A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 11 Issue 1
Jan.  2024

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(1): 170-180
G.-P. Liu, “Control strategies for digital twin systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 170–180, Jan. 2024. doi: 10.1109/JAS.2023.123834
Citation: G.-P. Liu, “Control strategies for digital twin systems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 170–180, Jan. 2024. doi: 10.1109/JAS.2023.123834
shu

Control Strategies for Digital Twin Systems

doi: 10.1109/JAS.2023.123834
Funds:  This work was supported in part by Shenzhen Key Laboratory of Control Theory and Intelligent Systems (ZDSYS20220330161800001) and the National Natural Science Foundation of China (62173255, 62188101)
More Information
    Abstract
  • With the continuous breakthrough in information technology and its integration into practical applications, industrial digital twins are expected to accelerate their development in the near future. This paper studies various control strategies for digital twin systems from the viewpoint of practical applications. To make full use of advantages of digital twins for control systems, an architecture of digital twin control systems, adaptive model tracking scheme, performance prediction scheme, performance retention scheme, and fault tolerant control scheme are proposed. Those schemes are detailed to deal with different issues on model tracking, performance prediction, performance retention, and fault tolerant control of digital twin systems. Also, the stability of digital twin control systems is analysed. The proposed schemes for digital twin control systems are illustrated by examples.

     

