Improved path following for kites with input delay compensation

In kite power systems, substantial input delay between the actuator and the tethered kite can severely hinder the performance of the control algorithm, limiting the capability of the system to track power-optimal loops. We propose a method that deals with this impediment by using a data-based adaptive filter that predicts future states despite variations in wind conditions, other exogenous disturbances and model mismatch. Moreover, we exploit the geometry of the path on a hemisphere to enhance the guidance algorithm for such kites at a fixed length tether. The objective is to improve the automatic crosswind operation of an airborne wind energy system. To test this under realistic conditions, a small-scale prototype was employed for a series of experiments. The robustness to disturbances and the performance of the algorithm in path following was evaluated for a number of different paths. © 2015 IEEE

Predictive Control of Autonomous Kites in Tow Test Experiments

In this paper we present a model-based control approach for autonomous flight of kites for wind power generation. Predictive models are considered to compensate for delay in the kite dynamics. We apply Model Predictive Control (MPC), with the objective of guiding the kite to follow a figure-of-eight trajectory, in the outer loop of a two level control cascade. The tracking capabilities of the inner-loop controller depend on the operating conditions and are assessed via a frequency domain robustness analysis. We take the limitations of the inner tracking controller into account by encoding them as optimisation constraints in the outer MPC. The method is validated on a kite system in tow test experiments.Comment: The paper has been accepted for publication in the IEEE Control Systems Letters and is subject to IEEE Control Systems Society copyright. Upon publication, the copy of record will be available at http://ieeexplore.ieee.or

Predictive Guidance Control for Autonomous Kites with Input Delay

In this paper we consider the design of a model predictive guidance controller in a cascaded control scheme for an autonomous kite with significant input delay. The input rate of the guidance is bounded to ensure robust performance of the underlying tracking controller. This is achieved by analysisng the limitations of the tracking controller arising from model parameter uncertainty and input delay. The delay is accounted for in the control design by predicting the values of the feedback variables ahead of time based on the past inputs and the system models. To account for changing operating conditions the model parameters are updated online. The proposed method has been tested in a real-time hardware-in-the-loop simulation study

L0 and L1 Guidance and Path-Following Control for Airborne Wind Energy Systems

For an efficient and reliable operation of an Airborne Wind Energy System, it is widely accepted that the kite should follow a pre-defined optimized path. In this article, we address the problem of designing a trajectory controller so that such path is closely followed. The path-following controllers investigated are based on a well-known nonlinear guidance logic termed L1 and on a proposed modification of it, which we termed L0. We have developed and implemented both L0 and L1 controllers for an AWES. The two controllers have an easy implementation with an explicit expression for the control law based on the cross-track error, on the heading angle relative to the path, and on a single parameter L (L0 or L1, depending on each controller) that we are able to tune. The L0 controller has an even easier implementation since the explicit control law can be used without the need to switch controllers. Since the switching of controllers might jeopardize stability, the L0 controller has an important theoretical advantage in being able to guarantee stability on a larger domain of attraction.The simulation study shows that both nonlinear guidance logic controllers exhibit appropriate performance when the L parameter is adequately tuned, with the L0 controller showing a better performance when measured in terms of the average cross-track error. (c) 2022 by the authors. Licensee MDPI, Basel, Switzerland