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Then, the desired estimator matrix gain is characterized in terms of the solution to these LMIs.
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When σ = 1.285 × 10 25, by the Matlab LMIs Control Toolbox, a solution to the LMIs (13) in Corollary 3 can be obtained as.
To this end, the control problem has been solved in terms of a solution to the LMIs (8) and (24).
By the Matlab LMI Control Toolbox, we find a solution to the LMIs in (9) and (10), and obtain the gain matrix K as.
Parameterized characterizations for stabilizing the controller are given in terms of the feasibility solutions to the LMIs.
Based on the solutions to the LMIs, an algorithm for the gain matrices of LPV filter is presented.
A parameterized characterization of the controllers is given in terms of the feasible solutions to the LMIs, which can be solved by various convex optimization algorithms.
By constructing an appropriate Lyapunov-Krasovskii functional and a novel integral inequality, which gives a tighter upper bound than Jensen's inequality and Bessel-Legendre inequality, some sufficient conditions are established and desired feedback controllers are designed in terms of the solution to certain LMIs.
Assume that the sliding function is given by (5) with, where P 1 and Y is a feasible solution to LMIs (9) and (10).
For prescribed decay rate δ, we can choose μ 1 = 2 δ + 1 β ln ( μ 2 ) to find the feasible solution to LMIs (3.2) and (3.3) by tuning parameter μ 2. Let us now design a memoryless state feedback controller of the following form: u ( t ) = K x ( t ) (3.13).
Fourth, for the case of state feedback we prove that the H∞ criterion is satisfied if and only if there exists a feasible solution to an LMI.
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