TY  - CHAP
A1  - Borggrafe, Andreas
A1  - Ohndorf, Andreas
A1  - Dachwald, Bernd
A1  - Seboldt, Wolfgang
T1  - Analysis of interplanetary solar sail trajectories with attitude dynamics
T2  - Dynamics and Control of Space Systems 2012
N2  - We present a new approach to the problem of optimal control of solar sails for low-thrust trajectory optimization. The objective was to find the required control torque magnitudes in order to steer a solar sail in interplanetary space. A new steering strategy, controlling the solar sail with generic torques applied about the spacecraft body axes, is integrated into the existing low-thrust trajectory optimization software InTrance. This software combines artificial neural networks and evolutionary algorithms to find steering strategies close to the global optimum without an initial guess. Furthermore, we implement a three rotational degree-of-freedom rigid-body attitude dynamics model to represent the solar sail in space. Two interplanetary transfers to Mars and Neptune are chosen to represent typical future solar sail mission scenarios. The results found with the new steering strategy are compared to the existing reference trajectories without attitude dynamics. The resulting control torques required to accomplish the missions are investigated, as they pose the primary requirements to a real on-board attitude control system.
Y1  - 2012
SN  - 978-0-87703-587-9
SP  - 1553
EP  - 1569
PB  - Univelt Inc
CY  - San Diego
ER  - 
TY  - CHAP
A1  - Dachwald, Bernd
A1  - Ohndorf, Andreas
T1  - Global optimization of continuous-thrust trajectories using evolutionary neurocontrol
T2  - Modeling and Optimization in Space Engineering
N2  - Searching optimal continuous-thrust trajectories is usually a difficult and time-consuming task. The solution quality of traditional optimal-control methods depends strongly on an adequate initial guess because the solution is typically close to the initial guess, which may be far from the (unknown) global optimum. Evolutionary neurocontrol attacks continuous-thrust optimization problems from the perspective of artificial intelligence and machine learning, combining artificial neural networks and evolutionary algorithms. This chapter describes the method and shows some example results for single- and multi-phase continuous-thrust trajectory optimization problems to assess its performance. Evolutionary neurocontrol can explore the trajectory search space more exhaustively than a human expert can do with traditional optimal-control methods. Especially for difficult problems, it usually finds solutions that are closer to the global optimum. Another fundamental advantage is that continuous-thrust trajectories can be optimized without an initial guess and without expert supervision.
Y1  - 2019
SN  - 978-3-030-10501-3
SN  - 978-3-030-10500-6
U6  - http://dx.doi.org/10.1007/978-3-030-10501-3_2
N1  - Springer Optimization and Its Applications, vol 144
gedruckt unter der Signatur 21 ZSS 46 in der Bereichsbibliothek Eupener Str. vorhanden
SP  - 33
EP  - 57
PB  - Springer
CY  - Cham
ER  -