Vibration control and angular tracking of a flexible link via neural networks

In this paper, we employ the adaptive Neural Network (NN) control to supress the vibration a flexible robotic manipulator with a proper tip-payload. The model of the flexible manipulator system is presented with the Lumped Spring-Mass method which can enhance the accuracy in reflecting the elastic vibrations of the original system. Full-state feedback control is carried out to approximate the unmeasured variables in this paper. Uniform ultimate boundedness (UUB) of the manipulator system is achieved via the Lyapunov´s direct theory. Simulations for the lumped flexible manipulator system are presented to verify the effectiveness of the proposed NN control strategies, and the control performance is compared with a PD control strategy.