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The filter matrices can be solved directly by using the existing LMIs optimization techniques.
The non-fragile passive controller and observer parameters can be solved directly by using the existing LMIs optimization techniques.
The controller and observer gains can be solved directly by using the existing LMIs optimization techniques.
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The control and observer matrices can be solved directly by using the existing LMI optimization techniques.
Moreover, by using the existing LMI optimization techniques, a suboptimal fuzzy observer-based controller in the sense of minimizing the cost bound is proposed.
Furthermore, using the existing LMI optimization technique, a suboptimal robust adaptive neural modal observer can be obtained in the sense of minimizing an upper bound of the peak gains in the ultimate bound.
Furthermore, by utilizing the existing LMI optimization technique, a suboptimal controller is obtained in the sense of minimizing an upper bound of H2 performance.
Hence, the design approach discussed in this paper is more general than that based on the existing LMI approaches to discrete T-S fuzzy control system designs.
"We're using the companies' existing protocols.
The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB.
By using the Matlab LMI toolbox [31], we solve the LMIs in Theorem 3.1 and obtain (4.2).
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