Adjacent-Agent Dynamic Linearization-Based Iterative Learning Formation Control

The dynamical relationship of the multiple agents' behavior in a networked system is explored and utilized to enhance the control performance of the multiagent formation in this paper. An adjacent-agent dynamic linearization is first presented for nonlinear and nonaffine multiagent systems (MAS...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2020-10, Vol.50 (10), p.4358-4369
Main Authors: Chi, Ronghu, Hui, Yu, Huang, Biao, Hou, Zhongsheng
Format: Article
Language:English
Subjects:
Adjacent-agent dynamic linearization
Communication
Computer simulation
data-driven control approach
First principles
iterative learning formation control
Iterative methods
Learning
Learning systems
Linearization
Multi-agent systems
Multiagent systems
nonlinear nonaffine multiagent systems (MASs)
Nonlinear systems
Task analysis
Uncertainty
Vehicle dynamics
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Summary:The dynamical relationship of the multiple agents' behavior in a networked system is explored and utilized to enhance the control performance of the multiagent formation in this paper. An adjacent-agent dynamic linearization is first presented for nonlinear and nonaffine multiagent systems (MASs) and a virtual linear difference model is built between two adjacent agents communicating with each other. Considering causality, the agents are assigned as parent and child, respectively. Communication is from parent to child. Taking the advantage of the repetitive characteristics of a large class of MASs, an adjacent-agent dynamic linearization-based iterative learning formation control (ADL-ILFC) is proposed for the child agent using 3-D control knowledge from iterations, time instants, and the parent agent. The ADL-ILFC is a data-driven method and does not depend on a first-principle physical model but the virtual linear difference model. The validity of the proposed approach is demonstrated through rigorous analysis and extensive simulations.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2019.2899654