使用基于自适应控制器的优化深度政策梯度来稳定人类心跳
1Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, 23890, Saudi Arabia.
Computers in biology and medicine
|June 17, 2025
概括
这项研究引入了一种新的自适应控制系统,使用高阶滑动模式控制和深度强化学习来稳定心律. 它提高了对干扰的稳定性,以更好地管理心律失常.
科学领域:
- 生物医学工程 生物医学工程
- 控制系统 控制系统
- 计算神经科学是一种神经科学.
背景情况:
- 稳定心律对于心血管健康和预防心律失常至关重要.
- 由于心脏系统的复杂,动态性质,传统的控制方法面临挑战.
- 生理学决定因素和外部因素带来了显著的变化和干扰.
研究的目的:
- 开发一种新的闭环控制框架,用于适应性,实时心律稳定.
- 将高阶滑动模式控制 (HO-SMC) 与优化深度政策梯度 (ODPG) 强化学习相结合.
- 为了增强控制器对心律不确定性和干扰的稳定性.
主要方法:
- 结合HO-SMC与ODPG强化学习进行自适应参数调节.
- 利用两个神经网络 (NN) 来动态调整控制收益.
- 使用强化学习来评估用于系统稳定的参数配置.
- 在各种生理和病理条件下验证了该方法.
主要成果:
- 与传统控制器相比,证明了更高的心律稳定功效.
- 通过自适应参数调整,展示了对不确定性和干扰的增强强性.
- 在各种模拟的生理和病理情景中验证了框架的有效性.
结论:
- 开创了滑动模式控制和深度强化学习的自适应协同作用,用于心律管理.
- 拟议的智能控制系统为生物医学控制提供了重大进步.
- 这种方法可以更有效地管理心律不规则.
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