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Recent years have seen the development of wind energy conversion systems that can exploit the superior wind resource that exists at altitudes above current wind turbine technology. One class of these systems incorporates a flying wing tethered to the ground which drives a winch at ground level. The wings often resemble sports kites, being composed of a combination of fabric and stiffening elements. Such wings are subject to load dependent deformation which makes them particularly difficult to model and control. Here we apply the techniques of evolutionary robotics i.e. evolution of neural network controllers using genetic algorithms, to the task of controlling a steerable kite. We introduce a multibody kite simulation that is used in an evolutionary process in which the kite is subject to deformation. We demonstrate how discrete time recurrent neural networks that are evolved to maximise line tension fly the kite in repeated looping trajectories similar to those seen using other methods. We show that these controllers are robust to limited environmental variation but show poor generalisation and occasional failure even after extended evolution. We show that continuous time recurrent neural networks (CTRNNs) can be evolved that are capable of flying appropriate repeated trajectories even when the length of the flying lines are changing. We also show that CTRNNs can be evolved that stabilise kites with a wide range of physical attributes at a given position in the sky, and systematically add noise to the simulated task in order to maximise the transferability of the behaviour to a real world system. We demonstrate how the difficulty of the task must be increased during the evolutionary process to deal with this extreme variability in small increments. We describe the development of a real world testing platform on which the evolved neurocontrollers can be tested.

No description available.

Thesis (Ph. D.)--University of Nevada, Reno, 2005. / "December, 2005." Includes bibliographical references (leaves 65-66). Online version available on the World Wide Web.

Thesis (M.A.)--York University, 2004. Graduate Programme in Geography. / Typescript. Includes bibliographical references (leaves 170-178). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL:http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99350

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Anette M. Karlsson, Dept. of Mechanical Engineering. Includes bibliographical references.

Thesis (Ph.D.)--University of Texas at Arlington, 2009.

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Knox T. Millsaps. Includes bibliographical references (p. 41). Also available online.

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2004. / Includes bibliographical references (p. 107-110).

Thesis (M.S.)--Florida State University, 2003. / Advisor: Dr. Andrew A. Dzurik, Florida State University, FAMU-FSU College of Engineering, Dept. of Civil and Environmental Engineering. Title and description from dissertation home page (February 25, 2004). Includes bibliographical references.