Keywords: distributed optimization; nabla fractional difference; active disturbance rejection control; Lyapunov method
@article{10_14736_kyb_2024_1_0090,
author = {Zeng, Yikun and Wei, Yiheng and Zhou, Shuaiyu and Yue, Dongdong},
title = {Distributed optimization via active disturbance rejection control: {A} nabla fractional design},
journal = {Kybernetika},
pages = {90--109},
year = {2024},
volume = {60},
number = {1},
doi = {10.14736/kyb-2024-1-0090},
mrnumber = {4730702},
zbl = {07893449},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2024-1-0090/}
}
TY - JOUR AU - Zeng, Yikun AU - Wei, Yiheng AU - Zhou, Shuaiyu AU - Yue, Dongdong TI - Distributed optimization via active disturbance rejection control: A nabla fractional design JO - Kybernetika PY - 2024 SP - 90 EP - 109 VL - 60 IS - 1 UR - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2024-1-0090/ DO - 10.14736/kyb-2024-1-0090 LA - en ID - 10_14736_kyb_2024_1_0090 ER -
%0 Journal Article %A Zeng, Yikun %A Wei, Yiheng %A Zhou, Shuaiyu %A Yue, Dongdong %T Distributed optimization via active disturbance rejection control: A nabla fractional design %J Kybernetika %D 2024 %P 90-109 %V 60 %N 1 %U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2024-1-0090/ %R 10.14736/kyb-2024-1-0090 %G en %F 10_14736_kyb_2024_1_0090
Zeng, Yikun; Wei, Yiheng; Zhou, Shuaiyu; Yue, Dongdong. Distributed optimization via active disturbance rejection control: A nabla fractional design. Kybernetika, Tome 60 (2024) no. 1, pp. 90-109. doi: 10.14736/kyb-2024-1-0090
[1] Chen, Y. Q., Gao, Q., Wei, Y. H., Wang, Y.: Study on fractional order gradient methods. Appl. Math. Comput. 314 (2017), 310-321. | DOI | MR
[2] Chen, Z. Q., Liang, S.: Distributed aggregative optimization with quantized communication. Kybernetika 58 (2022), 1, 123-144. | DOI | MR
[3] Cheng, S. S., Liang, S.: Distributed optimization for multi-agent system over unbalanced graphs with linear convergence rate. Kybernetika 56 (2020), 3, 559-577. | DOI | MR
[4] Cheng, S. S., Liang, S., Fan, nd Y.: Distributed solving Sylvester equations with fractional order dynamics. Control Theory and Technology 19 (2021), 2, 249-259. | DOI | MR
[5] Ding, C., Wei, R., Liu, F.: Prescribed-time distributed optimization for time-varying objective functions: a perspective from time-domain transformation. J. Franklin Inst. 359 (2021), 17, 10,267-10,280. | DOI | MR
[6] Duan, S. Q., Chen, S., Zhao, Z. L.: Active disturbance rejection distributed optimization algorithm for first order multi-agent disturbance systems. Control and Decision 37 (2022), 3, 1559-1566.
