Reachability and observability of positive discrete-timelinear systems with integer positive and negative powers of the state Frobenius matrices

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS
T. Kaczorek
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引用次数: 2

Abstract

A dynamical system is called positive if its state variables and outputs take nonnegative values for all nonnegative inputs and nonnegative initial conditions. The positive linear and nonlinear continuous-time and discrete-time systems have been addressed in many papers and books [1–23]. Positive descriptor systems have been analyzed in [1–3, 6–11, 15, 17, 22, 23] and positive nonlinear systems in [18, 19]. The minimum energy control of positive systems has been investigated in [9–12, 14] and the stability of positive systems in [4, 14, 21, 23]. The positive systems consisting of n subsystems with different fractional orders have been introduced in [13, 16]. In this paper the reachability and observability of positive discrete-time linear systems with integer positive and negative powers of state monomial generalized Frobenius matrices will be addressed. The paper is organized as follows. In Section 2 the basic definitions and theorems concerning positive linear systems are recalled. The notion of monomial generalized Frobenius matrices has been introduced and the reachability of positive linear systems with these state matrices has been analyzed in Section 3.
状态Frobenius矩阵正、负整数幂正离散时间线性系统的可达性和可观察性
如果一个动态系统的状态变量和输出在所有非负输入和非负初始条件下都取非负值,则该系统称为正系统。许多论文和书籍已经讨论了正线性和非线性连续时间和离散时间系统[1-23]。在[1 - 3,6 - 11,15,17,22,23]和[18,19]中分析了正广义系统和正非线性系统。[9 - 12,14]研究了正系统的最小能量控制,[4,14,21,23]研究了正系统的稳定性。文献[13,16]中引入了由n个不同分数阶的子系统组成的正系统。本文研究具有状态单广义Frobenius矩阵正、负整数幂的正离散线性系统的可达性和可观察性问题。本文组织如下。在第2节中,回顾了关于正线性系统的基本定义和定理。引入了单项式广义Frobenius矩阵的概念,并在第3节中分析了具有这些状态矩阵的正线性系统的可达性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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