Radial basis function neural network vibration control of a flexible planar parallel manipulator based on acceleration feedback

The self-excited vibration of flexible planar 3-RRR parallel manipulators is converted from the residual vibration after high-speed motion and is a resonance of the strongly coupled and nonlinear electromechanical system. This makes the active vibration control quite a challenging task. In this study, we attempt to adopt the radial basis function neural network control algorithm based on acceleration feedback for suppressing the self-excited vibration and guarantee its position accuracy. The stability of the controlled system is proved by the Lyapunov concept. Self-excited vibration control experiments are conducted near the singular region. Experimental results demonstrate the effectiveness of our adopted controller.