System Identification for Minimization of Model Mismatch in Optimal Control

Institut

Lehrstuhl für Raumfahrtantriebe

Typ

Bachelorarbeit /

Inhalt

theoretisch /  

Beschreibung

The space sector is currently gaining more and more interest due to the commercialization of Space (OneWeb, SpaceX Starlink) and upcoming human missions, such as the return to the lunar surface and the goal to land humans on Mars. The remoteness of these missions and their high complexity demand high autonomy in the spacecraft's control systems.

 

Spacecrafts, especially the propulsion systems, are complex machines made up of several subsystems.

Accurate control is essential for mission success, particularly for landing manoeuvres. The current traditional industry standard uses a hierarchy of conventional control algorithms, mainly due to their predictable behaviour and the ability to prove stability. For landing manoeuvres, optimal control algorithms have shown promising results by making use of knowledge of system dynamics and by being able to enforce boundary conditions. One challenge of optimal control is the reliance on an accurate model of the system dynamics, which can be complex to derive or unknown.

This ties into the ASCENT project at the chair of space mobility and propulsion, where a vertical take-off vertical landing rocket hopper is in development as a research platform for advanced control algorithms, including optimal control.

 

The goal of this thesis is to increase the robustness of optimal control algorithms by reducing model-reality mismatch. This shall be done by implementing a basic system model of a subscale hopper for data generation, and then comparing, implementing, and testing system identification methods to bridge model mismatch for the use with optimal control algorithms.

Möglicher Beginn

sofort

Kontakt

Jan Kayser, M.Sc.
jan.kaysertum.de

Ausschreibung