@article{Dachwald2004, author = {Dachwald, Bernd}, title = {Optimization of Interplanetary Solar Sailcraft Trajectories Using Evolutionary Neurocontrol}, series = {Journal of guidance, control, and dynamics. 27 (2004), H. 1}, journal = {Journal of guidance, control, and dynamics. 27 (2004), H. 1}, isbn = {0162-3192}, pages = {66 -- 72}, year = {2004}, language = {en} } @article{Dachwald2004, author = {Dachwald, Bernd}, title = {Interplanetary Mission Analysis for Non-Perfectly Reflecting Solar Sailcraft Using Evolutionary Neurocontrol}, series = {Astrodynamics 2003 : proceedings of the AAS/AIAA Astrodynamics Conference held August 3 - 7, 2003, Big Sky, Montana / ed. by Jean de Lafontaine. - Pt. 2. - (Advances in the astronautical sciences ; 116,2)}, journal = {Astrodynamics 2003 : proceedings of the AAS/AIAA Astrodynamics Conference held August 3 - 7, 2003, Big Sky, Montana / ed. by Jean de Lafontaine. - Pt. 2. - (Advances in the astronautical sciences ; 116,2)}, publisher = {Univelt}, address = {San Diego, Calif.}, isbn = {0-87703-509-1}, pages = {1247 -- 1262}, year = {2004}, language = {en} } @article{Dachwald2004, author = {Dachwald, Bernd}, title = {Low-Thrust Trajectory Optimization and Interplanetary Mission Analysis Using Evolutionary Neurocontrol}, series = {Deutscher Luft- und Raumfahrtkongress 2004 : Dresden, 20. bis 23. September 2004, Motto: Luft- und Raumfahrt - Br{\"u}cke f{\"u}r eine wissensbasierte Gesellschaft / Deutsche Gesellschaft f{\"u}r Luft- und Raumfahrt - Lilienthal-Oberth e.V. (DGLR). [Red.: Peter Brandt (verantwortlich)]. - Bd. 2. - (Jahrbuch ... der Deutschen Gesellschaft f{\"u}r Luft- und Raumfahrt)}, journal = {Deutscher Luft- und Raumfahrtkongress 2004 : Dresden, 20. bis 23. September 2004, Motto: Luft- und Raumfahrt - Br{\"u}cke f{\"u}r eine wissensbasierte Gesellschaft / Deutsche Gesellschaft f{\"u}r Luft- und Raumfahrt - Lilienthal-Oberth e.V. (DGLR). [Red.: Peter Brandt (verantwortlich)]. - Bd. 2. - (Jahrbuch ... der Deutschen Gesellschaft f{\"u}r Luft- und Raumfahrt)}, address = {Bonn}, pages = {917 -- 926}, year = {2004}, language = {en} } @article{Dachwald2004, author = {Dachwald, Bernd}, title = {Evolutionary Neurocontrol: A Smart Method for Global Optimization of Low-Thrust Trajectories}, series = {22nd AIAA Applied Aerodynamics Conference and Exhibit - AIAA/AAS Astrodynamics Specialist Conference and Exhibit - AIAA Guidance, Navigation, and Control Conference and Exhibit - AIAA Modeling and Simulation Technologies Conference and Exhibit - AIAA Atmospheric Flight Mechanics Conference and Exhibit : 16 - 19 August 2004, Providence, Rhode Island / American Institute of Aeronautics and Astronautics. - (AIAA meeting papers on disc ; 2004,14-15)}, journal = {22nd AIAA Applied Aerodynamics Conference and Exhibit - AIAA/AAS Astrodynamics Specialist Conference and Exhibit - AIAA Guidance, Navigation, and Control Conference and Exhibit - AIAA Modeling and Simulation Technologies Conference and Exhibit - AIAA Atmospheric Flight Mechanics Conference and Exhibit : 16 - 19 August 2004, Providence, Rhode Island / American Institute of Aeronautics and Astronautics. - (AIAA meeting papers on disc ; 2004,14-15)}, publisher = {American Inst. of Aeronautics and Astronautics}, address = {Reston, Va.}, pages = {2 CD-ROMs}, year = {2004}, language = {en} } @article{Dachwald2004, author = {Dachwald, Bernd}, title = {Optimal Solar Sail Trajectories for Missions to the Outer Solar System}, series = {22nd AIAA Applied Aerodynamics Conference and Exhibit - AIAA/AAS Astrodynamics Specialist Conference and Exhibit - AIAA Guidance, Navigation, and Control Conference and Exhibit - AIAA Modeling and Simulation Technologies Conference and Exhibit - AIAA Atmospheric Flight Mechanics Conference and Exhibit : 16 - 19 August 2004, Providence, Rhode Island / American Institute of Aeronautics and Astronautics. - (AIAA meeting papers on disc ; 2004,14-15)}, journal = {22nd AIAA Applied Aerodynamics Conference and Exhibit - AIAA/AAS Astrodynamics Specialist Conference and Exhibit - AIAA Guidance, Navigation, and Control Conference and Exhibit - AIAA Modeling and Simulation Technologies Conference and Exhibit - AIAA Atmospheric Flight Mechanics Conference and Exhibit : 16 - 19 August 2004, Providence, Rhode Island / American Institute of Aeronautics and Astronautics. - (AIAA meeting papers on disc ; 2004,14-15)}, publisher = {American Inst. of Aeronautics and Astronautics}, address = {Reston, Va.