Skip to main content

Multi-layered Sampled-Data Tracking Under Cooperative–Antagonistic Interactions

  • 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))

  • 43 Accesses

Abstract

Nowadays, ILC has been widely applied in a variety of fields, such as robotic manipulators (Frénay and Verleysen 2016; Li et al. 2016), data-driven control (Chi et al. 2015, 2016; Janssens et al. 2013), nonlinear systems (Liu et al. 2016; Wei et al. 2015, 2016), linear systems (Bu and Hou 2018; Bu et al. 2019), multi-agent systems (Meng and Moore 2016; Xiong et al. 2018), and rapid thermal processing (Lee et al. 2001; Yang et al. 2003).

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

Access this chapter

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

  • Altafini C (2013) Consensus problems on networks with antagonistic interactions. IEEE Trans Autom Control 58(4):935–946

    Article  MathSciNet  Google Scholar 

  • Boccaletti S, Bianconi G, Criado R, Del Genio CI, Gómez-Gardenes J, Romance M, Sendina-Nadal I, Wang Z, Zanin M (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122

    Article  MathSciNet  Google Scholar 

  • Bu XH, Hou ZS (2018) Adaptive iterative learning control for linear systems with binary-valued observations. IEEE Trans Neural Netw Learn Syst 29(1):232–237

    Article  MathSciNet  Google Scholar 

  • Bu XH, Yu QX, Hou ZS, Qian W (2019) Model free adaptive iterative learning consensus tracking control for a class of nonlinear multiagent systems. IEEE Trans Syst Man Cybern Syst 49(4):677–686

    Article  Google Scholar 

  • Chi RH, Liu Y, Hou ZS, Jin ST (2015) Data-driven terminal iterative learning control with high-order learning law for a class of nonlinear discrete-time multiple-input-multiple output systems. IET Control Theory Appl 9(7):1075–1082

    Article  MathSciNet  Google Scholar 

  • Chi RH, Huang B, Wang DW, Zhang RK, Feng YJ (2016) Data-driven optimal terminal iterative learning control with initial value dynamic compensation. IET Control Theory Appl 10(12):1357–1364

    Article  MathSciNet  Google Scholar 

  • De Domenico M, Granell C, Porter MA, Arenas A (2016) The physics of spreading processes in multilayer networks. Nat Phys 12(10):901–906

    Article  Google Scholar 

  • Frénay B, Verleysen M (2016) Reinforced extreme learning machines for fast robust regression in the presence of outliers. IEEE Trans Cybern. 46(12):3351–3363

    Article  Google Scholar 

  • Fridman E (2010) A refined input delay approach to sampled-data control. Automatica 46(2):421–427

    Article  MathSciNet  Google Scholar 

  • He WL, Xu ZW, Du WL, Chen GR, Kubota N, Qian F (2017) Synchronization control in multiplex networks of nonlinear multi-agent systems. Chaos 27(12):123104

    Article  MathSciNet  Google Scholar 

  • He WL, Chen GR, Han QL, Du WL, Cao JD, Qian F (2017) Multiagent systems on multilayer networks: synchronization analysis and network design. IEEE Trans Syst Man Cybern Syst 47(7):1655–1667

    Article  Google Scholar 

  • Janssens P, Pipeleers G, Swevers J (2013) A data-driven constrained norm-optimal iterative learning control framework for LTI systems. IEEE Trans Control Syst Tech 21(2):546–551

    Article  Google Scholar 

  • Kumar SV, Raja R, Anthoni SM, Cao JD, Tu ZW (2018) Robust finite-time non-fragile sampled-data control for TS fuzzy flexible spacecraft model with stochastic actuator faults. Appl Math Comput 321:483–497

    MathSciNet  Google Scholar 

  • Lee KS, Lee J, Chin I, Choi J, Lee JH (2001) Control of wafer temperature uniformity in rapid thermal processing using an optimal iterative learning control technique. Ind Eng Chem Res 40(7):1661–1672

    Article  Google Scholar 

  • Li HY, Jing XJ, Lam HK, Shi P (2014) Fuzzy sampled-data control for uncertain vehicle suspension systems. IEEE Trans Cybern 44(7):1111–1126

    Article  Google Scholar 

  • Li XF, Ren QY, Xu JX (2016) Precise speed tracking control of a robotic fish via iterative learning control. IEEE Trans Ind Electron 63(4):2221–2228

    Google Scholar 

  • Liu YJ, Jing Li, Tong SC, Chen CP (2016) Neural network control-based adaptive learning design for nonlinear systems with full-state constraints. IEEE Trans Neural Netw Learn Syst 27(7):1562–1571

    Article  MathSciNet  Google Scholar 

  • Manivannan R, Samidurai R, Cao JD, Perc M (2018) Design of resilient reliable dissipativity control for systems with actuator faults and probabilistic time-delay signals via sampled-data approach. IEEE Trans Syst Man Cybern Syst 50(11):4243–4255

    Article  Google Scholar 

  • Meng DY (2018) Dynamic distributed control for networks with cooperative-antagonistic interactions. IEEE Trans Autom Control 63(8):2311–2326

    Article  MathSciNet  Google Scholar 

  • Meng DY, Moore KL (2016) Learning to cooperate: networks of formation agents with switching topologies. Automatica 64:278–293

    Article  MathSciNet  Google Scholar 

  • Meng DY, Jia YM, Du JP (2016) Finite-time consensus for multiagent systems with cooperative and antagonistic interactions. IEEE Trans Neural Netw Learn Syst 27(4):762–770

    Article  MathSciNet  Google Scholar 

  • Peng ZH, Wang D, Zhang HW, Sun G (2014) Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems. IEEE Trans Neural Netw Learn Syst 25(8):1508–1519

    Article  Google Scholar 

  • Peng ZH, Wang D, Wang J (2017) Predictor-based neural dynamic surface control for uncertain nonlinear systems in strict-feedback form. IEEE Trans Neural Netw Learn Syst 28(9):2156–2167

    Article  MathSciNet  Google Scholar 

  • Sun C, Hu GQ, Xie LH (2017) Controllability of multiagent networks with antagonistic interactions. IEEE Trans Autom Control 62(10):5457–5462

    Article  MathSciNet  Google Scholar 

  • Wei QL, Liu DR, Yang X (2015) Infinite horizon self-learning optimal control of nonaffine discrete-time nonlinear systems. IEEE Trans Neural Netw Learn Syst 26(4):866–879

    Article  MathSciNet  Google Scholar 

  • Wei QL, Liu DR, Lin HQ (2016) Value iteration adaptive dynamic programming for optimal control of discrete-time nonlinear systems. IEEE Trans Cybern 46(3):840–853

    Article  Google Scholar 

  • Wu YQ, Su HY, Shi P, Shu Z, Wu ZG (2016) Consensus of multiagent systems using aperiodic sampled-data control. IEEE Trans Cybern 46(9):2132–2143

    Article  Google Scholar 

  • Xiong WJ, Xu L, Ho DWC, Cao JD, Huang TW (2018) Synchronous and asynchronous iterative learning strategies of TS fuzzy systems with measurable and unmeasurable state information. IEEE Trans Fuzzy Syst 26(5):3042–3053

    Article  Google Scholar 

  • Yang DR, Lee KS, Ahn HJ, Lee JH (2003) Experimental application of a quadratic optimal iterative learning control method for control of wafer temperature uniformity in rapid thermal processing. IEEE Trans Semicond Manuf 16(1):36–44

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

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). Multi-layered Sampled-Data Tracking Under Cooperative–Antagonistic Interactions. 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_10

Download citation

Publish with us

Policies and ethics