Online Modeling and Sequential Convex Programming for Lunar Landing Trajectory Optimization

Abstract

This paper presents a guidance framework for lunar powered descent and landing that combines sequential convex programming (SCP) with real-time online model identification. A nonconvex energy-optimal landing problem is developed and then reformulated into a sequence of convex second-order cone programs (SOCPs) through a change of variables, successive linearization, and a lossless second-order cone relaxation of the thrust direction constraint. An online identification layer, built from a recursive least squares (RLS) filter with exponential forgetting and an exponential moving average (EMA) smoother, estimates unknown gravitational, thrust-scale, and mass-gauging perturbations from noisy navigation measurements and injects a corrected bias term into the dynamics constraint of each convex subproblem at every guidance cycle. Building on this architecture and my prior work in this area, a baseline SCP algorithm and a receding-horizon online SCP algorithm with model identification are developed. Also, I try to explore some theoretical foundations, establishing the losslessness of the convex relaxation, the mean-square stability and convergence of the identification filters, the guaranteed convergence of the SCP iteration, and explicit convergence radius and convergence rate results. Numerical simulations across four perturbation scenarios of increasing complexity are implemented in MATLAB using YALMIP and the ECOS solver. The results show that the proposed online algorithm consistently reduces landing position and velocity error and better tracks the true propellant consumption relative to an uncorrected nominal trajectory, while retaining the predictable convergence and real-time computational properties of convex optimization.