Dynamic Model Identification for Industrial Robots

Abstract

The use of periodic excitation is the key feature of the presented robot identification method. Periodic excitation allows us to integrate the experiment design, signal processing, and parameter estimation. This integration simplifies the identification procedure and yields accurate models. Experimental results on an industrial robot manipulator show that the estimated dynamic robot model can accurately predict the actuator torques for a given robot motion. Accurate actuator torque prediction is a fundamental requirement for robot models that are used for offline programming, task optimization, and advanced model-based control. A payload identification approach is derived from the integrated robot identification method, and possesses the same favorable properties.