Skip to main content
Log in

Optimal Regulation Performance of MIMO Networked Time-delay Systems With Limited Bandwidth and Interference Signals

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

Abstract

In this paper, we investigate the optimal regulation performance of networked time delay systems with limited bandwidth and interference signals. Communication networks are primarily influenced by parameters including bandwidth, packet dropouts, coding and decoding, interference signals, and channel noise. For a given system, non-minimum phase zeros, unstable poles, and time delay are considered. The corresponding regulation performance expressions are derived using coprime decomposition, spectral decomposition techniques, and norm correlation theory in the frequency domain. Results indicate that regulation performance is dependent on the location and direction of non-minimum phase zeros and unstable poles of a given system, as well as the internal time delay of the controlled plant. In addition, network communication parameters such as bandwidth, channel noise, packet dropouts, and external interference signals influence the performance of the regulation. Finally, simulation examples are provided to demonstrate the theory’s validity.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Y. Tian, H. Yan, H. Zhang, X. Zhan, and Y. Peng, “Resilient static output feedback control of linear semi-Markov jump systems with incomplete semi-markov kernel,” IEEE Transactions on Automatic Control, vol. 69, no. 9, pp. 4274–4281, September 2021.

    Article  MathSciNet  Google Scholar 

  2. Z. Liu, X. Zhan, T. Han, and H. Yan, “Distributed adaptive finite-time bipartite containment control of linear multi-agent systems,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 11, pp. 4354–4358, November 2022.

    Google Scholar 

  3. J. Cheng, H. Zhang, W. Zhang, and H. Zhang, “Quasi-projective synchronization for caputo type fractional-order complex-valued neural networks with mixed delays,” International Journal of Control, Automation and Systems, vol. 20, no. 5, pp. 1723–1734, April 2022.

    Article  Google Scholar 

  4. Y. Wang, Z. Zeng, X. Liu, and Z. Liu, “Input-to-state stability of switched linear systems with unstabilizable modes under DoS attacks,” Automatica, vol. 146, no. 110607, 2022.

  5. H. Zhang, J. Cheng, H. Zhang, W. Zhang, and J. Cao, “Quasi-uniform synchronization of caputo type fractional neural networks with leakage and discrete delays,” Chaos, Solitons & Fractals, vol. 152, 111432, November 2021.

    Article  MathSciNet  Google Scholar 

  6. X. Du, X. Zhan, J. Wu, and H. Yan, “Performance analysis of MIMO information time-delay system under bandwidth, cyber-attack, and gaussian white noise,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2329–2338, 2023.

    Article  Google Scholar 

  7. G. Lin, H. Li, C. Ahn, and D. Yao, “Event-based finite-time neural control for human-in-the-loop UAV attitude systems,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11, 2022. DOI: https://doi.org/10.1109/TNNLS.2022.3166531

  8. X. Zhang, J. Wu, X. Zhan, T. Han, and H. Yan, “Observer-based adaptive time-varying formation-containment tracking for multiagent system with bounded unknown input,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 3, pp. 1479–1491, 2023.

    Article  Google Scholar 

  9. X. W. Jiang, M. Chi, X. Chen, H. Yan, and T. Huang, “Tracking and regulation performance limitations of networked control systems over erasure channel with input quantization,” IEEE Transactions on Automatic Control, vol. 67, no. 9, pp. 4862–4869, September 2022.

    Article  MathSciNet  Google Scholar 

  10. X. Zheng, H. Li, C. Ahn, and D. Yao, “Nn-based fixed-time attitude tracking control for multiple unmanned aerial vehicles with nonlinear faults,” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 2, pp. 1738–1748, 2023.

    Google Scholar 

  11. H. Ma, H. Ren, Q. Zhou, H. Li, and Z. Wang, “Observer-based neural control of N-link flexible-joint robots,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11, 2022. DOI: https://doi.org/10.1109/TNNLS.2022.3203074

  12. Y. Chang, H. Yan, W. Huang, R. Quan, and Y. Zhang, “A novel starting method with reactive power compensation for induction motors,” IET Power Electronics, vol. 16, no. 3, pp. 402–412, 2023.

    Article  Google Scholar 

  13. B. Du, K. Qian, C. Claudel, and D. Sun, “Parallelized active information gathering using multisensor network for environment monitoring,” IEEE Transactions on Control Systems Technology, vol. 30, no. 2, pp. 625–638, March 2022.

    Article  Google Scholar 

  14. R. Dutta, L. Sun, and D. Pack, “A decentralized formation and network connectivity tracking controller for multiple unmanned systems,” IEEE Transactions on Control Systems Technology, vol. 26, no. 6, pp. 2206–2213, November 2018.

    Article  Google Scholar 

  15. M. Kalidass, H. Su, Y. Wu, and S. Rathinasamy, “H∞ filtering for impulsive networked control systems with random packet dropouts and randomly occurring nonlinearities,” International Journal of Robust and Nonlinear Control, vol. 25, no. 12, pp. 1767–1782, August 2015.

    Article  MathSciNet  Google Scholar 

  16. X. Zhan, L. Cheng, J. Wu, and H. Yan, “Modified tracking performance limitation of networked time-delay systems with two-channel constraints,” Journal of the Franklin Institute, vol. 356, no. 12, pp. 6401–6418, August 2019.

    Article  MathSciNet  Google Scholar 

  17. X.-C. Shangguan, Y. He, C. Zhang, L. Jin, W. Yao, L. Jiang, and M. Wu, “Control performance standards-oriented event-triggered load frequency control for power systems under limited communication bandwidth,” IEEE Transactions on Control Systems Technology, vol. 30, no. 2, pp. 860–868, March 2022.

