Position control based on static neural networks of anthropomorphic robotic fingers

  • Authors:

  • Juan Ignacio Mulero-Martínez;Francisco García-Córdova;Juan López-Coronado

  • Affiliations:

  • Department of System Engineering and Automatic, Polytechnic University of Cartagena, Cartagena, Murcia, Spain;Department of System Engineering and Automatic, Polytechnic University of Cartagena, Cartagena, Murcia, Spain;Department of System Engineering and Automatic, Polytechnic University of Cartagena, Cartagena, Murcia, Spain

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

A position neurocontroller for robot manipulators with a tendon-driven transmission system has been developed allowing to track desired trajectories and reject external disturbances. The main problem to control tendons proceeds from the different dimensions between the joint and the tendon spaces. In order to solve this problem we propose a static neural network in cascade with a torque resolutor. The position controller is built as a parametric neural network by using basis functions obtained directly from the finger structure. This controller insure that the tracking error converges to zero and the weights of the network are bounded. The implementation has been improved partitioning the neural network into subnets and using the Kronecker product. Both control and weight updating laws have been designed by means of a Lyapunov energy function. In order to improve the computational efficient of the neural network, this has been split up into subnets to compensate inertial, Coriolis/centrifugal and gravitational effects. The NN weights are initialised at zero and tuned on-line with no ”off-line learning phase”. This scheme has been applied to an anthropomorphic robotic finger with a transmission system based on tendons.