Convex optimization based autonomous trajectory capture for strictly-regressive orbit

To meet the operational requirements of on-board autonomy and robustness for the initialization of satellite strictly-regressive orbits, a low-thrust autonomous guidance and control method is adopted, aiming to achieve fuel-optimal precise capture of large-scale spatial trajectories under given mission duration and tube diameter constraints. Using non-singular relative orbital elements as state variables, a dynamic optimization model for strictly-regressive orbits is established based on the mapping relationship between tube diameter deviations and relative orbital elements. The state transition matrix incorporates multiple perturbation effects, including atmospheric drag, solar and lunar third-body gravitational perturbations. By expressing the geometric tube diameter constraints in second-order cone form, a convex optimization problem is formulated to enable real-time on-board optimization with stable convergence. The convex optimization-derived thrust commands are regularized to generate thrust sequences that conform to the actual on-off characteristics of thrusters. Through a receding horizon optimization framework embedded with model predictive control, the closed-loop control is transformed into a time-sequential iterative convex optimization problem, thereby ensuring high precision in spatial trajectory capture.