SELF-COMPETITION LEADER–FOLLOWER MULTI-AUV FORMATION CONTROL BASED ON IMPROVED PSO ALGORITHM WITH ENERGY CONSUMPTION ALLOCATION

Yue Li,∗ Xin Li,∗ Daqi Zhu,∗ and Simon X. Yang∗∗

References

  1. [1] D.Q. Zhu, X.L. Cheng, L. Yang, Y.S. Chen, S.X. Yang, Information fusion fault diagnosis method for deep-sea human occupied vehicle thruster Based on deep belief network, IEEE Transactions on Cybernetics, 2021, doi: 10.1109/TCYB.2021.3055770.
  2. [2] L. Paull, S. Saeedi, M. Seto, and H. Li, AUV navigation and localization: a review, IEEE Journal of Oceanic Engineering, 39(1), 2014, 131–149.
  3. [3] X. Li, D. Zhu, and Y. Qian, A Survey on formation control algorithms for multi-AUV system, Unmanned Systems, 2(4), 2014, 351–359.
  4. [4] P.K.C. Wang, Navigation strategies for multiple autonomous mobile robots moving in formation, Intelligent Robots and Systems, 8(2), 1991, 177–195.
  5. [5] J. Li and X. Du, Underactuated multi-AUV robust formation control based on virtual leader, 2018 IEEE International Conf. on Mechatronics and Automation, Changchun, China, 2018, 1568–1573.
  6. [6] J. Li, X. Zhang, H. Zhang, and X. Du, Trajectory tracking control of multi-AUVs formation based on virtual leader, 2019 IEEE International Conference on Mechatronics and Automation, Tianjin, China, 2019, 291–296.
  7. [7] W. Ren, Decentralization of virtual structures in formation control of multiple vehicle systems via consensus strategies, European Journal of Control, 14(2), 2008, 93–103.
  8. [8] W.W. Pan, D.P. Jiang, Y.J. Pang, et al., A multiAUV formation algorithm combining artificial potential field and virtual structure, Acta Armamentarii, 38(2), 2017, 326–334.
  9. [9] N.E. Leonard and E. Fiorelli, Virtual leaders, Artificial potentials and coordinated control of groups, Proc. the 40th IEEE Conf. on Decision and Control, vol. 3, Orlando, FL, 2001, 2968–2973.
  10. [10] S. Cifuentes, J.M. Gir´on-Sierra, and J. Jimnez, Virtual fields and behavior blending for the coordinated navigation of robot teams: some experimental results, Expert Systems with Applications, 42(10), 2015, 4778–4796.
  11. [11] T. Balch and R.C. Arkin, Behavior-based formation control for multi-robot teams, IEEE Transactions on Robotics and Automation, 14(6), 1998, 926–939.
  12. [12] P. Ogren, M. Egerstedt, and X. Hu, A control Lyapunov function approach to multi-agent coordination, IEEE Transactions on Robotics and Automation, 18(5), 2002, 847–851.
  13. [13] W. Ren and R.W. Beard, Formation feedback control for multiple spacecraft via virtual structures, Control Theory and Applications, 151(3), 2004, 357–368.
  14. [14] W. Ren and R.W. Beard, Decentralized scheme for spacecraft formation flying via the virtual structure approach, Journal of Guidance, Control, and Dynamics, 27(1), 2004, 73–82.
  15. [15] G. Wen, Y. Zhao, Z. Duan, and G. Chen, Containment of higher-order multi-leader multi-agent systems: a dynamic output approach, IEEE Transactions on Automatic Control, 61(1), 2016, 1135–1140.
  16. [16] B.D.O. Anderson, C. Yu, B. Fidan, and J.M. Hendrickx, Rigid graph control architectures for autonomous formations, IEEE Control Systems Magazine, 28(6), 2008, 48–63.
  17. [17] J. Kennedy and R. Eberhart, Particle swarm optimization, Proc.1995 IEEE International Conference on Neural Networks, Piscataway, 1942–1948.
  18. [18] Y. Gao and X. Gao, Particle Swarm Optimization Algorithm and Its Application in Bionic Intelligent Calculation, vols. 2 and 3 (Beijing: Science Publishing House, 2018), 11–19, 29–36.
