Skip to main content
Log in

On Block-decoupling of Boolean Control Networks

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

Abstract

In this paper, two block-decoupling problems of Boolean control networks (BCNs), one of which is related to system decomposition and the other is not, are investigated by using the method of the semi-tensor product of matrices. Necessary and sufficient graphic conditions for block-decoupling of BCNs in the two cases are obtained. Moreover, a logical coordinate transformation is constructed to achieve block-decoupling related to system decomposition when the corresponding graphic conditions are satisfied. Finally, two illustrative examples are provided to illustrate the theoretical results.

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. S. A. Kauffman, “Metabolic stability and epigenesis in randomly constructed genetic nets,” Journal of Theoretical Biology, vol. 22, no. 3, pp. 437–467, March 1969.

    Article  MathSciNet  Google Scholar 

  2. H. de Jong, “Modeling and simulation of genetic regulatory systems: A literature review,” Journal of Computational Biology, vol. 9, no. 1, pp. 67–103, January 2002.

    Article  MathSciNet  Google Scholar 

  3. I. Shmulevich, E. R. Dougherty, S. Kim, and W. Zhang, “Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks,” Bioinformatics, vol. 18, no. 2, pp. 261–274, February 2002.

    Article  Google Scholar 

  4. D. Cheng, H. Qi, and Z. Li, Analysis and Control of Boolean Networks: A Semi-tensor Product Approach, Springer, London, 2010.

    Google Scholar 

  5. D. Cheng and H. Qi, “Controllability and observability of Boolean control networks,” Automatica, vol. 45, no. 7, pp. 1659–1667, July 2009.

    Article  MathSciNet  MATH  Google Scholar 

  6. D. Laschov and M. Margaliot, “Controllability of Boolean control networks via the Perron-Frobenius theory,” Automatica, vol. 48, no. 6, pp. 1218–1223, June 2012.

    Article  MathSciNet  MATH  Google Scholar 

  7. E. Fornasini and M. E. Valcher, “Observability, reconstructibility and state observers of Boolean control networks,” IEEE Transactions on Automatic Control, vol. 58, no. 6, pp. 1390–1401, November 2012.

    Article  MathSciNet  MATH  Google Scholar 

  8. K. Zhang, L. Zhang, and L. Xie, “Finite automata approach to observability of switched Boolean control networks,” Nonlinear Analysis: Hybrid Systems, vol. 19, pp. 186–197, February 2016.

    MathSciNet  MATH  Google Scholar 

  9. D. Cheng, H. Qi, Z. Li, and J. B. Liu, “Stability and stabilization of Boolean networks,” International Journal of Robust and Nonlinear Control, vol. 21, no. 2, pp. 134–156, January 2011.

    Article  MathSciNet  MATH  Google Scholar 

  10. D. Cheng, Z. Li, and H. Qi, “Realization of Boolean control networks,” Automatica, vol. 46, no. 1, pp. 62–69, January 2010.

    Article  MathSciNet  MATH  Google Scholar 

  11. J. Lu, J. Zhong, C. Huang, and J. Cao, “On pinning controllability of Boolean control networks,” IEEE Transactions on Automatic Control, vol. 61, no. 6, pp. 1658–1663, September 2015.

    Article  MathSciNet  MATH  Google Scholar 

  12. E. Fornasini and M. E. Valcher, “Optimal control of Boolean control networks,” IEEE Transactions on Automatic Control, vol. 59, no. 5, pp. 1258–1270, December 2013.

    Article  MathSciNet  MATH  Google Scholar 

  13. Q. Zhu, Y. Liu, J. Lu, and J. Cao, “On the optimal control of Boolean control networks,” SIAM Journal on Control and Optimization, vol. 56, no. 2, pp. 1321–1341, January 2018.

    Article  MathSciNet  MATH  Google Scholar 

  14. Y. Zou and J. Zhu, “Kalman decomposition for Boolean control networks,” Automatica, vol. 54, pp. 65–71, April 2015.

    Article  MathSciNet  MATH  Google Scholar 

  15. Y. Li and J. Zhu, “Observability decomposition of Boolean control networks,” IEEE Transactions on Automatic Control, February 2022. DOI:https://doi.org/10.1109/TAC.2022.3149970

  16. D. Cheng, “Disturbance decoupling of Boolean control networks,” IEEE Transactions on Automatic Control, vol. 56, no. 1, pp. 2–10, May 2010.

    Article  MathSciNet  MATH  Google Scholar 

  17. M. E. Valcher, “Input/output decoupling of Boolean control networks,” IET Control Theory & Applications, vol. 11, no. 13, pp. 2081–2088, May 2017.

