非线性多智能体系统的无模型自适应聚类一致性控制

Model-free adaptive cluster consensus control for nonlinear multi-agent systems

针对一类模型未知的离散时间非线性多智能体系统聚类一致性问题,提出一种无模型自适应控制算法.首先,假设系统具有固定拓扑,利用伪偏导数概念得到系统的数据关系模型,在考虑多智能体之间耦合系数条件下给出聚类一致性误差,在此基础上设计一种数据驱动的聚类一致性跟踪控制协议;然后,采用压缩映射方法在理论上分析了跟踪误差的收敛性,结果表明所提出算法不需要智能体模型信息即可完成跟踪任务,是一种数据驱动的控制方法;最后,将结果拓展至随机切换拓扑结构的多智能体系统中,数值仿真结果验证了所提出算法的有效性.

To address cluster consensus of discrete-time nonlinear multi-agent systems with unknown models under a fixed topology,this paper proposes a data-driven model-free adaptive control algorithm.Firstly,it is assumed that the system has a fixed topology,using the conception of pseudo partial derivative,the equivalent dynamic linearization model of the agent system is obtained.Under the consideration of the coupling coefficient among multiple agents,the cluster consensus error is proposed,and a data-driven cluster consensus control protocol is designed,then the convergence of tracking error is theoretically proved by using a compression mapping method,which shows that the proposed algorithm can complete the tracking task without the information of the agent model.Finally,the results are extended to multi-agent systems with a randomly switching topology.The effectiveness of the algorithm is verified by simulation examples.