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A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Control moment gyroscopes (CMGs) are a favorable choice for spacecraft attitude control systems thanks to their torque amplification capability. However, their performance is hindered by geometric singularities, which can severely limit control capabilities during attitude maneuvers. This paper proposes a hybrid attitude control system incorporating a single reaction wheel into the standard minimum redundant four-CMG pyramid configuration, providing an additional degree-of-freedom to avoid and escape singularities effectively. A comprehensive mathematical analysis of the system’s Jacobian matrix, using row echelon form, is performed alongside a geometrical analysis of the hybrid configuration’s momentum envelope. These analyses demonstrate the reflection of a reaction wheel inclusion into the system of four CMGs in the improvement of the system’s capacity to avoid/escape singularity. Additionally, an asymptotic control law and an adapted steering law are developed to optimize control performance. The effectiveness of the proposed hybrid system is validated through simulation, which includes a comparative analysis with traditional CMG-only system. The results highlight the superiority of the hybrid system in handling singularities.