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Improving trajectory tracking of a three axis SCARA robot using neural networks

In this paper, a neural-network based robust adaptive controller is proposed to control an industrial robot considering non- linearities, uncertainties and external perturbations. Three-axis SCARA robots is used to test the performance of this controller. The nonlinear system is treated as a partial...

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Main Authors: Raafat, Safanah M., Said, Waladin K., Akmeliawati, Rini, Tariq, Nagham M.
Format: Conference or Workshop Item
Language:English
Published: 2009
Subjects:
TJ212 Control engineering
Online Access:http://irep.iium.edu.my/5371/1/isiea2009_scara.pdf
http://irep.iium.edu.my/5371/
http://www.i-cerg.net/isiea2009/
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spelling my.iium.irep.53712011-11-21T22:02:50Z http://irep.iium.edu.my/5371/ Improving trajectory tracking of a three axis SCARA robot using neural networks Raafat, Safanah M. Said, Waladin K. Akmeliawati, Rini Tariq, Nagham M. TJ212 Control engineering In this paper, a neural-network based robust adaptive controller is proposed to control an industrial robot considering non- linearities, uncertainties and external perturbations. Three-axis SCARA robots is used to test the performance of this controller. The nonlinear system is treated as a partially known system. The known dynamic is used to design a nominal feedback controller based on the well-known feedback linearization method and PD controller. A Variable Structure Controller is added to the PD loop to provide robustness to uncertainties in the model of the system in order to improve accuracy of the trajectory tracking. A Neural Network (NN) based robust adaptive tracking controller is applied to further improves the control action. The outputs of the NNs are used to compensate the effects of the system uncertainties and to improve the tracking performance. Using this scheme, strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, the output tracking error between the plant output and the desired reference output can asymptotically converge to zero as well. This controller exhibited superior performance characteristics where the maximum absolute error for the three-axis SCARA robot is considerably reduced. 2009-10-04 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/5371/1/isiea2009_scara.pdf Raafat, Safanah M. and Said, Waladin K. and Akmeliawati, Rini and Tariq, Nagham M. (2009) Improving trajectory tracking of a three axis SCARA robot using neural networks. In: 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), 4-6 October 2009, Kuala Lumpur, Malaysia. http://www.i-cerg.net/isiea2009/ doi:10.1109/ISIEA.2009.5356448
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TJ212 Control engineering
spellingShingle TJ212 Control engineering
Raafat, Safanah M.
Said, Waladin K.
Akmeliawati, Rini
Tariq, Nagham M.
Improving trajectory tracking of a three axis SCARA robot using neural networks
description In this paper, a neural-network based robust adaptive controller is proposed to control an industrial robot considering non- linearities, uncertainties and external perturbations. Three-axis SCARA robots is used to test the performance of this controller. The nonlinear system is treated as a partially known system. The known dynamic is used to design a nominal feedback controller based on the well-known feedback linearization method and PD controller. A Variable Structure Controller is added to the PD loop to provide robustness to uncertainties in the model of the system in order to improve accuracy of the trajectory tracking. A Neural Network (NN) based robust adaptive tracking controller is applied to further improves the control action. The outputs of the NNs are used to compensate the effects of the system uncertainties and to improve the tracking performance. Using this scheme, strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, the output tracking error between the plant output and the desired reference output can asymptotically converge to zero as well. This controller exhibited superior performance characteristics where the maximum absolute error for the three-axis SCARA robot is considerably reduced.
format Conference or Workshop Item
author Raafat, Safanah M.
Said, Waladin K.
Akmeliawati, Rini
Tariq, Nagham M.
author_facet Raafat, Safanah M.
Said, Waladin K.
Akmeliawati, Rini
Tariq, Nagham M.
author_sort Raafat, Safanah M.
title Improving trajectory tracking of a three axis SCARA robot using neural networks
title_short Improving trajectory tracking of a three axis SCARA robot using neural networks
title_full Improving trajectory tracking of a three axis SCARA robot using neural networks
title_fullStr Improving trajectory tracking of a three axis SCARA robot using neural networks
title_full_unstemmed Improving trajectory tracking of a three axis SCARA robot using neural networks
title_sort improving trajectory tracking of a three axis scara robot using neural networks
publishDate 2009
url http://irep.iium.edu.my/5371/1/isiea2009_scara.pdf
http://irep.iium.edu.my/5371/
http://www.i-cerg.net/isiea2009/
_version_ 1643605525489778688
score 13.211869

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