[7] Gharesifard, B., Cortés, J.: Distributed continuous-time convex optimization on weight-balanced digraphs. IEEE Trans. Automat. Control 59 (2014), 3, 781-786. | DOI | MR
[8] Goodrich, C., Peterson, A. C.: Discrete Fractional Calculus. Springer, Cham 2015. | MR
[9] Guo, G., Zhang, R. Y. K., Zhou, I. D.: A local-minimization-free zero-gradient-sum algorithm for distributed optimization. Automatica 157 (2023), 111,247. | DOI | MR
[10] Han, J. Q.: From PID to active disturbance rejection control. IEEE Trans. Industr. Electron. 56 (2009), 3, 900-906. | DOI
[11] Hong, X. L., Wei, Y. H., Zhou, S. Y., Yue, D. D.: Nabla fractional distributed optimization algorithms over undirected/directed graphs. J. Franklin Inst. 361 (2024), 3, 1436-1454. | DOI | MR
[12] Hong, X. L., Wei, Y. H., S., Zhou, Y., Yue, D. D., Cao, J. D.: Nabla fractional distributed optimization algorithm over unbalanced graphs. IEEE Control Systems Lett. 8 (2024), 241-246. | DOI
[13] Kia, S. S., Cortés, J., J., Martínez, S.: Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication. Automatica 55 (2015), 254-264. | DOI | MR
[14] Liu, C. Y., Dou, X. H., Fan, Y., Cheng, S. S.: A penalty ADMM with quantized communication for distributed optimization over multi-agent systems. Kybernetika 59 (2023), 3, 392-417. | DOI | MR
[15] Pu, Y. F., Zhou, J. L., Zhang, Y., Zhang, N., Huang, G., Siarry, P.: Fractional extreme value adaptive training method: fractional steepest descent approach. IEEE Trans. Neural Networks Learning Systems 26 (2015), 4, 653-662. | DOI | MR
[16] Song, C. X., Qin, S. T., Zeng, Z. G.: Multiple Mittag-Leffler stability of almost periodic solutions for fractional-order delayed neural networks: Distributed optimization approach. IEEE Trans. Neural Networks Learning Systems (2023). | DOI
[17] Wang, J., Elia, N.: Control approach to distributed optimization. In: The 48th Annual Allerton Conference on Communication, Control, and Computing, Monticello 2010, pp. 557-561.
[18] Wang, K., Gong, P., Ma, Z. Y.: Fixed-time distributed time-varying optimization for nonlinear fractional-order multiagent systems with unbalanced digraphs. Fractal and Fractional 7 (2023), 11, 813. | DOI | MR
[19] Wang, Y., Song, Y.: Leader-following control of high-order multi-agent systems under directed graphs: pre-specified finite time approach. Automatica 87 (2018), 113-120. | DOI | MR
[20] Wang, Y. J., Song, Y. D., J., D., Hill, Krstic, M.: Prescribed-time consensus and containment control of networked multiagent systems. IEEE Trans. Cybernet. 49 (2019), 4, 1138-1147. | DOI
[21] Wei, Y. H.: Lyapunov stability theory for nonlinear nabla fractional order systems. IEEE Trans. Circuits Systems II: Express Briefs 68 (2021), 10, 3246-3250. | DOI
[22] Wei, Y. H.: Nabla Fractional Order Systems Theory: Analysis and Control. Science Press, Beijing 2023.
[23] Wei, Y. H., Chen, Y. Q., Liu, T. Y., Wang, Y.: Lyapunov functions for nabla discrete fractional order systems. ISA Trans. 88 (2019), 82-90. | DOI
[24] Wei, Y. H., Chen, Y. Q., Zhao, X., Cao, J. D.: Analysis and synthesis of gradient algorithms based on fractional-order system theory. IEEE Trans. Systems Man Cybernet.: Systems 53 (2023), 3, 1895-1906. | DOI | MR
[25] Wei, Y. H., Kang, Y., Yin, W. D., Wang, Y.: Generalization of the gradient method with fractional order gradient direction. J. Franklin Inst. 357 (2020), 4, 2514-2532. | DOI | MR
[26] Xu, Y., Han, T., Cai, K., Lin, Z., G, Yan, Fu, M.: A distributed algorithm for resource allocation over dynamic digraphs. IEEE Trans. Signal Process.65 (2017), 10, 2600-2612. | DOI | MR
[27] Yang, X. J., Zhao, W. M., Yuan, J. X., Chen, T., Zhang, C., Wang, L. Q.: Distributed optimization for fractional-order multi-agent systems based on adaptive backstepping dynamic surface control technology. Fractal and Fractional 6 (2022), 11, 642. | DOI
[28] Yang, X. L., Yuan, J. X., Chen, T., Yang, H.: Distributed adaptive optimization algorithm for fractional high-order multiagent systems based on event-triggered strategy and input quantization. Fractal and Fractional 7 (2023), 10, 749. | DOI
[29] Zhu, Y. N.: Research on Solving Network Distributed Optimization Problem Using Continuous-time Algorithms. Ph D. Thesis, Southeast University, Nanjing 2019.
Cité par Sources :