Data Driven Control Eigensystem Realization

In this lecture, we introduce the

In this lecture, we describe the

Overview lecture on linear system identification and model reduction. This lecture discusses how we obtain reduced-order models ...

In this lecture, we explore the observer Kalman filter identification (OKID) and

The slides may be obtained at: http://

This lecture discusses the eigenvalue

Overview lecture for series on

In this lecture, we connect the

In this lecture, we discuss the overarching goal of balanced model reduction: Identifying key states that are most jointly ...

In this lecture, we introduce the balancing proper orthogonal decomposition (BPOD) to approximate balanced truncation for ...

In this lecture, we explore balanced truncation and BPOD on a numerical example in Matlab.

In this lecture, we introduce the observer Kalman filter identification (OKID) algorithm. OKID takes natural input--output

This lecture discusses degrees of controllability using the controllability Gramian and the singular value decomposition of the ...

In this lecture, we introduce the output projection for balancing proper orthogonal decomposition (BPOD), to reduce the number of ...

In this lecture, we describe how the discrete-time impulse response is used in the