针对航天器对空间目标近距离绕飞背景下的姿轨一体化控制任务，考虑惯量参数不确定性与轨道摄动的影响，提出一种基于全驱系统参数化方法与自适应神经网络的预设性能控制方法。基于Lie群SE（3）描述刚体航天器近距离绕飞空间目标的六自由度运动，建立精确且简洁的航天器姿轨一体化误差动力学模型；通过引入指数形式的预设性能函数，先验定量地约束航天器姿轨状态误差的动态和稳态过程；考虑到航天器相对运动模型的非线性特性，设计了基于全驱系统方法的反馈控制项，使闭环系统成为具有可配置特征结构的常线性系统，降低了后续控制器设计难度；进一步设计了包括自适应神经网络项的积分滑模控制器，以实现存在惯量不确定性和轨道摄动情况下的航天器高精度一体化控制；此外，利用改进粒子群算法进一步优化控制器参数，提高了控制器的工程应用性。数值模拟结果显示，在所提出的控制方法作用下，能够实现高精度姿轨一体绕飞，航天器状态轨迹始终处于预设性能包络内且控制输出无明显抖振，证明了所设计控制器的有效性和可应用性。

An attitude-orbit integrated controller with prescribed performance based on fully actuated system parameterization approach and adaptive neural network is proposed for close flying-around of the space target in the presence of inertia parameter uncertainty and orbit perturbation. The six degree of freedom motion of rigid spacecraft is derived under the framework of Lie group SE（3）， and an accurate and concise attitude-orbit integrated error dynamic model is established. An exponential-form based performance function is introduced to perform prior and quantitative constraints on the dynamic and stable processes of attitude error and position error. Considering the nonlinear characteristics of the dynamic model， a feedback control term based on the fully actuated system approach is designed to obtain a constant linear closed-loop system with an arbitrarily assignable eigenstructure， thus reducing the difficulty of the subsequent controller design process. An adaptive neural network based integral sliding mode controller is further designed to compensate for the loss of control accuracy caused by inertia parameter uncertainty and orbit perturbation. In addition， to further enhance the engineering utility， an improved particle swarm optimization algorithm is developed to optimize the controller parameters. Numerical simulation results indicate that the high-precision attitude-orbit integrated flying-around is achieved without significant control chatter while satisfying the predesigned performance constraints， thus verifying the effectiveness and feasibility of the proposed controller.