Rotation angle vibration suppression for variable-length flexible manipulator based on neural network identification with sliding-mode controller

Abstract

The variable-length flexible manipulator has an impact on the flexibility of joints and the flexibility load, in which vibration at the angle of rotation will occur. To suppress the vibration, a control law combining neural network identification, sliding mode control and angle-independent method is presented. To begin with, the variable-length flexible manipulator dynamic equations are established considering friction torque and two-dimensional deformation. Then, An adaptive law for the neural network weight coefficients is devised using the Lyapunov stability theorem .At last, the simulated analysis and controlled experiments are performed. The experimental finding demonstrates this: The control accuracy of rotation angle can be improved by using neural network compensation for the uncertain part. The flexible load vibration can be suppressed by the angle-independent method. The mean absolute error of rotation angle can be decrease by 15.85% using the combination control strategy. This proves that the control strategy presented can effectually.