Abstract:

To address the degradation of landing accuracy resulting from thrust magnitude uncertainty in the lander's engine, a method integrating Powered Explicit Guidance (PEG) with Recursive Quadratic Programming (RQP) is proposed. The descent trajectory was divided into two phases with distinct thrust levels. PEG was applied in each phase to generate the corresponding guidance law, with the altitude and velocity at the thrust-switching point treated as optimization variables. These parameters were optimized using RQP framework, in which the Hessian matrix was approximated via a variable-metric method to compute the search direction. To further mitigate convergence to local minima, gradient clipping and stochastic parameter perturbation were introduced. Monte Carlo simulations were carried out to validate the effectiveness and robustness of the proposed method.  The results demonstrate reliable convergence across a range of test conditions, including variations in the desired downrange, initial parameters, and thrust deviation. Furthermore, compared with the conventional PEG approach, the proposed method significantly reduces both downrange error and fuel consumption. The proposed method effectively improves the lander’s pinpoint landing accuracy in the presence of thrust uncertainty, offering a reliable and technically feasible approach for future lunar soft-landing missions.

Key words:

Thrust magnitude uncertainty, Pinpoint landing, Recursive Quadratic Programming (RQP), Powered Explicit Guidance (PEG), Parameter optimization