Robust and Collision-Free Formation Control of Multi-Agent Systems with Limited Information
Date
2023
Authors
Fei, Y.
Shi, P.
Lim, C.C.
Editors
Advisors
Journal Title
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Type:
Journal article
Citation
IEEE Transactions on Neural Networks and Learning Systems, 2023; 34(8):4286-4295
Statement of Responsibility
Yang Fei, Peng Shi, and Cheng-Chew Lim
Conference Name
Abstract
This article investigates the collision-free cooperative formation control problem for second-order multiagent systems with unknown velocity, dynamics uncertainties, and limited reference information. An observer-based sliding mode control law is proposed to ensure both the convergence of the system’s tracking error and the boundedness of the relative distance between each pair of agents. First, two new finite-time neural-based observer designs are introduced to estimate both the agent velocity and the system uncertainty. The sliding mode differentiator is then employed for every agent to approximate the unknown derivatives of the formation reference to further construct the limited-information-based sliding mode controller. To ensure that the system is collision-free, artificial potential fields are introduced along with a time-varying topology. An example of a multiple omnidirectional robot system is used to conduct numerical simulations, and necessary comparisons are made to justify the effectiveness of the proposed limited information-based control scheme.
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Dissertation Note
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Description
Published 8 August 2023
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