Skip to main content
SpringerLink
Log in

Predictive current control of a switched reluctance machine in the direct-drive manipulator of cloud robotics

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud robotics has undergone rapid development. As an important candidate for direct-drive manipulator, switched reluctance machines (SRMs) face significant challenge in terms of control used in cloud robotics because of latency and package losses in network communication. In this paper, predictive current control of SRMs is extended to use in controller upon cloud in the face of latency and package losses. The starting point of predictive model is modified to eliminate errors caused by latency in sensor-controller communication, and the execution of control command sequence is dynamically regulated according to the arrival time of the following sequence to adapt for latency and package losses in controller-actuator communication. The proposed control method is evaluated in a 1.5 kW SRM test platform and comparison with a conventional control method is performed; the results show that the proposed control method has better tracking performance in face of time delay and package losses in transmission.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Aghili, F., Buehler, M., Hollerbach, J.M.: Development of a high-performance direct-drivejoint. Adv.Robot. 16, 233–250 (2002). doi:10.1109/IROS.2000.895289

    Article  Google Scholar 

  2. Chen, H., Zhang, D.: The switched reluctance motor drive for the direct-drive joint of the robot. In: Proceedings of the 18th IEEE International Conference on Robotics and Automation, May 2001. Vol. 2, pp. 1439–1443. doi:10.1109/ROBOT.2001.932812

  3. Cao, J.Y., Chen, Y.P., Zhou, Z.D.: Robust control of switched reluctance motors for direct-driverobotic applications. Int. J. Adv. Manuf.Tech. 22(3–4), 184–190 (2003). doi:10.1007/s00170-002-1458-0

    Article  Google Scholar 

  4. Cheung, N.C.: A compact and robust variable reluctance actuator for grasping applications. In: Proc. 24th Annu. Conf. IEEE, Volume. 2 (1998). doi:10.1109/IECON.1998.724244

  5. Schulz, S.E., Rahman, K.M.: High-performance digital PI current regulator for EV switchedreluctance323 motor drives. IEEE Trans. Ind.Appl 39(4), 1118–1126 (2003). doi:10.1109/IAS.2002.1043751

    Article  Google Scholar 

  6. Sahoo, S.K., Panda, S.K., Xu, J.X.: Indirect torque control of switched reluctance motors usingiterative learning control. IEEE Trans. Power.Electron 20(1), 200–208 (2005). doi:10.1109/TPEL.2004.839807(410)

    Article  Google Scholar 

  7. Rafiq, M., Rehman, S., Rehman, F., Butt, Q.R., Awan, I.: A second order sliding mode control design of a switchedreluctance motor using super twisting algorithm. Simulation Modelling Practiceand Theory. Simul. Model. Pract.Theory 25(6), 106–117 (2012). doi:10.1016/j.sim.pat.2012.03.001

    Article  Google Scholar 

  8. Loria, A., Espinosa-Perez, G., Chumacer, E.: Robust passivity-based control of switched reluctancemotors. Int. J. Robust. Nonlinear.Control 25(17), 3384–3403 (2014). doi:10.1002/rnc.3270

    Article  MATH  Google Scholar 

  9. Bristow, D., Alleyne, A.: A high precision motion control system with application tomicroscale robotic deposition. IEEE Trans. ControlSyst.Tech 16, 1008–1020 (2006). doi:10.1109/TCST.2006.880189

    Article  Google Scholar 

  10. Kehoe, B., Patil, S., Abbeel, P., Goldberg, K.: A survey of research on cloud robotics andautomation. IEEE Trans. Autom. Sci.Eng. 12(2), 398–409 (2015). doi:10.1109/TASE.2014.2376492

    Article  Google Scholar 

  11. Wan, J., Tang, S., Yan, H., Li, D., Wang, S., Vasilakos, A.: Cloud robotics: current status and openissues. IEEEAccess 4, 2797–2807 (2016). doi:10.1109/ACCESS.2016.2574979

    Google Scholar 

  12. Addad, B., Amari, S., Lesage, J.: Client-server networked automation systems reactivity:deterministic and probabilistic analysis. IEEETrans. Autom. Sci.Eng. 8(3), 540–548 (2011). doi:10.1109/TASE.2011.2116118

    Article  Google Scholar 

  13. Lian, F.: Analysis, design,modelling and control of networked control systems. PhD Dissertation,Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI,USA, (2001)

  14. Branicky, M.S., Phillips, S.M., Zhang, W.: Stability of networked control systems: explicit analysis of delay. In: Proc. Amer. Control Conf., Chicago, IL, pp. 2352–2357 (2000). doi:10.1109/ACC.2000.878601

  15. Georges, J.P., Rondeau, E., Divoux, T.: Confronting the performances of switched Ethernet network withindustrial constraints by using networkcalculus. Commun.Syst. 18(9), 877–903 (2005). doi:10.1109/ITC.2013.6662955

    Article  Google Scholar 

  16. Lee, K.C., Lee, S.: Performance evaluation of switched Ethernet for real-timeindustrial communications. Comput. Stand.Interfaces 24(5), 411–423 (2002). doi:10.1016/S0920-5489(02)00070-3

    Article  Google Scholar 

  17. Addad, B., Amari, S., Lesage, J.-J.: Analytic calculus of response time in networked automationsystems. IEEE Trans. Autom. Sci.Eng. 7(4), 858–869 (2010). doi:10.1109/TASE.2010.2047499

    Article  Google Scholar 

  18. Witsch, H.D., Vogel-Heuser, B., Faure, J.-M., Poulard-Marsal, G.: Performance analysis of industrial Ethernet networks by means of timed model-checking. In: Proc. 12th IFAC INCOM, St-Etienne, France, pp. 101–106 (2006). doi:10.1016/B978-008044654-7/50151-5