}, pages = {2 CD-ROMs}, year = {2004}, language = {en} } @inproceedings{Dachwald2007, author = {Dachwald, Bernd}, title = {Low-Thrust Mission Analysis and Global Trajectory Optimization Using Evolutionary Neurocontrol: New Results}, series = {European Workshop on Space Mission Analysis ESA/ESOC, Darmstadt, Germany 10 { 12 Dec 2007}, booktitle = {European Workshop on Space Mission Analysis ESA/ESOC, Darmstadt, Germany 10 { 12 Dec 2007}, year = {2007}, abstract = {Interplanetary trajectories for low-thrust spacecraft are often characterized by multiple revolutions around the sun. Unfortunately, the convergence of traditional trajectory optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess for the control function (if a direct method is used) or for the starting values of the adjoint vector (if an indirect method is used). Especially when many revolutions around the sun are re- quired, trajectory optimization becomes a very difficult and time-consuming task that involves a lot of experience and expert knowledge in astrodynamics and optimal control theory, because an adequate initial guess is extremely hard to find. Evolutionary neurocontrol (ENC) was proposed as a smart method for low-thrust trajectory optimization that fuses artificial neural networks and evolutionary algorithms to so-called evolutionary neurocontrollers (ENCs) [1]. Inspired by natural archetypes, ENC attacks the trajectoryoptimization problem from the perspective of artificial intelligence and machine learning, a perspective that is quite different from that of optimal control theory. Within the context of ENC, a trajectory is regarded as the result of a spacecraft steering strategy that maps permanently the actual spacecraft state and the actual target state onto the actual spacecraft control vector. This way, the problem of searching the optimal spacecraft trajectory is equivalent to the problem of searching (or "learning") the optimal spacecraft steering strategy. An artificial neural network is used to implement such a spacecraft steering strategy. It can be regarded as a parameterized function (the network function) that is defined by the internal network parameters. Therefore, each distinct set of network parameters defines a different network function and thus a different steering strategy. The problem of searching the optimal steering strategy is now equivalent to the problem of searching the optimal set of network parameters. Evolutionary algorithms that work on a population of (artificial) chromosomes are used to find the optimal network parameters, because the parameters can be easily mapped onto a chromosome. The trajectory optimization problem is solved when the optimal chromosome is found. A comparison of solar sail trajectories that have been published by others [2, 3, 4, 5] with ENC-trajectories has shown that ENCs can be successfully applied for near-globally optimal spacecraft control [1, 6] and that they are able to find trajectories that are closer to the (unknown) global optimum, because they explore the trajectory search space more exhaustively than a human expert can do. The obtained trajectories are fairly accurate with respect to the terminal constraint. If a more accurate trajectory is required, the ENC-solution can be used as an initial guess for a local trajectory optimization method. Using ENC, low-thrust trajectories can be optimized without an initial guess and without expert attendance. Here, new results for nuclear electric spacecraft and for solar sail spacecraft are presented and it will be shown that ENCs find very good trajectories even for very difficult problems. Trajectory optimization results are presented for 1. NASA's Solar Polar Imager Mission, a mission to attain a highly inclined close solar orbit with a solar sail [7] 2. a mission to de ect asteroid Apophis with a solar sail from a retrograde orbit with a very-high velocity impact [8, 9] 3. JPL's \2nd Global Trajectory Optimization Competition", a grand tour to visit four asteroids from