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This paper utilizes the linear matrix inequalities' techniques (LMI) for designing a robust collective pitch controller (CPC) for large wind turbines.
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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.
Sufficient conditions for the solvability of the two problems are obtained by utilizing a Lyapunov functional together with the linear matrix inequality (LMI) approach.
For instance, in [1], by utilizing the Lyapunov-Krasovkii functional and combining with the linear matrix inequalities (LMIs) approach, the authors analyzed the global exponential stability of neutral-type impulsive NNs.
The BMI-based condition is converted into the linear matrix inequality (LMI -based condition by utiLMI -basednge of variables for straightforward conditionon of the obyerver gain matrix.
After that, we utilize the linear discriminant analysis [14] to learn the transformation matrix too.
Subsequently, we vectorize the six magnitude subimages into a large vector which dimension is 384 and utilize the linear discriminant analysis (LDA) [14] to learn the subspace of the training faces and then record the transformation matrix.
Therefore, we utilized the linear regression function for UDP-Glc.
The method utilizes linear matrix inequalities (LMI) to produce control structures that are suitable for a multiprocessor environment.
Using Lyapunov functional and free-weighting matrix method, a delay-dependent stability criterion is obtained and formulated in the form of linear matrix inequalities, which can easily be checked by utilizing Matlab linear matrix inequality toolbox.
Utilizing the Lyapunov method and linear matrix inequality technology, new bounded input bounded output stability criteria are derived.
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