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Performance Assessment for Leader-Following Multi-Agent Systems with Unpredictable Disturbances and Switching Topologies Via Belief Rule Base

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Abstract

Performance assessment is crucial for managing the health of complex systems, including single-agent and multi-agent systems. However, research on the latter has been relatively sparse. Approaches to assessing system performance must consider both assessment result accuracy and process explainability, especially in cases with small or unbalanced samples, which limits the application of data-driven approaches. In this paper, the Belief Rule Base (BRB) grey-box expert system is adopted to assess the performance of leader-following multi-agent systems subject to switching topologies and unpredictable disturbances. By fusing historical data and expert knowledge, the precision of assessment results and the traceability of the assessment process are balanced. Two time-varying features based on the states and distributed communication characteristics of the leader-following MASs are designed as model input. The robustness of the feature matching degree is quantitatively analysed, and the obtained robustness factors can reflect their effectiveness on the final performance utility produced by the BRB model. This approach helps raise the cognitive ability of specialists and devise more rational model structures and initial parameters. In comparative studies, better prediction accuracy of the proposed BRB performance assessment model is demonstrated compared to other classical models based on small training samples, while avoiding an opaque inference process.

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References

  1. Zhao, F., Zhou, Z., Hu, C., Cao, Y., Han, X., Feng, Z.: A new safety assessment method based on evidential reasoning rule with a prewarning function. IEEE Access 6, 31862–31871 (2018). https://doi.org/10.1109/ACCESS.2018.2815631

    Article  Google Scholar 

  2. Zhou, Z., Cao, Y., Hu, G., Zhang, Y., Tang, S., Chen, Y.: New health-state assessment model based on belief rule base with interpretability. Sci. China Inf. Sci. 64(7), 172214 (2021). https://doi.org/10.1007/s11432-020-3001-7

    Article  Google Scholar 

  3. Chen, Z., Wu, M., Zhao, R., Guretno, F., Yan, R., Li, X.: Machine remaining useful life prediction via an attention-based deep learning approach. IEEE Trans. Industr. Electron. 68(3), 2521–2531 (2021). https://doi.org/10.1109/TIE.2020.2972443

    Article  Google Scholar 

  4. Bashiri Mosavi, A., Amiri, A., Hosseini, H.: A learning framework for size and type independent transient stability prediction of power system using twin convolutional support vector machine. IEEE Access 6, 69937–69947 (2018). https://doi.org/10.1109/ACCESS.2018.2880273

    Article  Google Scholar 

  5. Xu, X., Yang, R., Fu, Y.: Situation assessment for air combat based on novel semi-supervised naive bayes. J. Syst. Eng. Electron. 29(4), 768–779 (2018). https://doi.org/10.21629/JSEE.2018.04.11

    Article  Google Scholar 

  6. Mukherjee, R., De, A.: Development of an ensemble decision tree-based power system dynamic security state predictor. IEEE Syst. J. 14(3), 3836–3843 (2020). https://doi.org/10.1109/JSYST.2020.2978504

    Article  Google Scholar 

  7. Akırmak, O.O., Altan A.: Estimation of extrusion process parameters in tire manufacturing industry using random forest classifier. Balkan J. Electr. Comput. Eng. 11(2), 138–143 (2023). https://doi.org/10.17694/bajece.1232811

  8. He, W., Qiao, P., Zhou, Z., Hu, G., Feng, Z., Wei, H.: A new belief-rule-based method for fault diagnosis of wireless sensor network. IEEE Access 6, 9404–9419 (2018). https://doi.org/10.1109/ACCESS.2018.2808605

    Article  Google Scholar 

  9. Hu, G., Qiao, P.: Cloud belief rule base model for network security situation prediction. IEEE Commun. Lett. 20(5), 914–917 (2016). https://doi.org/10.1109/LCOMM.2016.2524404

    Article  Google Scholar 

  10. Tang, S., Zhou, Z., Hu, C., Zhao, F., Cao, Y.: A new evidential reasoning rule-based safety assessment method with sensor reliability for complex systems. IEEE Trans. Cybern. 52(5), 4027–4038 (2022). https://doi.org/10.1109/TCYB.2020.3015664

