• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

利用自适应反推算法控制 1 型糖尿病患者餐后血糖

Control of blood glucose induced by meals for type-1 diabetics using an adaptive backstepping algorithm.

机构信息

Department of Mechanical Engineering, Faculty of Engineering, Kharazmi University, Tehran, P.O.B. 15719-14911, Iran.

出版信息

Sci Rep. 2022 Jul 18;12(1):12228. doi: 10.1038/s41598-022-16535-2.

DOI:10.1038/s41598-022-16535-2
PMID:35851835
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9293929/
Abstract

In this study, an adaptive backstepping method is proposed to regulate the blood glucose induced by meals for type-1 diabetic patients. The backstepping controller is used to control the blood glucose level and an adaptive algorithm is utilized to compensate for the blood glucose induced by meals. Moreover, the effectiveness of the proposed method is evaluated by comparing results in two different case studies: in the presence of actuator faults and the loss of control input for a short while during treatment. Effects of unannounced meals three times a day are investigated for a nominal patient in every case. It is argued that adaptive backstepping is the preferred control method in either case. The Lyapunov theory is used to prove the stability of the proposed method. Obtained results, indicated that the adaptive backstepping controller is stable, and the desired level of glucose concentration is being tracked efficiently.

摘要

在这项研究中,提出了一种自适应反推方法来调节 1 型糖尿病患者的餐后血糖。反推控制器用于控制血糖水平,自适应算法用于补偿餐后引起的血糖。此外,通过比较两种不同案例研究中的结果来评估所提出方法的有效性:在存在执行器故障和治疗期间短时间失去控制输入的情况下。在每种情况下,都对一名名义患者每天吃三次不通知的食物进行了研究。有人认为,自适应反推是这两种情况下的首选控制方法。李雅普诺夫理论用于证明所提出方法的稳定性。所得结果表明,自适应反推控制器是稳定的,并且能够有效地跟踪所需的血糖浓度水平。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/28bb1e2beee2/41598_2022_16535_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/f64ebb5b181e/41598_2022_16535_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/5e578cef64bb/41598_2022_16535_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/baa9def2de9f/41598_2022_16535_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/f86c5a9f46e3/41598_2022_16535_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/88de1a21b31b/41598_2022_16535_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/1c87a6ef7491/41598_2022_16535_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/a5081c93aef9/41598_2022_16535_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/df08bc9db0ac/41598_2022_16535_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/112898806592/41598_2022_16535_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/d08b25e4cab4/41598_2022_16535_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/54e565f98b4d/41598_2022_16535_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/28bb1e2beee2/41598_2022_16535_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/f64ebb5b181e/41598_2022_16535_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/5e578cef64bb/41598_2022_16535_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/baa9def2de9f/41598_2022_16535_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/f86c5a9f46e3/41598_2022_16535_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/88de1a21b31b/41598_2022_16535_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/1c87a6ef7491/41598_2022_16535_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/a5081c93aef9/41598_2022_16535_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/df08bc9db0ac/41598_2022_16535_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/112898806592/41598_2022_16535_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/d08b25e4cab4/41598_2022_16535_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/54e565f98b4d/41598_2022_16535_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3168/9293929/28bb1e2beee2/41598_2022_16535_Fig12_HTML.jpg

相似文献

1
Control of blood glucose induced by meals for type-1 diabetics using an adaptive backstepping algorithm.利用自适应反推算法控制 1 型糖尿病患者餐后血糖
Sci Rep. 2022 Jul 18;12(1):12228. doi: 10.1038/s41598-022-16535-2.
2
Fault tolerant control for modified quadrotor via adaptive type-2 fuzzy backstepping subject to actuator faults.基于自适应型 2 模糊反推的故障容错控制在执行器故障下的改进四旋翼飞行器
ISA Trans. 2019 Dec;95:330-345. doi: 10.1016/j.isatra.2019.04.034. Epub 2019 May 14.
3
Positive input observer-based controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach.基于正输入观测器的 1 型糖尿病患者血糖调节控制器设计:一种反推方法。
IET Syst Biol. 2022 Sep;16(5):157-172. doi: 10.1049/syb2.12049. Epub 2022 Aug 17.
4
Variable structure robust controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach.1 型糖尿病患者血糖调节的变结构鲁棒控制器设计:回溯法。
IET Syst Biol. 2021 Aug;15(6):173-183. doi: 10.1049/syb2.12032. Epub 2021 Jul 8.
5
Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.多输入多输出非线性系统中存在执行器和传感器故障的间接自适应模糊容错跟踪控制。
ISA Trans. 2018 Aug;79:45-61. doi: 10.1016/j.isatra.2018.04.014. Epub 2018 May 10.
6
Wavelet adaptive backstepping control for a class of nonlinear systems.一类非线性系统的小波自适应反步控制
IEEE Trans Neural Netw. 2006 Sep;17(5):1175-83. doi: 10.1109/TNN.2006.878122.
7
Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model.基于改进 LuGre 摩擦模型的光电跟踪系统鲁棒自适应积分反步控制。
ISA Trans. 2018 Sep;80:312-321. doi: 10.1016/j.isatra.2018.07.016. Epub 2018 Aug 2.
8
Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems.将未宣布的进餐和运动纳入多变量人工胰腺系统个性化模型的自适应学习中。
J Diabetes Sci Technol. 2018 Sep;12(5):953-966. doi: 10.1177/1932296818789951. Epub 2018 Jul 31.
9
An adaptive robust backstepping improved control scheme for mobile manipulators robot.移动机械臂的自适应鲁棒反步改进控制方案。
ISA Trans. 2023 Jun;137:446-456. doi: 10.1016/j.isatra.2023.01.005. Epub 2023 Jan 6.
10
Parametric adaptive estimation and backstepping control of electro-hydraulic actuator with decayed memory filter.具有衰减记忆滤波器的电液执行器的参数自适应估计与反步控制
ISA Trans. 2016 May;62:202-14. doi: 10.1016/j.isatra.2016.02.009. Epub 2016 Feb 23.

