Abstract
Based on Feedback-Error-Learning (FEL), an adaptive dynamic inverse control approach for single-axis rotational maneuver of spacecraft with flexible appendages by use of on-off thrusters is discussed. This approach uses a conventional feedback controller (CFC) concurrently with a Nonlinear Auto-Regressive Exogenous Input (NARX) neural network, and the NARX neural network can act dynamic adaptive inverse feed-forward controller, which is adapted on-line using the output of the CFC as its error signal, to improve the performance of a conventional non-adaptive feedback controller. The neural network (NN) does not need training in advance, and can utilize input and output on-line information to learn the systematic parameter change and unmodeled dynamics, so that the self-adaptation of control parameter is adjusted. However, the CFC should at least be able to stabilize the system under control when used without the neural network. The numerical simulations have shown that the control strategy is effective and feasible.
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Liu, Y., Ma, G., Hu, Q. (2004). FEL-Based Adaptive Dynamic Inverse Control for Flexible Spacecraft Attitude Maneuver. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_7
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DOI: https://doi.org/10.1007/978-3-540-28648-6_7
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