Yifeng Ma

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.

This work focuses on the problem of collision avoidance with space debris for large-scale deployment of tethered space robot. To this end, a general scheme that contains offline training and online execution is presented for safe deployment of tethered space robot. Specifically, inspired by contraction theory, a feedback controller is learned from data to guarantee the superior tracking performance in the offline phase. Furthermore, the “tube” where state of tethered space robot would stay within is optimized simultaneously. In the online execution phase, when the space debris are detected, the motion planner generates nominal trajectory by considering safety constraints. Then, in the presence of disturbances, the feedback controller learned offline tracks this nominal trajectory safely without collisions. The proposed scheme allows for the comprehensive utilization of prior knowledge for designing the tracking controller in the offline phase, thereby enhancing the online tracking performance. Finally, the numerical simulations demonstrate effectiveness of the proposed framework.

The traditional control methods 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 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 tension force control strategy is proposed to reveal this kind of interaction and maximize its advantage. Specifically, a neutral 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 control framework is established to optimize the control input and tension force collectively. Through prediction and optimization, MUs actively manage their impacts on each other, thereby actively controlling the influence of tension force on the tracking errors of others. Finally, numerical simulations are conducted to showcase the effectiveness of the proposed scheme.

The Tethered Space Net Robot (TSNR) is an innovative solution for active space debris capture and removal. Its large envelope and simple capture method make it an attractive option for this task. However, capturing maneuverable debris with the flexible and elastic underactuated net poses significant challenges. To address this, a novel formation control method for the TSNR is proposed through the integration of differential game theory and robust adaptive control in this paper. Specifically, the trajectory of the TSNR is obtained through the solution of a real-time feedback pursuit-evasion game with a dynamic target, where the primary condition is to ensure the stability of the TSNR. Furthermore, to minimize tracking errors and maintain a specific configuration, a robust adaptive formation control scheme with Artificial Potential Field (APF) based on a Finite-Time Convergent Extended State Observer (FTCESO) is investigated. The proposed control method has a key advantage in suppressing complex oscillations by a new adaptive law, thus precisely maintaining the configuration. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed scheme.