Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems
作者：
Highlights：
• Establish a dynamic data model along the iteration axis for unknown nonlinear describe-time Multiagent systems that reduces computation burdens.
• Propose a quantized distributed model-free adaptive iterative learning bipartite consensus tracking approach, which reduces the consumption of communication resources by only using the incomplete input/output data.
• Study collaborative and antagonistic relationships of multi-agent systems with fixed and switching topologies.
• Investigate the relationship between the convergence rate and the quantization level.
摘要
•Establish a dynamic data model along the iteration axis for unknown nonlinear describe-time Multiagent systems that reduces computation burdens.•Propose a quantized distributed model-free adaptive iterative learning bipartite consensus tracking approach, which reduces the consumption of communication resources by only using the incomplete input/output data.•Study collaborative and antagonistic relationships of multi-agent systems with fixed and switching topologies.•Investigate the relationship between the convergence rate and the quantization level.
论文关键词：Data-driven control,Multi-agent systems,Bipartite consensus,Data quantization,Iterative learning,Model-free adaptive control
论文评审过程：Received 3 December 2020, Revised 6 July 2021, Accepted 4 August 2021, Available online 17 August 2021, Version of Record 17 August 2021.
论文官网地址：https://doi.org/10.1016/j.amc.2021.126582
