空间非合作目标惯性参数的Adaline网络辨识方法

空间在轨操作中，航天器在对空间非合作目标的抓捕行动常常导致航天器本体的姿态和空间轨迹发生变化。为克服空间非合作目标对航天器本体动力学、运动学的影响，使控制系统做出精准及时的姿控策略调整，确保航天器正常在轨工作和轨迹姿态的高精度，需对抓捕的非合作目标的惯性参数进行辨识。针对传统辨识方法依赖广义逆求解导致的辨识过程运算量大，且数值容易产生剧烈振荡，造成辨识结果不稳定等不足，采用基于归一化最小均方（NLMS）准则的Adaline神经网络方法进行空间非合作目标惯性参数的辨识。首先，基于动量守恒理论建立抓捕后的航天器-机械臂-空间非合作目标系统模型；然后将辨识方程的系数矩阵作为网络的输入和输出，空间非合作目标的惯性参数作为神经网络的训练权重，基于迭代步长可变的NLMS准则实现对目标惯量参数的快速、准确辨识；最后，在构造的ADAMS/MATLAB联合仿真平台上进行了验证。仿真结果表明，基于NLMS准则的Adaline神经网络是一种快速、准确辨识目标惯量参数的有效方法。

Adaline network-based identification method of inertial parameters for space uncooperative targets

During the operation in space, the spacecraft's attitude and trajectory are often affected by capturing the uncooperative target. In order to overcome the influence of uncooperative target on the dynamics and kinematics of spacecraft and ensure the high-precision attitude control strategy to be made and normal in-orbit condition, a process of identifying the inertial parameters of uncooperative targets should be accommodated. In order to avoid a large amount of computation induced by generalized inverse operation of traditional method in the identification process, which also causes severe vibration and unstability to numerical results, an Adaline neural network identification method based on normalized least mean square(NLMS) criterion is adopted. First of all, a system model composed of spacecraft, manipulator and uncooperative target is established based on the theory of momentum conservation. Then the weight parameters of the neural network representing the inertial parameters of uncooperative target are trained by the coefficient matrix of the identification equation as the input and output of the neural network via algorithm of NLMS with variable iterative step, and a fast and accurate process of identification is achieved. Finally, an ADAMS/MATLAB co-simulation platform is established, on which the proposed identification method is verified. The simulation results show that the Adaline neural network based on NLMS criterion is a fast and accurate method for identifying the target's inertia parameters.