Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Model predictive guidance control for autonomous kites


A. Romero Aguilar

Semester Thesis, SS16 (10520)

In this project the development of a non-linear model predictive control strategy, based on real-time iteration, for the guidance of autonomous kites is presented. We consider a non-linear unicycle model with input delay and, specifically, we focus on tracking eight-figure reference paths. To allow for changes in model parameters and still follow a fixed reference path, an optimization based path-planning algorithm has shown to be a suitable solution. This results in a control strategy that is adaptive to moderate changes in the system dynamics. For the delay compensation, a prediction feedback approach has been applied.
The model predictive controller allows us to account for various constraints: state constraints, input constraints and input rate constraints. The proposed adaptive, non-linear model predictive controller has been implemented using FORCES Pro and demonstrated in a high fidelity kite simulation environment.

Supervisors: Tony Wood, Eva Ahbe, Dr. Henrik Hesse, Prof. Roy Smith


Type of Publication:

(13)Semester/Bachelor Thesis

R. S. Smith

File Download:

Request a copy of this publication.
(Uses JavaScript)
% Autogenerated BibTeX entry
@PhdThesis { Xxx:2016:IFA_5506
Permanent link