Hostname: page-component-848d4c4894-75dct Total loading time: 0 Render date: 2024-06-01T13:59:00.125Z Has data issue: false hasContentIssue false

Synchronization control of blanket remote maintenance robot based on MPC-CCC algorithm

Published online by Cambridge University Press:  14 August 2023

Dongyi Li
Affiliation:
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China University of Science and Technology of China, Hefei 230026, China Anhui Extreme Environment Robot Engineering Laboratory, Hefei 230031, China Lappeenranta University of Technology, Lappeenranta, Finland
Kun Lu
Affiliation:
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Yong Cheng
Affiliation:
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China Anhui Extreme Environment Robot Engineering Laboratory, Hefei 230031, China
Huapeng Wu*
Affiliation:
Lappeenranta University of Technology, Lappeenranta, Finland
Heikki Handroos
Affiliation:
Lappeenranta University of Technology, Lappeenranta, Finland
Xuanchen Zhang
Affiliation:
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China University of Science and Technology of China, Hefei 230026, China Anhui Extreme Environment Robot Engineering Laboratory, Hefei 230031, China
Xinpeng Guo
Affiliation:
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China University of Science and Technology of China, Hefei 230026, China Anhui Extreme Environment Robot Engineering Laboratory, Hefei 230031, China
Songzhu Yang
Affiliation:
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China Anhui Extreme Environment Robot Engineering Laboratory, Hefei 230031, China
Liansheng Du
Affiliation:
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China Anhui Extreme Environment Robot Engineering Laboratory, Hefei 230031, China
Yu Zhang
Affiliation:
Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China Anhui Extreme Environment Robot Engineering Laboratory, Hefei 230031, China
*
Corresponding author: Huapeng Wu; Email: huapeng.wu@lut.fi

Abstract

This paper studies the synchronization control of the blanket remote maintenance robot (BRMR) of the China fusion engineering test reactor (CFETR). First, the general state space mathematical model of BRMR was established by using a physical-based method. Second, based on the receding horizon optimization of model predictive control (MPC) and cross-coupling error reduction in cross-coupling control (CCC), the innovative MPC-CCC controller was proposed to realize the single-system and multisystem error convergence and high accuracy transportation of blanket through the high accuracy synchronization control of BRMR. Third, to verify the control effectiveness of the MPC-CCC controller, two types of simulations and experiments were implied compared with the original proportional-integral (PI) controller in Mover. Results showed that simulation and experiments were highly consistent. It is found that the use of an MPC-CCC controller can result in up to a 70% reduction in displacement error and up to a 59% reduction in synchronization error compared to the PI controller. And the accuracy of the MPC-CCC controller satisfies the real requirement of the maintenance process of the blanket. This work provides the theoretical basis and practical experience for the highly stable, safe, and efficient maintenance of blankets in the future.

