Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Vishwas Puttige"'
Autor:
Sreenatha G. Anavatti, Vishwas Puttige
Publikováno v:
International Journal of Aerospace Innovations. 2:81-92
In this paper a novel indirect adaptive control technique based on neural networks for Unmanned Aerial Vehicles (UAV) is described. The technique demonstrates the use of two interconnected neural networks to provide faster tracking of the commanded r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ca81978148e36ba65dd525a74a331c5e
https://doi.org/10.1260/1757-2258.2.1-2.81
https://doi.org/10.1260/1757-2258.2.1-2.81
Publikováno v:
2009 IEEE International Conference on Industrial Technology.
This paper describes a novel indirect adaptive control technique based on neural networks for Unmanned Aerial Vehicles (UAV). Two neural networks are amalgamated to provide faster tracking of the commanded reference. A pre-trained internal model netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::591694aae85c35066f141605e3669239
https://doi.org/10.1109/icit.2009.4939665
https://doi.org/10.1109/icit.2009.4939665
Autor:
Sreenatha G. Anavatti, Matthew Garratt, Vishwas Puttige, Abhijit G. Kallapur, Mahendra Kumar Samal
Publikováno v:
ITSC
This paper presents a novel system identification framework for small unmanned aerial vehicles (UAVs) by combining an unscented Kalman filter (UKF) estimator with a neural network (NN) identifier. The method is effective for systems with low-cost, er
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5dcaea35947eb13968a9129c65674def
https://doi.org/10.1109/itsc.2008.4732708
https://doi.org/10.1109/itsc.2008.4732708
Autor:
Sreenatha G. Anavatti, Vishwas Puttige
Publikováno v:
Journal of Computers. 3
In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonlinear system. Models for such MIMO system are expected to be adaptive
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34e9a6686300386c0fe5b27029bceebe
https://doi.org/10.4304/jcp.3.7.31-38
https://doi.org/10.4304/jcp.3.7.31-38
Publikováno v:
IJCNN
Dynamic multi-objective optimization (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd53b0455e6299b60bcb47b9e1886b54
https://doi.org/10.1109/ijcnn.2008.4633847
https://doi.org/10.1109/ijcnn.2008.4633847
Publikováno v:
AI 2007: Advances in Artificial Intelligence ISBN: 9783540769262
Australian Conference on Artificial Intelligence
Australian Conference on Artificial Intelligence
This paper sumarises a comparative study of multiple neural networks as applied for the identification of the dynamics of an Unmanned Aerial Vehicle (UAV). Each of the networks are based on non-linear autoregressive technique and are trained online.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ec75eb75fa54e465bb6dfd2efe44c7a
https://doi.org/10.1007/978-3-540-76928-6_14
https://doi.org/10.1007/978-3-540-76928-6_14
Autor:
Sreenatha G. Anavatti, Vishwas Puttige
Publikováno v:
SMC
This paper describes a system identification technique based on dynamic selection of multiple neural networks for the Unmanned Aerial Vehicle (UAV). The UAV is a multi- input multi-output (MIMO) nonlinear system. The neural network models are based o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1097c6ef687e6ca24a46968909d327a3
https://doi.org/10.1109/icsmc.2007.4413945
https://doi.org/10.1109/icsmc.2007.4413945
Autor:
Vishwas Puttige, Sreenatha G. Anavatti
Publikováno v:
IJCNN
In this paper a comparison of an offline and online neural network architecture for the identification of an unmanned aerial vehicle (UAV) is presented. The identification algorithm is based on autoregressive model aided by neural networks for the si
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35a0cbfefdc726cca7313b7b0a9a08a1
https://doi.org/10.1109/ijcnn.2007.4370992
https://doi.org/10.1109/ijcnn.2007.4370992
Publikováno v:
2006 IEEE International Conference on Control Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cbae8fc58d73f0c9faad50027f9c1e5
https://doi.org/10.1109/cca.2006.286197
https://doi.org/10.1109/cca.2006.286197
Publikováno v:
2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.
Unmanned aerial vehicles (UAVs) have been playing an increasingly important role in military and civilian applications. Identification of UAV model is an important process in the controller design. In this paper, identification of the attitude dynami
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d15e941b98f8f0f3e1c24f2dac050d6
https://doi.org/10.1109/cacsd-cca-isic.2006.4776971
https://doi.org/10.1109/cacsd-cca-isic.2006.4776971