Neuro sliding mode control of robotic manipulators

In this paper, a synergistic combination of neural networks with sliding mode control (SMC) methodology is proposed. As a result, the chattering is eliminated and error performance of SMC is improved. In the approach, two parallel Neural Networks (NNs) are utilized to realize a neuro-SMC. The equivalent control and the corrective control terms of SMC are the outputs of the NNs. The weight adaptations of NNs are based on the SMC equations in such a way that the use of the gradient descent method minimizes the control activity and the amount of chattering while optimizing the error performance. The approach is almost model-free, requiring a minimal amount of a priori knowledge and robust in the face of parameter changes. Experimental studies carried out on a direct drive arm are presented, indicating that the proposed approach is a good candidate for trajectory control applications. (C) 1999 Elsevier Science Ltd, All rights reserved.