Trajectory planning and diagonal recurrent neural network vibration control of a flexible manipulator using structural light sensor

Abstract

Abstract A new vibration measurement method and active control algorithm for a flexible manipulator are investigated. A non-contact vibration measurement method based on structural light sensor is proposed. During and after the point-to-point motion, the locally controlled autoregressive moving average (CARMA) model of the flexible manipulator is determined through experiments. Based on the model, the particle swarm optimization (PSO) algorithm is adopted to obtain the optimal vibration suppression trajectory. Moreover, considering the time-varying and nonlinear characteristics of the flexible manipulator, a diagonal recurrent neural network (DRNN) control algorithm is designed to suppress residual vibration, which consists of an on-line identifier and a vibration control signal generator. The experiment setup is constructed. Compared with trapezoidal trajectory and classic smooth trajectories, planning an optimal trajectory can cause less vibration under the same motion requirements. Experimental results demonstrated that the DRNN controller is superior to the classical PD controller on vibration suppression, especially for the small amplitude residual vibration. Furthermore, the hybrid control strategy of optimal trajectory planning and DRNN control has the advantages of ensuring smoother motion and faster residual vibration suppression speed.