    Article  Google Scholar 

  18. L. Cheng, X. Zhan, J. Wu, and T. Han, “An optimal tracking performance of MIMO NCS with quantization and bandwidth constraints,” Asian Journal of Control, vol. 21, no. 3, pp. 1377–1388, May 2019.

    Article  MathSciNet  Google Scholar 

  19. R. González, F. Vargas, and J. Chen, “Mean square stabilization over SNR-constrained channels with colored and spatially correlated additive noises,” IEEE Transactions on Automatic Control, vol. 64, no. 11, pp. 4825–4832, November 2019.

    Article  MathSciNet  Google Scholar 

  20. J. Chen, S. Meng, and J. Sun, “Stability analysis of networked control systems with aperiodic sampling and time-varying delay,” IEEE Transactions on Cybernetics, vol. 47, no. 8, pp. 2312–2320, August 2017.

    Article  Google Scholar 

  21. F. Xiao, Y. Shi, and T. Chen, “Robust stability of networked linear control systems with asynchronous continuous-and discrete-time event-triggering schemes,” IEEE Transactions on Automatic Control, vol. 66, no. 2, pp. 932–939, February 2021.

    Article  MathSciNet  Google Scholar 

  22. T. Zhao, M. Huang, and S. Dian, “Robust stability and stabilization conditions for nonlinear networked control systems with network-induced delay via T-S fuzzy model,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 3, pp. 486–499, March 2021.

    Article  Google Scholar 

  23. Y. Tian, H. Yan, H. Zhang, X. Zhan, and Y. Peng, “Dynamic output-feedback controlof linear semi-Markov jump systems with incomplete semi-Markov kernel,” Automatica, vol. 117, 108997, July 2020.

    Article  MathSciNet  Google Scholar 

  24. M. Lu, J. Wu, X. Zhan, T. Han, and H. Yan, “Consensus of second-order heterogeneous multi-agent systems with and without input saturation,” ISA Transactions, vol. 126, pp. 14–20, July 2022.

    Article  Google Scholar 

  25. L. Liu, Z. S. Wang, and H. Zhang, “Neural-network-based robust optimal tracking control for MIMO discrete-time systems with unknown uncertainty using adaptive critic design,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 4, pp. 1239–1251, April 2018.

    Article  Google Scholar 

  26. X. Jiang, X. Chen, T. Huang, and H. Yan, “Output tracking control of single-input-multioutput systems over an erasure channel,” IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 2609–2617, April 2022.

    Article  Google Scholar 

  27. L. Cheng, H. Yan, M. Chi, X. Zhan, and G. Zhou, “Optimal tracking performance analysis of MIMO control systems under multiple constraints,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2734–2743, May 2022.

    Article  Google Scholar 

  28. L. Li, L. Song, T. Li, and J. Fu, “Event-triggered output regulation for networked flight control system based on an asynchronous switched system approach,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 12, pp. 7675–7684, December 2021.

    Article  Google Scholar 

  29. J. Fan, Q. Wu, Y. Jiang, T. Chai, and F. Lewis, “Model-free optimal output regulation for linear discrete-time lossy networked control systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 11, pp. 4033–4042, November 2020.

    Article  Google Scholar 

  30. X. Jiang, Z. Guan, F. Yuan, and X. Zhang, “Performance limitations in the tracking and regulation problem for discrete-time systems,” ISA transactions, vol. 53, no. 2, pp. 251–257, March 2014.

    Article  Google Scholar 

  31. Y. Dong and S. Xu, “Cooperative output regulation problem of nonlinear multiagent systems with proximitygraph via output feedback control,” IEEE Transactions on Cybernetics, vol. 51, no. 8, pp. 4201–4211, August 2021.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xisheng Zhan.

Additional information

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

This work was partially supported by the National Natural Science Foundation of China under Grants 62072164, 61971181 and 62271195, and Outstanding Youth Science and Technology Innovation Team in Hubei Province under Grant T2022027 and 2023AFD006.

Qianhao Li is pursuing a M.S. degree in the College of Electrical Engineering and Automation, Hubei Normal University, Huangshi, China. His research interests include networked control system performance analysis.

Qingsheng Yang received his B.S. and M.S. degrees in detection technology and automatic equipment from Yangtze University, Jingzhou, China, in 2007 and 2010, respectively. He is currently an Associate Professor with the College of Mechatronics and Control Engineering, Hubei Normal University. His research interests include networked control systems and robust control.

Xisheng Zhan is a professor in the College of Mechatronics and Control Engineering, Hubei Normal University. He received his B.S. and M.S. degrees in control theory and control engineering from the Liaoning Shihua University, Fushun, China, in 2003 and in 2006, respectively. He received a Ph.D. degree in control theory and applications from the Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China, in 2012. His research interests include networked control systems, robust control, and iterative learning control.

Jie Wu is a professor in the College of Mechatronics and Control Engineering, Hubei Normal University. She received her B.S. and M.S. degrees in control theory and control engineering from the Liaoning Shihua University, Fushun, China, in 2004 and in 2007, respectively. Her research interests include networked control systems, robust control, and complex network.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Q., Yang, Q., Zhan, X. et al. Optimal Regulation Performance of MIMO Networked Time-delay Systems With Limited Bandwidth and Interference Signals. Int. J. Control Autom. Syst. 22, 387–395 (2024). https://doi.org/10.1007/s12555-022-0537-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-022-0537-2

Keywords

Navigation