Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Explore data-driven control methods for continuous-time linear time-invariant systems in this 59-minute research seminar presented by Alessandro Bosso from the University of Bologna. Discover how the control community has shifted from traditional model-based approaches to data-driven methods, influenced by developments in system identification, adaptive control, and reinforcement learning. Learn about the modern trend of computing controllers directly from data using linear matrix inequalities (LMIs) without requiring explicit system identification. Examine the specific problem of designing controllers for asymptotic reference tracking and disturbance rejection from a single experiment, known as data-driven output regulation. Understand how this LMI-based approach applies to multivariable continuous-time linear time-invariant systems, utilizing tools that connect with modern nonlinear estimation methods and reinterpret classical adaptive observers in a state-space setting. Gain insights into the theoretical foundations and practical applications of this approach, and engage with the discussion of open research questions that remain in this evolving field of control theory.
