Distributed continuous-time aggregative optimization and its applications to power generation systems

doi:10.23919/JSEE.2026.000015

Journal of Systems Engineering and Electronics ›› 2026, Vol. 37 ›› Issue (1): 1-8.doi: 10.23919/JSEE.2026.000015

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Distributed continuous-time aggregative optimization and its applications to power generation systems

Chengxin XIAN¹^,²(), Yu ZHAO¹^,*(), Yongfang LIU¹()

¹Department of Control Theory and Control Engineering, School of Automation, Northwestern Polytechnical University, Xi’an 710129, China
²Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China

Received:2025-11-19 Online:2026-02-18 Published:2026-03-09
Contact: Yu ZHAO E-mail:chengxinxian1@gmail.com;yuzhao5977@gmail.com;liuyongfang@nwpu.edu.cn
About author:
XIAN Chengxin was born in 1997. He received his B.E. degree in transportation equipment and control engineering, and Ph.D. degree in control science and engineering from the School of Automation, Northwestern Polytechnical University, Xi’an, China in 2019 and 2024, respectively. He is a post-doctoral fellow with the Department of Applied Mathematics, The Hong Kong Polytechnic University, China. His research interests include event-based control, distributed optimization, and cooperative control of multi-agent systems. E-mail: chengxinxian1@gmail.com

ZHAO Yu was born in 1986. He received his B.S. degree from the Department of Mathematics, Inner Mongolia University, China, in 2009, and Ph.D. degree from the Department of Mechanics and Engineering Science, College of Engineering, Peking University, China, in 2015. He is a tenured professor at the Department of Control Theory and Control Engineering, School of Automation, Northwestern Polytechnical University, China. His research interests include coordination control and optimization of autonomous intelligent systems and analysis and synthesis of complex networks with applications to aerospace engineering.E-mail: yuzhao5977@gmail.com

LIU Yongfang was born in 1986. She received her B.S. degree from the Department of Mathematics, Northeastern University, China, in 2009, and Ph.D. degree from the Department of Mechanics and Engineering Science, College of Engineering, Peking University, China in 2014, respectively. She is an associate professor with the Department of Control Theory and Control Engineering, School of Automation, Northwestern Polytechnical University, China. Her research interests include nonlinear control, optimal control, and distributed cooperative control of multi-agent systems.E-mail: liuyongfang@nwpu.edu.cn
Supported by:
This work was supported by the National Key Research and Development Program of China (2025YFE0213100)，the National Natural Science Foundation of China (62422315; 62573348), the Natural Science Basic Research Program of Shaanxi (2025JC-YBMS-667), and the “ShuangYiLiu” Construction Foundation (25GH02010366).

Abstract

Abstract:

This paper investigates the distributed continuous-time aggregative optimization problem for second-order multi-agent systems, where the local cost function is not only related to its own decision variables, but also to the aggregation of the decision variables of all the agents. By using the gradient descent method, the distributed average tracking (DAT) technique and the time-base generator (TBG) technique, a distributed continuous-time aggregative optimization algorithm is proposed. Subsequently, the optimality of the system’s equilibrium point is analyzed, and the convergence of the closed-loop system is proved using the Lyapunov stability theory. Finally, the effectiveness of the proposed algorithm is validated through case studies on multirobot systems and power generation systems.

Key words: distributed continuous-time aggregative optimization, distributed average tracking (DAT), time-base generator (TBG)

Chengxin XIAN, Yu ZHAO, Yongfang LIU. Distributed continuous-time aggregative optimization and its applications to power generation systems[J]. Journal of Systems Engineering and Electronics, 2026, 37(1): 1-8.

Figures/Tables 7

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Distributed continuous-time aggregative optimization and its applications to power generation systems

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