SpringerLink
Log in

Linear-fitting-based recursive filtering for nonlinear systems under encoding-decoding mechanism

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

This paper deals with a recursive filtering problem for a class of discrete time-varying nonlinear networked systems with the encoding-decoding mechanism. The linear fitting method is introduced to handle the nonlinearity. An encoding-decoding mechanism is constructed to describe the data transmission process in wireless communication networks (WCNs). To be specific, the measurement outputs are mapped by a quantizer to unique codewords for transmission in WCNs. Then, the codewords are decoded by the decoder to recover the measurement outputs which are sent to the filter. The processing/encoding delay and network delay have been considered. Firstly, on the premise that the upper bound of the filtering error covariance is minimum, the appropriate filtering gain is calculated. Then, the mean square exponential boundedness of the filtering error is analyzed. Finally, two simulation examples are presented to verify the effectiveness of the proposed algorithm.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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. Gan D, **e S Y, Liu Z X. Stability of the distributed Kalman filter using general random coefficients. Sci China Inf Sci, 2021. 64: 172204

    Article  MathSciNet  Google Scholar 

  2. Kalman R E. A new approach to linear filtering and prediction problems. J Basic Eng, 1960, 82: 35–45

    Article  MathSciNet  Google Scholar 

  3. Li X G, Feng S, Hou N, et al. Surface microseismic data denoising based on sparse autoencoder and Kalman filter. Syst Sci Control Eng, 2022, 10: 616–628

    Article  Google Scholar 

  4. Chen Y, Zhu M Z, Lu R Q, et al. Distributed filtering of nonlinear systems with random topology by an event-triggered protocol. Sci China Inf Sci, 2021, 64: 202204

    Article  MathSciNet  Google Scholar 

  5. Suo J H, Li N, Li Q. Event-triggered H∞ state estimation for discrete-time delayed switched stochastic neural networks with persistent dwell-time switching regularities and sensor saturations. Neurocomputing, 2021, 455: 297–307

    Article  Google Scholar 

  6. Wang N, Qian W, Xu X Z. H∞ performance for load frequency control systems with random delays. Syst Sci Control Eng, 2021, 9: 243–259

    Article  Google Scholar 

  7. Gollamudi S, Nagaraj S, Kapoor S, et al. Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size. IEEE Signal Process Lett, 1998, 5: 111–114

    Article  Google Scholar 

  8. Hlinka O, Slučiak O, Hlawatsch F, et al. Likelihood consensus and its application to distributed particle filtering. IEEE Trans Signal Process, 2012, 60: 4334–4349

    Article  MathSciNet  Google Scholar 

  9. Shen H, Huang Z G, Cao J D, et al. Exponential H∞ filtering for continuous-time switched neural networks under persistent dwell-time switching regularity. IEEE Trans Cybern, 2019, 50: 2440–2449

    Article  Google Scholar 

  10. Shrivastava P, Soon T K, Idris M Y I B, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries. Renew Sustain Energy Rev, 2019, 113: 109233

    Article  Google Scholar 

  11. Schmidt S F. The Kalman filter - Its recognition and development for aerospace applications. J Guidance Control, 1981, 4: 4–7

    Article  Google Scholar 

  12. Monache L D, Nipen T, Liu Y, et al. Kalman filter and analog schemes to postprocess numerical weather predictions. Mon Weather Rev, 2011, 139: 3554–3570

    Article  Google Scholar 

  13. Hoshiya M, Saito E. Structural identification by extended Kalman filter. J Eng Mech, 1984, 110: 1757–1770

    Article  Google Scholar 

  14. Hu G G, Gao B B, Zhong Y M, et al. Unscented Kalman filter with process noise covariance estimation for vehicular INS/GPS integration system. Inf Fusion, 2020, 64: 194–204

    Article  Google Scholar 

  15. Julier S, Uhlmann J, Durrant-Whyte H F. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans Automat Contr, 2000, 45: 477–482

    Article  MathSciNet  Google Scholar 

  16. **ong Y B, Zhong X H. Linear fitting Kalman filter. IET Signal Process, 2016, 10: 404–412

    Article  Google Scholar 

  17. Ho Q D, Gao Y, Le-Ngoc T. Challenges and research opportunities in wireless communication networks for smart grid. IEEE Wireless Commun, 2013, 20: 89–95

    Article  Google Scholar 

  18. You X H, Wang C-X, Huang J, et al. Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. Sci China Inf Sci, 2021, 64: 110301

    Article  Google Scholar 

  19. Liu S, Song Y, Wei G L, et al. RMPC-based security problem for polytopic uncertain system subject to deception attacks and persistent disturbances. IET Control Theory Appl, 2017, 11: 1611–1618

