TY - JOUR

T1 - End-effector trajectory control in a two-link flexible manipulator through reference joint angle values modification by neural networks

AU - Öke, Gülay

AU - Istefanopulos, Yorgo

PY - 2006/2

Y1 - 2006/2

N2 - The basic difficulty in the control of flexible link manipulators stems from the fact that the link deflections cannot be controlled directly. Since the number of control inputs, applied by the actuators, is less than the total number of variables to be controlled, control approaches aiming at the suppression of deflections and vibrations are generally insufficient. Another possible approach is to determine new joint trajectories to minimize the error of the end-effector in the operational space. In this paper, a neural network is designed to compute incremental changes for the reference values of the joint angles to achieve successful tip tracking in the operational space. Tip position errors in the x-and y-directions are utilized as inputs to the neural network. The cost function, which is minimized in training the neural network, is also chosen as the sum of squares of the tip position error in both directions. Joint angle control is provided by a PD controller. Simulations are carried out to evaluate the performance of the neural-network-based trajectory tracking method, and the results are depicted in both joint and operational spaces.

AB - The basic difficulty in the control of flexible link manipulators stems from the fact that the link deflections cannot be controlled directly. Since the number of control inputs, applied by the actuators, is less than the total number of variables to be controlled, control approaches aiming at the suppression of deflections and vibrations are generally insufficient. Another possible approach is to determine new joint trajectories to minimize the error of the end-effector in the operational space. In this paper, a neural network is designed to compute incremental changes for the reference values of the joint angles to achieve successful tip tracking in the operational space. Tip position errors in the x-and y-directions are utilized as inputs to the neural network. The cost function, which is minimized in training the neural network, is also chosen as the sum of squares of the tip position error in both directions. Joint angle control is provided by a PD controller. Simulations are carried out to evaluate the performance of the neural-network-based trajectory tracking method, and the results are depicted in both joint and operational spaces.

KW - End-effector position

KW - Flexible link manipulators

KW - Neural network

KW - Trajectory control

UR - http://www.scopus.com/inward/record.url?scp=32944456825&partnerID=8YFLogxK

U2 - 10.1177/1077546306059319

DO - 10.1177/1077546306059319

M3 - Article

AN - SCOPUS:32944456825

SN - 1077-5463

VL - 12

SP - 101

EP - 117

JO - JVC/Journal of Vibration and Control

JF - JVC/Journal of Vibration and Control

IS - 2

ER -