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Agent-based consensus on speed in the network-coupled DC motors

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Abstract

In this paper, a new agent-based method is proposed to address the speed synchronization problem in the network-connected motors. In this study, DC motor is used, driven by a buck chopper circuit. In the proposed method, the consensus protocol of the leader-following multi-agent system is modified, in order to make consensus on the speed among multiple motors in the network, so that they can attain synchronous speed. In order to have a stable system, a common Lyapunov function is developed such that consensus is said to be reached if the ith agent is controllable and observable. MATLAB is used for the purpose of simulation, and results obtained authorize the proposed methodology.

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References

  1. Sergaki ES, Stavrakakis GS, Pouliezos AD (2002) Optimal robot speed trajectory by minimization of the actuator motor electromechanical losses. J Intell Robot Syst 33(2):187–207

    Article  MATH  Google Scholar 

  2. González I, Salazar S, Torres J, Lozano R, Romero H (2013) Real-time attitude stabilization of a mini-uav quad-rotor using motor speed feedback. J Intell Robot Syst 70(1–4):93–106

    Article  Google Scholar 

  3. Sanchez A, García Carrillo LR, Rondon E, Lozano R, Garcia O (2011) Hovering flight improvement of a quad-rotor mini UAV using brushless DC motors. J Intell Robot Syst 61(1–4):85–101

    Article  Google Scholar 

  4. Nunez C, Alvarez R, Cervantes I (2004) Comparison of multi-motor synchronization techniques. In: Industrial electronics society, 2004. IECON 2004. 30th annual conference of IEEE, vol 2, pp 1670–1675. IEEE

  5. Lee H-H, Jeong U-H (2000) A study on speed synchronization for multi-motors using controller area network. In: Science and technology, 2000. KORUS 2000. Proceedings. The 4th Korea–Russia international symposium on, vol 2, pp 234–239. IEEE

  6. Pérez-Pinal FJ, Nunez C, Alvarez R (2005) Multi-motor synchronization technique applied in traction devices. In: IEEE international conference on electric machines and drives, 2005

  7. Chen S, Zhang K, Zhang W, Shi Z, Liu C, Chen J, Liu T (2010) Design of multi-motor synchronous control system. In: Proceedings of the 29th Chinese control conference, pp 3367–3371. IEEE

  8. Fucai L, Xuelian Z, Liwei L (2002) Synchronous control theory and practical study of multi-motor synchronous driving system. Control Eng China 9:87–90

    Google Scholar 

  9. Ren J, Li C-W, Zhao D-Z (2009) Linearizing control of induction motor based on networked control systems. Int J Autom Comput 6(2):192–197

    Article  Google Scholar 

  10. Zhao D, Zhang S, Li C, Stobart R (2013) Scheduling and control co-design of networked induction motor control systems. In: 2013 IEEE international conference on information and automation (ICIA), pp 880–885

  11. Zhao D, Li C, Ren J (2011) Fuzzy speed control and stability analysis of a networked induction motor system with time delays and packet dropouts. Nonlinear Anal Real World Appl 12(1):273–287

    Article  MathSciNet  MATH  Google Scholar 

  12. Matsuo K, Miura T, Taniguchi T (2006) Speed control of a dc motor system through delay time variant network. In: 2006 SICE-ICASE international joint conference, pp 399–404. IEEE

  13. Almutairi NB, Chow M-Y, Tipsuwan Y (2001) Network-based controlled DC motor with fuzzy compensation. In: Industrial electronics society, 2001. IECON’01. The 27th annual conference of the IEEE, vol 3, pp 1844–1849. IEEE

  14. Sheikholeslami M, Sheykholeslami FB, Khoshhal S, Mola-Abasia H, Ganji DD, Rokni HB (2014) Effect of magnetic field on Cu–water nanofluid heat transfer using GMDH-type neural network. Neural Comput Appl 25(1):171–178

    Article  Google Scholar 

  15. Sheikholeslami M, Ganji DD (2016) Heat transfer improvement in a double pipe heat exchanger by means of perforated turbulators. Energy Convers Manag 127:112–123

    Article  Google Scholar 

  16. Sheikholeslami M, Ganji DD (2016) Heat transfer enhancement in an air to water heat exchanger with discontinuous helical turbulators; experimental and numerical studies. Energy 116:341–352

    Article  Google Scholar 

  17. Olfati-Saber R, Fax A, Murray RM (2007) Consensus and cooperation in networked multi-agent systems. Proc IEEE 95(1):215–233

    Article  MATH  Google Scholar 

  18. Wang C, Ji H (2014) Leader-following consensus of multi-agent systems under directed communication topology via distributed adaptive nonlinear protocol. Syst Control Lett 70:23–29

    Article  MathSciNet  MATH  Google Scholar 

  19. Hu J, Hong Y (2007) Leader-following coordination of multi-agent systems with coupling time delays. Phys A Stat Mech Appl 374(2):853–863

    Article  Google Scholar 

  20. Xie T, Liao X, Li H (2016) Leader-following consensus in second-order multi-agent systems with input time delay: an event-triggered sampling approach. Neurocomputing 177:130–135

    Article  Google Scholar 

  21. Djaidja S, Wu QH (2015) Stochastic consensus of leader-following multi-agent systems under additive measurement noises and time-delays. Eur J Control 23:55–61

    Article  MathSciNet  MATH  Google Scholar 

  22. Hu A-H, Hu M, Guo L (2014) Consensus of a leader-following multi-agent system with negative weights and noises. IET Control Theory Appl 8(2):114–119

    Article  MathSciNet  MATH  Google Scholar 

  23. Hong Y, Hu J, Gao L (2006) Tracking control for multi-agent consensus with an active leader and variable topology. Automatica 42(7):1177–1182

    Article  MathSciNet  MATH  Google Scholar 

  24. Hu J, Feng G (2010) Distributed tracking control of leader–follower multi-agent systems under noisy measurement. Automatica 46(8):1382–1387

    Article  MathSciNet  MATH  Google Scholar 

  25. Godsil C, Royle GF (2013) Algebraic graph theory, vol 207. Springer Science & Business Media, New York

    MATH  Google Scholar 

  26. Li Z, Duan Z (2014) ”Cooperative control of multi-agent systems: a consensus region approach” Automation and Control Engineering. CRC Press, Boca Raton

    Book  Google Scholar 

  27. Mesbahi M, Egerstedt M (2010) Graph theoretic methods in multiagent networks. Princeton University Press, Princeton

    Book  MATH  Google Scholar 

  28. Peng K, Yang Y (2009) Leader-following consensus problem with a varying-velocity leader and time-varying delays. Physica A 388:193–208

    Article  Google Scholar 

  29. Horn RA, Johnson CR (2012) Matrix analysis. Cambridge University Press, Cambridge

    Book  Google Scholar 

  30. Xu W, Ho DWC, Li L, Cao J (2015) Leader-following consensus of general linear multi-agent systems: event-triggered schemes. In: Control conference (ASCC), 2015 10th Asian, pp 1–6. IEEE

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant 61273114, the Innovation Program of Shanghai Municipal Education Commission under Grant 14ZZ087, the Pujiang Talent Plan of Shanghai City China under Grant 14PJ1403800, the International Corporation Project of Shanghai Science and Technology Commission under Grants 14510722500, 15220710400.

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Correspondence to Suhaib Masroor.

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Masroor, S., Peng, C. Agent-based consensus on speed in the network-coupled DC motors. Neural Comput & Applic 30, 1647–1656 (2018). https://doi.org/10.1007/s00521-016-2773-y

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  • DOI: https://doi.org/10.1007/s00521-016-2773-y

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