D. Wachter,et al.  Power from the skies : Laddermill takes airborne wind energy to new heights , 2010 .

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

[7]  James B. Rawlings,et al.  Optimizing Process Economic Performance Using Model Predictive Control , 2009 .

[8]  Roland Schmehl,et al.  Improving reliability and safety of airborne wind energy systems , 2020, Wind Energy.

[9]  Roland Schmehl,et al.  Flight Path Planning in a Turbulent Wind Environment , 2018 .

[10]  Daniel Rixen,et al.  Dynamic Nonlinear Aeroelastic Model of a Kite for Power Generation , 2014 .

[11]  Storm Dunker,et al.  Tether and Bridle Line Drag in Airborne Wind Energy Applications , 2018 .

[12]  Dario Piga,et al.  Optimization of airborne wind energy generators , 2012 .

[13]  Mario Zanon,et al.  A relaxation strategy for the optimization of Airborne Wind Energy systems , 2013, 2013 European Control Conference (ECC).

[14]  Roland Schmehl,et al.  Traction Power Generation with Tethered Wings , 2013 .

[15]  F. W. Gay System Stability , 1941, Transactions of the American Institute of Electrical Engineers.

[16]  Sebastian Rapp,et al.  Vertical Takeoff and Landing of Flexible Wing Kite Power Systems , 2018 .

[17]  Michael Erhard,et al.  Theory and Experimental Validation of a Simple Comprehensible Model of Tethered Kite Dynamics Used for Controller Design , 2013 .

[18]  Uwe Fechner,et al.  A Methodology for the Design of Kite-Power Control Systems , 2016 .

[19]  Roland Schmehl,et al.  Quasi-Steady Model of a Pumping Kite Power System , 2017, Renewable Energy.

[20]  Philip Bechtle,et al.  AWEsome: An Affordable Standardized Open-Source Test Platform for AWE Systems , 2017 .

[21]  Leo Goldstein,et al.  Airborne Wind Energy Conversion Systems with Ultra High Speed Mechanical Power Transfer , 2013 .

[22]  Roland Schmehl,et al.  System identification, fuzzy control and simulation of a kite power system with fixed tether length , 2018 .

[23]  William W. Hager,et al.  Updating the Inverse of a Matrix , 1989, SIAM Rev..

[24]  Sang-Gu Lee,et al.  Simultaneous solutions of Sylvester equations and idempotent matrices separating the joint spectrum , 2011 .

[25]  R. Schieder,et al.  System stability , 1941, Electrical Engineering.

[26]  Roland Schmehl,et al.  Aerodynamic characterization of a soft kite by in situ flow measurement , 2018, Wind Energy Science.

[27]  Roland Schmehl,et al.  Design and Experimental Characterization of a Pumping Kite Power System , 2013 .

[28]  Roland Schmehl,et al.  Dynamic Model of a Pumping Kite Power System , 2014, ArXiv.

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

[30]  M. Diehl,et al.  Airborne Wind Energy , 2023, Green Energy and Technology.

[31]  K. Park,et al.  Second-order structural identification procedure via state-space-based system identification , 1994 .

[32]  Roland Schmehl,et al.  Aeroelastic Simulation of Flexible Membrane Wings based on Multibody System Dynamics , 2013 .

[33]  Vladimir Bobal,et al.  Digital Self-tuning Controllers: Algorithms, Implementation and Applications , 2005 .

[34]  Wubbo Johannes Ockels,et al.  Tracking control with adaption of kites , 2010, ArXiv.

[35]  Louis C. Westphal Adaptive and self-tuning control , 1995 .

[36]  Moritz Diehl,et al.  A quaternion‐based model for optimal control of an airborne wind energy system , 2015, ArXiv.

[37]  R. Plackett Some theorems in least squares. , 1950, Biometrika.