Sentence examples for matrix inequality relaxations from inspiring English sources

The synthesis conditions are given in terms of linear matrix inequality relaxations.

Linear matrix inequality relaxations based on polynomially parameter-dependent Lyapunov matrices and slack variables are proposed for the H2 and H∞ filter design.

The condition can be solved by means of linear matrix inequality relaxations with slack variables and Lyapunov matrices which are considered as homogeneous polynomials of arbitrary degree.

This paper presents new linear matrix inequality relaxations for full order parameter-dependent H∞ filter design for linear parameter varying systems with arbitrarily fast parameter variation.

By recognizing the polynomial nature of the phase mismatch, the design task is formulated as a nonconvex multivariate polynomial optimization problem, which is then solved through the latest convex programming techniques based on linear matrix inequality relaxations.

Then, a global optimisation algorithm, based on the theory of moments and linear matrix inequality relaxations suitable for the type of problems (polynomial programs) that appear in the fuzzy simulation algorithm, is used.

The implicit problem being to maximize the region in which the closed-loop stability can be ensured, some convex optimization problems with LMI (linear matrix inequalities) relaxations schemes are stated.

The algorithm used combines regular model predictive control with ideas from applications oriented input design and linear matrix inequality based convex relaxation techniques.

The condition takes the form of a linear matrix inequality.

A strict linear matrix inequality (LMI) design approach is developed.

This design problem becomes a linear matrix inequality (LMI) problem.