interval linear systems
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Author(s):  
Ирина Александровна Шарая ◽  
Сергей Петрович Шарый

В работе рассматриваются интервальные линейные включения Cx ⊆ d в полной интервальной арифметике Каухера. Вводится количественная мера выполнения этого включения, названная “резервом включения”, исследуются ее свойства и приложения. Показано, что резерв включения оказывается полезным инструментом при изучении АЕ-решений и кванторных решений интервальных линейных систем уравнений и неравенств. В частности, использование резерва включения помогает при определении положения точки относительно множества решений, при исследовании пустоты множества решений или его внутренности и т.п In this paper, we consider interval linear inclusions Cx ⊆ d in the Kaucher complete interval arithmetic. These inclusions are important both on their own and because they provide equivalent and useful descriptions for the so-called quantifier solutions and AE-solutions to interval systems of linear algebraic relations of the form Ax σ b , where A is an interval m × n -matrix, x ∈ R , b is an interval m -vector, and σ ∈ {= , ≤ , ≥} . In other words, these are interval systems in which equations and non-strict inequalities can be mixed. Considering the inclusion Cx ⊆ d in the Kaucher complete interval arithmetic allows studing simultaneously and in a uniform way all the different special cases of quantifier solutions and AE-solutions of interval systems of linear relations, as well as using interval analysis methods. A quantitative measure, called the “inclusion reserve”, is introduced to characterize how strong the inclusion Cx ⊆ d is fulfilled. In our work, we investigate its properties and applications. It is shown that the inclusion reserve turns out to be a useful tool in the study of AE-solutions and quantifier solutions of interval linear systems of equations and inequalities. In particular, the use of the inclusion reserve helps to determine the position of a point relative to a solution set, in investigating whether the solution set is empty or not, whether a point is in the interior of the solution set, etc


2020 ◽  
Author(s):  
Rafael M. Alves ◽  
André R. Fioravanti ◽  
Matheus Souza

In this paper, we address the H∞ control problem for uncertain sampled-data systems rewritten as hybrid systems. The conditions proposed are formulated as intervals to ensure stability and design controllers that guarantee an upper bound for an associated H∞ norm. A numerical example points out the main features of the proposed method.


Author(s):  
Kirti D. Mishra ◽  
K. Srinivasan

Abstract Many forms of system uncertainty result in interval description of linear systems, and numerically efficient design methods for the computation of robust iterative learning controllers with good learning transients for these systems are lacking. Using a Lyapunov framework, two design procedures that ensure robust convergence of the tracking error to zero with good learning transients are described in this study. Both methods are validated numerically for an application of position control, and robust and monotonic convergence of the tracking error to zero is demonstrated.


10.29007/ptx4 ◽  
2018 ◽  
Author(s):  
Jaroslav Horáček ◽  
Jan Horáček ◽  
Milan Hladík

In this paper we deal with detection of unsolvability of interval linear systems. Various methods based on existing algorithms or on existing sufficient conditions are developed. The methods are tested on a large variety of random systems and the results are visualized. The two strongest sufficient conditions are proved to be equivalent under a certain assumption. The topic of detecting solvability is also touched upon.


10.29007/1vkp ◽  
2018 ◽  
Author(s):  
Iwona Skalna ◽  
Milan Hladík

Solving systems of parametric linear equations with parameters varying within closed intervals is a hard computational problem. However, we may reduce the problem dimension and thus make the problem more tractable by utilizing the monotonicity of the solution components with respect to the parameters. In this paper, we propose two improvements of the standard monotonicity checking techniques. The first improvement relies on creating a system with original variables and their derivatives as unknowns, and the second one employs the so-called p-solution. By a series of numerical experiments we show that the improved monotonicity approach outperforms the standard one.


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