TY  - JOUR
T1  - Tracking Control for Robot Manipulator Based on Neural Networks with Adaptive Learning Rate
AU - , Noureddine Guersi AU - , Messaoud Djeghaba AU - , Dimitri Lefebvre AU - , Fabrice Druaux AU - , Edouard Leclercq 
JO  - Asian Journal of Information Technology
VL  - 4
IS  - 10
SP  - 927
EP  - 934
PY  - 2005
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2005.927.934
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2005.927.934
KW  - Multilayer neural networks
KW  -gradient descent method
KW  -adaptive learning rate
KW  -stable on-line MIMO control
KW  -robot trajectory tracking
AB  - The selection of learning rates to obtain satisfactory performances for neural network controllers is a challenging problem. In order to skip any time consuming experimentation for the choice of an appropriate value of the learning rate, this paper is concerned with an online adaptive learning rate algorithm derived from the convergence analysis of the usual gradient descent method. Based on the feedback linearization method, a multilayer neural network controller approximates online the unknown dynamics of the system including the non–linear behaviours. The proposed controller does not require any preliminary off-line training. A stability proof of this control scheme is given. Simulations and a comparison with a PD controller and several fixed learning rate neural controllers illustrate the effectiveness of the proposed algorithm in case of daptive control for robot trajectory tracking.
ER  -