JRM Vol.18 No.5 pp. 529-538
doi: 10.20965/jrm.2006.p0529


Neural Adaptive Approach-Application to Robot Force Control in an Unknown Environment

Yacine Amirat*, Karim Djouani*, Mohamed Kirad*,
and Nadia Saadia**

*LISSI - Université Paris 12, 120-122, rue Paul Armangot 94400 Vitry sur seine, France

**Electronics and Computer Science Faculty, USTHB University, BP 32, Bab-ezzouar, El Alia, Alger Algeria

July 29, 2005
March 13, 2006
October 20, 2006
robot-environment interaction, adaptive control, neural networks, force control, reference model

This paper presents an effective neural adaptive approach for robot force control with changing/unknown robot-environment interaction dynamic properties. In this approach, a multilayered neural network controller is trained at first off line from data collected during contact motion in order to perform a smooth transition from free to contact motion. Then, an adaptive process is implemented online through a desired impedance reference model such that the closed-loop system maintains a good performance and compensates for uncertain/unknown dynamics of the robot-environment interaction. The effectiveness of the proposed approach has been evaluated for the force control of a 6 DOF (Degree Of Freedom) C5-links parallel robot executing rectangular peg-in-hole insertions with weak tolerances. The experimental results demonstrate that the robot’s skill improves effectively and force control performances are good even if robot-environment interaction dynamic properties change.

Cite this article as:
Yacine Amirat, Karim Djouani, Mohamed Kirad, and
and Nadia Saadia, “Neural Adaptive Approach-Application to Robot Force Control in an Unknown Environment,” J. Robot. Mechatron., Vol.18, No.5, pp. 529-538, 2006.
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