Title: Indirect sliding mode neural-network control for holonomic constrained robot manipulators

Authors: Emna Zouari, Hanene Medhaffar, Nabil Derbel

Addresses: Intelligent Control, Design and Optimisation of Complex Systems (ICOS), Ecole Nationale des Ingenieurs de Sfax, Sfax, B.P.W. 3038, Tunisie. ' Intelligent Control, Design and Optimisation of Complex Systems (ICOS), Ecole Nationale des Ingenieurs de Sfax, Sfax, B.P.W. 3038, Tunisie. ' Intelligent Control, Design and Optimisation of Complex Systems (ICOS), Ecole Nationale des Ingenieurs de Sfax, Sfax, B.P.W. 3038, Tunisie

Abstract: This paper presents an adaptive neural network (NN) sliding mode control (NNSMC) for the motion and force control of constrained robot manipulators. Radial basis function (RBF) NNs are used as estimators to approximate the uncertainties in the problem formulation. Adaptive learning algorithms in NNSMC are derived from the Lyapunov stability analysis, so that the stability of the proposed control scheme is proved. Simulations are performed to demonstrate the effectiveness of the proposed controller.

Keywords: constrained manipulators; sliding mode control; RBF neural networks; estimation; adaptive control; holonomic constrained robots; motion control; force control; robot motion; adaptive learning; simulation; robot control.

DOI: 10.1504/IJISTA.2010.034318

International Journal of Intelligent Systems Technologies and Applications, 2010 Vol.9 No.2, pp.150 - 168

Received: 19 Nov 2008
Accepted: 21 Jul 2009
Published online: 31 Jul 2010 *

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