引用本文的文献

1
Automated blood glucose regulation for nonlinear model of type-1 diabetic patient under uncertainties: GWOCS type-2 fuzzy approach.不确定条件下1型糖尿病患者非线性模型的血糖自动调节:基于广义加权二阶滑模(GWOCS)的2型模糊方法
Biomed Eng Lett. 2023 Oct 1;14(1):127-151. doi: 10.1007/s13534-023-00318-3. eCollection 2024 Jan.

本文引用的文献

1
Neuroadaptive Performance Guaranteed Control for Multiagent Systems With Power Integrators and Unknown Measurement Sensitivity.具有功率积分器和未知测量灵敏度的多智能体系统的神经自适应性能保证控制
IEEE Trans Neural Netw Learn Syst. 2023 Dec;34(12):9771-9782. doi: 10.1109/TNNLS.2022.3160532. Epub 2023 Nov 30.
2
Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties.具有动态不确定性的非仿射非线性多智能体系统基于神经网络的事件触发自适应控制
IEEE Trans Neural Netw Learn Syst. 2021 May;32(5):2239-2250. doi: 10.1109/TNNLS.2020.3003950. Epub 2021 May 3.
3
Insulin Units and Conversion Factors: A Story of Truth, Boots, and Faster Half-Truths.
胰岛素单位与换算因子:一个关于真相、误导及半真半假信息的故事。
J Diabetes Sci Technol. 2019 May;13(3):597-600. doi: 10.1177/1932296818805074. Epub 2018 Oct 13.
4
Blood glucose regulation in type 1 diabetic patients: an adaptive parametric compensation control-based approach.1 型糖尿病患者的血糖调节:基于自适应参数补偿控制的方法。
IET Syst Biol. 2018 Oct;12(5):219-225. doi: 10.1049/iet-syb.2017.0093.
5
Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties.基于葡萄糖和速度相关控制罚函数的人工胰腺自适应区域模型预测控制。
IEEE Trans Biomed Eng. 2019 Apr;66(4):1045-1054. doi: 10.1109/TBME.2018.2866392. Epub 2018 Aug 21.
6
Blood glucose concentration control for type 1 diabetic patients: a non-linear suboptimal approach.1型糖尿病患者的血糖浓度控制:一种非线性次优方法。
IET Syst Biol. 2017 Aug;11(4):119-125. doi: 10.1049/iet-syb.2016.0044.
7
Type 1 diabetes mellitus.1 型糖尿病。
Nat Rev Dis Primers. 2017 Mar 30;3:17016. doi: 10.1038/nrdp.2017.16.
8
Continuous subcutaneous insulin infusion versus multiple daily injections in individuals with type 1 diabetes: a systematic review and meta-analysis.1型糖尿病患者持续皮下胰岛素输注与每日多次注射的比较:一项系统评价和荟萃分析。
Endocrine. 2017 Jan;55(1):77-84. doi: 10.1007/s12020-016-1039-x. Epub 2016 Aug 1.
9
Real-time estimation of plasma insulin concentration from continuous glucose monitor measurements.通过连续血糖监测测量实时估算血浆胰岛素浓度。
Comput Methods Biomech Biomed Engin. 2016;19(9):934-42. doi: 10.1080/10255842.2015.1077234. Epub 2015 Sep 7.
10
First use of model predictive control in outpatient wearable artificial pancreas.首个应用于门诊可穿戴人工胰腺的模型预测控制。
Diabetes Care. 2014;37(5):1212-5. doi: 10.2337/dc13-1631.