    Article  Google Scholar 

  11. Zhou, Z., Cao, Y., Hu, G., Zhang, Y., Tang, S., Chen, Y.: New health-state assessment model based on belief rule base with interpretability. Sci. China Inf. Sci. 64(7), 172214 (2021). https://doi.org/10.1007/s11432-020-3001-7

    Article  Google Scholar 

  12. Li, G., Zhou, Z., Hu, C., Chang, L., Zhou, Z., Zhao, F.: A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base. Saf. Sci. 93, 108–120 (2017). https://doi.org/10.1016/j.ssci.2016.11.011

    Article  Google Scholar 

  13. Yang, L., Wang, Y., Chang, L., Fu, Y.: A disjunctive belief rule-based expert system for bridge risk assessment with dynamic parameter optimization model. Comput. Ind. Eng. 113, 459–474 (2017). https://doi.org/10.1016/j.cie.2017.09.027

    Article  Google Scholar 

  14. Chang, L., Dong, W., Yang, J., Sun, X., Xu, X., Xu, X., Zhang, L.: Hybrid belief rule base for regional railway safety assessment with data and knowledge under uncertainty. Inf. Sci. 518, 376–395 (2020). https://doi.org/10.1016/j.ins.2019.12.035

    Article  Google Scholar 

  15. Feng, Z., Zhou, Z., Hu, C., Ban, X., Hu, G.: A safety assessment model based on belief rule base with new optimization method. Reliab. Eng. Syst. Saf. 203, 107055 (2020). https://doi.org/10.1016/j.ress.2020.107055

    Article  Google Scholar 

  16. Feng, Z., He, W., Zhou, Z., Ban, X., Hu, C., Han, X.: A new safety assessment method based on belief rule base with attribute reliability. IEEE/CAA J. Autom. Sin. 8(11), 1774–1785 (2021). https://doi.org/10.1109/JAS.2020.1003399

    Article  Google Scholar 

  17. Yang, R., Liu, L., Feng, G.: Cooperative output tracking of unknown heterogeneous linear systems by distributed event-triggered adaptive control. IEEE Trans. Cybern. 52(1), 3–15 (2022). https://doi.org/10.1109/TCYB.2019.2962305

    Article  Google Scholar 

  18. Yang, R., Liu, L., Feng, G.: Cooperative tracking control of unknown discrete-time linear multiagent systems subject to unknown external disturbances. IEEE Trans. Cybern. 1–13 (2022). https://doi.org/10.1109/TCYB.2022.3179467

  19. Qian, Y., Liu, L., Feng, G.: Distributed dynamic event-triggered control for cooperative output regulation of linear multiagent systems. IEEE Trans. Cybern. 50(7), 3023–3032 (2020). https://doi.org/10.1109/TCYB.2019.2905931

    Article  Google Scholar 

  20. Zhang, H., Chen, J., Wang, Z., Fu, C., Song, S.: Distributed event-triggered control for cooperative output regulation of multiagent systems with an online estimation algorithm. IEEE Trans. Cybern. 52(3), 1911–1923 (2022). https://doi.org/10.1109/TCYB.2020.2991761

    Article  Google Scholar 

  21. Bi, C., Xu, X., Liu, L., Feng, G.: Robust cooperative output regulation of heterogeneous uncertain linear multiagent systems with unbounded distributed transmission delays. IEEE Trans. Autom. Control 67(3), 1371–1383 (2022). https://doi.org/10.1109/TAC.2021.3069718

    Article  MathSciNet  MATH  Google Scholar 

  22. Altan A., Aslan O., Hacıoğlu R.f (2018) Real-time control based on narx neural network of hexarotor uav with load transporting system for path tracking. In: 2018 6th International Conference on Control Engineering and Information Technology (CEIT). pp 1–6 https://doi.org/10.1109/CEIT.2018.8751829

  23. Altan, A., Hacıoğlu, R.: Model predictive control of three-axis gimbal system mounted on uav for real-time target tracking under external disturbances. Mech. Syst. Signal Process. 138, 106548. https://doi.org/10.1016/j.ymssp.2019.106548

  24. Lu, M., Liu, L.: Leader-following consensus of multiple uncertain euler-lagrange systems subject to communication delays and switching networks. IEEE Trans. Autom. Control 63(8), 2604–2611 (2018). https://doi.org/10.1109/TAC.2017.2771318

