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Full-Text Articles in Engineering
Optimal And Robust Neural Network Controllers For Proximal Spacecraft Maneuvers, B. Cole George
Optimal And Robust Neural Network Controllers For Proximal Spacecraft Maneuvers, B. Cole George
Theses and Dissertations
Recent successes in machine learning research, buoyed by advances in computational power, have revitalized interest in neural networks and demonstrated their potential in solving complex controls problems. In this research, the reinforcement learning framework is combined with traditional direct shooting methods to generate optimal proximal spacecraft maneuvers. Open-loop and closed-loop feedback controllers, parameterized by multi-layer feed-forward artificial neural networks, are developed with evolutionary and gradient-based optimization algorithms. Utilizing Clohessy- Wiltshire relative motion dynamics, terminally constrained fixed-time, fuel-optimal trajectories are solved for intercept, rendezvous, and natural motion circumnavigation transfer maneuvers using three different thrust models: impulsive, finite, and continuous. In addition …
Comprehensive Study Of Study Of Optimal Synergetic Skip Entries With Dynamic Thrust Vectoring Control, Jeremiah M. Webb
Comprehensive Study Of Study Of Optimal Synergetic Skip Entries With Dynamic Thrust Vectoring Control, Jeremiah M. Webb
Theses and Dissertations
The atmospheric skip entry has been studied since London's presentation in 1962 describing a more fuel efficient means of altering the orbital inclination of satellites. Since London, research over the decades since has traversed many aspects of this field with varying degrees of success. The present research employs the use of modern optimal control software, complex dynamics with minor simplifications, and thrust vectoring to re-approach the aerocruise atmospheric skip entry. Using the aerodynamics of the X-34, the aerocruise problem is first compared to the un-powered aeroglide where it is shown that the aerocruise is capable of increasing the inclination change …