In this brief, we focus on the study of formation tracking problem for a class of multiagent systems with nonlinear dynamics and external disturbances in the presence of relative distance constraints. A novel distributed formation control strategy is proposed based on an integration of radial basis function neural network (NN) with artificial potential field method. The relative distance constraints between arbitrary adjacent agents can be ensured by the artificial potential function. Based on the NN approximation property, it has been proposed to neutralize the nonlinear dynamics in agents. To account for the negative influence of the approximation error and external disturbances, a robustness term is employed. Finally, based on algebraic graph theory, matrix theory, and Barbalat’s lemma, some sufficient conditions are established to accomplish the asymptotical stability of the systems for a given communication graph. The study is with application to tethered space net robot. The simulation results are performed to illustrate the performance of the proposed strategy.

中文翻译：

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约束多智能体系统的人工势能和神经网络分布式编队控制

在本文中，我们着重研究在相对距离约束下具有非线性动力学和外部干扰的一类多主体系统的编队跟踪问题。提出了一种基于径向基函数神经网络与人工势场法相结合的新型分布式编队控制策略。可以通过人工势函数来确保任意相邻代理之间的相对距离约束。基于神经网络的逼近特性，已提出中和代理中的非线性动力学。为了解决近似误差和外部干扰的负面影响，采用了稳健性项。最后，根据代数图论，矩阵论和Barbalat引理，建立了一些足够的条件，以实现给定通信图的系统的渐近稳定性。该研究及其在束缚空间网机器人中的应用。仿真结果表明了所提出策略的性能。