{"title":"Backward differentiation formula method and random forest method to solve continuous-time differential Riccati equations","authors":"Juan Zhang, Wenwen Zou, Chenglin Sui","doi":"10.1002/asjc.3494","DOIUrl":null,"url":null,"abstract":"In this paper, we explore the utilization of machine learning techniques for solving the numerical solutions of continuous-time differential Riccati equations. Specifically, we focus on generating a reduction matrix capable of transforming a high-order matrix into a low-order matrix. Additionally, we address the issue of differential terms in the continuous-time differential Riccati equation and incorporate the backward differentiation formula of the matrix to improve stability and accuracy. Finally, by training samples through neural networks and machine learning methods, we could predict the solutions for high-order matrix equations.","PeriodicalId":55453,"journal":{"name":"Asian Journal of Control","volume":"27 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Control","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/asjc.3494","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we explore the utilization of machine learning techniques for solving the numerical solutions of continuous-time differential Riccati equations. Specifically, we focus on generating a reduction matrix capable of transforming a high-order matrix into a low-order matrix. Additionally, we address the issue of differential terms in the continuous-time differential Riccati equation and incorporate the backward differentiation formula of the matrix to improve stability and accuracy. Finally, by training samples through neural networks and machine learning methods, we could predict the solutions for high-order matrix equations.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
