Distributed Optimization of Nonlinear Multi-Agent Systems: A Small-Gain Approach

Tengfei Liu, Zhengyan Qin, Yiguang Hong, Zhong Ping Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper studies the distributed optimal output agreement problem for multi-agent systems described by uncertain nonlinear models. By using 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 paper introduces distributed coordinators to calculate the ideal outputs, and designs reference-tracking controllers for the agents to follow the ideal outputs. To deal with the nonlinear uncertain dynamics, the closed-loop multi-agent system is considered as a dynamical network, and Sontag's input-to-state stability (ISS) properties are employed to characterize the interconnections. It is shown that output agreement in multi-agent nonlinear systems is achievable by means of distributed optimal coordinators via a small-gain approach. Numerical simulations are employed to show the effectiveness of the design.

Original languageEnglish (US)
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728113982
StatePublished - Dec 2019
Event58th IEEE Conference on Decision and Control, CDC 2019 - Nice, France
Duration: Dec 11 2019Dec 13 2019

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference58th IEEE Conference on Decision and Control, CDC 2019

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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