TY  - JOUR
T1  - Identification and Trajectory Control of a Manipulator Arm Using a Neuro-Fuzzy Technique
AU - , F. Arbaoui AU - , M.L. Saidi AU - , S. Kermiche AU - , H.A. Abbassi 
JO  - Asian Journal of Information Technology
VL  - 5
IS  - 6
SP  - 627
EP  - 632
PY  - 2006
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2006.627.632
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2006.627.632
KW  - neural network
KW  -on-line identification
KW  -fuzzy controller
KW  -robotic tracking control
AB  - Neural Network (NN) and Fuzzy Inference System (FIS) had been successfully employed in many
engineering applications and were adopted in identification and designing controllers for robotic manipulators,
the former because of its model-free feature and the other for its high flexibility. In this paper, we focus on the
application of a neuro-fuzzy technique to bring certain advantages over neural networks and fuzzy logic control
for identification and tracking control of a robot manipulator which is a complicated multivariable nonlinear
dynamical system. Neural network has the ability to learn by adjusting the interconnections between layers
while fuzzy inference system is a computing framework based on the concept of fuzzy sets, fuzzy if-then rules
and fuzzy reasoning. A Fuzzy Logic Controller (FLC) is combined with the neural network plant model trained
on-line by the backpropagation algorithm using an adaptive learning rate. Simulations and some results are
showed and discussed.
ER  -