JRM Vol.15 No.1 pp. 77-83
doi: 10.20965/jrm.2003.p0077


Stable Neural Network Controller Based Observer for Rigid Robot Manipulators

Boubaker Daâchi and Abdelaziz Benallegue

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

January 30, 2002
November 13, 2002
February 20, 2003
neural networks, adaptive control, observer, robot manipulators
We propose a neural network controller using only joint position measurements for rigid robot manipulators. The joint velocity needed for the control law is estimated using an observer based on sliding mode. A decomposed structure neural network approximates the unknown model of the system. Each neural network (MLP) approximates a separate element of the dynamical model. These approximations are used to conduct an adaptive stable control law. The TaylorYoung series was used to solve the nonlinearity problem of the MLP and to lead to the parameters adaptation algorithm. The corresponding parameters are the weights of the neural net. They are updated via the adaptation algorithm derived from stability study of the system in closed loop using the Lyapunov approach and intrinsic properties of robot manipulators. Simulations were conducted to show the conductance of the proposed controller.
Cite this article as:
B. Daâchi and A. Benallegue, “Stable Neural Network Controller Based Observer for Rigid Robot Manipulators,” J. Robot. Mechatron., Vol.15 No.1, pp. 77-83, 2003.
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