    Article  MathSciNet  Google Scholar 

  18. Y. Li, J. Zhu, B. Li, Y. Liu, and J. Lu, “A necessary and sufficient graphic condition for the original disturbance decoupling of Boolean networks,” IEEE Transactions on Automatic Control, vol. 66, no. 8, pp. 3765–3772, September 2021.

    Article  MathSciNet  MATH  Google Scholar 

  19. K. Sarda, A. Yerudkar, and C. del Vecchio, “Disturbance decoupling control design for Boolean control networks: A Boolean algebra approach,” IET Control Theory & Applications, vol. 14, no. 16, pp. 2339–2347, September 2020.

    Article  MathSciNet  Google Scholar 

  20. M. Yang, R. Li, and T. Chu, “Controller design for disturbance decoupling of Boolean control networks,” Automatica, vol. 49, no. 1, pp. 273–277, January 2013.

    Article  MathSciNet  MATH  Google Scholar 

  21. Y. Li and J. Zhu, “On disturbance decoupling problem of Boolean control networks,” Asian Journal of Control, vol. 21, no. 6, pp. 2543–2550, April 2019.

    Article  MathSciNet  MATH  Google Scholar 

  22. S. Fu, J. Zhao, and J. Wang, “Input-output decoupling control design for switched Boolean control networks,” Journal of the Franklin Institute, vol. 355, no. 17, pp. 8576–8596, November 2018.

    Article  MathSciNet  MATH  Google Scholar 

  23. J. Pan, J. Feng, J. Yao, and J. Zhao, “Input-output decoupling of Boolean control networks,” Asian Journal of Control, vol. 20, no. 6, pp. 2185–2194, January 2018.

    Article  MathSciNet  MATH  Google Scholar 

  24. Y. Li and J. Zhu, “Necessary and sufficient vertex partition conditions for input-output decoupling of Boolean control networks,” Automatica, vol. 137, p. 110097, March 2022.

    Article  MathSciNet  MATH  Google Scholar 

  25. Y. Yu, J. Feng, J. Pan, and D. Cheng, “Block decoupling of Boolean control networks,” IEEE Transactions on Automatic Control, vol. 64, no. 8, pp. 3129–3140, November 2018.

    Article  MathSciNet  MATH  Google Scholar 

  26. C. Commault, J. Dion, and J. Torres, “Invariant spaces at infinity of linear systems application to block decoupling,” IEEE Transactions on Automatic Control, vol. 35, no. 5, pp. 618–623, May 1990.

    Article  MathSciNet  MATH  Google Scholar 

  27. C. Commault, J. M. Dion, and J. Torres, “Minimal structure in the block decoupling problem with stability,” Automatica, vol. 27, no. 2, pp. 331–338, March 1991.

    Article  MathSciNet  MATH  Google Scholar 

  28. J. Dion, “Feedback block decoupling and infinite structure of linear systems,” International Journal of Control, vol. 37, no. 3, pp. 521–533, March 1983.

    Article  MathSciNet  MATH  Google Scholar 

  29. J. Dion, J. Torres, and C. Commault, “New feedback invariants and the block decoupling problem,” International Journal of Control, vol. 51, no. 1, pp. 219–235, January 1990.

    Article  MathSciNet  MATH  Google Scholar 

  30. M. Malabre and J. A. Torres-Munoz, “Block decoupling by precompensation revisited,” IEEE Transactions on Automatic Control, vol. 52, no. 5, pp. 922–925, May 2007.

    Article  MathSciNet  MATH  Google Scholar 

  31. X. Wei and J. E. Mottershead, “Block-decoupling vibration control using eigenstructure assignment,” Mechanical Systems and Signal Processing, vol. 74, pp. 11–28, June 2016.

    Article  Google Scholar 

  32. D. Cheng and H. Qi, “State-space analysis of Boolean networks,” IEEE Transactions on Neural Networks, vol. 21, no. 4, pp. 584–594, February 2010.

    Article  Google Scholar 

  33. Y. Zou and J. Zhu, “System decomposition with respect to inputs for Boolean control networks,” Automatica, vol. 50, no. 4, pp. 1304–1309, April 2014.

    Article  MathSciNet  MATH  Google Scholar 

  34. Y. Zou and J. Zhu, “Decomposition with respect to outputs for Boolean control networks,” IFAC Proceedings Volumes, vol. 47, no. 3, pp. 10331–10336, June 2014.

    Article  Google Scholar 

  35. J. R. Magnus and H. Neudecker, Matrix Differential Calculus with Applications in Statistics and Econometrics, John Wiley & Sons, New Jersey, 2019.

