Title :
Tracking control for robot arm using neural network with simultaneous perturbation learning rule
Author :
Onishi, Hidenori ; Maeda, Yutaka
Author_Institution :
Kansai Univ., Suita, Japan
Abstract :
We report tracking control for a robot arm using a neuro-controller. We adopt the simultaneous perturbation learning rule for the neuro-controller. The learning rule requires only two values of an error function. The twice operation yields modifying quantities of the weights in the network. Thus the neuro-controller can learn an inverse of robot kinematics. Some simulation results are shown.
Keywords :
learning (artificial intelligence); manipulator kinematics; neurocontrollers; nonlinear control systems; perturbation techniques; direct inverse control scheme; error function values; inverse robot kinematics; multi-layered neural networks; neuro-controller; nonlinear control; robot arm; simulation results; simultaneous perturbation learning rule; tracking control; Error correction; Jacobian matrices; Multi-layer neural network; Neural networks; Neurofeedback; Neurons; Robot control; Robot kinematics; Stochastic processes; Torque;
Conference_Titel :
SICE 2002. Proceedings of the 41st SICE Annual Conference
Print_ISBN :
0-7803-7631-5
DOI :
10.1109/SICE.2002.1195620
