Reinforcement-learning-based predefined-time relative orbit-attitude control for spacecraft formation flying with connectivity preserving

In this paper, the spacecraft formation control problem is investigated under the undirected tree communication topology subjected to connectivity preserving and collision avoidance constraint. To implement the connectivity preservation and collision prevention of initially connected spacecrafts without employing any potential functions, a novel appointed-time performance function is developed to impose restrictions on the relative orbit distance of the adjacent spacecrafts. Moreover, this function can also characterise the upper boundary of the convergence time and steady-state error explicitly. Then a reinforcement-learning-based relative orbit-attitude control scheme is proposed to propel the formation error to the vicinity of the origin. An actor-critic neural network architecture is utilised to online compensate the system uncertainties, in which the critic neural network is introduced to improve the approximate performance of the actor neural network for the unknown uncertainties by evaluating the consensus performance of the formation system. In addition, an auxiliary system is devised to cope with the input saturation constraint. By using the Lyapunov stability theory, the stability and convergence of the closed-loop system are analysed. Finally numerical simulations are conducted to illustrate the feasibility and effectiveness of the proposed formation control scheme.