A robust neural adaptive force controller for a C5 parallel robot

In this paper, a neural adaptive force control of a parallel robot is proposed to solve the trajectory tracking problems. Assuming that the dynamic model of the system is a black box one, we use an a priori learning for its neural identification. And then, we use this results to design and adaptive control law. All adaptation laws of neural parameters 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.