Fetching the paper…
Reading the bibliography…
Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the environment.
I. Cheeseman and W. Bennett, “The effect of ground on a helicopter rotor in forward flight,” 1955
1955
Earlier work this paper cites.
J. Slotine and W. Li, Applied Nonlinear Control . Prentice Hall, 1991
1991
Earlier work this paper cites.
S. Balakrishnan and R. Weil, “Neurocontrol: A literature survey,” Mathematical and Computer Modelling , vol. 23, no. 1-2, pp. 101–117, 1996
1996
Earlier work this paper cites.
H. Khalil, Nonlinear Systems , ser. Pearson Education. Prentice Hall, 2002
2002
Earlier work this paper cites.
P. Abbeel, A. Coates, and A. Y. Ng, “Autonomous helicopter aerobatics through apprenticeship learning,” The International Journal of Robotics Research , vol. 29, no. 13, pp. 1608–1639, 2010
2010
Earlier work this paper cites.
K. Nonaka and H. Sugizaki, “Integral sliding mode altitude control for a small model helicopter with ground effect compensation,” in American Control Conference (ACC), 2011 . IEEE, 2011, pp. 202–207
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
S.-J. Chung, S. Bandyopadhyay, I. Chang, and F. Y. Hadaegh, “Phase synchronization control of complex networks of Lagrangian systems on adaptive digraphs,” Automatica , vol. 49, no. 5, pp. 1148–1161, 2013
2013
Earlier work this paper cites.
M. T. Frye and R. S. Provence, “Direct inverse control using an artificial neural network for the autonomous hover of a helicopter,” in Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on . IEEE, 2014, pp. 4121–4122
2014
Earlier work this paper cites.
L. Danjun, Z. Yan, S. Zongying, and L. Geng, “Autonomous landing of quadrotor based on ground effect modelling,” in Control Conference (CCC), 2015 34th Chinese . IEEE, 2015, pp. 5647–5652
2015
Earlier work this paper cites.
A. Punjani and P. Abbeel, “Deep learning helicopter dynamics models,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on . IEEE, 2015, pp. 3223–3230
2015
Cited alongside, same era.
2016
Cited alongside, same era.
S. Bansal, A. K. Akametalu, F. J. Jiang, F. Laine, and C. J. Tomlin, “Learning quadrotor dynamics using neural network for flight control,” in Decision and Control (CDC), 2016 IEEE 55th Conference on . IEEE, 2016, pp. 4653–4660
2016
Cited alongside, same era.
T. Salimans and D. P. Kingma, “Weight normalization: A simple reparameterization to accelerate training of deep neural networks,” in Advances in Neural Information Processing Systems , 2016, pp. 901–909
2016
Cited alongside, same era.
H. Suprijono and B. Kusumoputro, “Direct inverse control based on neural network for unmanned small helicopter attitude and altitude control,” Journal of Telecommunication, Electronic and Computer Engineering (JTEC) , vol. 9, no. 2-2, pp. 99–102, 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
P. L. Bartlett, D. J. Foster, and M. J. Telgarsky, “Spectrally-normalized margin bounds for neural networks,” in Advances in Neural Information Processing Systems , 2017, pp. 6240–6249
2017
Later among the works it cites.
2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Bandyopadhyay, S.-J. Chung, and F. Y. Hadaegh, “Nonlinear attitude control of spacecraft with a large captured object,” Journal of Guidance, Control, and Dynamics , vol. 39, no. 4, pp. 754–769, 2016
2016
Cited alongside, same era.
D. Morgan, G. P. Subramanian, S.-J. Chung, and F. Y. Hadaegh, “Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming,” Int. J. Robotics Research , vol. 35, no. 10, pp. 1261–1285, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
Q. Li, J. Qian, Z. Zhu, X. Bao, M. K. Helwa, and A. P. Schoellig, “Deep neural networks for improved, impromptu trajectory tracking of quadrotors,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on . IEEE, 2017, pp. 5183–5189
2017
Cited alongside, same era.
S. Zhou, M. K. Helwa, and A. P. Schoellig, “Design of deep neural networks as add-on blocks for improving impromptu trajectory tracking,” in Decision and Control (CDC), 2017 IEEE 56th Annual Conference on . IEEE, 2017, pp. 5201–5207
2017
Cited alongside, same era.
Later among the works it cites.
2017
Later among the works it cites.
B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro, “Exploring generalization in deep learning,” in Advances in Neural Information Processing Systems , 2017, pp. 5947–5956
2017
Later among the works it cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Later among the works it cites.
C. Sánchez-Sánchez and D. Izzo, “Real-time optimal control via deep neural networks: study on landing problems,” Journal of Guidance, Control, and Dynamics , vol. 41, no. 5, pp. 1122–1135, 2018
2018
Closest in time.
2018
Closest in time.
X. Shi, K. Kim, S. Rahili, and S.-J. Chung, “Nonlinear control of autonomous flying cars with wings and distributed electric propulsion,” in 2018 IEEE Conference on Decision and Control (CDC) . IEEE, 2018, pp. 5326–5333
2018
Closest in time.