Sliding mode control of robot based on neural network model with positive definite inertia matrix

Authors:
Jakub Możaryn;Jerzy E. Kurek
Affiliations:
Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland;Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Year:
2010

Citing 3
Cited 0

Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Springer Handbook of Robotics

Springer Handbook of Robotics
Design of a neural network for an identification of a robot model with a positive definite inertia matrix

ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II

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Abstract

A synthesis of a sliding mode control law, for a robot arm, based on the robot model with a positive definite inertia matrix, identified with an artificial neural network, is presented. The structure of the neural network resemble a Lagrange-Euler mathematical model of the robot, and identifies the positive definite inertia matrix. A design of the neural model is based on the Cholesky decomposition of the identified inertia matrix.