    Article  MathSciNet  Google Scholar 

  20. Tao H M, Tan H L, Chen Q W, et al. H∞ state estimation for memristive neural networks with randomly occurring DoS attacks. Syst Sci Control Eng, 2022, 10: 154–165

    Article  Google Scholar 

  21. Wang X L, Sun Y, Ding D R. Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques. Int J Network Dyn Intell, 2022, 1: 85–98

    Google Scholar 

  22. Yao F, Ding Y L, Hong S G, et al. A survey on evolved LoRa-based communication technologies for emerging internet of things applications. Int J Network Dyn Intell, 2022, 1: 4–19

    Google Scholar 

  23. Yu L Y, Liu Y R, Cui Y, et al. Intermittent dynamic event-triggered state estimation for delayed complex networks based on partial nodes. Neurocomputing, 2021, 459: 59–69

    Article  Google Scholar 

  24. Zou L, Wang Z D, Hu J, et al. Partial-node-based state estimation for delayed complex networks under intermittent measurement outliers: a multiple-order-holder approach. IEEE Trans Neural Netw Learn Syst, 2023, 34: 7181–7195

    Article  MathSciNet  Google Scholar 

  25. Alsaadi F E, Liu Y R, Alharbi N S. Design of robust H∞ state estimator for delayed polytopic uncertain genetic regulatory networks: dealing with finite-time boundedness. Neurocomputing, 2022, 497: 170–181

    Article  Google Scholar 

  26. Liu L, Zhao X D, Wang B H, et al. Event-triggered state estimation for cyber-physical systems with partially observed injection attacks. Sci China Inf Sci, 2023, 66: 169202

    Article  Google Scholar 

  27. Qu F R, Zhao X, Wang X M, et al. Probabilistic-constrained distributed fusion filtering for a class of time-varying systems over sensor networks: a torus-event-triggering mechanism. Int J Syst Sci, 2022, 53: 1288–1297

    Article  MathSciNet  Google Scholar 

  28. Sun Y, Tian X, Wei G L. Finite-time distributed resilient state estimation subject to hybrid cyber-attacks: a new dynamic event-triggered case. Int J Syst Sci, 2022, 53: 2832–2844

    Article  MathSciNet  Google Scholar 

  29. **ao H C, Ding D R, Dong H L, et al. Adaptive event-triggered state estimation for large-scale systems subject to deception attacks. Sci China Inf Sci, 2022, 65: 122207

    Article  MathSciNet  Google Scholar 

  30. Amini A, Asif A, Mohammadi A. Formation-containment control using dynamic event-triggering mechanism for multi-agent systems. IEEE CAA J Autom Sin, 2020, 7: 1235–1248

    Article  MathSciNet  Google Scholar 

  31. Liu J X, Wu L G, Wu C W, et al. Event-triggering dissipative control of switched stochastic systems via sliding mode. Automatica, 2019, 103: 261–273

    Article  MathSciNet  Google Scholar 

  32. Zhang Z N, Niu Y G, Lam H K. Sliding-mode control of T-S fuzzy systems under weighted try-once-discard protocol. IEEE Trans Cybern, 2019, 50: 4972–4982

    Article  Google Scholar 

  33. Yu H Y, Hu J, Song B Y, et al. Resilient energy-to-peak filtering for linear parameter-varying systems under random access protocol. Int J Syst Sci, 2022, 53: 2421–2436

    Article  MathSciNet  Google Scholar 

  34. Shen B, Ding D R, Liu Q Y. Special issue on performance analysis and synthesis of networked systems under coding-decoding communication mechanisms. Int J Syst Sci, 2022, 53: 2709–2710

    Article  MathSciNet  Google Scholar 

  35. Wang L C, Wang Z D, Han Q L, et al. Synchronization control for a class of discrete-time dynamical networks with packet dropouts: a coding-decoding-based approach. IEEE Trans Cybern, 2017, 48: 2437–2448

    Article  Google Scholar 

  36. Wang L C, Wang Z D, Wei G L, et al. Observer-based consensus control for discrete-time multiagent systems with coding-decoding communication protocol. IEEE Trans Cybern, 2018, 49: 4335–4345

    Article  Google Scholar 

  37. Farhadi A, Ahmed N U. Tracking nonlinear noisy dynamic systems over noisy communication channels. IEEE Trans Commun, 2011, 59: 955–961

    Article  Google Scholar 

  38. Liberzon D. On stabilization of linear systems with limited information. IEEE Trans Automat Contr, 2003, 48: 304–307

    Article  MathSciNet  Google Scholar 

  39. Liu Z T, Lin W Y, Yu X H, et al. Approximation-free robust synchronization control for dual-linear-motors-driven systems with uncertainties and disturbances. IEEE Trans Ind Electron, 2022, 69: 10500–10509

