Trajectory tracking model predictive guidance for landing phase of reusable launch vehicles

Abstract

This thesis works on the optimal landing guidance for Reusable Launch Vehicles(RLVs) that utilize retro-propulsion for the landing. Recently developed RLVs proved the cost-effectiveness and even the improved flexibility in the operation by reusing the 1st stage booster. However, the guidance and control algorithms required for the reusable launch vehicle not only have to pursue the minimization of fuel usage but also satisfy the various path constraints of the launch vehicle while guaranteeing a precise and soft landing. To mitigate this difficulty, this study proposes optimal trajectory tracking guidance by model predictive control(MPC) framework to accomplish soft landing while satisfying the various constraints. The proposed algorithms comprise two phases: Attaining the fuel-optimal landing trajectory and tracking the optimal trajectory via MPC. In formulating the optimization problem for the MPC, the attitude control and throttling characteristics of the launch vehicle are augmented while retaining the required computational load. Then, sequential convex programming(SCP) is employed to solve the formulated non-convex optimization problem efficiently. Lastly, the proposed algorithm is verified by numerical simulation with a 6DOF model and control algorithm designed for the assumed launch vehicle and its landing scenario.