Hermite matrix in Lagrange basis for scaling static output feedback polynomial matrix inequalities

Details Hermite matrix in Lagrange basis for scaling static output feedback polynomial matrix inequalities Hermite matrix in Lagrange basis for scaling static output feedback polynomial matrix inequalities

2010-01-20

arxiv.org/abs/1001.3582v1

Mathematics

Optimization and Control

Scientific paper

Using Hermite's formulation of polynomial stability conditions, static output feedback (SOF) controller design can be formulated as a polynomial matrix inequality (PMI), a (generally nonconvex) nonlinear semidefinite programming problem that can be solved (locally) with PENNON, an implementation of a penalty method. Typically, Hermite SOF PMI problems are badly scaled and experiments reveal that this has a negative impact on the overall performance of the solver. In this note we recall the algebraic interpretation of Hermite's quadratic form as a particular Bezoutian and we use results on polynomial interpolation to express the Hermite PMI in a Lagrange polynomial basis, as an alternative to the conventional power basis. Numerical experiments on benchmark problem instances show the substantial improvement brought by the approach, in terms of problem scaling, number of iterations and convergence behavior of PENNON.

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