Distributed optimization for discrete-time multiagent systems with nonconvex control input constraints and switching topologies
Xiao-Yu Shen(沈小宇)1, Shuai Su(宿帅)2,3,†, and Hai-Liang Hou(侯海良)1
1 School of Automation, Central South University, Changsha 410083, China; 2 National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China; 3 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Abstract This paper addresses the distributed optimization problem of discrete-time multiagent systems with nonconvex control input constraints and switching topologies. We introduce a novel distributed optimization algorithm with a switching mechanism to guarantee that all agents eventually converge to an optimal solution point, while their control inputs are constrained in their own nonconvex region. It is worth noting that the mechanism is performed to tackle the coexistence of the nonconvex constraint operator and the optimization gradient term. Based on the dynamic transformation technique, the original nonlinear dynamic system is transformed into an equivalent one with a nonlinear error term. By utilizing the nonnegative matrix theory, it is shown that the optimization problem can be solved when the union of switching communication graphs is jointly strongly connected. Finally, a numerical simulation example is used to demonstrate the acquired theoretical results.
Fund: Project supported by the National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University (Grant No. NERC2019K002).
Corresponding Authors:
Shuai Su
E-mail: shuaisu123@163.com
Cite this article:
Xiao-Yu Shen(沈小宇), Shuai Su(宿帅), and Hai-Liang Hou(侯海良) Distributed optimization for discrete-time multiagent systems with nonconvex control input constraints and switching topologies 2021 Chin. Phys. B 30 120507
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