Robust data-driven dynamic model discovery of industrial robots with spatial manipulation capability using simple trajectory
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11071-024-09526-7.pdf
Reference51 articles.
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2. Guan, S., Zhuang, Z., Tao, H., Chen, Y., Stojanovic, V., Paszke, W.: Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths. Trans. Inst. Meas. Control. 45(11), 2015–2026 (2023). https://doi.org/10.1177/01423312221142564
3. Tao, H., Zheng, J., Wei, J., Paszke, W., Rogers, E., Stojanovic, V.: Repetitive process based indirect-type iterative learning control for batch processes with model uncertainty and input delay. J. Process Control 132(103), 112 (2023). https://doi.org/10.1016/j.jprocont.2023.103112
4. Lin, J.L., Huang, H.P., Lin, C.Y.: Iterative learning control for vibration suppression of a robotic arm. Appl. Sci. (2023). https://doi.org/10.3390/app13020828
5. Jin, J., Gans, N.: Parameter identification for industrial robots with a fast and robust trajectory design approach. Robot. Comput.-Integr. Manuf. 31, 21–29 (2015). https://doi.org/10.1016/j.rcim.2014.06.004
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