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A physicochemically inspired approach to flocking control of multiagent system
Nonlinear Dynamics ( IF 5.2 ) Pub Date : 2020-11-04 , DOI: 10.1007/s11071-020-06062-y
Guibin Sun , Rui Zhou , Bin Di , Yan Hu

Theoretical studies suggest that dispersion and repulsion are two important interactions that occur within non-polar particles. Enlightened by this, we propose a simple model for flocking of autonomous agents interacting by physicochemically inspired dispersion and repulsion interactions. This interdisciplinary effort provides a generic framework for design and analysis of distributed flocking by introducing virtual electrons (VEs). We innovatively utilize the functional theory to construct the energy functional with the distribution density of VEs as the basic variable and then solve the Lagrangian equation to derive the control law of multiagent flocking. Theoretical analysis reveals that the proposed protocol can ensure that autonomous agents asymptotically converge to an equilibrium configuration from chaotic movement and meanwhile all agents tend in time to a common velocity. Numerical simulations are presented to verify the effectiveness of theoretical results. We also give a comparison with the collective potential method and found that the proposed approach can realize the rapid transition of autonomous agents from disordered to ordered movement. This implies that the proposed framework can effectively avoid the limitation of constructing a complicated empirical field and thus may have broad application prospects.



中文翻译:

物理化学启发的多代理系统植绒控制方法

理论研究表明,分散和排斥是非极性粒子中发生的两个重要相互作用。受此启发,我们提出了一个简单的模型,用于通过物理化学方法激发的分散和排斥相互作用来聚集自主药物。这种跨学科的努力通过引入虚拟电子(VE)为分布式植绒的设计和分析提供了一个通用框架。我们创新地运用泛函理论,以VEs的分布密度为基本变量,构建了能量泛函,然后求解拉格朗日方程,推导了多主体植绒的控制律。理论分析表明,所提出的协议可以确保自主智能体从混沌运动渐近收敛到平衡构型,同时所有智能体及时趋于相同的速度。数值模拟被提出来验证理论结果的有效性。我们还与集体势能方法进行了比较,发现所提出的方法可以实现自主智能体从无序运动到有序运动的快速过渡。这意味着所提出的框架可以有效避免构建复杂的经验领域的局限性,因此具有广阔的应用前景。我们还与集体势能方法进行了比较,发现所提出的方法可以实现自主智能体从无序运动到有序运动的快速过渡。这意味着所提出的框架可以有效避免构建复杂的经验领域的局限性,因此具有广阔的应用前景。我们还与集体势能方法进行了比较,发现所提出的方法可以实现自主智能体从无序运动到有序运动的快速过渡。这意味着所提出的框架可以有效避免构建复杂的经验领域的局限性,因此具有广阔的应用前景。

更新日期:2020-11-04
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