Adaptive neural dynamic-based hybrid control strategy for stable retrieval of tethered satellite systems

This study proposes a novel adaptive neural dynamic-based hybrid control strategy for stable subsatellite retrieval of two-body tethered satellite systems. The retrieval speed is given analytically, ensuring a libration-free steady state. To mitigate the potential libration motion, a general control input signal is generated by an adaptive neural-dynamic (AND) algorithm and executed by adjusting the retrieval speed and thruster on the subsatellite. To address the limited retrieval speed and improve the control performance, the thruster controller is manipulated according to a novel advanced state fuzzy control law based on higher-order libration states, whereas the remaining control input is allocated to the speed controller. The Lyapunov stability of the control strategy is demonstrated analytically. Numerical simulations validate the proposed control strategy, demonstrating well-allocated control inputs for both controllers and good control performance.