Then, a robust controller is designed using the linear matrix inequalities (LMI) approach.
The corresponding parameters of the controller are obtained by using the linear matrix inequalities (LMIs) optimization.
The estimation of the state-matching error is presented using the linear matrix inequality (LMI) techniques.
The subsequent stated controller matrices are found by using the Linear Matrix Inequality approach.
The controllers are derived by using the linear matrix inequality approach.
The controller's parameters are obtained using the linear matrix inequalities (LMI) optimization.
The adopted framework is based on the Lyapunov theory and uses the Linear Matrix Inequalities (LMI) formalism.
Besides, many authors used the linear matrix inequality (LMI) and the Lyapunov-Krasovskii functional to study the dynamic behaviors of various kinds of neural networks and obtained many interesting new results [6, 16, 21, 25].
Using the linear MMSE weighting matrix G k, the MSE and SINR expressions can be rewritten, respectively as MSE k, s ≜ E k ( s, s ) = I + T k † R H k T k † − 1 ( s, s ), (5) γ k, s ≜ 1 MSE k, s − 1. (6).
Generally speaking, the time delay considered can be categorized as constant delay, time-varying delay, and distributed delay, and the methods used include the linear matrix inequality (LMI) approach, Lyapunov functional method, -matrix theory, topological degree theory, and techniques of inequality analysis.
Similar to wave propagation in homogeneous materials in the 1-D case, we have obtained very accurate results for composite and functionally graded materials using the linear elements with lumped mass matrix and the explicit central difference method.
