Neural network disturbance observer with extended weight matrix for spacecraft disturbance attenuation

A modified neural network-based disturbance observer with high-accuracy estimation performance is fabricated for spacecraft attitude control in this paper. To reduce the approximation error of the neural network with a limited number of neurons for complex disturbances, the weight matrix of the neural network is extended into a time series expansion and updated by a modified back propagation algorithm. Thus, the high-order dynamics of the weight matrix can be reconstructed, and the disturbance estimation accuracy is improved. Then, by introducing an integral state of the lumped disturbance into the disturbance model and utilizing it as the virtual disturbance measurement, an improved neural network-based disturbance observer is designed. The proposed observer takes advantages of the nonlinear approximation capability of the neural network and the high-order dynamics estimation ability of the extended observer structure. To realize high-performance attitude control of a spacecraft, a composite controller based on the proposed observer is designed, and then tested by comparative simulations. Simulation results show that, the estimation error of the designed observer can be reduced by an order of magnitude than the traditional observers. It is suitable for high-performance disturbance attenuation of verities of applications. (C) 2022 Elsevier Masson SAS. All rights reserved.