In [7, 8], Thong, et al. introduced a fast and efficient way to construct a measurement matrix, called the structurally random matrix (SRM), which attempted to improve the structure of an initial random measurement matrix using optimization techniques.
Based on this approach, the optimal learning parameters can be found by utilizing the linear matrix inequality (LMI) optimization techniques to achieve a predefined H∞ "noise" attenuation level.
The design method involves linear matrix inequality (LMI) optimization techniques and the generalized structured singular value üg.
First, a novel method for designing insensitive H∞ filters subjected to additive filter coefficient variations is given in terms of the linear matrix inequality (LMI) optimization techniques.
The finite difference method in space and the existing linear matrix inequality (LMI) optimization techniques are employed to approximately solve the suboptimal fuzzy control design problem.
Furthermore, in order to compute the gain matrices of SOF controllers, a two-step procedure is presented to solve the BMI feasibility problem via the existing linear matrix inequality (LMI) optimization techniques.
Furthermore, using the finite difference method and the standard linear matrix inequality (LMI) optimization techniques, recursive LMI algorithms for solving the SDLMIs in the analysis and synthesis are provided.
In fact, there are a lot of existing works addressing this problem, and various methods have been proposed, e.g., Riccati equation approach, rank-constrained condition, approach based on structural properties, bilinear matrix inequality (BMI) approaches, min-max optimization techniques, and linear matrix inequality approaches [28, 29].
By using linear matrix inequalities and convex optimization techniques, the controller design problem can be solved efficiently.
The selection of design parameters is achieved using the linear matrix inequality (LMI) optimization technique.
