The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform

Roland Schmehl - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Flight Path Control of Airborne Wind Energy Systems
    Energies, 2020
    Co-Authors: Tarek N. Dief, Uwe Fechner, Roland Schmehl, Shigeo Yoshida, Mostafa A. Rushdi
    Abstract:

    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.

  • Flight Path Control of kite power systems in a turbulent wind environment
    2016 American Control Conference (ACC), 2016
    Co-Authors: Uwe Fechner, Roland Schmehl
    Abstract:

    Converting the traction power of kites into electricity can be a low cost solution for wind energy. A reliable and robust Control system is considered to be crucial for the commercial success of the technology. The focus of this paper is the Control of the Flight Path projected onto the unit sphere. The proposed algorithm is straightforward to implement because it uses mainly LPV and PID Control components and is thus easy to certify by the authorities, it allows to define limits for the maximal turn rate to avoid sensor failures, and it allows to use a low gain in the feedback loop to be robust against Control loop delays up to 200 ms. This is achieved by splitting the Control of the Flight Path into two different modes of operation: Turn maneuvers and parts of the Flight Path, where the course angle is constant. During the turning maneuvers mainly feedforward Control is used, therefore reducing stability problems. During the straight Flight Path segments feedback Control in combination with Nonlinear Dynamic Inversion (NDI) is used and thus deviations from the planned Flight Path can be compensated. NDI is needed to compensate the effect of gravity on the turn rate, but also the changes of the steering sensitivity, depending on the apparent wind speed and the angle of attack. A dynamic 4-point model of the kite is used for the validation of the Controller performance. The kite is flown in a turbulent 3D wind field using the Mann-model for modeling the turbulence. The results show a low tracking error even in very turbulent wind conditions and even in the presence of large sensor errors and Control loop delays: At a turbulence intensity of 26.5% the elevation error was still lower than 1.5°.

  • ACC - Flight Path Control of kite power systems in a turbulent wind environment
    2016 American Control Conference (ACC), 2016
    Co-Authors: Uwe Fechner, Roland Schmehl
    Abstract:

    Converting the traction power of kites into electricity can be a low cost solution for wind energy. A reliable and robust Control system is considered to be crucial for the commercial success of the technology. The focus of this paper is the Control of the Flight Path projected onto the unit sphere. The proposed algorithm is straightforward to implement because it uses mainly LPV and PID Control components and is thus easy to certify by the authorities, it allows to define limits for the maximal turn rate to avoid sensor failures, and it allows to use a low gain in the feedback loop to be robust against Control loop delays up to 200 ms. This is achieved by splitting the Control of the Flight Path into two different modes of operation: Turn maneuvers and parts of the Flight Path, where the course angle is constant. During the turning maneuvers mainly feedforward Control is used, therefore reducing stability problems. During the straight Flight Path segments feedback Control in combination with Nonlinear Dynamic Inversion (NDI) is used and thus deviations from the planned Flight Path can be compensated. NDI is needed to compensate the effect of gravity on the turn rate, but also the changes of the steering sensitivity, depending on the apparent wind speed and the angle of attack. A dynamic 4-point model of the kite is used for the validation of the Controller performance. The kite is flown in a turbulent 3D wind field using the Mann-model for modeling the turbulence. The results show a low tracking error even in very turbulent wind conditions and even in the presence of large sensor errors and Control loop delays: At a turbulence intensity of 26.5% the elevation error was still lower than 1.5°.

Uwe Fechner - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Flight Path Control of Airborne Wind Energy Systems
    Energies, 2020
    Co-Authors: Tarek N. Dief, Uwe Fechner, Roland Schmehl, Shigeo Yoshida, Mostafa A. Rushdi
    Abstract:

    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.

  • Flight Path Control of kite power systems in a turbulent wind environment
    2016 American Control Conference (ACC), 2016
    Co-Authors: Uwe Fechner, Roland Schmehl
    Abstract:

    Converting the traction power of kites into electricity can be a low cost solution for wind energy. A reliable and robust Control system is considered to be crucial for the commercial success of the technology. The focus of this paper is the Control of the Flight Path projected onto the unit sphere. The proposed algorithm is straightforward to implement because it uses mainly LPV and PID Control components and is thus easy to certify by the authorities, it allows to define limits for the maximal turn rate to avoid sensor failures, and it allows to use a low gain in the feedback loop to be robust against Control loop delays up to 200 ms. This is achieved by splitting the Control of the Flight Path into two different modes of operation: Turn maneuvers and parts of the Flight Path, where the course angle is constant. During the turning maneuvers mainly feedforward Control is used, therefore reducing stability problems. During the straight Flight Path segments feedback Control in combination with Nonlinear Dynamic Inversion (NDI) is used and thus deviations from the planned Flight Path can be compensated. NDI is needed to compensate the effect of gravity on the turn rate, but also the changes of the steering sensitivity, depending on the apparent wind speed and the angle of attack. A dynamic 4-point model of the kite is used for the validation of the Controller performance. The kite is flown in a turbulent 3D wind field using the Mann-model for modeling the turbulence. The results show a low tracking error even in very turbulent wind conditions and even in the presence of large sensor errors and Control loop delays: At a turbulence intensity of 26.5% the elevation error was still lower than 1.5°.

