An integrated fuzzy and learning approach to performance improvement of model-based multi-agent robotic control systems

Erfu Yang, Dongbing Gu

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


This paper presents an integrated approach to improving the performance of model-based control for multi-agent robotic systems (MARS). The fuzzy logic and learning techniques are compactly and efficiently integrated into the proposed approach to yield an improved formation controller for MARS while ensuring the stability obtained from model-based control systems. As a case study the proposed approach is applied to a leader-follower MARS where the robotic leader agent has its own target and the robotic follower agent is constrained by formation tasks. Simulation results are presented to demonstrate the effectiveness of the integrated fuzzy and learning approach.

Original languageEnglish
Title of host publicationInternational Conference on Mechatronics and Automation, 2007. ICMA 2007
Place of PublicationPiscataway, NJ.
Number of pages6
ISBN (Print)9781424408283
Publication statusPublished - 2007
Event2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007 - Harbin, United Kingdom
Duration: 5 Aug 20078 Aug 2007


Conference2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007
Country/TerritoryUnited Kingdom


  • fuzzy logic and learning
  • model-based control
  • multi-agent systems
  • performance improvement
  • robotics

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