Guidance of Autonomous Aerospace Vehicles for Vertical Soft Landing using Nonlinear Control Theory

Abstract : Two vertical soft landing problems are investigated in this report. First, the soft landing problem of quadrotor in presence of unmodelled dynamics is investigated. A neural network-based disturbance estimation is adopted to capture the unmodeled quadrotor dynamics due to rotor blade flapping phenomenon. An adaptive guidance law with the Dynamic Inversion (DI) as baseline algorithm is illustrated for soft vertical touch down. Next, the autonomous landing of a spacecraft on the lunar surface is explored. To ensure the smooth touchdown of the spacecraft on the lunar surface, a nonlinear optimal control theory based Generalized model predictive static programming (G-MPSP) guidance is proposed. As the G-MPSP formulation incorporates the terminal condition as a hard constraint, it ensures the high terminal accuracy of position and velocity of the spacecraft. Also the vertical orientation of the spacecraft during touchdown is achieved through the soft constraint formulation by the proper selection of the control weight matrix. Effectiveness of the proposed guidance methods are demonstrated using simulation results.