Identification methods for excavator arm parameters

This paper presents the results of a study on parameters identification of a full scale unmanned excavator vehicle. Two identification methods, the generalized Newton method and the least square method are used and comparison between them in term of prediction accuracy, robustness to noise and computational speed are presented. The techniques are used to identify the link parameters (mass, inertia and length) and friction of a full-scale excavator arm. The identified parameters are compared with physical values. Further, the joint torques and positions computed by the proposed model using the identified parameters are validated against measured data. The comparison shows that the generalized Newton method is better in term of prediction accuracy, robustness to noise and computational speed.