Vibration reduction in flexible spacecraft using FEL-based adaptive dynamic inverse control

Minimizing vibrations of a flexible spacecraft actuated by on-off thrusters are a challenging task. 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 feedforward 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 CFC controller is a fixed gain proportional-plus-derivative (PD) controller, which at least guarantees the stability of the overall system when without the neural network used. 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. The numerical simulations have shown that the control strategy can significantly reduce the vibration of the flexible vibrations.