Type
Research Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Li, D., Lu, K., Cheng, Y., Zhao, W., Yang, S., Zhang, Y., Li, J. and Shi, S., “Dynamic analysis of multi-functional maintenance platform based on Newton-Euler method and improved virtual work principle,” Nucl. Eng. Technol. 52(11), 26302637 (2020).CrossRefGoogle Scholar
Yu, X., He, W., Li, Q., Li, Y. and Li, B., “Human-robot co-carrying using visual and force sensing,” IEEE Trans. Ind. Electron. 68(9), 86578666 (2021).CrossRefGoogle Scholar
Yu, X., Li, B., He, W., Feng, Y., Cheng, L. and Silvestre, C., “Adaptive-constrained impedance control for human-robot co-transportation,” IEEE Trans. Cybern. 52(12), 1323713249 (2021).CrossRefGoogle Scholar
Alleyne, A., Allgöwer, F., Ames, A., Amin, S., Anderson, J., Annaswamy, A., Antsaklis, P., Bagheri, N., Balakrishnan, H. and Bamieh, B., “Control for Societal-Scale Challenges: Road Map 2030,” 2022 IEEE CSS Workshop on Control for Societal-Scale Challenges (2023) pp. 114118.Google Scholar
Zhu, Q., Wang, J. and Xie, G., “Review of composite adaptive control for servo system,” Aeronaut. Manuf. Technol. 64(22), 1427 (2021).Google Scholar
Avanzini, G. B., Zanchettin, A. M. and Rocco, P., “Constrained model predictive control for mobile robotic manipulators,” Robotica 36(1), 1938 (2018).CrossRefGoogle Scholar
Yao, M. and Zhao, M., “Unmanned aerial vehicle dynamic path planning in an uncertain environment,” Robotica 33(3), 611621 (2015).CrossRefGoogle Scholar
Nascimento, T. P., Dórea, C. E. and Gonçalves, L. M. G., “Nonholonomic mobile robots’ trajectory tracking model predictive control: A survey,” Robotica 36(5), 676696 (2018).CrossRefGoogle Scholar
Ouyang, T., Lu, Y., Cheng, L. and Wang, J., “Controller design for electro-hydraulic actuator of heavy-duty automatic transmission using model predictive control algorithm,” IEEE Trans. Transp. Electrif. 11 (2023) https://doi.org/10.1109/TTE.2023.3249164.Google Scholar
Jose, J. T., Das, J. and Mishra, S. K., “Dynamic improvement of hydraulic excavator using pressure feedback and gain scheduled model predictive control,” IEEE Sens. J. 21(17), 1852618534 (2021).CrossRefGoogle Scholar
Cho, B., Kim, S.-W., Shin, S., Oh, J.-H., Park, H.-S. and Park, H.-W., “Energy-efficient hydraulic pump control for legged robots using model predictive control,” IEEE/ASME Trans. Mech. 28(1), (2022).Google Scholar
Varga, B., Meier, S., Schwab, S. and Hohmann, S., “Model Predictive Control and Trajectory Optimization of Large Vehicle-Manipulators,” IEEE International Conference on Mechatronics (ICM), vol. 12019 (2019) pp. 6066.Google Scholar
Shi, Q. and He, L., “A model predictive control approach for electro-hydraulic braking by wire,” IEEE Trans. Ind. Inform. 19(2), 13801388 (2022).CrossRefGoogle Scholar
Mei, M., Cheng, S., Mu, H., Pei, Y. and Li, B., “Switchable MPC-based multi-objective regenerative brake control via flow regulation for electric vehicles,” Front. Rob. AI 10 (2023). https://doi.org/10.3389/frobt.2023.1078253.Google ScholarPubMed
Heybroek, K. and Sjöberg, J., “Model predictive control of a hydraulic multichamber actuator: A feasibility study,” IEEE/ASME Trans. Mech. 23(3), 13931403 (2018).CrossRefGoogle Scholar
Zeng, X., Li, G., Yin, G., Song, D., Li, S. and Yang, N., “Model predictive control-based dynamic coordinate strategy for hydraulic hub-motor auxiliary system of a heavy commercial vehicle,” Mech. Syst. Signal Process. 101, 97120 (2018).CrossRefGoogle Scholar
Bender, F. A., Göltz, S., Bräunl, T. and Sawodny, O., “Modeling and offset-free model predictive control of a hydraulic mini excavator,” IEEE Trans. Autom. Sci. Eng. 14(4), 16821694 (2017).CrossRefGoogle Scholar
Dahunsi, O., Pedro, J. and Nyandoro, O., “Neural network-based model predictive control of a servo-hydraulic vehicle suspension system,” In: Proceedings of IEEE AFRICON 2009, Nairobi Kenya (2009). https://doi.org/10.1109/AFRCON.2009.5308111.CrossRefGoogle Scholar
Kalmari, J., Backman, J. and Visala, A., “Nonlinear model predictive control of hydraulic forestry crane with automatic sway damping,” Comput. Electron. Agric. 109, 3645 (2014).CrossRefGoogle Scholar
Yang, X., Wang, X., Wang, S., Wang, K. and Sial, M. B., “Finite-time adaptive dynamic surface synchronization control for dual-motor servo systems with backlash and time-varying uncertainties,” ISA Trans. 137, 248–262 (2022) https://doi.org/10.1016/j.isatra.2022.12.013.Google ScholarPubMed
Sun, D., Shao, X. and Feng, G., “A model-free cross-coupled control for position synchronization of multi-axis motions: Theory and experiments,” IEEE Trans. Control Syst. Technol. 15(2), 306314 (2007).CrossRefGoogle Scholar
Yuan, H. and Zhao, X., “A novel precision synchronization control via adaptive jerk control with parameter estimation for gantry servo system,” Int. J. Control Autom. Syst. 21(1), 188200 (2023).CrossRefGoogle Scholar
Kuang, Z., Gao, H. and Tomizuka, M., “Precise linear-motor synchronization control via cross-coupled second-order discrete-time fractional-order sliding mode,” IEEE/ASME Trans. Mech. 26(1), 358368 (2020).Google Scholar
Han, G., Lu, Z., Hong, J., Wu, M., Xu, S. and Zhu, B., “Speed synchronization control of dual-SRM drive with ISMC-based cross-coupling control strategy,” IEEE Trans. Transp. Electrif. 9(2), 2524–2534 (2022).Google Scholar
Zou, S., Zhao, W., Wang, C., Liang, W. and Chen, F., “Tracking and synchronization control strategy of vehicle dual-motor steer-by-wire system via active disturbance rejection control,” IEEE/ASME Trans. Mech. 28(1), 92–103 (2022).Google Scholar
Pandala, A. G., Ding, Y. and Park, H.-W., “qpSWIFT: A real-time sparse quadratic program solver for robotic applications,” IEEE Rob. Autom. Lett. 4(4), 33553362 (2019).CrossRefGoogle Scholar
Li, D., Lu, K., Cheng, Y., Zhao, W., Yang, S., Zhang, Y., Li, J. and Wu, H., “Fuzzy-PID controller for motion control of CFETR multi-functional maintenance platform,” Nucl. Eng. Technol. 53(7), 22512260 (2021).CrossRefGoogle Scholar