  19. [19] Y. Shi and R.C. Eberhart, Empirical study of particle swarm optimization, Proc. 1999 Congress on Evolutionary Computation, vol. 3, Washington, DC, 19, 1945–1950.
  20. [20] C. Sammut and G.I. Webb, Particle Swarm Optimization. Encyclopedia of Machine Learning, vol. 4 (New York: Springer US, 2011), 760–766.
  21. [21] H.S. Lim, S. Fan, C.K.H. Chin, S. Chai, et al., Particle swarm optimization algorithms with selective differential evolution for AUV path planning, International Journal of Robotics and Automation, 9(2), 2020, 94–112. 13
  22. [22] W. Gan, D. Zhu, and S.X. Yang, A speed jumping-free tracking controller with trajectory planner for unmanned underwater vehicle, International Journal of Robotics and Automation, 35(5), 2020.
  23. [23] J. Ke, J.X. Qian, and Y.Z. Qiao, A modified particle swarm optimization algorithm, Journal of Circuits and Systems, 8(5), 2003, 87–91.
  24. [24] F. Piltan, N. Sulaiman, A. Gavahian, et al., Design mathematical tunable gain PID-like sliding mode fuzzy controller with minimum rule base, International Journal of Robotic and Automation, 2(3), 2011, 146–156.
  25. [25] Y. Wang, X. Yang, and H. Yan, Reliable fuzzy tracking control of near-space hypersonic vehicle using aperiodic measurement information, IEEE Transactions on Industrial Electronics, 2019, 1.
  26. [26] Y. Wang, H.R. Karimi, H.K. Lam, et al., Fuzzy output tracking control and filtering for nonlinear discrete-time descriptor systems under unreliable communication links, IEEE Transactions on Cybernetics, 99, 2019, 1–11.
  27. [27] Y. Wang, W. Zhou, J. Luo, et al., Reliable intelligent path following control for a robotic airship against sensor faults, IEEE/ASME Transactions on Mechatronics, 24(6), 2020, 2572–2582.
  28. [28] Z. Li, H. Yan, H. Zhang, S. Jun, et al., Stability and stabilization with additive freedom for Delayed Takagi-Sugeno fuzzy systems by intermediary-polynomial-based functions, IEEE Transactions on Fuzzy Systems, 28(4), 2020, 692–705.
  29. [29] Z. Li, H. Yan, H. Zhang, X. Zhan, et al., Stability analysis for delayed neural networks via improved auxiliary polynomialbased functions, IEEE Transactions on Neural Networks and Learning Systems, 30(8), 2019, 2562–2568.
  30. [30] Z. Li, Y. Bai, C. Huang, H. Yan, et al., Improved stability analysis for delayed neural networks, IEEE Transactions on Neural Networks and Learning Systems, 29(9), 2018, 4535– 4541.
  31. [31] X. Li and D. Zhu, An adaptive SOM neural network method for distributed formation control of a group of AUVs, IEEE Transactions on Industrial Electronics, 65(10), 2018, 8260– 8270.
  32. [32] R. Liu and Y. Zhang, Task allocation of multiple autonomous underwater vehicles based on improved ant colony algorithm, Chinese Journal of Ship Research, 13(6), 2018, 107–112.
  33. [33] Z. Li, C. Huang, and H. Yan, Stability analysis for systems with time delays via new integral inequalities, IEEE Transactions on Systems Man & Cybernetics Systems, 48(12), 2018, 2495–2501.
  34. [34] Z. Li, H. Yan, H. Zhang, X. Zhan, et al., Improved inequalitybased functions approach for stability analysis of time delay system, Automatica, 108(10), 2019, 416–424.
  35. [35] Z. Li, H. Yan, H. Zhang, Y. Peng, et al., Stability analysis of linear systems with time-varying delay via intermediate polynomial-based functions, Automatica, 113(3), 2020, 756– 762.

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