Simulation and Application of Gpops for a Trajectory Optimization and Mission Planning Tool - Yaple, Danielle E.;Air Force Institute of Technology (U.S.). Graduate School of Enginee
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Rapid trajectory generation is crucial to prompt global warfare. To meet the USAF's objective of Persistent and Responsive Precision Engagement, a rapid mission planning tool is required. This research creates the framework for the mission planning tool and provides a sample optimal trajectory which is solved using the GPOPS software package. GPOPS employs a Gaussian psuedospectral method to solve the non-linear equations of motion with both end conditions and path constraints. By simultaneously solving the entire trajectory based on an initial guess and small number of nodes, this method is…mehr

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Rapid trajectory generation is crucial to prompt global warfare. To meet the USAF's objective of Persistent and Responsive Precision Engagement, a rapid mission planning tool is required. This research creates the framework for the mission planning tool and provides a sample optimal trajectory which is solved using the GPOPS software package. GPOPS employs a Gaussian psuedospectral method to solve the non-linear equations of motion with both end conditions and path constraints. By simultaneously solving the entire trajectory based on an initial guess and small number of nodes, this method is ideal for generating rapid solutions. The sample case is a multi-phase minimum time optimal control problem which is used to validate the planning tool. The developed framework includes different atmospheric models, gravity models, inclusion of no-flyzones and waypoints and the ability to create a library of sample cases. This versatile tool can be used for either trajectory generation or mission analysis. The results of this research show the complexities in solving an optimal control problem with states that change from one phase of the solution to another. The final resulting trajectory is calculated from a sectioned method, allowing changes in states to be done outside of the optimal control problem. This method should be the foundation for a state varying complete optimal control problem and the mission planning tool.