Skip to main content
SpringerLink
Log in

Congestion Tracking Control for Wireless TCP/AQM Network Based on Adaptive Integral Backstepping

  • Regular Paper
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

This paper extends the backstepping control strategy to wireless Transmission Control Protocol (TCP) network to solve the Active Queue Management (AQM) problem. Different from the wired network, the uplink and downlink of the wireless network are asymmetric and the packet loss may be caused by congestion and may occurs during the transmission over wireless links. Inspired by the existing backstepping design idea for a wired network model, an adaptive tracking controller is proposed to deal with the congestion problem in wireless network. The uplink and downlink packet losses are estimated by adaptive update laws. The presented method provides satisfactory tracking performance in the network. Meanwhile, the packet loss is small. The simulation results show the feasibility of the proposed method.

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

Similar content being viewed by others

References

  1. M. Mathis, J. Mahdavi, S. Floyd, and A. Romanow, “RFC 2018: TCP selective acknowledgment options,” Ietf Rfc, 1996.

  2. S. Floyd, T. Henderson, and A. Gurtov, “The newreno modification to TCP’s fast recovery algorithm,” Rfc, vol. 345, no. 2, pp. 414–418, 2012.

    Google Scholar 

  3. L. S. Brakmo and L. Peterson, “TCP vegas: End to end congestion avoidance on a global internet,” IEEE Journal on Selected Areas in Communications, vol. 13, no. 8, pp. 1465–1480, 2002.

    Article  Google Scholar 

  4. J. Kim and I. Yeom, “Reducing queue oscillation at a congested link,” IEEE Transactions on Parallel and Distributed Systems, vol. 19, pp. 394–407, 2008.

    Article  Google Scholar 

  5. J. Crowcroft, B. Davie, S. Deering, C. Systems, D. Estrin, S. Floyd, V. Jacobson, G. Minshall, C. Partridge, and L. Peterson, “Recommendation on queue management and congestion avoidance in the internet,” RFC 2309, 04 1998.

  6. S. Floyd and V. Jacobson, “Random early detection gateways for congestion avoidance,” IEEE/ACM Transactions on Networking, vol. 1, no. 4, pp. 397–413, 1993.

    Article  Google Scholar 

  7. S. Floyd, R. Gummadi, and S. Shenker, “Adaptive RED: An algorithm for increasing the robustness of red,” 2001.

  8. L. W. T. J. Ott and T. V. Lakshman, “SRED: stabilized RED,” 1999.

  9. L. Dong and R. Morris, “Dynamics of random early detection,” ACM SIGCOMM Computer Communication Review, vol. 27, no. 4, pp. 127–137, 1997.

    Article  Google Scholar 

  10. J. Aweya, M. Ouellette, and D. Montuno, An Optimization-oriented View of Random Early Detection, 2001.

  11. S. Floyd and K. Fall, “Promoting the use of end-to-end congestion control in the internet,” IEEE/ACM Transactions on Networking, vol. 7, no. 4, pp. 458–472, 1999.

    Article  Google Scholar 

  12. M. Christiansen, K. Jeffay, D. Ott, and F. D. Smith, “Tuning RED for web traffic,” IEEE/ACM Transactions on Networking, vol. 9, no. 3, pp. 249–264, 2001.

    Article  Google Scholar 

  13. V. Misra, “Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED,” Acm Sigcomm Stockholm Sweden, vol. 30, no. 4, pp. 151–160, 2000.

    Article  Google Scholar 

  14. Y. Hong, O. Yang, and C. Huang, “Self-tuning PI TCP flow controller for AQM routers with interval gain and phase margin assignment,” Proc. of IEEE Global Telecommunications Conference, 2004.

  15. J. Sun, G. Chen, K. T. Ko, S. Chan, and M. Zukerman, “PD-controller: a new active queue management scheme,” Proc. of IEEE Global Telecommunications Conference, 2003.

  16. S. K. Bisoy and P. K. Pattnaik, “Design of feedback controller for TCP/AQM networks,” Engineering Science and Technology An International Journal, vol. 20, no. 1, pp. 116–132, 2016.

    Article  Google Scholar 

  17. C. Zhou, J. He, and Q. Chen, “A robust active queue management scheme for network congestion control,” Computers & Electrical Engineering, vol. 39, no. 2, pp. 285–294, 2013.

    Article  Google Scholar 

  18. W. Dan, Y. Hao, J. Yuanwei, J. Nan, and Z. Siying, “Study on the congestion in complex network based on traffic awareness algorithm,” Acta Physica Sinica, vol. 58, no. 10, pp. 6802–6808, 2009.

