Sentence examples for matrix inequality feasibility from inspiring English sources

Enforcing well-posedness is reduced to a linear matrix inequality feasibility problem that can be solved during the anti-windup design stage.

By encapsulating the uncertain nonlinear system with a piecewise affine difference inclusion, we are able to state the synthesis problem as a linear matrix inequality feasibility problem with a spectral radius constraint on the product of two positive definite matrices.

For that aim, a robustness analysis condition is given in terms of a linear matrix inequality feasibility problem, producing less conservative results than recent parameter dependent Lyapunov based methods (which encompasses quadratic stability) for polytopic uncertainties in linear systems.

The design procedure combines nonlinear internal model control with linear matrix inequality feasibility or optimization problems, such that all robust stability and performance criteria are computable in polynomial-time using readily available software.

Sufficient conditions for stability and stabilization are given in polynomial matrix inequalities (PMIs), and these conditions can be verified by sum of squares technique which solves linear matrix inequality feasibility problem in essence.

The stability criteria is eventually formulated as a linear matrix inequality (LMI) feasibility problem.

The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem.

Sufficient conditions for the synthesis of the feedback action are provided in terms of linear matrix inequality (LMI) feasibility problems.

The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions.

Unlike previous approaches for SFDC problem, the observer parameters and controller gain are all obtained via Linear Matrix Inequality (LMI) feasibility conditions.

The design of the observer matrix gain is achieved by solving a Linear Matrix Inequality (LMI) feasibility problem, where constraints on the position in the complex plane of the poles of the estimation error dynamics are taken into account.