Comparison of neural network robot models with not inverted and inverted inertia matrix

  • Authors:

  • Jakub Możaryn;Jerzy E. Kurek

  • Affiliations:

  • Warsaw University of Technology, Institute of Automatic Control and Robotics, Warszawa, Poland;Warsaw University of Technology, Institute of Automatic Control and Robotics, Warszawa, Poland

  • Venue:
  • ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
  • Year:
  • 2005

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Abstract

The mathematical model of an industrial robot is usually described in the form of Lagrange-Euler equations, Newton-Euler equations or generalized d'Alambert equations. However, these equations require the physical parameters of a robot that are difficult to obtain. In this paper, two methods for calculation of a Lagrange-Euler model of robot using neural networks are presented and compared. The proposed network structure is based on an approach where either a not inverted or inverted inertia matrix is calculated. The presented models show good performance for different sets of data.