On V-Geometric Ergodicity Markov Chains of the Two-Inertia Systems
Feng-Rung Hu and
Jia-Sheng Hu ()
Additional contact information
Feng-Rung Hu: Department of Mathematics Education, National Taichung University of Education, Taichung 4403514, Taiwan
Jia-Sheng Hu: Department of Greenergy, National University of Tainan, Tainan 700301, Taiwan
Mathematics, 2024, vol. 12, issue 10, 1-11
Abstract:
This study employs the diffusion process to construct Markov chains for analyzing the common two-inertia systems used in industry. Two-inertia systems are prevalent in commonly used equipment, where the load is influenced by the coupling of external force and the drive shaft, leading to variations in the associated output states. Traditionally, the control of such systems is often guided by empirical rules. This paper examines the equilibrium distribution and convergence rate of the two-inertia system and develops a predictive model for its long-term operation. We explore the qualitative behavior of the load end at discrete time intervals. Our findings are applicable not only in control engineering, but also provide insights for small-scale models incorporating dual-system variables.
Keywords: two-inertia system; diffusion process; geometrical ergodicity; markov chain (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/2227-7390/12/10/1492/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/10/1492/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:10:p:1492-:d:1392170
Access Statistics for this article
Mathematics is currently edited by Ms. Emma He
More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().