Abstract
From real input/output data, different models of an unmanned aerial vehicle are obtained by applying adaptive neural networks. These models are control-oriented; their main objective is to help us to design, implement and simulate different intelligent controllers and to test them on real systems. The influence of the selected training data on the final model is also discussed. They have been compared to off-line learning neural models with satisfactory results in terms of accuracy and computational cost.
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Acknowledgments
The authors would like to acknowledge the data provided by the Control Engineering Group of the Spanish Committee of Automatic [10]. Author Matilde Santos would like to thank the Spanish Ministry of Science and Innovation (MICINN) for support under project DPI2013-46665-C2-1-R. Authors would also like to thank the reviewers for their useful comments.
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Enrique Sierra, J., Santos, M. (2015). Adaptive Neural Control-Oriented Models of Unmanned Aerial Vehicles. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_29
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DOI: https://doi.org/10.1007/978-3-319-19719-7_29
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