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Volume 9 Issue 5
May  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(5): 854-863
L. Jin, X. Zheng, and X. Luo, “Neural dynamics for distributed collaborative control of manipulators with time delays,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 854–863, May 2022. doi: 10.1109/JAS.2022.105446
Citation: L. Jin, X. Zheng, and X. Luo, “Neural dynamics for distributed collaborative control of manipulators with time delays,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 854–863, May 2022. doi: 10.1109/JAS.2022.105446
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Neural Dynamics for Distributed Collaborative Control of Manipulators With Time Delays

doi: 10.1109/JAS.2022.105446
  • 1.

    School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China

  • 2.

    State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China

  • 3.

    School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 4000654, China

  • 4.

    Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China

Funds:  This work was supported in part by the National Natural Science Foundation of China (62176109), the Natural Science Foundation of Gansu Province (21JR7RA531), the Team Project of Natural Science Foundation of Qinghai Province China (2020-ZJ-903), the State Key Laboratory of Integrated Services Networks (ISN23-10), the Gansu Provincial Youth Doctoral Fund of Colleges and Universities (2021QB-003), the Fundamental Research Funds for the Central Universities (lzujbky-2021-65), the Supercomputing Center of Lanzhou University, the Natural Science Foundation of Chongqing (cstc2019jcyjjqX0013), the CAAIHuawei MindSpore Open Fund (CAAIXSJLJJ-2021-035A), and the Pioneer Hundred Talents Program of Chinese Academy of Sciences
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    Abstract
  • Time-delay phenomena extensively exist in practical systems, e.g., multi-agent systems, bringing negative impacts on their stabilities. This work analyzes a collaborative control problem of redundant manipulators with time delays and proposes a time-delayed and distributed neural dynamics scheme. Under assumptions that the network topology is fixed and connected and the existing maximal time delay is no more than a threshold value, it is proved that all manipulators in the distributed network are able to reach a desired motion. The proposed distributed scheme with time delays considered is converted into a time-variant optimization problem, and a neural dynamics solver is designed to solve it online. Then, the proposed neural dynamics solver is proved to be stable, convergent and robust. Additionally, the allowable threshold value of time delay that ensures the proposed scheme’s stability is calculated. Illustrative examples and comparisons are provided to intuitively prove the validity of the proposed neural dynamics scheme and solver.

     

