Optimal trajectory tracking control based on reinforcement learning for the deployment process of space tether system

Abstract

Space tether system has a wide application prospect in space mission. Due to the characteristics of strong non-linearity and under-actuation, as well as the interference of complex space environment, it is difficult to model the tethered system accurately. Hence, the controller based on the parameters of the system model will cause large errors in the process of control. In this paper, an adaptive dynamic programming algorithm based on reinforcement learning theory is adopted. By training two Back Propagation (BP) neural networks, namely critic neural network (NN) and actor NN, the performance index function and control law of the system approach approximate optimal values respectively. The controller design is independent of the system model, so model-free control of the system is realized by implementing this control method. First, assuming that the out-of-plane motion of the system is stable, the optimal deployment trajectory of the tethered system is obtained by parameter optimization based on Nelder-Mead method. The optimal trajectory is taken as the nominal trajectory and the trajectory tracking is carried out by reinforcement learning controller. The simulation results show that the reinforcement learning algorithm has a good control effect on the in-plane trajectory tracking of the tethered system, which proves the feasibility and robustness of the control method.