The current studies for Tethered Space Net Robot (TSNR) typically treat the tension force induced by the net as a disturbance and employ passive suppression for compensation. However, these approaches not only result in excess fuel consumption but also overlook the intrinsic nature of the net dynamics. When one Maneuverable Unit (MU) maneuvers, it generates a tension force on the net that is transmitted to other MUs. This force not only affects the control accuracy of other MUs but also has a positive effect. In this paper, an Active Energy Management Distributed Formation Control (AEMC) strategy is proposed to reveal this kind of interaction and maximize its advantage. Firstly, an energy recovery framework is established, allowing each MU can effectively utilize the tension force due to the net. Specifically, a neural network estimator is designed to capture the hysteresis relationship in which MUs influence each other by transmitting forces through the net. Furthermore, to achieve the cooperative completion of tasks, a game based control scheme is proposed to optimize the control input and tension force collectively. Through prediction and optimization, MUs actively manage their impacts on each other, thereby controlling the influence of tension force on the tracking errors of others. Finally, numerical simulations are conducted to showcase the effectiveness of the proposed approach.

中文翻译：

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一种基于协同博弈论的系留空间网机器人主动能量管理分布式编队控制


目前对系留空间网络机器人 （TSNR） 的研究通常将网络感应的拉力视为干扰，并采用被动抑制进行补偿。然而，这些方法不仅导致过度的燃料消耗，而且还忽视了净动力学的内在本质。当一个机动单元 （MU） 机动时，它会在网络上产生一个张力，该张力会传递到其他 MU。这种力不仅影响其他 MU 的控制精度，而且具有积极的作用。该文提出了一种主动能量管理分布式编队控制 （AEMC） 策略来揭示这种相互作用并最大限度地发挥其优势。首先，建立了能量回收框架，使每个 MU 都能有效利用来自网络的拉力。具体来说，神经网络估计器旨在捕获磁滞关系，其中 MU 通过网络传递力来相互影响。此外，为了实现任务的协同完成，提出了一种基于游戏的控制方案，以集中优化控制输入和张力。通过预测和优化，MU 主动管理它们之间的影响，从而控制张力对其他 MU 跟踪误差的影响。最后，进行了数值仿真以证明所提方法的有效性。