Relative torque contribution based model simplification for robotic dynamics identification

It has been proved that minimizing the condition number of the observation matrix, which is calculated from the robot dynamic model and the associated exciting trajectories, is very effective for improving the identification accuracy of robotic dynamics. A relative simple dynamic model is beneficial for reduction of the associated condition number, and hence, several model simplification methods have been proposed in the literature. However, the existed methods cannot be used to efficiently process model structural errors, which will inevitably cause inaccurate estimation of the dynamics. Therefore, a novel model simplification method based on relative contribution of the undetermined parameters, is proposed to overcome the deficiency. Firstly, exciting trajectories for model simplification are designed by using finite Fourier series and optimized by using the condition number criteria. Then, the optimized exciting trajectory is implemented on the robot, and joint torques and motion data are recorded, which are used to calculate relative contribution of the undetermined parameters to joint torques. The model can be simplified repeatedly by neglecting the parameter that contributes least until the condition number is small enough. Finally, the performance of the proposed method is demonstrated by the identification and validation experiments conducted on a lower limb rehabilitation robot.