Iterative learning algorithms-based multiplicative thrust fault reconstruction and tolerant control for spacecraft formation flying systems

In this paper, the issues of multiplicative thruster fault reconstruction and reconfigurable fault-tolerant control for spacecraft formation flying system subject to loss of thruster effectiveness and a series of external space perturbations are investigated using iterative learning algorithms. Inspired by sliding mode methodology, a new robust iterative learning observer (RILO) is explored to reconstruct thrust effectiveness factor. Subsequently, a learning state-feedback fault-tolerant control approach is proposed based on the fault signals obtained from the RILO to guarantee the closed-loop spacecraft formation configuration is accurately maintained in the presence of multiplicative thrust faults and space perturbations. Finally, numerical simulations clearly validate the effectiveness and superiority of the proposed thrust fault-reconstructing and tolerant configuration maintenance control schemes for spacecraft formation flying systems.