Reference : Learning-While Controlling RBF-NN for Robot Dynamics Approximation in Neuro-Inspired ...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/36935
Learning-While Controlling RBF-NN for Robot Dynamics Approximation in Neuro-Inspired Control of Switched Nonlinear Systems
English
Klecker, Sophie mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Hichri, Bassem mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Plapper, Peter mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
2018
Artificial Neural Networks and Machine Learning; ICANN 2018 part 3
Springer
Lecture Notes in Computer Science; 11141
717-727
Yes
No
International
978-3-030-01423-0
Cham
Switzerland
27th International Conference on Artificial Neural Networks - ICANN 2018
from 04-10-2018 to 07-10-2018
European Neural Network Society - ENNS
Rhodes
Greece
[en] RBF-NN ; Learning-while controlling ; Switching constraints
[en] Radial Basis Function-Neural Networks are well-established function approximators. This paper presents an adaptive Gaussian RBF-NN with an extended learning-while controlling behaviour. The weights, function centres and widths are updated online based on a sliding mode control element. In this way, the need for fixing parameters a priori is overcome and the network is able to adapt to dynamically changing systems. The aim of this work is to present an extended adaptive neuro-controller for trajectory tracking of serial robots with unknown dynamics. The adaptive RBF-NN is used to approximate the unknown robot manipulator dynamics-function. It is combined with a conventional controller and a bio-inpsired extension for the control of a robot in the presence of switching constraints and discontinuous inputs. Its learned goal-directed output results from the complementary action of an actuator, A, and a prventer, P. The trigger is an incentive, I, based on the weighted perception of the enviornment. The concept is validated through simulations and implementation on a KUKA LWR4-robot.
http://hdl.handle.net/10993/36935
10.1007/978-3-030-01424-7_70

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