A Dynamic Parameter Identification Method for Flexible Joints Based on Adaptive Control

This paper investigates the dynamic parameter identification for flexible joints without additional sensors. The estimation accuracy of traditional methods based on the original dynamic model is limited by measurement of angular acceleration. Recently, approaches using integral operations are raised to solve this problem. However, the integral operations easily result in error accumulation and deteriorate the parameter estimation accuracy. This study proposes a new dynamic parameter identification method for flexible joints. By constructing an alternative system function and estimating its outputs through nonmodel-based adaptive control, we avoid the measurement of angular acceleration and integral operations. On this basis, dynamic parameters of flexible joints are estimated using the recursive least squares (RLS) method. The experimental results indicate that the proposed method possesses better estimation accuracy than the general RLS- and integral-based methods. It is also validated that the proposed method can provide accurate estimation even under a relatively low-resolution system. Applications to model-based motion control and physical human-robot interaction are also verified.