Skip to main content
SpringerLink
Log in

Consensus Under Limited Information Communication

  • Chapter
  • First Online:
Iterative Learning Control for Network Systems Under Constrained Information Communication

Part of the book series: Intelligent Control and Learning Systems ((ICLS,volume 12))

  • 41 Accesses

Abstract

In this chapter, we shall discuss the problem of quantized iterative learning for digital networks consisting of many packet radio terminals sent to a single hub station. Digital networks include packet data networks, cellular and micro-cellular networks, and mobile satellite networks. In general, a digital network is a complex circuit network composed of large assemblies of logic gates. Digital networks are more useful than analog circuit networks because they make it easier for an electronic device to switch to one of several known states rather than precisely reproducing a continuous range of values.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Carli R, Fagnani F, Frasca P, Zampieri S (2010) Gossip consensus algorithms via quantized communication. Automatica 46(1):70–80

    Article  MathSciNet  Google Scholar 

  • Dong HL, Wang ZD, Gao HJ (2012) Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts. IEEE Trans Signal Process 60(6):3164–3173

    Article  MathSciNet  Google Scholar 

  • Dong HL, Wang ZD, Gao HJ (2012) On design of quantized fault detection filters with randomly occurring nonlinearities and mixed time-delays. Signal Process 92(4):1117–1125

    Article  Google Scholar 

  • Horn RA, Johnson CR (2012) Matrix Analysis. Cambridge University Press, London

    Book  Google Scholar 

  • Huang DQ, Xu JX, Venkataramanan V, Huynh T (2014) High-performance tracking of piezoelectric positioning stage using current-cycle iterative learning control with gain scheduling. IEEE Trans Ind Electron 61(2):1085–1098

    Article  Google Scholar 

  • Kurek JE, Zaremba MB (1993) Iterative learning control synthesis based on 2-D system theory. IEEE Trans Automat Control 38(1):121–125

    Article  MathSciNet  Google Scholar 

  • Li JS, Li JM (2014) Adaptive iterative learning control for coordination of second-order multi-agent systems. Int J Robust Nonlin Control 24(18):3282–3299

    Article  MathSciNet  Google Scholar 

  • Li T, Xie LH (2011) Distributed consensus over digital networks with limited bandwidth and time-varying topologies. Automatica 47(9):2006–2015

    Article  MathSciNet  Google Scholar 

  • Li DQ, Liu QP, Wang XF, Yin ZX (2014) Quantized consensus over directed networks with switching topologies. Syst Control Lett 65:13–22

    Article  MathSciNet  Google Scholar 

  • Liu Y, Jia YM (2012) An iterative learning approach to formation control of multi-agent systems. Syst Control Lett 61(1):148–154

    Article  MathSciNet  Google Scholar 

  • Liu M, Ho DWC, Niu YG (2010) Robust filtering design for stochastic system with mode-dependent output quantization. IEEE Trans Signal Process 58(12):6410–6416

    Article  MathSciNet  Google Scholar 

  • Liu ZW, Guan ZH, Li T, Zhang XH, Xiao JW (2012a) Quantized consensus of multi-agent systems via broadcast gossip algorithms. Asian J Control 14(6):1634–1642

    Google Scholar 

  • Liu TF, Jiang ZP, Hill DJ (2012b) Small-gain based output-feedback controller design for a class of nonlinear systems with actuator dynamic quantization. IEEE Trans Autom Control 57(5):1326–1332

    Google Scholar 

  • Meng DY, Jia YM (2012) Iterative learning approaches to design finite-time consensus protocols for multi-agent systems. Syst Control Lett 61(1):187–194

    Article  MathSciNet  Google Scholar 

  • Meng DY, Jia YM, Du JP, Yu FS (2012) Tracking control over a finite interval for multi-agent systems with a time-varying reference trajectory. Syst Control Lett 61(7):807–818

    Article  MathSciNet  Google Scholar 

  • Meng DY, Jia YM, Du JP, Zhang J (2014) On iterative learning algorithms for the formation control of nonlinear multi-agent systems. Automatica 50(1):291–295

    Article  MathSciNet  Google Scholar 

  • Meng DY, Jia YM, Du JP (2015) Robust consensus tracking control for multiagent systems with initial state shifts, disturbances, and switching topologies. IEEE Trans Neural Netw Learn Syst 26(4):809–824

    Article  MathSciNet  Google Scholar 

  • Wang YC, Chien CJ, Teng CC (2004) Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network. IEEE Trans Syst Man Cybern 34(3):1348–1359

    Article  Google Scholar 

  • Wang ZD, Shen B, Shu HS, Wei GL (2012) Quantized control for nonlinear stochastic time-delay systems with missing measurements. IEEE Trans Autom Control 57(6):1431–1444

    Article  MathSciNet  Google Scholar 

  • Xia YQ, Yan JJ, Shi P, Fu MY (2013) Stability analysis of discrete-time systems with quantized feedback and measurements. IEEE Trans Ind Informat 9(1):313–324

    Article  Google Scholar 

  • Xu JX, Tan Y (2003) Linear and Nonlinear Iterative Learning Control. Springer, Berlin

    Google Scholar 

  • Yang GN, Shi P, Liu GP, Gao HJ (2011) Network-based feedback control for systems with mixed delays based on quantization and dropout compensation. Automatica 47(12):2805–2809

    Article  MathSciNet  Google Scholar 

  • Yang W, Liu M, Shi P (2012) H\(_\infty \) filtering for nonlinear stochastic systems with sensor saturation, quantization and random packet losses. Signal Process 92(6):1387–1396

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, Sichuan, China

    Wenjun Xiong

  2. School of Science, Civil Aviation Flight University of China, Guanghan, Sichuan, China

    Zijian Luo

  3. Department of Mathematics, City University of Hong Kong, Hong Kong, China

    Daniel W. C. Ho

Authors
  1. Wenjun Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zijian Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel W. C. Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenjun Xiong .

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Xiong, W., Luo, Z., Ho, D.W.C. (2024). Consensus Under Limited Information Communication. In: Iterative Learning Control for Network Systems Under Constrained Information Communication. Intelligent Control and Learning Systems, vol 12. Springer, Singapore. https://doi.org/10.1007/978-981-97-0926-7_3

Download citation

Publish with us

Policies and ethics

Search

Navigation