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  • [1]
    J. S. Wang, J. Wang, and Q. L. Han, “Neurodynamics-based model predictive control of continuous-time under-actuated mechatronic systems,” IEEE/ASME Trans. Mechatron., vol. 26, no. 1, pp. 311–322, Feb. 2021.
    [2]
    Z. H. Peng, J. Wang, and Q. L. Han, “Path-following control of autonomous underwater vehicles subject to velocity and input constraints via neurodynamic optimization,” IEEE Trans. Ind. Electron., vol. 66, no. 11, pp. 8724–8732, Nov. 2019. doi: 10.1109/TIE.2018.2885726
    [3]
    C. T. Xu and X. He, “A fully distributed approach to optimal energy scheduling of users and generators considering a novel combined neurodynamic algorithm in smart grid,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1325–1335, Jul. 2021. doi: 10.1109/JAS.2021.1004048
    [4]
    X. Luo, Y. Zhou, Z. G. Liu, and M. C. Zhou, “Fast and accurate non-negative latent factor analysis on high-dimensional and sparse matrices in recommender systems,” IEEE Trans. Knowl. Data Eng., to be published. DOI: 10.1109/TKDE.2021.3125252.
    [5]
    L. Jin, L. Wei, and S. Li, “Gradient-based differential neural-solution to time-dependent nonlinear optimization,” IEEE Trans. Autom. Control, to be published. DOI: 10.1109/TAC.2022.3144135.
    [6]
    N. Liu and S. T. Qin, “A novel neurodynamic approach to constrained complex-variable pseudoconvex optimization,” IEEE Trans. Cybern., vol. 49, no. 11, pp. 3946–3956, Nov. 2019. doi: 10.1109/TCYB.2018.2855724
    [7]
    S. P. Madruga, A. H. B. M. Tavares, S. O. D. Luiz, T. P. Do Nascimento, and A. M. N. Lima, “Aerodynamic effects compensation on multi-rotor UAVs based on a neural network control allocation approach,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 295–312, Feb. 2022. doi: 10.1109/JAS.2021.1004266
    [8]
    X. Luo, H. Wu, Z. Wang, J. J. Wang, and D. Y. Meng, “A novel approach to large-scale dynamically weighted directed network representation,” IEEE Trans. Pattern Anal. Mach. Intell., to be published. DOI: 10.1109/TPAMI.2021.3132503.
    [9]
    Y. Saad and H. A. Van Der Vorst, “Iterative solution of linear systems in the 20th century,” J. Comput. Appl. Math., vol. 123, no. 1−2, pp. 1–33, Nov. 2000. doi: 10.1016/S0377-0427(00)00412-X
    [10]
    R. Olfati-Saber and R. M. Murray, “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Trans. Autom. Control, vol. 49, no. 9, pp. 1520–1533, Sep. 2004. doi: 10.1109/TAC.2004.834113
    [11]
    L. Moreau, “Stability of multiagent systems with time-dependent communication links,” IEEE Trans. Autom. Control, vol. 50, no. 2, pp. 169–182, Feb. 2005. doi: 10.1109/TAC.2004.841888
    [12]
    U. Münz, A. Papachristodoulou, and F. Allgöwer, “Delay robustness in consensus problems,” Automatica, vol. 46, no. 8, pp. 1252–1265, Aug. 2010. doi: 10.1016/j.automatica.2010.04.008
    [13]
    D. Wu, X. Luo, M. S. Shang, Y. He, G. Y. Wang, and X. D. Wu, “A data-characteristic-aware latent factor model for web services QoS prediction,” IEEE Trans. Knowl. Data Eng., to be published. DOI: 10.1109/TKDE.2020.3014302.
    [14]
    X. M. Zhang, Q. L. Han, and X. H. Ge, “Novel stability criteria for linear time-delay systems using Lyapunov-Krasovskii functionals with a cubic polynomial on time-varying delay,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 77–85, Jan. 2021.
    [15]
    X. M. Zhang, Q. L. Han, and X. H. Ge, “The construction of augmented Lyapunov-Krasovskii functionals and the estimation of their derivatives in stability analysis of time-delay systems: A survey,” Int. J. Syst. Sci., to be published. DOI: 10.1080/00207721.2021.2006356.
    [16]
    X. Wang, A. Saberi, A. A. Stoorvogel, H. F. Grip, and T. Yang, “Consensus in the network with uniform constant communication delay,” Automatica, vol. 49, no. 8, pp. 2461–2467, Aug. 2013. doi: 10.1016/j.automatica.2013.04.023
    [17]
    Y. Y. Chen and Y. Shi, “Consensus for linear multiagent systems with time-varying delays: A frequency domain perspective,” IEEE Trans. Cybern., vol. 47, no. 8, pp. 2143–2150, Aug. 2017. doi: 10.1109/TCYB.2016.2590480
