Precision Motion Control of Robotized Industrial Hydraulic Excavators via Data-Driven Model Inversion
نویسندگان
چکیده
This work proposes a novel precision motion control framework of robotized industrial hydraulic excavators via data-driven model inversion. Rather than employing single neural network to approximate the whole excavator dynamics, including input delays and dead-zones, we construct physics-inspired with modular structure. The is then inverted in fashion which benefits training speed. its inversion are trained offline supervised manner using real operational data since online learning methods can damage machine surroundings. entire consists that compensates for dynamics proportional determines enhance robustness. experimentally validated commercial 38-ton class digging grading tasks, achieving precise performance (i.e., root-mean-square path following error under $2 \;[\rm cm]$) even severe soil interactions.
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2022
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2022.3142389