Learning Observer and Performance Tuning-Based Robust Consensus Policy for Multiagent Systems

Chengxi Zhang, Jin Wu, Choon Ki Ahn, Zhongyang Fei, Caisheng Wei

Research output: Contribution to journalArticlepeer-review


This article addresses the multiagent systems consensus control problem via a learning observer-based performance tuning control policy. Specifically, a novel learning observer is presented to reconstruct the compound nonlinear terms and system states simultaneously. Based on the learning observer’s reconstructed information, a novel performance tuning control policy is proposed to deal with the internal nonlinear terms and external disturbances acting on the system while providing prescribed consensus performance. The proposed learning observer can guarantee the uniformly ultimately bounded estimation while saving computing resources, which is beneficial to the multiagent system. The proposed control policy, combined with observer and performance tuning, ensures the robustness to nonideal perturbations and the high accuracy control performance simultaneously. Mathematical simulations verify the effectiveness of the control algorithm.

Original languageEnglish
JournalIEEE Systems Journal
Publication statusAccepted/In press - 2021


  • Consensus control
  • Estimation error
  • Graph theory
  • learning observer
  • Linear matrix inequalities
  • Multi-agent systems
  • nonlinear system
  • Observers
  • prescribed performance multiagent system
  • Symmetric matrices
  • Tuning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Learning Observer and Performance Tuning-Based Robust Consensus Policy for Multiagent Systems'. Together they form a unique fingerprint.

Cite this