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Link to original content: https://doi.org/10.1007/s11424-024-3167-3
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Approximate Synchronization of Multi-Agent Systems over Finite Fields

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Abstract

In this paper, the approximate synchronization of leader-follower multiagent systems (MASs) over finite fields is studied in regard to local and global synchronization. First, the approximately synchronous state set (ASSS) is obtained. Second, combined with ASSS and transient periods, some criteria for the local and global approximate synchronization of systems are given. Moreover, the algorithms for calculating the maximum approximately synchronous basin (MASB) and the maximum control invariant set (MCIS) are presented. Third, the global approximate synchronization of the system is achieved by designing the state feedback control, and a design algorithm of the controller using the truth matrix method is proposed. Moreover, the results of approximate synchronization are degenerated to complete synchronization. Last, two examples are shown to demonstrate the results of this paper.

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References

  1. Zhang C W, Li X H, Li S X, et al., Dynamically analyzing cell interactions in biological environments using multiagent social learning framework, Journal of Biomedical Semantics, 2017, 8(1): 31.

    Article  Google Scholar 

  2. Guido B, Gabriella P, and Leendert V D T, Five guidelines for normative multiagent systems, Proceedings of the 22nd Annual Conference on Legal Knowledge and Information Systems, 2009, 205: 21–30.

    Google Scholar 

  3. Carolina H F, Carlos J P de L, and Jean P B, Modeling of open normative multiagent systems, Proceedings of the 1st International Conference on Agents and Artificial Intelligence, 2009.

  4. Wang J, Yang T Y, Staskevich G, et al., Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee, International Journal of Systems Science, 2017, 48(5): 909–920.

    Article  MathSciNet  Google Scholar 

  5. Santos F P, Social norms of cooperation in multiagent systems, Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems, 2017, 1859–1860.

  6. Chen L P, Li X M, Chen Y Q, et al., Leader-follower non-fragile consensus of delayed fractional-order nonlinear multi-agent systems, Applied Mathematics and Computation, 2022, 414: 126688.

    Article  MathSciNet  Google Scholar 

  7. Rezaee H, Parisini T, and Polycarpou M M, Resiliency in dynamic leader-follower multiagent systems, Automatica, 2021, 125: 109384.

    Article  MathSciNet  Google Scholar 

  8. Shao J L, Zheng W X, Huang T Z, et al., Leader-follower consensus with switching topologies: An analysis inspired by pigeon hierarchies, IEEE Transaction on Automatic Control, 2018, 63(10): 3588–3593.

    Article  MathSciNet  Google Scholar 

  9. Liu B, Chu T G, Wang L, et al., Controllability of a leader-follower dynamic network with switching topology, IEEE Transaction on Automatic Control, 2008, 53(4): 1009–1013.

    Article  MathSciNet  Google Scholar 

  10. Guan Y Q, Ji Z J, Zhang L, et al., Controllability of multi-agent systems under directed topology, International Journal of Robust and Nonlinear Control, 2017, 27(18): 4333–4347.

    Article  MathSciNet  Google Scholar 

  11. Pasqualetti F, Borra D, and Bullo F, Consensus networks over finite fields, Automatica, 2014, 50(2): 349–358.

    Article  MathSciNet  Google Scholar 

  12. Li Y L, Li H T, Ding X Y, et al., Leader-follower consensus of multiagent systems with time delays over finite fields, IEEE Transactions on Cybernetics, 2019, 49(8): 3203–3208.

    Article  Google Scholar 

  13. Li H T, Wang Y Z, and Guo P L, Consensus of finite-field networks with switching topologies and linear protocols, Proceedings of the 33rd Chinese Control Conference, 2014, 2475–2480.

  14. Zhang J, Lu J Q, Xing M P, et al., Synchronization of finite field networks with switching multiple communication channels, IEEE Transactions on Network Science and Engineering, 2021, 8(3): 2160–2169.

    Article  MathSciNet  Google Scholar 

  15. Meng M, Li X X, and Xiao G X, Synchronization of networks over finite fields, Automatica, 2020, 115: 108877.

    Article  MathSciNet  Google Scholar 

  16. Li Y L and Li H, Controllability of multi-agent systems over finite fields via semi-tensor product method, Proceedings of the 38th Chinese Control Conference, 2019, 5606–5611.

  17. Lu Z H, Zhang L, and Wang L, Controllability analysis of multiagent systems with switching topology over finite fields, SCIENCE CHINA Information Science, 2019, 62(1): 012201.

    Article  MathSciNet  Google Scholar 

  18. Sundaram S and Hadjicostis C N, Structural controllability and observability of linear systems over finite fields with applications to multi-agent systems, IEEE Transactions on Automatic Control, 2013, 58(1): 60–73.

    Article  MathSciNet  Google Scholar 

  19. Anantharaman R and Sule V, Koopman operator approach for computing structure of solutions and observability of nonlinear dynamical systems over finite fields, Mathematics of Control Signals and Systems, 2021, 33(2): 331–358.

    Article  MathSciNet  Google Scholar 

  20. Xu J Q, Zhang J X, and Tang W S, Approximate synchronization of uncertain complex delayed networks with non-identical nodes, International Journal of Computer Mathematics, 2013, 90(5): 921–936.