    [18]
    Z. J. Li and C. Y. Su, “Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties,” IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 9, pp. 1400–1413, Sep. 2013. doi: 10.1109/TNNLS.2013.2258681
    [19]
    Z. B. Li, S. Li, and X. Luo, “An overview of calibration technology of industrial robots,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 23–36, Jan. 2021. doi: 10.1109/JAS.2020.1003381
    [20]
    A. H. Khan, Z. L. Shao, S. Li, Q. X. Wang, and N. Guan, “Which is the best PID variant for pneumatic soft robots? An experimental study” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 451–460, Mar. 2020. doi: 10.1109/JAS.2020.1003045
    [21]
    B. X. Wu, J. P. Zhong, and C. G. Yang, “A visual-based gesture prediction framework applied in social robots,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 510–519, Mar. 2022. doi: 10.1109/JAS.2021.1004243
    [22]
    D. Nakhaeinia, P. Payeur, and R. Laganiere, “A mode-switching motion control system for reactive interaction and surface following using industrial robots,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 3, pp. 670–682, May 2018. doi: 10.1109/JAS.2018.7511069
    [23]
    H. Zhang, H. Z. Jin, Z. X. Liu, Y. B. Liu, Y. H. Zhu, and J. Zhao, “Real-time kinematic control for redundant manipulators in a time-varying environment: Multiple-dynamic obstacle avoidance and fast tracking of a moving object,” IEEE Trans. Ind. Informat., vol. 16, no. 1, pp. 28–41, Jan. 2020. doi: 10.1109/TII.2019.2917392
    [24]
    M. Yang, Y. N. Zhang, Z. J. Zhang, and H. F. Hu, “Adaptive discrete ZND models for tracking control of redundant manipulator,” IEEE Trans. Ind. Informat., vol. 16, no. 12, pp. 7360–7368, Dec. 2020. doi: 10.1109/TII.2020.2976844
    [25]
    Y. Y. Zhang, S. Li, J. Gui, and X. Luo, “Velocity-level control with compliance to acceleration-level constraints: A novel scheme for manipulator redundancy resolution,” IEEE Trans. Ind. Informat., vol. 14, no. 3, pp. 921–930, Mar. 2018. doi: 10.1109/TII.2017.2737363
    [26]
    Z. T. Xie, L. Jin, X. Luo, S. Li, and X. C. Xiao, “A data-driven cyclic-motion generation scheme for kinematic control of redundant manipulators,” IEEE Trans. Control Syst. Technol., vol. 29, no. 1, pp. 53–63, Jan. 2021. doi: 10.1109/TCST.2019.2963017
    [27]
    Z. J. Zhang, Z. J. Li, Y. N. Zhang, Y. M. Luo, and Y. Q. Li, “Neural-dynamic-method-based dual-arm CMG scheme with time-varying constraints applied to humanoid robots,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 12, pp. 3251–3262, Dec. 2015. doi: 10.1109/TNNLS.2015.2469147
    [28]
    J. Z. Zhang, L. Jin, and C. G. Yang, “Distributed cooperative kinematic control of multiple robotic manipulators with an improved communication efficiency,” IEEE/ASME Trans. Mechatron., vol. 27, no. 1, pp. 149–158, Feb. 2022. doi: 10.1109/TMECH.2021.3059441
    [29]
    L. Huang, M. C. Zhou, K. R. Hao, and E. Hou, “A survey of multi-robot regular and adversarial patrolling,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 4, pp. 894–903, Jul. 2019. doi: 10.1109/JAS.2019.1911537
    [30]
    X. Luo, Y. Yuan, S. L. Chen, N. Y. Zeng, and Z. D. Wang, “Position-transitional particle swarm optimization-incorporated latent factor analysis,” IEEE Trans. Knowl. Data Eng., to be published. DOI: 10.1109/TKDE.2020.3033324.
    [31]
    Z. Y. Zuo, Q. L. Han, B. D. Ning, X. H. Ge, and X. M. Zhang, “An overview of recent advances in fixed-time cooperative control of multiagent systems,” IEEE Trans. Ind. Informat., vol. 14, no. 6, pp. 2322–2334, Jun. 2018. doi: 10.1109/TII.2018.2817248
    [32]
    X. H. Ge and Q. L. Han, “Distributed formation control of networked multi-agent systems using a dynamic event-triggered communication mechanism,” IEEE Trans. Ind. Electron., vol. 64, no. 10, pp. 8118–8127, Oct. 2017. doi: 10.1109/TIE.2017.2701778
    [33]
    Y. M. Ju, D. R. Ding, X. He, Q. L. Han, and G. L. Wei, “Consensus control of multi-agent systems using fault-estimation-in-the-loop: Dynamic event-triggered case,” IEEE/CAA J. Autom. Sinica, to be published. DOI: 10.1109/JAS.2021.1004386.
