通过多剂在政策上的强化学习,通过多剂在政策上的强化学习进行自适应性平均动脉压控制
Xiaofeng Hong1, Walid Ayadi2, Khalid A Alattas3
1Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang, 322100, China.
Scientific reports
|January 3, 2025
概括
这项研究介绍了一种新的无模型超局部模型 (MFULM) 控制器,使用多代理在政策强化学习 (MAOPRL) 通过精确的药物剂量进行远程血压调节. 该系统在各种条件下展示了有效的,适应性的控制和稳定性.
科学领域:
- 生物医学工程 生物医学工程
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 远程血压调节需要精确的药物剂量.
- 现有的控制系统可能缺乏适应患者变化和干扰的适应性.
研究的目的:
- 引入一个无模型的超局部模型 (MFULM) 控制器,与多代理的政策强化学习 (MAOPRL) 集成.
- 通过智能药物剂量,实现精确,适应性和稳定的远程血压调节.
主要方法:
- 开发一个结合MFULM控制器,观察器和MAOPRL算法的闭环系统.
- 在MAOPRL中使用演员-关键方法进行自适应参数优化.
- 整合一个观察者来估计状态和干扰,以提高稳定性.
主要成果:
- 该MFULM控制器证明了有效的血压调节和适应性药物剂量调整.
- MAOPRL系统显示出增强的适应性和稳定性,特别是对抗干扰.
- 评估证实了该方法在正常情况下的效率和可行性,患者间的适应性和混合干扰.
结论:
- 拟议的MFULM与MAOPRL的控制器为远程血压管理提供了一个强大而适应性的解决方案.
- 这种智能控制系统显示出在闭环治疗干预中改善患者结果的巨大潜力.
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