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.
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