MAE 5830

Astronautic Optimization

Course Description

Course information provided by the Courses of Study 2022-2023. Courses of Study 2022-2023 is scheduled to publish mid-June.

This course provides a brief review of several topics in sufficient detail to amplify student success: estimation, allocation, and control; classical feedback; sensor noise; and Monte Carlo analysis. The review leads to application of the methods of Pontryagin applied to examples including single-gimballed rocket engines, guidance, and control problems including least squares estimation, and the famous Brachistochrone problem as a motivating example illustrating the minimum time solution is not necessarily the minimum path-length solution, particularly in a gravity field. After taking this course, students will be able to apply their expertise to actual systems in advanced courses or in laboratory settings leveraging analytic (non-numerical) nonlinear programming and real-time optimal control. Graduates will understand the application of constrained (smooth constrained, box constrained, with brief introduction to inequality constrained) and unconstrained optimization; linear-quadratic programming; and Bellman's principle of optimality.

When Offered Fall, Spring.

Permission Note Enrollment limited to: graduate students.
Prerequisites/Corequisites Prerequisite: undergraduate-level coursework in dynamics, calculus (understanding of extrema), and classical feedback control or system dynamics. Recommended prerequisite: coursework or understanding of spacecraft attitude control or rotational mechanics.

• The student will be able to apply their expertise to actual systems in space in advanced courses or in spacecraft attitude control laboratory settings leveraging nonlinear programming and real time optimal control.
• The student will be able to understand the application of constrained (smooth constrained, box constrained, inequality constrained) and unconstrained optimization.
• The student will be able to understand the application of linear-quadratic programming; and Bellman's principle of optimality; all strictly applied to the problem of spacecraft attitude control.

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