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Model predictive guidance control for autonomous kites

Author(s):

A. Romero Aguilar
Conference/Journal:

Semester Thesis, SS16 (10520)
Abstract:

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

Year:

2016
Type of Publication:

(13)Semester/Bachelor Thesis
Supervisor:

R. S. Smith

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2016:IFA_5506
}
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