A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 6.171, Top 11% (SCI Q1)
    CiteScore: 11.2, Top 5% (Q1)
    Google Scholar h5-index: 51, TOP 8
Turn off MathJax
Article Contents
Qi Wu, Li Yu, Yao-Wei Wang and Wen-An Zhang, "LESO-based Position Synchronization Control for Networked Multi-Axis Servo Systems With Time-Varying Delay," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1116-1123, July 2020. doi: 10.1109/JAS.2020.1003264
Citation: Qi Wu, Li Yu, Yao-Wei Wang and Wen-An Zhang, "LESO-based Position Synchronization Control for Networked Multi-Axis Servo Systems With Time-Varying Delay," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1116-1123, July 2020. doi: 10.1109/JAS.2020.1003264

LESO-based Position Synchronization Control for Networked Multi-Axis Servo Systems With Time-Varying Delay

doi: 10.1109/JAS.2020.1003264
Funds:  This work was supported by the National Natural Science Foundation of China (NSFC) (61822311) and the NSFC-Zhejiang Joint Fund for the Intergration of Industrialization and Informatization (U1709213)
More Information
  • The position synchronization control (PSC) problem is studied for networked multi-axis servo systems (NMASSs) with time-varying delay that is smaller than one sampling period. To improve the control performance of the system, time-varying delays, modeling uncertainties, and external disturbances are first modeled as a lumped disturbance. Then, a linear extended state observer (LESO) is devised to estimate the system state and the lumped disturbance, and a linear feedback controller with disturbance compensation is designed to perform individual-axis tracking control. After that, a cross-coupled control approach is used to further improve synchronization performance. The bounded-input-bounded-output (BIBO) stability of the closed-loop control system is analyzed. Finally, both simulation and experiment are carried out to demonstrate the effectiveness of the proposed method.


