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Performance comparison of type-1 and type-2 neuro-fuzzy controllers for a flexible joint manipulator
conference contribution
posted on 2019-01-01, 00:00 authored by A S Jokandan, Abbas KhosraviAbbas Khosravi, Saeid Nahavandi© Springer Nature Switzerland AG 2019. Flexible joint manipulators are extensively used in several industries and precise control of their nonlinear dynamics has proven to be a challenging task. In this work, we want to compare two intelligent controllers by proposing two Takagi-Sugeno-Kang Neuro-Fuzzy Approaches (Type-1 and Type-2) to control a flexible joint. For both controllers, The inverse models are found using identification techniques, then they are put in series as inverse controllers to control the flexible joint in an online structure. Interval weights are trained by gradient descent approaches using backpropagation algorithms. Results reveal that, without any knowledge about the dynamic of the robot, the methods can control the flexible joint which is highly unstable. As illustrated in result section, One level more fuzziness of Type-2 in compare to type-1 fuzzy controllers helps this controller to more effectively deals with information from a knowledge base. The proposed models can effectively handle uncertainties arising from friction and other structural nonlinearities.
History
Event
Asia-Pacific Neural Network Society. Conference (2019 : 26th : Sydney, N.S.W.)Series
Lecture Notes in Computer Science ; v.11953Pagination
621 - 632Publisher
SpringerLocation
Sydney, N.S.W.Place of publication
Berlin, GermanyPublisher DOI
Start date
2019-12-12End date
2019-12-15ISSN
0302-9743eISSN
1611-3349ISBN-13
9783030367077Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
T Gedeon, K Wong, M LeeTitle of proceedings
ICONIP 2019 : Proceedings of the 26th International Conference on Neural Information ProcessingUsage metrics
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