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For all admissible uncertainties, time delays, and controller gain variations, the problem is to design a memoryless state feedback control laws such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound of a given cost function.
Let J _ ∗ denote the optimal value of the lower bound of a given cost function in Problem 1.
The difficulty to improve energy level is increased as the predicted energy level approaches to a known lower bound of a given protein.
The design condition realizes guaranteed cost control by minimizing the upper bound of a given performance function.
The objective is to provide a fully distributed estimation&control scheme that stabilizes the plant while the upper bound of a given quadratic performance index is minimized.
The obtained result on stability analysis is then utilized to synthesize a suboptimal state feedback controller that minimizes the upper bound of a given infinite-horizon cost function.
Similar(49)
Guaranteed cost control problem [9 12] has the advantage of providing an upper bound on a given system performance index and thus the system performance degradation incurred by the uncertainties or time delays is guaranteed to be less than this bound.
A guaranteed cost control problem [9 12] has the advantage of providing an upper bound on a given system performance index, and, thus, the system performance degradation, incurred by the uncertainties or time delays, is guaranteed to be less than this bound.
By analogy to (32), it is easy straight forward to derive the double-truncated K-W function for any low threshold value t (t = 1, 2,...) of the number of TFs bound to a given TFBS, and next using the re-normalized K-W function, to estimate parameters of the double-truncated K-W function using the optimization algorithm reported in [ 20].
Including Yap6, Cin5, Skn7, and Phd1 as recruiter proteins strengthens the positive relationship between the number of recruiters bound to a given locus and the level of Tup1 and Ssn6 enrichment observed (Figure 5B) (Dataset S2).
No saturation effects are present and the true signal intensity (disregarding the technical measurement error) is proportional to the amount of RNA bound to a given probe set with a constant (non-random) coefficient of proportionality.
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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