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Coversheet for Thesis in Sussex Research Online A University of Sussex DPhil thesis Available online via Sussex Research Online: http://sro.sussex.ac.uk/ This thesis is protected by copyright which belongs to the author. This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the Author The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the Author When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given Please visit Sussex Research Online for more information and further details Evolutionary Robotics in High Altitude Wind Energy Applications Allister David John Furey January 2011 Centre for Computational Neuroscience and Robotics Department for Informatics University of Sussex Falmer BN1 9QG A thesis submitted for the degree of Doctor of Philosophy of the University of Sussex UNIVERSITY OF SUSSEX Evolutionary Robotics in High Altitude Wind Energy Applications Allister Furey Summary 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. Submitted for the degree of Doctor of Philosophy University of Sussex, UK Submitted: January, 2011 ii Publications The following publications have arisen from work described in this thesis: Furey, A., & Harvey, I. (2007). Evolution of neural networks for active control of tethered airfoils. In F. A. e Costa, L. M. Rocha, E. Costa, Inman Harvey, & A. Coutinho (Eds.), Advances in Artificial Life, 9th European Conference, ECAL 2007 (pp. 746–755). Berlin, Heidelberg: Springer. Furey, A., & Harvey, I. (2008). Robust adaptive control for kite wind energy using evolutionary robotics. Proc. Biological Approaches for Engineering, 17-19 March 2008 (pp. 117-120). Southampton Furey, A., & Harvey, I. (2008). Adaptive Behavioural Modulation and Hysteresis in an Analogue of a Kite Control Task. In M. Asada, J. C. T. Hallam, J.-A. Meyer, & J. Tani (Eds.), From Animals to Animats 10 (Vol. 5040, pp. 509-518). Berlin, Heidelberg, Springer iii Acknowledgements I have been so utterly dependent on the goodwill and support of so many people, this work would have been truly impossible without them. I’ll try and go some way to acknowledging them here. First thanks must go to Inman Harvey for his support and advice and for agreeing, presumably in an uncharacteristic moment of poor judgement, to let me do this work at all. Thanks also to Sam Metcalf who started this whole thing off with lunchtime kite flying sessions in Aylestone park and in speculating with me what might be possible, those few hours set off a trajectory of over four years and hopefully more. Thanks to Bill Bigge for building the first kite robot and introducing me to the wonders of the robot lab. Thanks to everyone in the CCNR who submitted to coming down to the field and/or the downs to be dragged around and spend time relaunching kites, that’s all of Jose Fernandes Leon, Lucas Wilkins, Nick Tomko, Tom Parslow. Tom you are also a legend for resurrecting the Gantry robot. Andy Philipedes for his no nonsense advice –‘I think you’re being somewhat naïve’ which definitely kept my feet on the ground. I must thank Martin Ling, Alex and DK Arvind from Specknet at the University of Edinburgh for allowing me to fly their expensive IMUs and talk with the group there. Thanks must go to Bill Hampton and John Prewer from Kitetech who provided the push needed to get the robots working, nothing like a test being scheduled to provide the motivation to fix the robot! Along the same lines, I have to thank Colm O’ Gairbhith and Rob Creighton at Windlift the vision system was born on those windless days in Salvo NC. Thanks to Dr Olly Woodford for his orientation code, Naser Sayma and Sanjeev Sanghi for helping out with the CFD. Thanks to Henry Rebbeck for being interested from word go, and for going out of his way to help out. Thanks to Roland Schmehl and the crew at TU Delft for letting me come over and test on their amazing kite system, seeing the team in action was an inspiration. Thanks to my family for their support and for not questioning why I didn’t get a proper job and Clare and Nicky for putting me up in the last stretch. Thanks most of all to Camilla Bowkett, who has been there the whole time, spent more time behind a kite than everyone else put together and listened to endless hours of kite relate boredom. This thesis is dedicated to you. iv Table of Contents Summary .............................................................................................................................................................................. i Publications ....................................................................................................................................................................... ii Acknowledgements ...................................................................................................................................................... iii List of figures ................................................................................................................................................................. viii 1 Evolutionary robotics in high altitude wind energy applications .................................................... 2 1.1 Motivation ...................................................................................................................................................... 2 1.2 Specific considerations of this task ..................................................................................................... 4 1.3 Evolutionary robotics as a potential solution ................................................................................. 6 1.4 Novel contributions ................................................................................................................................... 7 2 Literature review................................................................................................................................................ 10 2.1 Introduction to evolutionary robotics: ........................................................................................... 10 2.1.1 Traditional approach to artificial neural networks .............................................................. 10 2.1.2 Limitations of these approaches .................................................................................................. 12 2.1.3 Achieving adaptive autonomy in robots ................................................................................... 13 2.1.4 Internally dynamic neurocontrollers as a route to adaptive robotics ......................... 14 2.1.5 Genetic algorithms as a method of artificial evolution ....................................................... 16 2.1.6 Applications of the evolutionary robotics paradigm ........................................................... 18 2.2 Introduction to airborne wind energy ............................................................................................ 42 2.2.1 Background and motivation for airborne wind energy ...................................................... 43 2.2.2 Basics of kite flight .............................................................................................................................. 49 2.2.3 Modelling, control and hardware for kite systems .............................................................. 52 2.3 Summary ...................................................................................................................................................... 58 3 Considering kite control as a task for ER ................................................................................................
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