    Article  MathSciNet  MATH  Google Scholar 

  25. Lu, M., Liu, L.: Leader-following consensus of multiple uncertain euler-lagrange systems with unknown dynamic leader. IEEE Trans. Autom. Control 64(10), 4167–4173 (2019). https://doi.org/10.1109/TAC.2019.2892384

    Article  MathSciNet  MATH  Google Scholar 

  26. Wu, Y., Zhang, H., Wang, Z., Zhang, C., Huang, C.: Leader-following and leaderless consensus of linear multiagent systems under directed graphs by double dynamic event-triggered mechanism. IEEE Trans. Syst. Man Cybern. Syst 52(10), 6426–6438 (2022). https://doi.org/10.1109/TSMC.2022.3145575

    Article  Google Scholar 

  27. Wu, Z., Xu, Y., Lu, R., Wu, Y., Huang, T.: Event-triggered control for consensus of multiagent systems with fixed/switching topologies. IEEE Trans. Syst. Man Cybern. Syst 48(10), 1736–1746 (2018). https://doi.org/10.1109/TSMC.2017.2744671

    Article  Google Scholar 

  28. Yu, W., Wang, H., Cheng, F., Yu, X., Wen, G.: Second-order consensus in multiagent systems via distributed sliding mode control. IEEE Trans. Cybern. 47(8), 1872–1881 (2017). https://doi.org/10.1109/TCYB.2016.2623901

    Article  Google Scholar 

  29. Rong, L., Liu, X., Jiang, G., Xu, S.: Event-driven multiagent consensus disturbance rejection with input uncertainties via adaptive protocols. IEEE Trans. Syst. Man Cybern. Syst. 52(5), 2911–2919 (2022). https://doi.org/10.1109/TSMC.2021.3055398

  30. Su, Y., Huang, J.: Stability of a class of linear switching systems with applications to two consensus problems. IEEE Trans. Autom. Control 57(6), 1420–1430 (2012). https://doi.org/10.1109/TAC.2011.2176391

  31. Feng, Q., Hai, X., Sun, B., Ren, Y., Wang, Z., Yang, D., Hu, Y., Feng, R.: Resilience optimization for multi-uav formation reconfiguration via enhanced pigeon-inspired optimization. Chin. J. Aeronaut. 35(1), 110–123 (2022). https://doi.org/10.1016/j.cja.2020.10.029

    Article  Google Scholar 

  32. Belge, E., Altan, A., Hacıoğlu, R.F.: Metaheuristic optimization-based path planning and tracking of quadcopter for payload hold-release mission. Electronics 11(8) (2022) https://doi.org/10.3390/electronics11081208

  33. Altan A (2020) Performance of metaheuristic optimization algorithms based on swarm intelligence in attitude and altitude control of unmanned aerial vehicle for path following. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). pp. 1–6 https://doi.org/10.1109/ISMSIT50672.2020.9255181

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Funding

This work was supported partly by the National Natural Science Foundation of China under Grant No. 62203461 and No. 62203365, partly by the Fundamental Research Funds for the Central Universities under Grant No. D5000210690, partly by the Natural Science Basic Research Program of Shaanxi under Grant No. 2022JQ-580, partly by the Young Talent Promotion Program of Shaanxi Association for Science and Technology under Grant No.20220121, partly by the Harbin Normal University Graduate Student Innovation Program under Grant No. HSDSSCX2023-4. Authors Wei He and Zhichao Feng have received research support from the State Key Laboratory of Intelligent Control and Decision of Complex Systems.

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Author notes

  1. Haoran Zhang and Wei He contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Information Engineering, Harbin Normal University, 150500, Harbin, China

    Haoran Zhang & Wei He

  2. Department of Missile Engineering, Rocket Force University of Engineering, 710025, Xi’an, China

    Wei He & Zhichao Feng

  3. School of Electronics and Information, Northwestern Polytechnical University, 710072, Xi’an, China

    Ruohan Yang

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  1. Haoran Zhang
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  2. Wei He
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  3. Ruohan Yang
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  4. Zhichao Feng
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Haoran Zhang. The first draft of the manuscript was written by Haoran Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ruohan Yang.

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Zhang, H., He, W., Yang, R. et al. Performance Assessment for Leader-Following Multi-Agent Systems with Unpredictable Disturbances and Switching Topologies Via Belief Rule Base. J Intell Robot Syst 109, 62 (2023). https://doi.org/10.1007/s10846-023-01990-4

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