  • loading
  • [1]
    N. Uchiyama, Y. Ogawa, A. El Khalick M, and S. Sano, “Energy saving in five-axis machine tools using synchronous and contouring control and verification by machining experiment,” IEEE Trans. Ind. Electron., vol. 62, no. 9, pp. 5608–5618, Sep. 2015. doi: 10.1109/TIE.2015.2437354
    J. X. Yang and Y. Altintas, “A generalized on-line estimation and control of five-axis contouring errors of CNC machine tools,” Int. J. Mach. Tools Manuf., vol. 88, pp. 9–23, Jan. 2015. doi: 10.1016/j.ijmachtools.2014.08.004
    Z. Y. Jia, J. W. Ma, D. N. Song, F. J. Wang, and W. Liu, “A review of contouring-error reduction method in multi-axis CNC machining,” Int. J. Mach. Tools Manuf., vol. 125, pp. 34–54, Feb. 2018. doi: 10.1016/j.ijmachtools.2017.10.008
    J. Lee, J. Hong, K. Nam, R. Ortega, L. Praly, and A. Astolfi, “Sensorless control of surface-mount permanent-magnet synchronous motors based on a nonlinear observer,” IEEE Trans. Power Electron., vol. 25, no. 2, pp. 290–297, Feb. 2010. doi: 10.1109/TPEL.2009.2025276
    H. Yang, Y. T. Fu, and D. H. Wang, “Multi-ANFIS model based synchronous tracking control of high-speed electric multiple unit,” IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp. 1472–1484, Jun. 2018. doi: 10.1109/TFUZZ.2017.2725819
    R. J. Wai and R. Muthusamy, “Design of fuzzy-neural-network-inherited backstepping control for robot manipulator including actuator dynamics,” IEEE Trans. Fuzzy Syst., vol. 22, no. 4, pp. 709–722, Aug. 2014. doi: 10.1109/TFUZZ.2013.2270010
    D. Nojavanzadeh and M. Badamchizadeh, “Adaptive fractional-order non-singular fast terminal sliding mode control for robot manipulators,” IET Control Theory Appl., vol. 10, no. 13, pp. 1565–1572, Aug. 2016. doi: 10.1049/iet-cta.2015.1218
    L. Roveda, G. Pallucca, N. Pedrocchi, F. Braghin, and L. M. Tosatti, “Iterative learning procedure with reinforcement for high-accuracy force tracking in robotized tasks,” IEEE Trans. Ind. Inform., vol. 14, no. 4, pp. 1753–1763, Apr. 2018. doi: 10.1109/TII.2017.2748236
    Q. K. Han, L. N. Hao, H. Zhang, and B. C. Wen, “Achievement of chaotic synchronization trajectories of master-slave manipulators with feedback control strategy,” Acta Mech. Sinica, vol. 26, no. 3, pp. 433–439, Apr. 2010. doi: 10.1007/s10409-010-0340-9
    F. J. Torres, G. V. Guerrero, C. D. Garcia, J. F. Gomez, M. Adam, and R. F. Escobar, “Master-slave synchronization of robot manipulators driven by induction motors,” IEEE Latin Am. Trans., vol. 14, no. 9, pp. 3986–3991, Sep. 2016. doi: 10.1109/TLA.2016.7785923
    D. Sun, X. Y. Shao, and G. Feng, “A model-free cross-coupled control for position synchronization of multi-axis motions: Theory and experiments,” IEEE Trans. Control Syst. Technol., vol. 15, no. 2, pp. 306–314, Mar. 2007. doi: 10.1109/TCST.2006.883201
    C. S. Chen and L. Y. Chen, “Cross-coupling position command shaping control in a multi-axis motion system,” Mechatronics, vol. 21, no. 3, pp. 625–632, Apr. 2011. doi: 10.1016/j.mechatronics.2011.01.004
    T. N. Shi, H. Liu, Q. Geng, and C. L. Xia, “Improved relative coupling control structure for multi-motor speed synchronous driving system,” IET Electr. Power Appl., vol. 10, no. 6, pp. 451–457, Jul. 2016. doi: 10.1049/iet-epa.2015.0515
    G. L. Zhong, H. Deng, G. Y. Xin, and H. S. Wang, “Dynamic hybrid control of a hexapod walking robot: Experimental verification,” IEEE Trans. Ind. Electron., vol. 63, no. 8, pp. 5001–5011, Aug. 2016.
    J. Na, Q. Chen, X. M. Ren, and Y. Guo, “Adaptive prescribed performance motion control of servo mechanisms with friction compensation,” IEEE Trans. Ind. Electron., vol. 61, no. 1, pp. 486–494, Jan. 2014. doi: 10.1109/TIE.2013.2240635
    S. J. Dong, L. Yu, W. A. Zhang, and B. Chen, “Recursive identification for Wiener non-linear systems with non-stationary disturbances,” IET Control Theory Appl., vol. 13, no. 16, pp. 2648–2657, Nov. 2019. doi: 10.1049/iet-cta.2018.6413
    W. He, T. T. Meng, X. Y. He, and S. S. Ge, “Unified iterative learning control for flexible structures with input constraints,” Automatica, vol. 96, pp. 326–336, Oct. 2018. doi: 10.1016/j.automatica.2018.06.051
    G. L. Zhong, Y. Kobayashi, Y. Hoshino, and T. Emaru, “System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty,” Nonlinear Dyn., vol. 73, no. 1–2, pp. 167–182, Jul. 2013. doi: 10.1007/s11071-013-0776-0
    S. C. Qu, X. H. Xia, and J. F. Zhang, “Dynamics of discrete-time sliding-mode-control uncertain systems with a disturbance compensator,” IEEE Trans. Ind. Electron., vol. 61, no. 7, pp. 3502–3510, Jul. 2014. doi: 10.1109/TIE.2013.2279369
