JRM Vol.16 No.1 pp. 90-96
doi: 10.20965/jrm.2004.p0090


Neural Networks for Redundant Robot Manipulators Control with Obstacles Avoidance

B. Daachi*, A. Benallegue**, T. Madani**,
and M. E. Daachi***

*Laboratoire d’Informatique Industrielle et d’Automatique, 122, rue Paul Armangot, 94400 Vitry sur Seine, France

**Laboratoire de Robotique de Versailles, 10-12, avenue de l’Europe, 78140 Velizy, France

***Universite Ferhat Abbas, 9000 Setif, Algeria

June 12, 2003
December 12, 2003
February 20, 2004
neural networks, adaptive control, redundant robots, stability, obstacle avoidance

In this paper, neural networks of MLP type are used to control constrained redundant robot manipulators with obstacles. The proposed controller is determined using extended Cartesian space to minimise the joint displacements and to avoid obstacles. The neural networks have been used to approximate separately, the functions of the dynamic model of the robot manipulator expressed in the Cartesian space. The adaptation laws weights of each neural network, are obtained via stability study in Lyapunov sense of the system in closed loop. The performances of the proposed control approach are tested on a 3-degree of freedom robot manipulators involving in the vertical space.

Cite this article as:
B. Daachi, A. Benallegue, T. Madani, and
and M. E. Daachi, “Neural Networks for Redundant Robot Manipulators Control with Obstacles Avoidance,” J. Robot. Mechatron., Vol.16, No.1, pp. 90-96, 2004.
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Last updated on Mar. 05, 2021