Dynamic Modeling and Analysis of Industrial Robots for Enhanced Manufacturing Precision

Abstract

This study addresses the challenge of accurately modeling the dynamic behavior of industrial robots for precision manufacturing applications. Using a comprehensive experimental approach with modal impulse hammer testing and triaxial acceleration measurements, 360 frequency response functions were recorded along orthogonal measurement paths for a KUKA KR10 robot. Two dynamic models with different parameter dimensions (12-parameter and 24-parameter) were developed in Matlab/Simscape, and their parameters were identified using genetic algorithm optimization. The KUKA KR10 features Harmonic Drives at each joint, whose high transmission ratio and zero backlash characteristics significantly influence rotational dynamics and allow for meaningful static structural measurements. Objective functions based on the Frequency Response Assurance Criterion (FRAC) and Root Mean Square Error (RMSE) metrics were employed, utilizing a frequency-dependent weighting function. The performance of the models was evaluated across different robot configurations and frequency ranges. The 24-parameter model demonstrated significantly superior performance, achieving 70% overall average Global FRAC in the limited frequency range (≤200 Hz) compared to 41% for the 12-parameter model when optimized using a representative subset of 9 measurement points. Both models showed substantially better performance in the limited frequency range than in the full spectrum. This research provides a validated methodology for dynamic characterization of industrial robots and demonstrates that higher-dimensional models, incorporating transverse joint compliance, can accurately represent robot dynamics up to approximately 200 Hz. Future work will investigate nonlinear effects such as torsional stiffness hysteresis, particularly relevant for Harmonic Drive systems.