通过直流编程优化正马尔可夫跃迁线性系统的概率率

IF 2.7 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Asian Journal of Control Pub Date : 2024-04-14 DOI:10.1002/asjc.3364

Chengyan Zhao, Bohao Zhu, Masaki Ogura, James Lam

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引用次数: 0

摘要

我们通过优化正马尔可夫跃迁线性系统的过渡概率率来研究其稳定问题。通过利用正余弦的凸特性和处理凸函数差的标准数学编程，我们证明过渡概率率综合问题可以通过凸差分（DC）编程来解决。我们用一个数值示例来说明我们结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Probability rate optimization of positive Markov jump linear systems via DC programming

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Probability rate optimization of positive Markov jump linear systems via DC programming

We investigate the stabilization problem of positive Markov jump linear systems by optimizing their transition probability rates. By using the convex property of posynomials and the standard mathematical programming that deals with the difference in convex functions, we show that transition probability rate synthesis problems can be solved via difference-of-convex (DC) programming. A numerical example is used to illustrate the effectiveness of our results.

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来源期刊

Asian Journal of Control 工程技术-自动化与控制系统

CiteScore

4.80

自引率

25.00%

发文量

253

审稿时长

7.2 months

期刊介绍： The Asian Journal of Control, an Asian Control Association (ACA) and Chinese Automatic Control Society (CACS) affiliated journal, is the first international journal originating from the Asia Pacific region. The Asian Journal of Control publishes papers on original theoretical and practical research and developments in the areas of control, involving all facets of control theory and its application. Published six times a year, the Journal aims to be a key platform for control communities throughout the world. The Journal provides a forum where control researchers and practitioners can exchange knowledge and experiences on the latest advances in the control areas, and plays an educational role for students and experienced researchers in other disciplines interested in this continually growing field. The scope of the journal is extensive. Topics include: The theory and design of control systems and components, encompassing: Robust and distributed control using geometric, optimal, stochastic and nonlinear methods Game theory and state estimation Adaptive control, including neural networks, learning, parameter estimation and system fault detection Artificial intelligence, fuzzy and expert systems Hierarchical and man-machine systems All parts of systems engineering which consider the reliability of components and systems Emerging application areas, such as: Robotics Mechatronics Computers for computer-aided design, manufacturing, and control of various industrial processes Space vehicles and aircraft, ships, and traffic Biomedical systems National economies Power systems Agriculture Natural resources.