Extraction and computation of identifiable parameters in robot dynamic models: theory and application

Abstract

The accuracy of robot dynamic models plays an important role in the design of stable and robust robot controllers. The accuracy of these dynamic models is largely dependent on the precise measurement of robot dynamic parameters (e.g first moments, radius of gyration matrix, etc.). Towards this end, algorithms for the identification of these parameters need to be developed to obtain dynamic models with high fidelity, which will ultimately improve the future designs of robot controllers. This work aims to provide a viable solution to this problem. A simplified model of the dynamics is used to extract and group the dynamic parameters of a robot manipulator. The resulting equations are mapped onto a network of parallel processors to speed up the rate of computations and enable real-time implementations. The efficiency of the algorithm is demonstrated by a case study.