  • ACC - Flight Path Control of kite power systems in a turbulent wind environment
    2016 American Control Conference (ACC), 2016
    Co-Authors: Uwe Fechner, Roland Schmehl
    Abstract:

    Converting the traction power of kites into electricity can be a low cost solution for wind energy. A reliable and robust Control system is considered to be crucial for the commercial success of the technology. The focus of this paper is the Control of the Flight Path projected onto the unit sphere. The proposed algorithm is straightforward to implement because it uses mainly LPV and PID Control components and is thus easy to certify by the authorities, it allows to define limits for the maximal turn rate to avoid sensor failures, and it allows to use a low gain in the feedback loop to be robust against Control loop delays up to 200 ms. This is achieved by splitting the Control of the Flight Path into two different modes of operation: Turn maneuvers and parts of the Flight Path, where the course angle is constant. During the turning maneuvers mainly feedforward Control is used, therefore reducing stability problems. During the straight Flight Path segments feedback Control in combination with Nonlinear Dynamic Inversion (NDI) is used and thus deviations from the planned Flight Path can be compensated. NDI is needed to compensate the effect of gravity on the turn rate, but also the changes of the steering sensitivity, depending on the apparent wind speed and the angle of attack. A dynamic 4-point model of the kite is used for the validation of the Controller performance. The kite is flown in a turbulent 3D wind field using the Mann-model for modeling the turbulence. The results show a low tracking error even in very turbulent wind conditions and even in the presence of large sensor errors and Control loop delays: At a turbulence intensity of 26.5% the elevation error was still lower than 1.5°.

Mostafa A. Rushdi - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Flight Path Control of Airborne Wind Energy Systems
    Energies, 2020
    Co-Authors: Tarek N. Dief, Uwe Fechner, Roland Schmehl, Shigeo Yoshida, Mostafa A. Rushdi
    Abstract:

    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.

Kathleen C Howell - One of the best experts on this subject based on the ideXlab platform.

  • look ahead Flight Path Control for solar sail spacecraft
    Proceedings of the Institution of Mechanical Engineers Part G: Journal of Aerospace Engineering, 2013
    Co-Authors: Geoffrey Wawrzyniak, Kathleen C Howell
    Abstract:

    Recent investigations of trajectory options that incorporate solar sails have been motivated by missions to observe planetary poles or to communicate with an outpost at the lunar south pole. Designing reference trajectories and understanding their fundamental dynamics are the necessary first steps toward flying spacecraft in dynamically complicated regimes. However, the existence of a reference orbit alone is insufficient for Flight operations. Two variations of a turn-and-hold strategy are examined for Flight-Path Control: an approach that implements multiple turns to achieve a target in an error-free scenario and an approach that incorporates a look-ahead strategy to accommodate representative errors.

  • Look-ahead Flight Path Control for solar sail spacecraft
    Proceedings of the Institution of Mechanical Engineers Part G: Journal of Aerospace Engineering, 2012
    Co-Authors: Geoffrey Wawrzyniak, Kathleen C Howell
    Abstract:

    Recent investigations of trajectory options that incorporate solar sails have been motivated by missions to observe planetary poles or to communicate with an outpost at the lunar south pole. Design...

Uav Special - One of the best experts on this subject based on the ideXlab platform.

  • Research on Counteracting Side Wind in Landing Control for Fly-Wing UAV
    Computer Simulation, 2009
    Co-Authors: Ma Song, Uav Special
    Abstract:

    Tailless flying wing UAV brings difficulties to its lateral stability and Control,especially for its Flight Path Control with side wind.There are two strategies widely used to counteract side wind,one is keeping the course with a fixed sideslip angle,the other is keeping the course with a drift angle.Advantage and disadvantage of each strategy are commented,and different lateral Controllers are designed for each strategy.According to the characteristics of flying wing UAV,different strategies are used in various phases.Keeping the course with a drift angle is used in approach and early glide phase,while keeping the course with a fixed sideslip angle is used in later glide and landing phase.Simulation results show that the strategy and Controller is good at counteracting side wind.