Manipulator Trajectory Control by Momentum Change Inverse Models Using Multilayer Neural Networks

Abstract

This paper proposes a learning method of inverse manipulator dynamics model using only position and velocity. The direct inverse modeling method that was proposed as a learning method using neural network requires sensing manipulator position, velocity, and acceleration, because this method is formularized on the basis of manipulator. motion equation. However, since it is difficult at present to sense accurately manipulator acceleration, we could hardly implement this method by original formula. In the momentum change inverse modeling; the learning method that we proposed in this paper, manipulator motion causality is modeled not on the basis of manipulator motion equation but on the manipulator momentum change equation. With this formulation, sensing acceleration becomes unnecessary, inverse manipulator dynamics model can be learned using sensible position and velocity.