应用于姿态敏感器的SORNN微小故障诊断方法

为实现空间外部干扰和测量噪声存在情况下，航天器姿态敏感器微小故障的有效检测，以及方位敏感器和惯性敏感器之间的故障隔离，提出了一种基于自组织循环神经网络(self-organizing recurrent neural network, SORNN)的微小故障诊断方法。首先，设计了SORNN模型，包括网络结构自组织算法、终止条件和调整条件，实现对网络隐藏层神经元数量和循环记忆深度的自适应调节，用以提升网络的拟合性能。然后，针对姿态运动学子系统设计了基于SORNN的干扰观测器，给出网络权值更新算法并证明了状态估计误差的收敛性。将系统输出估计误差通过低通滤波器以抑制星敏感器测量噪声，推导更严格的残差和检测阈值进而提高对微小故障的检测能力。最后，针对姿态动力学子系统设计了故障隔离观测器，通过干扰解耦和干扰观测器的补偿消除未知扰动和噪声对残差的影响，利用动力学和运动学的冗余关系解决了两类敏感器故障的隔离问题。仿真结果表明，验证了所提方法对扰动和噪声掩盖下的星敏感器和陀螺微小故障检测与隔离的有效性。

SORNN small fault diagnosis method for attitude sensors

To achieve effective detection of small faults in spacecraft attitude sensors and fault isolation of orientation sensors and inertial sensors in the presence of disturbance and measurement noise, we proposed a self-organizing recurrent neural network (SORNN) based small fault diagnosis method. Firstly, a SORNN model, including the self-organizing algorithm, termination condition, and adjustment condition, was designed to realize the adaptive adjustment of the number of hidden layer neurons and memory depth, thereby improving the fitting performance of the network. Then, a SORNN-based disturbance observer was designed for the kinematics subsystem. The network weight update algorithm was given, and the state estimation error convergence was proved. The output estimation error was passed through a low-pass filter to suppress the measurement noise of the star sensor. More rigorous residual and detection threshold were derived to improve the detection ability of small faults. Furthermore, a fault isolation observer was designed for the dynamic subsystem. The influence of unknown disturbance and noise on residual was eliminated by disturbance decoupling and disturbance observer compensation. The problem of fault isolation of different sensors was solved by using the redundancy relationship between dynamics and kinematics. Finally, the simulation results verified the effectiveness of the proposed method for detecting and isolating small faults of star sensors and gyros under the cover of disturbance and noise.