Adjacent-Agent Dynamic Linearization-Based Iterative Learning Formation Control

• 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
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2019.2899654

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.