    Google Scholar 

  19. T. Zhang, M. Xia, and Y. Yi, “Adaptive neural dynamic surface control of strictfeedback nonlinear systems with full state constraints and unmodeled dynamics,” Automatica, vol. 81, pp. 232–239, 2017.

    Article  MathSciNet  MATH  Google Scholar 

  20. S. Mohammadi, H. M. Pour, M. Jafari, and A. Javadi, “Fuzzy-based PID active queue manager for TCP/IP networks,” Proc. of International Conference on Information Sciences Signal Processing and Their Applications, 2010.

  21. C. Liu, H. Wang, X. Liu, and Y. Zhou, “Adaptive finite-time fuzzy funnel control for nonaffine nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019. DOI: https://doi.org/10.1109/TSMC.2019.2917547

  22. C. Han, M. Li, Y. Jing, L. Lei, Z. Pang, and D. Sun, “Nonlinear model predictive congestion control for networks,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 552–557, 2017.

    Article  Google Scholar 

  23. S. S. Kunniyur, R. Srikant, and S. Member, “An adaptive virtual queue algorithm for active,” IEEE/ACM Transactions on Networking, vol. 12, no. 2, pp. 286–299, 2004.

    Article  Google Scholar 

  24. S. Athuraliya, V. H. Li, S. H. Low, and Q. Yin, “Rem: Active queue management,” IEEE Network the Magazine of Global Internetworking, vol. 15, no. 3, pp. 48–53, 2001.

    Article  Google Scholar 

  25. X. Deng, S. Yi, G. Kesidis, C. R. Das, X. Deng, G. Kesidis, and C. R. Das, “Stabilized virtual buffer (svb) — an active queue management scheme for internet quality-of-service,” Proc. of IEEE Global Telecommunications Conference, 2002.

  26. N. Wang, J. C. Sun, M. Han, Z. Zheng, and M. J. Er, “Adaptive approximation based regulation control for a class of uncertain nonlinear systems without feedback linearizability,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3747–3760, 2017.

    MathSciNet  Google Scholar 

  27. F. Mazenc and P. A. Bliman, “Backstepping design for time-delay nonlinear systems,” IEEE Transactions on Automatic Control, vol. 51, no. 1, pp. 149–154, 2006.

    Article  MathSciNet  MATH  Google Scholar 

  28. C. Hua, F. Gang, and X. Guan, “Robust controller design of a class of nonlinear time delay systems via backstepping method,” Automatica, vol. 44, no. 2, pp. 567–573, 2008.

    Article  MathSciNet  MATH  Google Scholar 

  29. Y. Wang and H. Wu, “Adaptive robust backstepping control for a class of uncertain dynamical systems using neural networks,” Nonlinear Dynamics, vol. 81, no. 4, pp. 1597–1610, 2015.

    Article  MathSciNet  MATH  Google Scholar 

  30. J. Yu, S. Peng, and Z. Lin, “Finite-time command filtered backstepping control for a class of nonlinear systems,” Automatica, vol. 92, pp. 173–180, 2018.

    Article  MathSciNet  MATH  Google Scholar 

  31. G. Wen, S. S. Ge, and F. Tu, “Optimized backstepping for tracking control of strict-feedback systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3850–3862, 2018.

    Article  MathSciNet  Google Scholar 

  32. X. Zheng, N. Zhang, M. Georigi, Dimirovski, and Y. Jing, “Adaptive sliding mode congestion control for diffserv network,” Ifac Proceedings Volumes, vol. 41, no. 2, pp. 12983–12987, 2008.

    Article  Google Scholar 

  33. K. Wang, Y. Jing, and S. Zhang, “Minimax controller design based on backstepping sliding mode for TCP network systems,” Proc. of 29th Chinese Control And Decision Conference (CCDC), 2017.

  34. Z. Li, Y. Liu, and Y. Jing, “Design of adaptive backstepping congestion controller for TCP networks with UDP flows based on minimax,” ISA Transactions, vol. 95, pp. 27–34, 2019.

    Article  Google Scholar 

  35. Y. Jing, Z. Li, and G. Dimirovski, “Minimax based congestion control for TCP network systems with UDP flows,” MATEC Web of Conferences, vol. 210, p. 03005, 2018.

    Article  Google Scholar 

  36. K. Wang, Y. Jing, S. Zhang, and G. M. Dimirovski, “Hamiltonian theory applied to ameliorate the complexity of TCP network congestion control,” IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2579–2584, 2017.

  37. X. H. Yang and Z. Q. Wang, “NPFC-VRTT: nonlinear AQM algorithm based on variable RTT,” Control and Decision, vol. 25, no. 1, pp. 69–42, 2010.

