Published on:

28 July 2023

Primary Category:

Optimization and Control

Paper Authors:

Jhon Manuel Portella Delgado,

Ankit Goel

•

Presents method to compute invariant zeros from state-space matrices

•

Transforms system into 'zero-subspace' form where zeros emerge

•

Shows zeros are eigenvalues of a partition of the transformed dynamics matrix

•

Allows computing zeros by solving a standard eigenvalue problem

•

Applies to square MIMO systems and is robust to minimality

Computing Zeros of Linear Systems from State Space Matrices

This paper presents a technique to compute the invariant zeros of a linear system from its state-space realization. By transforming the system into a 'zero-subspace' form, the zeros are shown to be the eigenvalues of a partition of the transformed dynamics matrix. This allows computing zeros via an eigenvalue problem rather than a generalized eigenvalue problem. The result holds for square MIMO systems, is unaffected by lack of controllability/observability, and connects to the zero dynamics of the system.

Predictable integration of constrained dynamic systems

Simplifying nonlinear dynamics with eigenfunctions

Locating zeros of quaternion polynomials with matrices

Asymptotic behavior of eigenvalues for a cooperative quasilinear system

Data-driven stabilization of nonlinear systems

A clear and approachable overview of quantum graph nodal patterns

No comments yet, be the first to start the conversation...

Sign up to comment on this paper