Online Network-Based Identification and its Application in Satellite Attitude Control Systems

Most satellite attitude control models are built analytically, which requires a clear understanding of the kinematic and dynamic equations of the satellites and the various disturbance models to deal with the interferences and uncertainties in the space environment. This article studies a system identification method to build a system model for satellite attitude control. The satellite model is based on a neural network model, i.e., the RBF-ARX model, which requires only observation data. Unlike the existing offline identification methods for the RBF-ARX model, this article proposes an online identification method that can constantly correct the model according to the newest data. To reduce the computational complexity of identifying the satellite model, a decomposition scheme is also developed. The computational efficiency of the proposed method is analyzed. Finally, the efficacy of the proposed identification method is demonstrated by applying it to two satellite attitude control systems.