Dynamic Event-Triggered Bipartite Formation for MIMO Multiagent Systems With Quantized Data
针对动力学模型未知的非线性多输入多输出多智能体系统，提出一种数据驱动的动态事件触发二分编队控制方法，利用量化信息降低通信负担并加快收敛，仿真和实验验证了有效性。
This article deals with fully distributed data-driven bipartite formation control for nonlinear discrete-time multi-input-multi-output multiagent systems (MASs) with unknown dynamics models and quantized information. Initially, a distributed combined measurement error function (DCMEF) is developed for MASs characterized by cooperative and competitive interactions. This function is designed to transform bipartite formation challenges into traditional consensus problems. Subsequently, a distributed compact form dynamic linearization model is established based on the designed DCMEF and input-output data of the MASs, eliminating the need for a strongly connected communication topology. Following this, a logarithmic quantization scheme and a dynamic event-triggered communication mechanism are devised to reduce the communication burden and enhance convergence speed. Finally, a data-driven fully distributed dynamic event-triggered bipartite formation control method is proposed, and its convergence is rigorously proven. Simulation studies and hardware experiments are conducted to validate the effectiveness of the proposed method.