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Subject offerings of new and developing areas of knowledge in mechanical engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Prerequisites Linear Systems Theory ? Probability and Random Processes ? Introductory Control Theory (feedback design, Lyapunov methods) ? Familiarity with convex optimization and basic machine learning helpful but not required. This course introduces methods for modeling, analysis, and control of uncertain continuous time systems with emphasis on distributional robustness. It begins with stochastic processes and stochastic differential equations, including probability theory, Brownian motion and It?o calculus, and pathwise and distributional representations. Stability of uncertain stochastic systems is studied using Lyapunov methods and generalized robustness concepts defined through metrics on probability measures. The course then covers adaptive control design, starting with deterministic systems and finite-time robustness guarantees, and extending to robust adaptive control in the space of probability measures. Topics include controller design for stochastic systems, compa