LMI-based techniques for solving quadratic distance problems

Abstract

The computation of the minimum distance from a point to a surface in a finite dimensional space is a key issue in several system analysis and control problems. This paper presents a general framework in which some classes of minimum distance problems are tackled via LMI techniques. Exploiting a suitable representation of homogeneous forms, a lower bound to the solution of a canonical quadratic distance problem is obtained by solving a one-parameter family of LMI optimization problems. Several properties of the proposed technique are discussed. In particular, tightness of the lower bound is investigated, providing both a simple algorithmic procedure for a posteriori optimality testing and a structural condition on the related homogeneous form that ensures optimality a priori. Extensive numerical simulations are reported showing promising performances of the proposed method.