    W. He, Y. T. Dong, and C. Y. Sun, “Adaptive neural impedance control of a robotic manipulator with input saturation,” IEEE Trans. Syst.,Man,Cybern.,Syst., vol. 46, no. 3, pp. 334–344, Mar. 2016. doi: 10.1109/TSMC.2015.2429555
    W. He and S. S. Ge, “Cooperative control of a nonuniform gantry crane with constrained tension,” Automatica, vol. 66, pp. 146–154, Apr. 2016. doi: 10.1016/j.automatica.2015.12.026
    C. L. Lai and P. L. Hsu, “Design the remote control system with the time-delay estimator and the adaptive smith predictor,” IEEE Trans. Ind. Inform., vol. 6, no. 1, pp. 73–80, Feb. 2010. doi: 10.1109/TII.2009.2037917
    W. A. Zhang, L. Yu, and H. B. Song, “A switched system approach to networked control systems with time-varying delays,” in Proc. 27th Chinese Control Conf., Kunming, China, 2008, pp. 16-18.
    Q. X. Chen and A. D. Liu, “D-stability and disturbance attenuation properties for networked control systems: Switched system approach,” J. Syst. Eng. Electron., vol. 27, no. 5, pp. 1108–1114, Oct. 2016. doi: 10.21629/JSEE.2016.05.18
    W. A. Zhang and L. Yu, “BIBO stability and stabilization of networked control systems with short time-varying delays,” Int. J. Robust Nonlinear Control, vol. 21, no. 3, pp. 295–308, Feb. 2011. doi: 10.1002/rnc.1592
    J. Chen, R. J. Patton, and H. Y. Zhang, “Design of unknown input observers and robust fault detection filters,” Int. J. Control, vol. 63, no. 1, pp. 85–105, Feb. 1996. doi: 10.1080/00207179608921833
    K. Yang, Y. Choi, and W. K. Chung, “On the tracking performance improvement of optical disk drive servo systems using error-based disturbance observer,” IEEE Trans. Ind. Electron., vol. 52, no. 1, pp. 270–279, Feb. 2005. doi: 10.1109/TIE.2004.841069
    J. H. She, M. X. Fang, Y. Ohyama, H. Hashimoto, and M. Wu, “Improving disturbance-rejection performance based on an equivalent-input-disturbance approach,” IEEE Trans. Ind. Electron., vol. 55, no. 1, pp. 380–389, Jan. 2008. doi: 10.1109/TIE.2007.905976
    J. Q. Han, “From PID to active disturbance rejection control,” IEEE Trans. Ind. Electron, vol. 56, no. 3, pp. 900–906, Mar. 2009. doi: 10.1109/TIE.2008.2011621
    S. Zhao and Z. Q. Gao, “Modified active disturbance rejection control for time-delay systems,” ISA Trans., vol. 53, no. 4, pp. 882–888, Jul. 2014. doi: 10.1016/j.isatra.2013.09.013
    S. Chen, W. C. Xue, Y. Huang, and P. Liu, “On comparison between Smith Predictor and Predictor Observer based ADRCs for nonlinear uncertain systems with output delay,” in Proc. Conf. American Control, Seattle, USA, 2017, pp. 5083-5088.
    W. C. Xue, P. Liu, S. Chen, and Y. Huang, “On extended state predictor observer based active disturbance rejection control for uncertain systems with sensor delay,” in Proc. 16th Int. Conf. Control, Autom. and Systems, Gyeongju, South Korea, 2016, pp. 1267-1271.
    S. Chen, W. C. Xue, S. Zhong, and Y. Huang, “On comparison of modified ADRCs for nonlinear uncertain systems with time delay,” Sci. China Inform. Sci., vol. 61, no. 7, pp. 70223, Jun. 2018. doi: 10.1007/s11432-017-9403-x
    Z. Q. Gao, “Scaling and bandwidth-parameterization based controller tuning,” in Proc. American Control Conf., Denver, USA, 2003, pp. 4989-4996.
    S. H. Li, J. Yang, W. H. Chen, and X. S. Chen, “Generalized extended state observer based control for systems with mismatched uncertainties,” IEEE Trans. Ind. Electron., vol. 59, no. 12, pp. 4792–4802, Dec. 2012. doi: 10.1109/TIE.2011.2182011
    Q. Wu, Y. W. Wang, W. A. Zhang, and L. Yu, “Model identification of PMSM based on the comprehensive learning particle swarm optimization,” Mach. Des. Manuf. Eng., vol. 46, no. 11, pp. 78–82, Nov. 2017.


    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(7)  / Tables(3)

    Article Metrics

    Article views (788) PDF downloads(45) Cited by()


    • It is demonstrated that the proposed approach can deal with the effects of system uncertainty, external disturbance, and short time-varying for the NMASS.
    • It is rigorously proved that the closed-loop control system under the proposed controller is bounded-input-bounded-output (BIBO) stable.
    • It is verified that the proposed method has better tracking and synchronization performance than the improve PID-based method by testing on a four-axis NMASS experimental platform.
    • The bandwidth-parameterization tuning method is applied in both controller design and observer design, so that the number of parameters that need to be adjusted is greatly reduced.


    DownLoad:  Full-Size Img  PowerPoint