Parameter identification for industrial robots with a fast and robust trajectory design approach

Abstract

Model-based, torque-level control can offer precision and speed advantages over velocity-level or position-level robot control. However, the dynamic parameters of the robot must be identified accurately. Several steps are involved in dynamic parameter identification, including modeling the system dynamics, joint position/torque data acquisition and filtering, experimental design, dynamic parameters estimation and validation. In this paper, we propose a novel, computationally efficient and intuitive optimality criterion to design the excitation trajectory for the robot to follow. Experiments are carried out for a 6 degree of freedom (DOF) Staubli TX-90 robot. We validate the dynamics parameters using torque prediction accuracy and compare to existing methods. The RMS errors of the prediction were small, and the computation time for the new, optimal objective function is an order of magnitude less than for existing approaches. HighlightsWe propose a fast, robust trajectory design for use in parameter identification.Computation time for our design is approximately 1/20 the time for prominent, existing methods.We estimate the dynamic parameters for a 6 DOF robot, the Staubli Tx-90, which has not previously been identified, to our knowledge.The RMS errors of the torque prediction were small (< 24 Nm).