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The synthesis problem involves multiple Lyapunov functions and is formulated as a linear matrix inequality problem.
The synthesis problem is formulated as a linear matrix inequality problem.
A design of the fault-tolerant compensation controller is reformulated as a linear matrix inequality problem.
The consensus problem is reduced to solve the matrix inequality problem under actuator saturation.
Weights are either based on the known measurement noise or given by the solution of a linear matrix inequality problem.
First, it is based on a new reformulation of this control problem into a class of BMI (Bilinear Matrix Inequality) problem.
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Both analysis and design problems are reduced to robust linear matrix inequality problems that can be solved by using a matrix SOS technique.
To overcome the bilinear matrix inequality problems involved in the delay dependent conditions; a cone complementary linearization method is used to find a feasible solution set.
The major part of our synthesis procedure consists of solving generalized eigenvalue problems and/or linear matrix inequality problems, which can be efficiently solved by recently developed interior point methods.
To achieve the prescribed disturbance attenuation performance index, feedback gains of controllers are designed by solving linear matrix inequality problems so that lumped disturbance attenuation with respect to the controlled output is ensured in the L2-gain sense.
The H∞ fuzzy robust control design problem is parameterized in terms of a linear matrix inequality (LMI) problem, and the LMI problem can be solved very efficiently using the convex optimization techniques.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com