This course covers the synthesis of optimal control laws for linear and nonlinear dynamical systems, with a strong focus on aerospace engineering applications. Topics covered include: necessary conditions for optimal control based on the Pontryagin Minimum Principle will be introduced, and including cases of fixed and free terminal time and boundary conditions; will be discussed. Feedback optimal control will be discussed, and the Hamilton-Jacobi-Bellman equation will be introduced. The special case of linear quadratic optimal control; basic numerical techniques such as pseudospectral optimization; and modern machine learning techniques such as reinforcement learning will be discussed. Examples throughout the course will be based on air- and space vehicle applications, such as flight trajectory optimization. Assignments and term project (if any) will introduce basic numerical techniques and introduce software packages for optimal control. Prerequisites: Fluency with the theory of linear dynamical systems and control(AE 5331 or similar) and with MATLAB programming. Students cannot receive credit for this course if they have taken AE 5222 “Optimal Control”.
AE 5333: Optimal Control for Aerospace Applications
Department