Numerical Optimal Control

Advanced topics and numerical methods for optimal control problems form the core of the curriculum. This includes ways to formulate optimal control problems, numerical methods and software to solve them, and analysis of their performance and properties from a control and numerical point of view.

Knowledge - Thorough understanding of the methods and theoretical foundation for direct methods for formulation of nonlinear optimal control and model predictive control as numerical optimization problems, in particular related to stability, robustness, feasibility and numerical properties. - Basic understanding of indirect methods for optimal control, such as Pontryagin´s maximum principle and the theory of dynamic programming. - Basic understanding of numerical methods for optimization, in particular quadratic programming and nonlinear programming. - Basic understanding of numerical linear algebra theory for solving optimization problems Skills - Ability to formulate well-conditioned optimization problems in standard forms - Proficiency in use of numerical software for convex and nonlinear programming - Knowledge of mathematical modeling tools for numerical optimal control - Ability to select linear algebraic methods in order to exploit structural properties such as sparsity and band structure. Transferable skills - Presentation of advanced scientific topics, and scientific discussion (colloquia) - Scientific writing skills (project report)

The course is based on a combination of lectures, colloquia where students present topics, and a project.