A Neural Network System for Calculation of Inverse Dynamics for Manipulators

Motoji Yamamoto, Hisashi Suematsu

Research output: Contribution to journalArticlepeer-review


This paper proposes a method to calculate manipulator inverse dynamics using a neural network. In this method, two sets of neural networks are prepared. One is for the elements of inertia moment matrix, and the other is for gravitational force. Each input for the network is only a joint position. Teacher signals of each network are also calculated using only a joint position, and therefore learning of each network is fast. The neural network, which acquires a model of inertia moment matrix, is used to calculate inertial force, centrifugal force and Coriolis force. In particular, the terms of centrifugal force and Coriolis force are calculated using a characteristic of manipulator dynamics and structure of the neural network. This method can be applied to the wide area data of joint positions, joint velocities and joint accelerations to calculate manipulator inverse dynamics. To show the validity of this method, the inverse dynamics of a two-dimensional manipulator are calculated.

Original languageEnglish
Pages (from-to)839-844
Number of pages6
Journaltransactions of the japan society of mechanical engineers series c
Issue number559
Publication statusPublished - 1993

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'A Neural Network System for Calculation of Inverse Dynamics for Manipulators'. Together they form a unique fingerprint.

Cite this