@inproceedings{97d2f4fce05449c990f0b1195a051e30,
title = "A Simulation Modeling Method for Electromechanical Control of Large-inertia Manipulator Based on Deep Koopman Operator",
abstract = "This paper proposes a deep extended dynamic mode decomposition architecture with residual modules and feedback channels (RDEDMD) to construct a highdimensional linearized dynamic model of 6-DOF manipulator using Koopman operator. Basing on this foundation, the RKMPC model predictive control system is designed and integrated with the developed electromechanical simulation model for high-inertia manipulator, thereby establishing a unified electromechanical control simulation framework based on deep Koopman operators. Experimental results demonstrate that the proposed simulation model achieves significantly higher accuracy than traditional mechanismbased modeling approaches, while the RK-MPC controller exhibits superior prediction and control performance compared with conventional control methods, thus validating the effectiveness of the linearized model constructed by the RDEDMD.",
keywords = "6-DOF Manipulator, Deep Learning, Electromechanical control simulation model, Koopman Operator",
author = "Zhuoqi Manthe and Junyu Wu and Xuanming Cao and Yubin Liu",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 2026 International Conference on Robotics, Automation and Intelligent Transportation Systems, RAITS 2026 ; Conference date: 23-01-2026 Through 25-01-2026",
year = "2026",
doi = "10.1109/RAITS68656.2026.11580208",
language = "英语",
series = "Proceedings - 2026 International Conference on Robotics, Automation and Intelligent Transportation Systems, RAITS 2026",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2026 International Conference on Robotics, Automation and Intelligent Transportation Systems, RAITS 2026",
address = "美国",
}