Publication

Adaptive Fuzzy Behavioral Control of Second-Order Autonomous Agents With Prioritized Missions: Theory and Experiments

Huang, J., Zhou, N. & Cao, M., Dec-2019, In : IEEE Transactions on Industrial Electronics. 66, 12, p. 9612-9622 11 p.

Research output: Contribution to journalArticleAcademicpeer-review

Copy link to clipboard

Documents

  • Adaptive Fuzzy Behavioral Control of Second-Order Autonomous Agents With Prioritized Missions

    Final publisher's version, 3 MB, PDF document

    Request copy

DOI

In this paper, we study the adaptive fuzzy formation control of multiple autonomous agents with prioritized missions. For a platoon of autonomous agents in an unknown environment containing multiple obstacles, formation control is investigated, where each agent is modeled by a second-order nonlinear system under unknown external disturbance in the Brunovsky form. We introduce the systematic procedure of null-space-based projection to convert the prioritized multimission control problem into a behavioral control problem. Then, we further develop a class of nonlinear-fast-terminal-sliding-mode-based adaptive control strategies that combine the fuzzy logic systems by jointly considering both kinematic and dynamic levels of the agents. The proposed controllers can guarantee each individual agent to achieve the predesigned desired pattern and drive the entire systems to achieve the prescribed missions. A simulation example with five agents demonstrates the effectiveness of the algorithm. Finally, the strategies are experimentally validated using a platoon of Pioneer 3AT and 3DX mobile robots.

Original languageEnglish
Pages (from-to)9612-9622
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Volume66
Issue number12
Early online date17-Jan-2019
Publication statusPublished - Dec-2019

    Keywords

  • Autonomous agents, behavioral control, Brunovsky system, multiagent systems (MASs), Pioneer mobile robot, prioritized mission, MULTIAGENT SYSTEMS, SYNCHRONIZATION, COORDINATION, CONSENSUS

ID: 101231670