Dynamic and Friction Parameters of an Industrial Robot: Identification, Comparison and Repetitiveness Analysis

Abstract

This paper describes the results of dynamic tests performed to study the robustness of a dynamics model of an industrial manipulator. The tests show that the joint friction changes during the robot operation. The variation can be identified in a double exponential law and thus the variation can be predicted. The variation is due to the heat generated by the friction. A model is used to estimate the temperature and related friction variation. Experimental data collected on two robots EFORT ER3A-C60 are presented and discussed. Repetitive tests performed on different days showed that the inertial and friction parameters can be robustly estimated and that the value of the measured joint friction can be used to estimate the unexpected conditions of the joints. Future applications may include sensorless identification of collisions, predictive maintenance programs, or human–robot interaction.

A Review of Key Technologies for Friction Nonlinearity in an Electro-Hydraulic Servo System

“…Stribeck [10,11] found that many frictions have static properties, as shown in Figure 2. By summarizing the observed friction phenomena, the friction characteristics were described as Stribeck curves.…”

An improved iterative approach with a comprehensive friction model for identifying dynamic parameters of collaborative robots

“…In addition, it extends the Generalized Maxwell-Slip model to represent the observed friction phenomena at near-zero velocities. Hao [29] described the relationship between friction and temperature with a double exponential model and adjusted its parameters by genetic algorithms. However, separated identification of the friction model introduces extra outliers in the near-zero-velocity region, that will lead to bias in the dynamic model identification [11].…”

The Study of the Dynamics of a Cebâşev Manipulator

“…Trajectory control of industrial robots using multilayer neural networks driven by iterative learning control can be found in the paper (Chen and Wen, 2021). Dynamic and friction parameters of an industrial robot with repeatability identification, comparison and analysis are other important aspects of dynamic and robotic processes in the industry (Hao et al, 2021). The impact of gravity compensation on reinforcement learning in goalsetting tasks for robotic manipulators is a relatively new problem in dynamic disciplines (Fugal et al, 2021).…”