Design of robust state feedback controllers via eigenvalue/eigenvector assignment

Abstract

A new method of selecting a multivariable state feedback controller is presented. The resulting controller simultaneously realizes arbitrary closed-loop eigenvalues, approximates specified modal behavior and achieves low eigensystem sensitivity with respect to plant parameter variations. The method characterizes a vector space slightly larger than the null space of the closed-loop system matrix and projects a desired modal matrix onto this space. Sensitivity of eigenvalues and eigenvectors is then minimized local to the desired modal matrix using a gradient search technique. A tutorial example to illustrate the design procedure is given.