Spacecraft attitude tracking control is a challenge for different space moving target observation tasks, especially when uncertainties exist. Traditional controllers have poor adaptability, and iterative learning controllers are time consuming. This paper focuses on the spacecraft attitude tracking control problem with uncertainties, including an unknown inertia matrix, desired attitude and desired angular velocity. To improve the attitude tracking accuracies under uncertainties, a reinforcement learning (RL)-based sliding mode observer is designed in which the uncertainties are estimated by the learning process using RL. To guarantee the convergence of the prescribed sliding mode surface, a controller based on the combination of traditional feedback control and RL is developed. The traditional feedback controller is used to speed up the learning process and to reject the aperiodic disturbance, while RL is applied to solve the uncertainty and to reject the periodic disturbance. The proposed controller can maintain high attitude tracking accuracies for different tracking missions in which the unknown inertia matrix, the desired attitude and attitude angular velocity are different without adjusting parameters. Finally, comparison results of the numerical simulation illustrate the effectiveness of the proposed method.

航天器姿态跟踪控制是不同空间运动目标观测任务的挑战，尤其是在存在不确定性的情况下。传统控制器适应性差，迭代学习控制器耗时。本文重点研究具有不确定性的航天器姿态跟踪控制问题，包括未知惯性矩阵、期望姿态和期望角速度。为了提高不确定性下的姿态跟踪精度，设计了一种基于强化学习 (RL) 的滑模观测器，其中通过使用 RL 的学习过程来估计不确定性。为了保证指定滑模面的收敛，开发了一种基于传统反馈控制和RL相结合的控制器。传统的反馈控制器用于加速学习过程并抑制非周期性扰动，而RL用于解决不确定性和抑制周期性扰动。所提出的控制器可以在不调整参数的情况下，对未知惯性矩阵、期望姿态和姿态角速度不同的不同跟踪任务保持较高的姿态跟踪精度。最后，数值模拟的对比结果说明了所提方法的有效性。在不调整参数的情况下，期望的姿态和姿态角速度是不同的。最后，数值模拟的对比结果说明了所提方法的有效性。在不调整参数的情况下，期望的姿态和姿态角速度是不同的。最后，数值模拟的对比结果说明了所提方法的有效性。