Control Bootcamp: Full-State Estimation

This video describes full-state estimation. An estimator dynamical system is constructed, and it is shown that the estimate converges to the true state. Further, the eigenvalues of the estimator system may be placed arbitrarily for observable systems. These lectures follow Chapters 1 & 3 from: Machine learning control, by Duriez, Brunton, & Noack https://www.amazon.com/Machine-Learning-Control-Turbulence-Applications-ebook/dp/B01MDUPONF/ Chapters available at: http://faculty.washington.edu/sbrunton/mlcbook/ This video was produced at the University of Washington