Combined multi-layer perceptron neural network and sliding mode technique for parallel robots control : An adaptive approach

In this paper, an adaptive control of a parallel robot is proposed for trajectory tracking problems. This approach is based on adaptive multi-layer perceptron (MLP) neural network and sliding mode technique. The aim of this study is to design a robust controller with respect to external disturbances in order to improve the trajectory tracking. In fact, an adaptive MLP neural network is developed to estimate the gravitational force, frictions and other dynamics. To overcome the non-linearity problem presented in the neural network, we used the Taylor series expansion. The control law combining a neural network and sliding mode is synthesized in order to attract states model to the sliding surface. All adaptation laws of neural parameters and sliding mode term are based on the stability of the closed loop system in the Lyapunov sense. This approach has been implemented on a C5 parallel robot, and the experimental results show the effectiveness of the proposed method in presence of external disturbances.