Abstract:
In this thesis, we first construct a basic model representing the soft landing problem. The aim of the modeling effort is to transparently represent the process of landing a spacecraft on the surface of a celestial body. The process of landing is an interesting problem because there are two main contradictory performance criteria to be met simultaneously; the landing duration should be as short as possible, but at the same time crashing the spacecraft to the surface should be avoided. In this work, we studied four different control heuristics for the soft landing problem. The first heuristic is adapted from the mass-spring-damper model using the similarity of the equations of the soft landing model developed to the equations of the mass-spring-damper model. The second one is a bang-bang heuristic that first allows the spacecraft to fall freely, but after a critical point is reached, it uses the reverse force thruster at its maximum power until the touchdown. Bang-bang heuristic minimizes the time needed to land. The third heuristic is a combination of the bang-bang and mass-spring-damper heuristics. This new heuristic also borrows the concept Weight of Supply Line from System Dynamics literature. This new heuristic reconciles the two heuristics reducing their respective problematic behaviors. The last heuristic is the terminal guidance heuristic. The mass-spring-damper, bang-bang, new, and terminal guidance heuristics are compared in terms of their performances in the presence of an error in the parameter estimates, an error in the height readings, and an overlooked factor such as a delay in changing the level of the force created by the reverse force thruster, which is known as actuator delay. Terminal guidance heuristic and new heuristic lie in between mass-spring-damper heuristic and bang-bang heuristic in the sense that they require a more reasonable time to land as compared to the mass-spring-damper heuristic and they are not as sensitive as the bang-bang heuristic to the deviations from the original model. Finally, constant mass assumption is relaxed to observe a potential change in the behaviors generated by the heuristics, including the deviations due to errors and actuator delay. This relaxation also enables a comparison for the fuel consumption values of the heuristics.
