A Backpropagation Learning Method for Dynamic Parameter Identification of Industrial Robots

Abstract

Precise dynamic parameters are of great significance to the model-based controller of industrial robots. The least square (LS) method is widely used to identify the dynamic parameters in reality, but it is sensitive to the measurement noise and may obtain a biased solution. Besides, the nonlinear dynamics are the key and difficult point of identification, such as the nonlinear friction and the coupling effect of adjacent joint, but they are seldom solved simply and effectively. To cope with the above issues, we propose a backpropagation learning (BPL) method for parameter identification. The mean square error between the measured torque and the calculated torque from the dynamic model is taken as the loss function. The gradient of the loss function with respect to the parameters is computed, and the parameters are updated in the negative gradient direction to minimize the loss function until finding the optimal parameters with the minimum loss function. The optimal parameters will not only fit the modeled torque but also compensate for the torque caused by the unmodeled factors, thus improving the parameter accuracy. The proposed method is essentially a supervised learning method, so the impact of measurement noise is reduced by continuous training with a large amount of valid data. The proposed method is verified in an industrial robot platform, and the experimental results show that the proposed method has smaller errors than the weighted least squares (WLS) method and achieves similar accuracy to the semiparametric model (SPM) but has a better generalization ability.