State-space representation of linear dynamical systems. Eigenvalues of non-symmetric matrices. Left and right eigenvectors, with dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input/multi-output systems, impulse and step matrices. Convolution and transfer-matrix descriptions. Control, reachability, and state transfer. Observability and least-squares state estimation. Positive systems and Perron-Frobenius theory. Response of linear dynamical systems to Gaussian random inputs. The linear-quadratic regulator and the Kalman filter. Applications from a broad range of disciplines including circuits, signal processing, machine learning, and control systems.
Prerequisites: Linear algebra as in EE263.
Andrei Kanavalau
Emi Soroka
Tuesdays and Thursdays, 10:30am - 11:50am
First lecture 3/31, last lecture 6/2
Location: CoDa B60
This class will be recorded, and videos will be available on Canvas.