    Article  Google Scholar 

  40. Savkin A V, Cheng T M. Detectability and output feedback stabilizability of nonlinear networked control systems. IEEE Trans Automat Contr, 2007, 52: 730–735

    Article  MathSciNet  Google Scholar 

  41. Wong W S, Brockett R W. Systems with finite communication bandwidth constraints. II. Stabilization with limited information feedback. IEEE Trans Automat Contr, 1999, 44: 1049–1053

    Article  MathSciNet  Google Scholar 

  42. ** Z P, Gupta V, Murray R M. State estimation over packet drop** networks using multiple description coding. Automatica, 2006, 42: 1441–1452

    Article  MathSciNet  Google Scholar 

  43. Li T, **e L H. Distributed consensus over digital networks with limited bandwidth and time-varying topologies. Automatica, 2011, 47: 2006–2015

    Article  MathSciNet  Google Scholar 

  44. Wen P Y, Li X R, Hou N, et al. Distributed recursive fault estimation with binary encoding schemes over sensor networks. Syst Sci Control Eng, 2022, 10: 417–427

    Article  Google Scholar 

  45. Yu M, Yan C, **e D M. Event-triggered control for couple-group multi-agent systems with logarithmic quantizers and communication delays. Asian J Control, 2017, 19: 681–691

    Article  MathSciNet  Google Scholar 

  46. Zou L, Wang Z D, Shen B, et al. Encrypted finite-horizon energy-to-peak state estimation for time-varying systems under eavesdrop** attacks: tackling secrecy capacity. IEEE CAA J Autom Sin, 2023, 10: 985–996

    Article  Google Scholar 

  47. Ling Q. Bit rate conditions to stabilize a continuous-time scalar linear system based on event triggering. IEEE Trans Automat Contr, 2017, 62: 4093–4100

    Article  MathSciNet  Google Scholar 

  48. Ling Q. Stabilizing bit rate conditions for a scalar continuous time linear system with bounded processing delay and bounded process noise. In: Proceedings of the IEEE 55th Conference on Decision and Control, Las Vegas, 2016. 4849–4854

  49. Julier S, Uhlmann J. New extension of the Kalman filter to nonlinear systems. In: Proceedings of SPIE, 1997. 3068: 182–193

    Article  Google Scholar 

  50. Zhang J H, Gao S S, Li G, et al. Distributed recursive filtering for multi-sensor networked systems with multi-step sensor delays, missing measurements and correlated noise. Signal Processing, 2021, 181: 107868

    Article  Google Scholar 

  51. **a J, Gao S S, Zhong Y M, et al. A novel fitting H-infinity Kalman filter for nonlinear uncertain discrete-time systems based on fitting transformation. IEEE Access, 2019, 8: 10554–10568

    Article  Google Scholar 

  52. Hu J, Wang Z D, Liu G P, et al. Variance-constrained recursive state estimation for time-varying complex networks with quantized measurements and uncertain inner coupling. IEEE Trans Neural Netw Learn Syst, 2020, 31: 1955–1967

    Article  MathSciNet  Google Scholar 

  53. Theodor Y, Shaked U. Robust discrete-time minimum-variance filtering. IEEE Trans Signal Process, 1996, 44: 181–189

    Article  Google Scholar 

  54. Reif K, Gunther S, Yaz E, et al. Stochastic stability of the discrete-time extended Kalman filter. IEEE Trans Automat Contr, 1999, 44: 714–728

    Article  MathSciNet  Google Scholar 

  55. Dong H L, Wang Z D, Ding S X, et al. On estimation of randomly occurring faults for a class of nonlinear time-varying systems with fading channels. IEEE Trans Automat Contr, 2015, 61: 479–484

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. U21A2019, 61873058, 62103095), Hainan Province Science and Technology Special Fund (Grant No. ZDYF2022SHFZ105), Natural Science Foundation of Heilongjiang Province of China (Grant No. LH2021F005), and Alexander von Humboldt Foundation of Germany.

Author information

Authors and Affiliations

  1. Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, 163318, China

    Bo Jiang, Hongli Dong, Yuxuan Shen & Fan Yang

  2. Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Daqing, 163318, China

    Bo Jiang, Hongli Dong, Yuxuan Shen & Fan Yang

  3. School of Electrical & Information Engineering, Northeast Petroleum University, Daqing, 163318, China

    Zhiwei Gao

  4. Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya, 572024, China

    Hongli Dong & Yuxuan Shen

Authors
  1. Bo Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongli Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiwei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuxuan Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuxuan Shen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, B., Dong, H., Gao, Z. et al. Linear-fitting-based recursive filtering for nonlinear systems under encoding-decoding mechanism. Sci. China Inf. Sci. 67, 152203 (2024). https://doi.org/10.1007/s11432-023-3905-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-023-3905-1

Keywords

Search

Navigation