    [34]
    D. R. Ding, Q. L. Han, X. H. Ge, and J. Wang, “Secure state estimation and control of cyber-physical systems: A survey,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 51, no. 1, pp. 176–190, Jan. 2021. doi: 10.1109/TSMC.2020.3041121
    [35]
    W. Chen, D. R. Ding, X. H. Ge, Q. L. Han, and G. L. Wei, “H∞ containment control of multiagent systems under event-triggered communication scheduling: The finite-horizon case,” IEEE Trans. Cybern., vol. 50, no. 4, pp. 1372–1382, Apr. 2020. doi: 10.1109/TCYB.2018.2885567
    [36]
    Y. Wan, J. D. Cao, A. Alsaedi, and T. Hayat, “Distributed observer-based stabilization of nonlinear multi-agent systems with sampled-data control,” Asian J. Control, vol. 19, no. 3, pp. 918–928, May 2017. doi: 10.1002/asjc.1415
    [37]
    S. Li, J. B. He, Y. M. Li, and M. U. Rafique, “Distributed recurrent neural networks for cooperative control of manipulators: A game-theoretic perspective,” IEEE Trans. Neural Netw. Learn. Syst., vol. 28, no. 2, pp. 415–426, Feb. 2017. doi: 10.1109/TNNLS.2016.2516565
    [38]
    L. Jin, S. Li, X. Luo, Y. M. Li, and B. Qin, “Neural dynamics for cooperative control of redundant robot manipulators,” IEEE Trans. Ind. Informat., vol. 14, no. 9, pp. 3812–3821, Sep. 2018. doi: 10.1109/TII.2018.2789438
    [39]
    M. Mesbahi and M. Egerstedt, Graph Theoretic Methods in Multiagent Networks. Princeton, USA: Princeton University Press, 2010.
    [40]
    C. L. Liu, L. Shan, Y. Y. Chen, and Y. Zhang, “Average-consensus filter of first-order multi-agent systems with disturbances,” IEEE Trans. Circuits Syst. II:Express Briefs, vol. 65, no. 11, pp. 1763–1767, Nov. 2018. doi: 10.1109/TCSII.2017.2762723
    [41]
    W. W. Yu, G. R. Chen, and M. Cao, “Some necessary and sufficient conditions for second-order consensus in multi-agent dynamical systems,” Automatica, vol. 46, no. 6, pp. 1089–1095, Jun. 2010. doi: 10.1016/j.automatica.2010.03.006
    [42]
    S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, UK: Cambridge University Press, 2004.
    [43]
    G. F. Liu, J. J. Xu, X. Wang, and Z. X. Li, “On quality functions for grasp synthesis, fixture planning, and coordinated manipulation,” IEEE Trans. Autom. Sci. Eng., vol. 1, no. 2, pp. 146–162, Oct. 2004. doi: 10.1109/TASE.2004.836760
    [44]
    A. P. Kypson, L. W. Nifong, and W. R. Chitwood Jr., “Robotic cardiac surgery,” J. Long-Term Effects Med. Implants, vol. 13, no. 6, pp. 451–464, Feb. 2003.
    [45]
    T. H. Zhang, J. H. Xiao, L. Li, C. Wang, and G. M. Xie, “Toward coordination control of multiple fish-like robots: Real-time vision-based pose estimation and tracking via deep neural networks,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1964–1976, Dec. 2021. doi: 10.1109/JAS.2021.1004228
    [46]
    Y. Aiyama, M. Hara, T. Yabuki, J. Ota, and T. Arai, “Cooperative transportation by two four-legged robots with implicit communication,” Robot. Auton. Syst., vol. 29, no. 1, pp. 13–19, Oct. 1999. doi: 10.1016/S0921-8890(99)00034-2
    [47]
    J. Welch, D. Backer, L. Blitz, D. C. J. Bock, G. C. Bower, C. Cheng, et al., “The Allen telescope array: The first widefield, panchromatic, snapshot radio camera for radio astronomy and SETI,” Proc. IEEE, vol. 97, no. 8, pp. 1438–1447, Aug. 2009. doi: 10.1109/JPROC.2009.2017103
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    Highlights

    • It considers time delay for the first time when investigating the distributed collaborative control of redundant manipulators and analyzing their kinematic properties
    • It establishes allowable upper bound of time delay based on theoretical analyses and verifies the stability, convergence, and robustness of the designed distributed collaborative controller of redundant manipulators rigorously
    • It provides illustrative examples on CoppeliaSim and comparisons to prove the validity of the proposed neural dynamics scheme

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