Event-triggered model predictive control for Halo orbit station-keeping

This study aims to balance orbit-keeping accuracy, propellant consumption and computational efficiency for spacecraft station-keeping near Earth-Moon libration points. Traditional nonlinear model predictive control (NMPC) imposes a heavy onboard computational burden because it solves complex nonlinear optimization at every control step. A high-fidelity model is developed based on the circular restricted three-body problem combined with low-thrust orbital dynamics. The research introduces an event-triggered mechanism (ETM) and proposes two event-triggered NMPC (ET-NMPC) algorithms. Instead of fixed-period execution, the controller initiates online receding-horizon optimization only when the real-time position tracking error exceeds a preset threshold. Between triggers, when tracking performance remains satisfactory, two low-computation strategies are applied alternately: control-input freezing and control-input nullification. This design avoids numerous unnecessary online optimizations. Long-term numerical simulations show that the ET-NMPC strategies maintain control precision while greatly lowering computational load. With suitable thresholds, both methods reduce average computation time by over 50 and decrease the average triggering frequency by more than 77 compared with standard NMPC. The approach successfully balances control performance with limited onboard computational resources. The proposed control framework offers a novel on-demand optimization solution for deep-space orbital control under strict resource constraints. It significantly expands the practical applicability of nonlinear model predictive control in complex aerospace dynamical systems.