Part of a Book
Refine
Year of publication
Document Type
- Part of a Book (43) (remove)
Keywords
- Wind Tunnel (3)
- Flight Test (2)
- Pitching Moment (2)
- Wave Drag (2)
- Aerodynamic Drag (1)
- Carsharing (1)
- Certification Rule (1)
- Drag Reduction (1)
- Electrical vehicle (1)
- Engine Efficiency (1)
- Epistemische Neugier (1)
- Evacuation Rule (1)
- Friction Drag (1)
- Ice melting probe (1)
- Ice penetration (1)
- Icy moons (1)
- Leading Edge Vortex (1)
- Mach Number (1)
- Mars (1)
- Multidisciplinary Design Optimization (1)
Institute
- Fachbereich Luft- und Raumfahrttechnik (43) (remove)
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.