An adaptive neural network sliding controller for robotic manipulators

In this paper, an adaptive neural network sliding mode controller (ANNSMC) for robotic manipulators is proposed to alleviate the problems met in practical implementation using classical sliding mode controllers. The chattering phenomenon is eliminated by substituting single-input single-output radial-basis-function neural networks (RBFNN´s), which are nonlinear and continuous, in lieu of the discontinuous part of the control signals present in classical forms. The weights of the hidden layer of the RBFNN´s are updated in an online manner to compensate the system uncertainties. The key feature of this scheme is that prior knowledge of the system uncertainties is not required to guarantee the stability. Moreover, a theoretical proof of the stability and convergence of the proposed scheme using Lyapunov method is presented. To demonstrate the effectiveness of the proposed approach, a practical situation in robot control is simulated