    • FullText(HTML)
  • loading
    • References(41)
  • [1]
    A. Redelinghuys, A. Basson, and K. Kruger, “A six-layer architecture for the digital twin: A manufacturing case study implementation,” J. Intelligent Manufacturing, vol. 31, no. 6, pp. 1383–1402, 2020. doi: 10.1007/s10845-019-01516-6
    [2]
    M. W. Grieves, “Product lifecycle management: The new paradigm for enterprises,” Int. J. Product Development, vol. 2, no. 1–2, pp. 71–84, 2005.
    [3]
    Y. H. Kim, C. M. Kim, Y. H. Han, Y. S. Jeong, and D. S. Park, “An efficient strategy of nonuniform sensor deployment in cyber physical systems,” The J. Supercomputing, vol. 66, no. 1, pp. 70–80, 2013. doi: 10.1007/s11227-013-0977-9
    [4]
    E. J. Tuegel, A. R. Ingraffea, T. G. Eason, and S. M. Spottswood, “Reengineering aircraft structural life prediction using a digital twin,” Int. J. Aerospace Engineering, vol. 2011, p. 154798, 2011.
    [5]
    E. Glaessgen and D. Stargel, “The digital twin paradigm for future NASA and U.S. Air Force vehicles,” in Proc. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conf., 2012, pp. 1818−1832.
    [6]
    R. Soni, M. Bhatia, and T. Singh, “Digital twin: Intersection of mind and machine,” Int. J. Computational Intelligence and IoT, vol. 2, no. 3, pp. 667–670, 2019.
    [7]
    S. Haag and R. Anderl, “Digital twin-proof of concept,” Manufacturing Letters, pp. 64–66, 2018.
    [8]
    P. D. U. Coronado, R. Lynn, W. Louhichi, M. Parto, E. Wescoat, and T. Kurfess, “Part data integration in the shop floor digital twin: Mobile and cloud technologies to enable a manufacturing execution system,” J. Manufacturing Systems, vol. 48, pp. 25–33, 2018. doi: 10.1016/j.jmsy.2018.02.002
    [9]
    Y. Fang, C. Peng, P. Lou, Z. Zhou, J. Hu, and J. Yan, “Digital-twin based job shop scheduling towards smart manufacturing,” IEEE Trans. Industrial Informatics, vol. 15, no. 2, pp. 6425–6435, 2019. doi: 10.1109/TII.2019.2938572
    [10]
    I. M. Cavalcante, E. M. Frazzon, F. A. Forcellini, and D. Ivanov, “A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing,” Int. J. Information Management, vol. 49, pp. 86–97, 2019. doi: 10.1016/j.ijinfomgt.2019.03.004
    [11]
    A. El Saddik, “Digital twins: The convergence of multimedia technologies,” IEEE MultiMedia, vol. 25, no. 2, pp. 87–92, 2018. doi: 10.1109/MMUL.2018.023121167
    [12]
    D. Smith and S. Singh, “Approaches to multisensor data fusion in target tracking: A survey,” IEEE Trans. Knowledge and Data Engineering, vol. 18, no. 12, pp. 1696–1710, 2006. doi: 10.1109/TKDE.2006.183
    [13]
    Y. He, J. Guo, and X. Zheng, “From surveillance to digital twin: Challenges and recent advances of signal processing for industrial internet of things,” IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 120–129, 2018. doi: 10.1109/MSP.2018.2842228
    [14]
    B. Yi, X. Li, and Y. Yang, “Heterogeneous model integration of complex mechanical parts based on semantic feature fusion,” Engineering Computers, vol. 33, no. 4, pp. 797–805, 2017. doi: 10.1007/s00366-016-0498-2
    [15]
    G. N. Schroeder, C. Steinmetz, C. E. Pereira, and D. B. Espindola, “Digital twin data modeling with AutomationML and a communication methodology for data exchange,” IFAC-PapersOnLine, vol. 49, no. 30, pp. 12–17, 2016. doi: 10.1016/j.ifacol.2016.11.115
    [16]
    B. Schleich, N. Anwer, L. Mathieu, and S. Wartzack, “Shaping the digital twin for design and production engineering,” CIRP Annals, vol. 66, no. 1, pp. 141–144, 2017. doi: 10.1016/j.cirp.2017.04.040
    [17]
    F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, “Digital twin in industry: State-of-the-art,” IEEE Trans. Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, 2019. doi: 10.1109/TII.2018.2873186
    [18]
    K. Zhang, T. Qu, D. Zhou, H. Jiang, Y. Lin, P. Li, H. Guo, Y. Liu, C. Li, and G. Huang, “Digital twin-based opti-state control method for a synchronized production operation system,” Robotics and Computer-Integrated Manufacturing, vol. 63, p. 101892, 2020.
    [19]
    L. Chang, L. Zhang, C. Fu, and Y. Chen, “Transparent digital twin for output control using belief rule base,” IEEE Trans. Cyber., vol. 52, no. 10, pp. 10364–10378, 2022. doi: 10.1109/TCYB.2021.3063285
    [20]
    A. McClellan, J. Lorenzetti, M. Pavone, and C. Farhat, “A physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing,” Philosophical Trans. the Royal Society A-Mathematical Physical and Engineering Sciences, vol. 380, p. 2229, 2022.
    [21]
    X. Zhu and Y. Ji, “A digital twin-driven method for online quality control in process industry,” Int. J. Advanced Manufacturing Technology, vol. 119, no. 5−6, pp. 3045–3064, 2022. doi: 10.1007/s00170-021-08369-5
    [22]
