Neuro-sliding mode control of robot manipulators

Abstract

In this paper, a synergistic combination of neural networks with sliding mode control (SMC) is proposed. As a result, the chattering is eliminated and error performance of SMC is improved. In such an approach, two parallel Neural Networks (NNs) are proposed to realize the SMC. The equivalent control and the collective control term of SMC are the outputs of the NNs. The weight adaptation of NNs are based on the SMC equations with using a gradient decent method to minimize the control and chattering while optimize the error performance. This novel approach is applied to control of a scara type robot manipulator and experimental results are given.