    Book  MATH  Google Scholar 

  36. D. Cheng and H. Qi, “A linear representation of dynamics of Boolean networks,” IEEE Transactions on Automatic Control, vol. 55, no. 10, pp. 2251–2258, February 2010.

    Article  MathSciNet  MATH  Google Scholar 

  37. Z. Li, H. Yan, H. Zhang, Y. Peng, J. H. Park, and Y. He, “Stability analysis of linear systems with time-varying delay via intermediate polynomial-based functions,” Automatica, vol. 113, p. 108756, March 2020.

    Article  MathSciNet  MATH  Google Scholar 

  38. Z. Li, H. Yan, H. Zhang, X. Zhan, and C. Huang, “Improved inequality-based functions approach for stability analysis of time delay system,” Automatica, vol. 108, p. 108416, October 2019.

    Article  MathSciNet  Google Scholar 

  39. Z. Li, H. Yan, H. Zhang, H. K. Lam, and M. Wang, “Aperiodic sampled-data-based control for interval type-2 fuzzy systems via refined adaptive event-triggered communication scheme,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 2, pp. 310–321, August 2020.

    Article  Google Scholar 

  40. Z. Li, H. Zhang, H. Yan, H.-K. Lam, and C. Huang, “Aperiodic sampled-data Takagi-Sugeno fuzzy extended state observer for a class of uncertain nonlinear systems with external disturbance and unmodeled dynamics,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2678–2692, 2022.

    Article  Google Scholar 

  41. M. S. Ali, L. Palanisamy, N. Gunasekaran, A. Alsaedi, and B. Ahmad, “Finite-time exponential synchronization of reaction-diffusion delayed complex-dynamical networks,” Discrete & Continuous Dynamical Systems-S, vol. 14, no. 4, pp. 1465–1477, April 2021.

    Article  MathSciNet  MATH  Google Scholar 

  42. N. Gunasekaran, G. Zhai, and Q. Yu, “Sampled-data synchronization of delayed multi-agent networks and its application to coupled circuit,” Neurocomputing, vol. 413, pp. 499–511, November 2020.

    Article  Google Scholar 

  43. M. S. Ali, Q. Zhu, S. Pavithra, and N. Gunasekaran, “A study on 〈(Q, S, R) ․ γ〉-dissipative synchronisation of coupled reaction-diffusion neural networks with time-varying delays,” International Journal of Systems Science, vol. 49, no. 4, pp. 755–765, January 2018.

    Article  MathSciNet  MATH  Google Scholar 

  44. N. Gunasekaran, R. Saravanakumar, Y. H. Joo, and H. S. Kim, “Finite-time synchronization of sampled-data T-S fuzzy complex dynamical networks subject to average dwell-time approach,” Fuzzy Sets and Systems, vol. 374, pp. 40–59, November 2019.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiandong Zhu.

Additional information

Lei Wang received her B.Sc. degree in mathematics from Henan University, Kaifeng, China, in 2018. She is currently pursuing a Ph.D. degree from the School of Mathematical Sciences, Nanjing Normal University, Nanjing, China. Her research interests include control theory of Boolean control networks and game theory.

Yifeng Li received his B.Sc. degree in mathematics from Chongqing Normal University, Chongqing, China, in 2015, his M.Sc. and Ph.D. degrees in operations research and cybernetics from Nanjing Normal University, Nanjing, China, in 2018 and 2021, respectively. He is a lecturer of National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, China. His research interests include Boolean control network and game theory.

Jiandong Zhu received his B.Sc. degree from Xuzhou Normal University, Xuzhou, China, in 1996, his M.Sc. and Ph.D. degrees from Shandong University, Jinan, China, in 1999 and 2002, respectively. Currently, he is a Professor of the School of Mathematical Sciences, Nanjing Normal University. He was a Postdoctoral Research Associate of Southeast University, Nanjing, China, from 2002 to 2004, a Visiting Academic in RMIT University, Melbourne, Australia, from 2010 to 2011, and a Visiting Scholar in University of Texas at San Antonio, USA, from 2016 to 2017. His research interests include Boolean control networks, multi-agent systems, and stability of nonlinear systems.

Publisher’s Note

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

The research work of Yifeng Li is supported by Chongqing Normal University Foundation under Grant 21XLB045, and the research work of Jiandong Zhu is supported by National Natural Science Foundation (NNSF) of China under Grants 61673012 and 11971240.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Li, Y. & Zhu, J. On Block-decoupling of Boolean Control Networks. Int. J. Control Autom. Syst. 21, 40–51 (2023). https://doi.org/10.1007/s12555-021-0907-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-021-0907-1

Keywords

Navigation