基于索伦森糖尿病患者模型的血糖控制硬件设计,使用强大的,不断发展的基于云计算的控制器
Subasri Chellamuthu Kalaimani1, Vijay Jeyakumar1
1Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
Computer methods in biomechanics and biomedical engineering
|November 1, 2023
概括
本研究介绍了一种基于索伦森的糖尿病模型和一种新的RECCo控制器,用于管理1型糖尿病患者的血糖水平. 该系统有效调节葡萄糖,防止诸如低血糖和高血糖等并发症.
科学领域:
- 生物医学工程 生物医学工程
- 控制系统工程 控制系统工程
- 计算智能是一种计算智能.
背景情况:
- 糖尿病 (DM) 带来了重大的公共卫生挑战,需要先进的工程解决方案来预防并发症.
- 现有的模型往往缺乏考虑动态患者因素的能力,如压力,食和运动,影响葡萄糖调节.
研究的目的:
- 开发一种非线性糖尿病模型,包括身体,精神和生活方式因素.
- 设计和实施一种新的RECCo控制器,使用ANYA模糊的基于规则的系统,用于1型糖尿病患者的适应性血糖调节.
- 通过硬件实验和比较分析来验证控制器的性能.
主要方法:
- 基于索伦森的糖尿病模型的开发,考虑身体特征,精神状态,压力,食,运动和胰岛素敏感性 (IS).
- 基于ANYA模糊的基于规则的在线自适应控制系统的RECCo控制器的设计.
- 实现一个简单的胰岛素用于硬件实验.
- 使用N-BEATS算法验证模型准确性 (98%准确性).
- 使用模型预测控制 (MPC) 和模型参考适应控制 (MRAC) 的比较分析.
主要成果:
- 在一个硬件实验中成功实现了RECCo控制器,以有效调节血糖.
- 通过精确的血糖控制,已证明可以预防低血糖和高血糖.
- 在比较分析中,拟议的控制器与MPC和MRAC相比表现优越.
- N-BEATS算法验证了该模型的准确性约为98%.
结论:
- 开发的基于索伦森的糖尿病模型和RECCo控制器为管理1型糖尿病提供了强大的解决方案.
- 基于ANYA模糊规则的系统的适应性使得在不确定的条件下能够有效调节葡萄糖.
- 硬件验证证实了控制器在预防糖尿病并发症方面的实际适用性和成功.
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