    MATH  Google Scholar 

  38. Z.-H. Li, Y. Liu, and Y. Jing, “Active queue management algorithm for TCP networks with integral backstepping and minimax,” International Journal of Control Automation and Systems, vol. 17, pp. 1059–1066, 2019.

    Article  Google Scholar 

  39. X. Liu, Y. Liu, Y. Jing, Z. Zhang, and X. Chen, “Congestion tracking control for uncertain TCP/AQM network based on integral backstepping,” ISA Transactions, vol. 89, pp. 131–138, 2019.

    Article  Google Scholar 

  40. Y. Liu, X. Liu, Y. Jing, and S. Zhou, “Adaptive backstepping H∞ tracking control with prescribed performance for internet congestion,” Isa Trans, vol. 72, pp. 92–99, 2017.

    Article  Google Scholar 

  41. K. Wang, Y. Liu, X. Liu, Y. Jing, and S. Zhang, “Adaptive fuzzy funnel congestion control for TCP/AQM network,” ISA Transations, vol. 95, pp. 11–17, 2019.

    Article  Google Scholar 

  42. J. Dong, Y. Wu, and G. Yang, “A new sensor fault isolation method for T-S fuzzy systems,” IEEE Transactions on Cybernetics, vol. 47, no. 9, pp. 2437–2447, Sep. 2017.

    Article  Google Scholar 

  43. Y. Li, X. Min, and S. Tong, “Adaptive fuzzy inverse optimal control for uncertain strict-feedback nonlinear systems,” IEEE Transactions on Fuzzy Systems, 2019. DOI: https://doi.org/10.1109/TFUZZ.2019.2935693

  44. Y. Li, X. Shao, and S. Tong, “Adaptive fuzzy prescribed performance control of non-triangular structure nonlinear systems,” IEEE Transactions on Fuzzy Systems, 2019. DOI: https://doi.org/10.1109/TFUZZ.2019.2937046

  45. H. Wang, P. X. Liu, X. Zhao, and X. Liu, “Adaptive fuzzy finite-time control of nonlinear systems with actuator faults,” IEEE Transactions on Cybernetics, 2019. DOI: https://doi.org/10.1109/TCYB.2019.2902868

  46. X. Zhao, X. Wang, L. Ma, and G. Zong, “Fuzzy-approximation-based asymptotic tracking control for a class of uncertain switched nonlinear systems,” IEEE Transactions on Fuzzy Systems, 2019. DOI: https://doi.org/10.1109/TFUZZ.2019.2912138

  47. H. Wang, S. Liu, and X. Yang, “Adaptive neural control for non-strict-feedback nonlinear systems with input delay,” Information Sciences, 2019.

  48. H. Wang, P. X. Liu, J. Bao, X. Xie, and S. Li, “Adaptive neural output-feedback decentralized control for large-scale nonlinear systems with stochastic disturbances,” IEEE Transactions on Neural Networks and Learning Systems, 2019. DOI: https://doi.org/10.1109/TNNLS.2019.2912082

  49. X. Zhao, X. Wang, S. Zhang, and G. Zong, “Adaptive neural backstepping control design for a class of nonsmooth nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 9, pp. 1820–1831, Sep. 2019.

    Article  Google Scholar 

  50. Y. Li, K. Li, and S. Tong, “Adaptive neural network finite-time control for multi-input and multi-output nonlinear systems with positive powers of odd rational numbers,” IEEE Transactions on Neural Networks and Learning Systems, 2019. DOI: https://doi.org/10.1109/TNNLS.2019.2933409

  51. B. Chen, H. Zhang, X. Liu, and C. Lin, “Neural observer and adaptive neural control design for a class of nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4261–4271, Sep. 2018.

    Article  Google Scholar 

  52. M. Liu, D. Li, C. Xu, J. Zhou, and W. Huang, “Discovery of multimodal sensor data through webpage exploration,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5232–5245, June 2019.

    Article  Google Scholar 

  53. M. Liu, D. Li, Q. Chen, J. Zhou, K. Meng and S. Zhang, “Sensor information retrieval from internet of things: representation and indexing,” IEEE Access, vol. 6, pp. 36509–36521, 2018.

    Article  Google Scholar 

  54. F. Zheng and J. Nelson, “An approach to congestion control design for aqm routers supporting TCP flows in wireless access networks,” Computer Networks, vol. 51, no. 6, pp. 1684–1704, 2007.

    Article  MATH  Google Scholar 

  55. J. Yang, D. Lim, and W. Oh, “LQ-servo congestion control for TCP/AQM system in wireless network environment,” International Journal of Control and Automation, vol. 6, 2013.