different classes with a NEP spacecraft}, language = {en} } @inproceedings{Dachwald2005, author = {Dachwald, Bernd}, title = {Global optimization of low-thrust space missions using evolutionary neurocontrol}, series = {Proceedings of the international workshop on global optimization}, booktitle = {Proceedings of the international workshop on global optimization}, pages = {85 -- 90}, year = {2005}, abstract = {Low-thrust space propulsion systems enable flexible high-energy deep space missions, but the design and optimization of the interplanetary transfer trajectory is usually difficult. It involves much experience and expert knowledge because the convergence behavior of traditional local trajectory optimization methods depends strongly on an adequate initial guess. Within this extended abstract, evolutionary neurocontrol, a method that fuses artificial neural networks and evolutionary algorithms, is proposed as a smart global method for low-thrust trajectory optimization. It does not require an initial guess. The implementation of evolutionary neurocontrol is detailed and its performance is shown for an exemplary mission.}, language = {en} } @incollection{Dachwald2017, author = {Dachwald, Bernd}, title = {Light propulsion systems for spacecraft}, series = {Optical nano and micro actuator technology}, booktitle = {Optical nano and micro actuator technology}, editor = {Knopf, George K. and Otani, Yukitoshi}, publisher = {CRC Press}, address = {Boca Raton}, isbn = {9781315217628 (eBook)}, pages = {577 -- 598}, year = {2017}, language = {en} } @incollection{Dachwald2010, author = {Dachwald, Bernd}, title = {Solar sail dynamics and control}, series = {Encyclopedia of Aerospace Engineering}, booktitle = {Encyclopedia of Aerospace Engineering}, publisher = {Wiley}, address = {Hoboken}, doi = {10.1002/9780470686652.eae292}, year = {2010}, abstract = {Solar sails are large and lightweight reflective structures that are propelled by solar radiation pressure. This chapter covers their orbital and attitude dynamics and control. First, the advantages and limitations of solar sails are discussed and their history and development status is outlined. Because the dynamics of solar sails is governed by the (thermo-)optical properties of the sail film, the basic solar radiation pressure force models have to be described and compared before parameters to measure solar sail performance can be defined. The next part covers the orbital dynamics of solar sails for heliocentric motion, planetocentric motion, and motion at Lagrangian equilibrium points. Afterwards, some advanced solar radiation pressure force models are described, which allow to quantify the thrust force on solar sails of arbitrary shape, the effects of temperature, of light incidence angle, of surface roughness, and the effects of optical degradation of the sail film in the space environment. The orbital motion of a solar sail is strongly coupled to its rotational motion, so that the attitude control of these soft and flexible structures is very challenging, especially for planetocentric orbits that require fast attitude maneuvers. Finally, some potential attitude control methods are sketched and selection criteria are given.}, language = {en} } @article{Dachwald2005, author = {Dachwald, Bernd}, title = {Optimization of very-low-thrust trajectories using evolutionary neurocontrol}, series = {Acta Astronautica}, volume = {57}, journal = {Acta Astronautica}, number = {2-8}, publisher = {Elsevier}, address = {Amsterdam [u.a.]}, isbn = {1879-2030}, pages = {175 -- 185}, year = {2005}, abstract = {Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult and time-consuming task that involves much experience and expert knowledge in astrodynamics and optimal control theory. This is because the convergence behavior of traditional local optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find, especially for very-low-thrust trajectories that necessitate many revolutions around the sun. The obtained solutions are typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. The performance of the method is assessed for three different interplanetary missions with a thrust to mass ratio <0.15mN/kg (solar sail and nuclear electric).}, language = {en} }