  19. Greifeneder, J., Frey, G.: Optimizing quality of control in networked automation systems using probabilistic models. In: Proc. 11th IEEE Int. Conf. ETFA, Prague, Czech Republic, pp. 372–379 (2006). doi:10.1109/ETFA.2006.355428

  20. Denis, B., Ruel, S., Faure, J.M., Marsal, G.: Measuring the impact of vertical integration on response times in Ethernet fieldbuses. In: Proc. 12th IEEE Int. Conf. Emerging Technol. Factory Autom., Patras, Greece, pp. 332–339 (2007). doi:10.1109/EFTA.2007.4416815

  21. Vallabhan, M., Seshadhri, S., Ashok, S.,Ramaswmay, S., Ayyagari, R.: An analytical framework for analysis and design ofnetworked control systems with random delays and packet losses. In: Proc. 25thCCECE. Conf., Quebec, Canada (2012)

  22. Liu, F.C., Yao, Y.: Modeling and analysis of networked control systems using hidden markov models. In: Proc. 4th Int. Conf. Machine Learning and Cybernetics, pp. 18–21 (2005). doi:10.1109/ICMLC.2005.1527076

  23. Lee, S., Lee, S.H., Lee, K.C.: Remotefuzzy logic control for networked control system. In: 27th IECON, pp. 1822–1827(2001). doi:10.1109/IECON.2001.975567

  24. Huo, Z., Fang, H.: Robust\(H_\infty \) filter design for networked control system with random time delays.In: Proc. 10th IEEE Int. Conf. Eng. Complex Computer Systems, pp. 333–0,(2005). doi:10.1109/ICMLC.2007.4370205

  25. Sinopoli, B., Schenato, L., Franceschetti, M., Polla, K., Jordan, M.I., Sastry, S.S.: Kalman filtering with intermittentobservations. IEEE Trans. Autom.Control 49(9), 1453–1464 (2004). doi:10.1109/TAC.2004.834121

    Article  MATH  Google Scholar 

  26. Liu, G.P., Chai, S.C., Mu, J.X., Rees, D.: Networked predictive control of systems with random delay insignal transmission channels. Int. J. Syst.Sci. 39(11), 1055–1064 (2008). doi:10.3182/20050703-6-CZ-1902.00909

    Article  MATH  Google Scholar 

  27. Tang, P., C. Silva, C.: Compensation for transmission delays in an Ethernet-based control network using variable-horizon predictive control. In: IEEE Trans. Contr., Syst. Technol., vol. 14, pp. 707–718 (2006). doi:10.1109/TCST.2006.876640

  28. Kouro, S., Cortés, P., Vargas, R., Ammann, U., Rodríguez, J.: Model predictive control—a simple and powerful method to controlpower converters. IEEE Trans. Ind.Electron. 56(6), 1826–1838 (2009). doi:10.1109/TIE.2008.2008349

    Article  Google Scholar 

  29. Geyer, T., Papafotiou, G., Morari, M.: Model predictive direct torque control—Part I: concept,algorithm, and analysis. IEEE Trans. Ind.Electron. 56(6), 1894–1905 (2009). doi:10.1109/TIE.2008.2007030

    Article  Google Scholar 

  30. Geyer, T.: Model predictive direct current control: formulation of thestator current bounds and the concept of the switchinghorizon. IEEE Ind. Appl.Mag. 18(2), 47–59 (2012). doi:10.1109/MIAS.2011.2175518

    Article  Google Scholar 

  31. Mikail, R., Husain, I., Sozer, Y., Islam, M.S., Sebastian, T.: Torque ripple minimization of switched reluctance machinesthrough current profiling. IEEE Trans. Ind.Appl. 49(3), 1258–1267 (2013). doi:10.1109/TIA.2013.2252592

    Article  Google Scholar 

  32. Kioskeridis, I., Mademlis, C.: Maximum efficiency in single pulse controlled switched reluctancemotor drives. IEEE Trans. Energy.Convers. 20, 809–817 (2005). doi:10.1109/TEC.2005.853738

    Article  Google Scholar 

  33. Ye, J., Bilgin, B., Emadi, A.: An offline torque sharing function for torque ripple reduction inswitched reluctance motor drives. IEEE Trans.Energy.Convers. 30(2), 726–735 (2015). doi:10.1109/TEC.2014.2383991

    Article  Google Scholar 

  34. Li, X., Shamsi, P.: Model predictive current control of switched reluctance motorswith inductance auto-calibration. IEEE Trans. Ind.Electron. 63(6), 3934–3941 (2016). doi:10.1109/TIE.2015.2497301

    Article  Google Scholar 

  35. Zhou, Q., Luo, J.: The study on evaluation method of urban network security in the big data era. Intell. Autom. Soft Comput. (2017). doi:10.1080/10798587.2016.1267444

  36. Xue, X.D., Cheng, K.W.E., Ho, S.L.: Optimization and evaluation of torque-sharing functions fortorque ripple minimization in switched reluctance motordrives. IEEE Trans. PowerElectron. 24(9), 2076–2090 (2009). doi:10.1109/TPEL.2009.2019581

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 51305258).

Author information

Authors and Affiliations

  1. Shanghai JiaoTong University, 800 Dongchuan Road, Shanghai, China

    Bingchu Li, Xiao Ling, Yixiang Huang, Liang Gong & Chengliang Liu

Authors
  1. Bingchu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yixiang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liang Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengliang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengliang Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, B., Ling, X., Huang, Y. et al. Predictive current control of a switched reluctance machine in the direct-drive manipulator of cloud robotics. Cluster Comput 20, 3037–3049 (2017). https://doi.org/10.1007/s10586-017-0983-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0983-4

Keywords

Search

Navigation