  56. H. Mukaidani, C. Lin, and X. Shen, “Stable queue management for supporting TCP flows over wireless networks,” Proc. of IEEE International Conference on Communications, 2011.

  57. K. Chavan, R. G. Kumar, M. N. Belur, and A. Karandikar, “Robust active queue management for wireless networks,” IEEE Transactions on Control Systems Technology, vol. 19, no. 6, pp. 1630–1638, 2011.

    Article  Google Scholar 

  58. L. Khoshnevisan and F. R. Salmasi, “A switched rate-based robust queue management for wireless access networks,” Proc. of Iranian Conference on Electrical Engineering (ICEE), pp. 846–850, 2018.

  59. Y. Qian, W. Hu, X. Lin, and B. Wang, “Fractional order proportional integral controller for active queue management of wireless network,” Proc. of Control Conference, 2011.

  60. T. Alvarez and D. Martinez, “Handling the congestion control problem of TCP/AQM wireless networks with PID controllers,” Lecture Notes in Electrical Engineering, vol. 229, no. 3, pp. 365–379, 2013.

    Article  Google Scholar 

  61. G. Long and X. Yang, “Congestion control algorithm based on minimum variance theory in wired/wireless network,” Nanjing LI Gong Daxue Xuebao/journal of Nanjing University of Science and Technology, vol. 41, no. 2, pp. 207–211, 2017.

    Google Scholar 

  62. W. Peng, C. Li, and G. Zhang, “Interval type-2 fuzzy logic based transmission power allocation strategy for lifetime maximization of WSNs,” Engineering Applications of Artificial Intelligence, vol. 87, 2020.

Download references

Author information

Authors and Affiliations

  1. Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, 250101, China

    Lujuan Ma, Huanqing Wang & Yucheng Zhou

  2. Faculty of Engineering, Lakehead University, Thunder Bay, ON, P7B 5E1, Canada

    Xiaoping Liu

Authors
  1. Lujuan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huanqing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yucheng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoping Liu.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Jiuxiang Dong under the direction of Editor Guang-Hong Yang. This project was supported by Taishan Scholar Project of Shandong Province (TSQN201812092,TSQN201812093, 2015162) and Major Scientific and Technological Innovation Project of Shandong Province (2019JZZY010120)

Lujuan Ma received her B.S. degree in Communication Engineering from Shandong Normal University in 2008, and her Ph.D. degree in Communication and Information System from Wuhan University in 2013. She is currently with the Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University. Her research interests include congestion control and wireless communication network.

Xiaoping Liu received his B.Sc., M.Sc., and Ph.D. degrees from Northeastern University, China, in 1984, 1987, and 1989, respectively. He spent more than 10 years in the School of Information Science and Engineering at Northeastern University, China. In 2001, he joined the Department of Electrical Engineering at Lakehead University, Canada. Since 2017, he has been a visiting professor in Shandong Jianzhu University, Jinan, China. His research interests are nonlinear control systems, singular systems, and adaptive control. He is a member of the Professional Engineers of Ontario.

Huanqing Wang received his B.Sc. degree in mathematics from Bohai University, Jinzhou, China, in 2003, an M.Sc. degree in mathematics from Inner Mongolia University, Huhhot, China, in 2006, and a Ph.D. degree from the Institute of Complexity Science, Qingdao University, Qingdao, China, in 2013. He was a Post-Doctoral Fellow with the Department of Electrical Engineering, Lakehead University, Thunder Bay, Canada, in 2014, and was a Post-Doctoral Fellow with the Department of Systems and Computer Engineering, Carleton University, Ottawa, Canada. He has authored or co-authored over 50 papers in top international journals. His current research interests include adaptive backstepping control, fuzzy control, neural networks control, and stochastic nonlinear systems. Dr. Wang serves as an Associate Editor for several journals, including Neural Computing and Applications, the International Journal of Control, Automation, and Systems, and the IEEE ACCESS.

Yucheng Zhou received his B.S. degree in mathematics from Harbin Normal University, Haerbin, China in 1982, an M.S. degree in Computer science and technology from Yanshan University, Qinhuangdao, China in 1988. and a Ph.D. degree in automation from Northeastern University, Shenyang, China. In 1998, he joined research institute of wood industry in Chinese Academy of forestry. Since 2015, he has been a Taishan scholar in Shandong Jianzhu University, Jinan, China, His research interest is control of complex system.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, L., Liu, X., Wang, H. et al. Congestion Tracking Control for Wireless TCP/AQM Network Based on Adaptive Integral Backstepping. Int. J. Control Autom. Syst. 18, 2289–2296 (2020). https://doi.org/10.1007/s12555-019-0724-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-019-0724-y

Keywords

Search

Navigation