Skip to main content
SpringerLink
Log in

Recent advances on micro-control for near-critical complex systems

  • Letter
  • Published:
Control Theory and Technology Aims and scope Submit manuscript

Abstract

In this paper, we review some existing control methodologies for complex systems with particular emphasis on those that are near critical. Due to the shortage of the classical control theory in handling complex systems, the reviewed control methods are mainly associated with machine learning techniques, game-theoretical approaches, and sparse control strategies. Additionally, several interesting and promising directions for future research are also proposed.

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

References

  1. Stanley, H. E. (1987). Introduction to Phase Transitions and Critical Phenomena. Oxford University Press.

  2. Ott, E. (2002). Chaos in Dynamical Systems (2nd ed.). Cambridge University Press.

  3. Klamser, P. P., & Romanczuk, P. (2021). Collective predator evasion: Putting the criticality hypothesis to the test. PLoS Computational Biology, 17, 3.

    Article  Google Scholar 

  4. Bertschinger, N., & Natschläger, T. (2004). Real-time computation at the edge of chaos in recurrent neural networks. Neural Computing, 16, 7.

    Article  MATH  Google Scholar 

  5. Su, W., Chen, G., & Hong, Y. (2017). Noise leads to quasi-consensus of Hegselmann-Krause opinion dynamics. Automatica, 85, 448–454.

    Article  MathSciNet  MATH  Google Scholar 

  6. Mourat, G., Servagent, N., & Bosch, T. M. (1998). Influence of a weak optical feedback on the spectral characteristics of a semiconductor laser for sensing applications. In Pierre Galarneau (Ed.), Laser Diodes and Applications III: 109–121.

  7. Chen, J., Chen, B. M., & Sun, J. (2019). Complex system and intelligent control: Theories and applications. Frontiers of Information Technology & Electronic Engineering, 20, 1–3.

    Article  Google Scholar 

  8. Roli, A., Villani, M., Filisetti, A., & Serra, R. (2018). Dynamical criticality: Overview and open questions. Journal of Systems Science and Complexity, 31, 647–663.

    Article  MATH  Google Scholar 

  9. Tami, R., Soualmi, B., Doufene, A., Ibanez, J., & Dauwels, J. (2019). Machine learning method to ensure robust decision-making of AVs. IEEE Intelligent Transportation Systems Conference,(pp. 1217–1222). Auckland, New Zealand.

  10. Lu, X., Baraldi, P., & Zio, E. (2020). A data-driven framework for identifying important components in complex systems. Reliability Engineering & System Safety, 204, 2.

    Article  Google Scholar 

  11. Lewis, F. L., & Vamvoudakis, K. G. (2011). Reinforcement learning for partially observable dynamic processes: Adaptive dynamic programming using measured output data. IEEE Transactions on Systems, Man, and Cybernetics, 41(1), 14–25.

    Article  Google Scholar 

  12. Binney, J. J., Dowrick, N. J., Fisher, A. J., & Newman, M. (1992). The Theory of Critical Phenomena: An Introduction to the Renormalization Group. Oxford University Press.

  13. Ma, J., & Guo, Z. (2014). The parameter basin and complex of dynamic game with estimation and two-stage consideration. Applied Mathematics and Computation, 248, 131–142.

    Article  MathSciNet  MATH  Google Scholar 

  14. Attanasi, A., Cavagna, A., Castello, L. D., et al. (2014). Finite-size scaling as a way to probe near-criticality in natural swarms. Physical Review Letters, 113, 2.

    Article  Google Scholar 

  15. Askar, S. S. (2020). Duopolistic Stackelberg game: Investigation of complex dynamics and chaos control. Operational Research, 20, 1685–1699.

    Article  Google Scholar 

  16. Ikeda, T., & Kashima, K. (2019). On sparse optimal control for general linear systems. IEEE Transactions on Automatic Control, 64(5), 2077–2083.

    Article  MathSciNet  MATH  Google Scholar 

  17. Stadler, G. (2009). Elliptic optimal control problems with \({\cal{L}}_1\)-control cost and applications for the placement of control devices. IEEE Transactions on Automatic Control, 44, 159.

    MATH  Google Scholar 

  18. Caponigro, M., Fornasier, M., Piccoli, B., & Trélat, E. (2015). Sparse stabilization and control of alignment models. Mathematical Models and Methods in Applied Sciences, 25(3), 521–564.

    Article  MathSciNet  MATH  Google Scholar 

  19. Kaiser, E., Kutz, J. N., & Brunton, S. L. (2018). Sparse identification of nonlinear dynamics for model predictive control in the low-data limit. Proceedings of the Royal Society, 474, 2219.

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Control Science and Engineering & Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, China

    Di Zhao & Yi Dong

Authors
  1. Di Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Dong.

Additional information

This work was supported in part by the Shanghai Municipal Science and Technology, China Major Project (No. 2021SHZDZX0100), in part by the National Natural Science Foundation of China (Nos. 62073241, 62088101, 62103303), in part by the Shanghai Municipal Commission of Science and Technology, China Project (No. 19511132101), and in part by the Fundamental Research Funds for the Central Universities (No. 22120200077)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, D., Dong, Y. Recent advances on micro-control for near-critical complex systems. Control Theory Technol. 20, 287–290 (2022). https://doi.org/10.1007/s11768-022-00082-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11768-022-00082-2

Keywords

Search

Navigation