    Z. Lei, H. Zhou, W. Hu, G. Liu, S. Guan, and X. Feng, “Towards a web-based digital twin thermal power plant,” IEEE Trans. Industrial Informatics, vol. 18, no. 3, pp. 1716–1725, 2022. doi: 10.1109/TII.2021.3086149
    [23]
    G. Liu and S. Daley, “Optimal-tuning PID controller design in the frequency domain with application to a rotary hydraulic system,” Control Engineering Practice, vol. 7, pp. 821–830, 1999. doi: 10.1016/S0967-0661(99)00047-7
    [24]
    G. Liu and S. Daley, “Optimal-tuning nonlinear PID control for hydraulic systems,” Control Engineering Practice, vol. 8, no. 9, pp. 1045–1053, 2000. doi: 10.1016/S0967-0661(00)00042-3
    [25]
    Q. Wei, H. Li, and F. Wang, “Parallel control for continuous-time linear systems: A case study,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 919–928, 2020. doi: 10.1109/JAS.2020.1003216
    [26]
    J. Yang, X. Wang, and Y. Zhao, “Parallel manufacturing for industrial metaverses: A new paradigm in smart manufacturing,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2063–2070, 2022. doi: 10.1109/JAS.2022.106097
    [27]
    L. Merkle, A. S. Segura, J. T. Grummel, and M. Lienkamp, “Architecture of a digital twin for enabling digital services for battery systems,” in Proc. IEEE Int. Conf. Industrial Cyber Physical Systems, 2019, pp. 155−160.
    [28]
    T. Defraeye, G. Tagliavini, W. Wu, et al., “Digital twins probe into food cooling and biochemical quality changes for reducing losses in refrigerated supply chains,” Resources,Conservation and Recycling, vol. 149, pp. 778–794, 2019. doi: 10.1016/j.resconrec.2019.06.002
    [29]
    A. Dalstam, M. Engberg, D. Nafors, B. Johansson, and A. Sundblom, “A stepwise implementation of the virtual factory in manufacturing industry,” in Proc. Winter Simulation Conf., 2018, pp. 3229−3240.
    [30]
    F. Tao and M. Zhang, “Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing,” IEEE Access, vol. 5, pp. 20418–20427, 2017. doi: 10.1109/ACCESS.2017.2756069
    [31]
    N. Nikolakis, K. Alexopoulos, E. Xanthakis, and G. Chryssolouris, “The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor,” Int. J. Computer Integrated Manufacturing, vol. 32, no. 1, pp. 1–12, 2019. doi: 10.1080/0951192X.2018.1529430
    [32]
    K. Ding, F. Chan, X. Zhang, G. Zhou, and F. Zhang, “Defining a digital twin-based cyber-physical production system for autonomous manufacturing in smart shop floors, ” Int. J. Production Research, vol. 57, no. 20, pp. 6315−6334. 2019.
    [33]
    R. Soderberg, K. Wormefjord, J. S. Carlson, and L. Lindkvist, “Toward a digital twin for real-time geometry assurance in individualized production,” CIRP Annals, vol. 66, no. 1, pp. 137–140, 2017. doi: 10.1016/j.cirp.2017.04.038
    [34]
    Q. Wang, W. Jiao, P. Wang, and Y. Zhang, “Digital twin for human-robot interactive welding and welder behavior analysis,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 334–343, 2021. doi: 10.1109/JAS.2020.1003518
    [35]
    Y. Wang, Y. Tian, J. Wang, Y. Cao, S. Li, and B. Tian, “Integrated inspection of QoM, QoP, and QoS for AOI industries in metaverses,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2071–2078, 2022. doi: 10.1109/JAS.2022.106091
    [36]
    G. Liu and S. Daley, “Optimal-tuning PID control for industrial systems,” Control Engineering Practice, vol. 9, no. 11, pp. 1185–1194, 2001. doi: 10.1016/S0967-0661(01)00064-8
    [37]
    G. Liu, Nonlinear Identification and Control: A Neural Network Approach, London, UK: Springer-Verlag Ltd., 2001.
    [38]
    G. Liu, “Tracking control of multi-agent systems using a networked predictive PID tracking scheme,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 216–225, 2023. doi: 10.1109/JAS.2023.123030
    [39]
    G. Liu, J. Yang, and J. Whidborne, Multiobjective Optimisation and Control, Baldock, UK: Research Studies Press Ltd., 2003.
    [40]
    Z. Wang, P. Li, R. Lu, and C. Wang, Performance Analysis of Multiple Time-Delay Discrete Systems, Beijing, China: Science and Technology Press, 2022.
    [41]
    G. Liu, “Coordination of networked nonlinear multi-agents using a high-order fully actuated predictive control strategy,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 615–623, 2022. doi: 10.1109/JAS.2022.105449
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(15)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (310) PDF downloads(86) Cited by()

    Highlights

    • A novel architecture of digital twin control systems is proposed for practical control applications
    • To make full use of the advantages of digital twins, several digital twin control schemes are presented
    • The proposed digital twin control strategies can effectively deal with practical control challenging issues

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return