Evolution of neural networks for active control of tethered airfoils

Authors:
Allister Furey;Inman Harvey
Affiliations:
Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, United Kingdom;Centre for Computational Neuroscience and Robotics, University of Sussex, Brighton, United Kingdom
Venue:
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Year:
2007

Citing 3
Cited 1

Center-crossing recurrent neural networks for the evolution of rhythmic behavior

Neural Computation
Artificial Evolution: A Continuing SAGA

ER '01 Proceedings of the International Symposium on Evolutionary Robotics From Intelligent Robotics to Artificial Life
Evolving Physically Simulated Flying Creatures for Efficient Cruising

Artificial Life

Adaptive Behavioural Modulation and Hysteresis in an Analogue of a Kite Control Task

SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent development in tethered airfoil i.e. kite technology allows the possibility of exploitation of wind energy at higher altitudes than achievable with traditional wind turbines, with greater efficiency and reduced costs. This study describes the use of evolutionary robotics techniques to build neurocontrollers that maximize energy recoverable from wind by kite control systems in simulation. From initially randomized starting conditions, neurocontrollers rapidly develop under evolutionary pressure to fly the kite in figure eight trajectories that have previously been shown to be an optimal path for power generation. Advantages of this approach are discussed and data is presented which demonstrates the robustness of trajectory control to environmental perturbation.