Online Course Catalog

Stochastic Control for Motion and Mission Planning Stochastic control processes are often used to describe system behavior in complex, unexplored, or changing environments – the future playing fields of autonomous systems. However, performing even the simplest classical control tasks in such a framework requires the development of new theories of dynamical systems, optimal control, reachability, and adaptation. This course will serve as a gentle first introduction to that endeavor. Topics covered will include: basics of formal probability and measure theory, Markov chains, Markov decision processes, dynamic programming, optimal policies, partial observability and beliefs, robust control for uncertain processes, exploration-exploitation tradeoff and value of information, multi-agent planning, and automata and product spaces.

4 hours

BS degree in engineering or science from an accredited college in the United States or an approved institution of higher learning abroad and familiarity with graph theory, probability, and random variables

• Aerospace Engineering