Distributed Optimization of Nonlinear Multiagent Systems: A Small-Gain Approach

• 成果类型: Article
• 署名作者: Liu, Tengfei; Qin, Zhengyan; Hong, Yiguang; Zhong-Ping Jiang
• 署名单位: Northeastern University - China; Tongji University; Tongji University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3053549
• 发表日期: 2022
• 页码: 676-691
• 关键词: Multi-agent systems; optimization; linear programming; nonlinear dynamical systems; uncertainty; CONVERGENCE; TOPOLOGY; input-to-state stability (ISS); multiagent systems; nonlinear proportional-integral (PI) control; nonlinear uncertain dynamics; optimal output agreement

摘要

This article studies the distributed optimal output agreement problem for multiagent systems described by uncertain nonlinear models. By using the partial information of an objective function, the design aims to steer the outputs of the agents to an agreement on the optimal solution to the objective function. To solve this problem, this article introduces distributed coordinators to calculate the desired outputs, and designs reference-tracking controllers for the agents to follow the desired outputs. To deal with the nonlinear uncertain dynamics, the closed-loop multiagent system is considered as a dynamical network, and Sontag's input-to-state stability is employed to characterize the interconnections. It is shown that output agreement in multiagent nonlinear systems is achievable by means of distributed optimal controllers via a small-gain approach. The proposed design features a three-layer architecture, and the reference-tracking controllers can be implemented as successive nonlinear proportional-integral loops. A numerical example is employed to show the effectiveness of the design.