    Article  MathSciNet  Google Scholar 

  21. Sorrentino F and Pecora L, Approximate cluster synchronization in networks with symmetries and parameter mismatches, Chaos, 2016, 26(9): 094823.

    Article  MathSciNet  Google Scholar 

  22. Zhao R, Feng J E, and Wang B, Approximate synchronization of coupled multi-valued logical networks, Information Sciences, 2023, 626: 19–41.

    Article  Google Scholar 

  23. Cheng D Z, An Introduction to Semi-Tensor Product of Matrices and Its Applications, World Scientific, Singapore, 2012.

    Book  Google Scholar 

  24. Sun L J, Lu J Q, Liu Y, et al., Variable structure controller design for Boolean networks, Neural Networks, 2017, 97: 107–155.

    Article  Google Scholar 

  25. Wu Y H, Guo Y Q, and Toyoda M, Policy iteration approach to the infinite horizon average optimal control of probabilistic Boolean networks, IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(7): 2910–2924.

    Article  MathSciNet  Google Scholar 

  26. Li R, Yang M, and Chu T G, Synchronization of Boolean networks with time delays, Applied Mathematics and Computation, 2012, 219(3): 917–927.

    Article  MathSciNet  Google Scholar 

  27. Cheng D Z, He F H, Qi H S, et al., Modeling, analysis and control of networked evolutionary games, IEEE Transactions on Automatic Control, 2015, 60(9): 2402–2415.

    Article  MathSciNet  Google Scholar 

  28. Cheng D Z, Xu T T, and Qi H S, Evolutionarily stable strategy of networked evolutionary games, IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(7): 1335–1345.

    Article  Google Scholar 

  29. Le S T, Wu Y H, and Toyoda M, A congestion game framework for service chain composition in NFV with function benefit, Information Sciences, 2019, 514: 521–522.

    MathSciNet  Google Scholar 

  30. Zhang Z P, Chen Z Q, and Liu Z X, Modeling and reachability of probabilistic finite automata based on semi-tensor product of matrices, SCIENCE CHINA Information Sciences, 2018, 61(12): 129202.

    Article  MathSciNet  Google Scholar 

  31. Yan Y Y, Chen Z Q, Yue J M, et al., STP approach to model controlled automata with application to reachability analysis of DEDS, Asian Journal of Control, 2016, 18(6): 2027–2036.

    Article  MathSciNet  Google Scholar 

  32. Wu J H, Zhong J, Liu Y, et al., State estimation of networked finite state machine with communication delays and losses, IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(3): 1372–1376.

    Google Scholar 

  33. Liu Y S, Song M J, Li H T, et al., Containment problem of finite-field networks with fixed and switching topology, Applied Mathematics and Computation, 2021, 411: 126519.

    Article  MathSciNet  Google Scholar 

  34. Xu X R and Hong Y G, Leader-following consensus of multi-agent systems over finite fields, Proceedings of the 53rd Annual Conference on Decision and Control, 2014, 2999–3004.

  35. Cheng D Z, Qi H S, and Li Z Q, Analysis and Control of Boolean Networks: A Semi-Tensor Product Approach, Springer, London, 2011.

    Book  Google Scholar 

  36. Lee J S and Kim D W, Classifying categorical data based on adoptive hamming distance, IEICE Transactions on Information and Systems, 2010, 93(1): 189–192.

    Article  Google Scholar 

  37. Jia Y Z, Cheng D Z, and Feng J E, State feedback stabilization of generic logic systems via Ledley antecedence soulution, Mathematical Methods in the Applied Sciences, 2021, https://doi.org/10.1002/mma.7554.

  38. Cheng D Z and Qi H S, A linear representation of dynamics of Boolean networks, IEEE Transactions on Automatic Control, 2010, 55(10): 2251–2258.

    Article  MathSciNet  Google Scholar 

  39. Guo Y Q, Wang P, Gui W H, et al., Set stability and set stabilization of Boolean control networks based on invariant subsets, Automatica, 2015, 61: 106–112.

    Article  MathSciNet  Google Scholar 

  40. Zhou R P, Guo Y Q, Wu Y H, et al., Asymptotical feedback set stabilization of probabilistic Boolean control networks, IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(11): 4524–4537.

    Article  MathSciNet  Google Scholar 

  41. Li H T, Yang X R, and Wang S L, Robustness for stability and stabilization of Boolean networks with stochastic function perturbations, IEEE Transactions on Automatic Control, 2020, 66(3): 1231–1237.

    Article  MathSciNet  Google Scholar 

  42. Li X D, Li H T, Li Y L, et al., Function perturbation impact on stability in distribution of probabilistic Boolean networks, Mathematics and Computers in Simulation, 2020, 177: 1–12.

    Article  MathSciNet  Google Scholar 

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Correspondence to Jun-e Feng.

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The authors declare no conflict of interest.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant Nos. 62373178, 62273201, and 62103176, the Research Fundfor the Taishan Scholar Project of Shandong Province of China under Grant Nos. tstp20221103 and tstp20221103.

This paper was recommended for publication by Editor DONG Yi.

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Yu, M., Feng, Je., Xia, J. et al. Approximate Synchronization of Multi-Agent Systems over Finite Fields. J Syst Sci Complex 37, 1561–1580 (2024). https://doi.org/10.1007/s11424-024-3167-3

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  • DOI: https://doi.org/10.1007/s11424-024-3167-3

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