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基于元强化模型的规划和故障耐受性控制,用于和潜水脱污染脱压室
Nan Zhang1, Qijing Lin1, Zhuangde Jiang1
1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
和潜水需要在压缩室中精确控制. 本研究介绍了一种使用人工智能的耐故障系统,用于在高压环境中实现更安全,更适应的控制.
科学领域:
- 超高压工程 超高压工程
- 控制系统工程 控制系统工程
- 安全系统中的人工智能
背景情况:
- 和潜水对于水下操作至关重要,需要先进的生命支持系统.
- 解压室需要精确的压力管理,以确保在长时间暴露于高压时的潜水员安全.
- 动态条件和潜在的系统故障在高压环境中构成重大风险.
研究的目的:
- 设计一个强大的控制系统,用于和潜水脱污除压室.
- 在动态和故障条件下确保安全的净化和通风控制.
- 开发适应性,耐故障的控制框架,用于人类居住的高压系统.
主要方法:
- 开发了一个新的基于模型的规划和容错控制框架.
- 由政策网络指导的轨迹规划算法被实施,以提高效率和稳定性.
- 提出了一个基于meta-learning的容错控制策略来处理干扰和故障.
主要成果:
- 与传统方法相比,拟议的方法显示了更高的累积回报和更快的趋同.
- 实验结果证实,在模拟故障条件下,系统性能稳定性得到改善.
- 该系统实现了适应性响应和弹性性能,这对于安全关键操作至关重要.
结论:
- 开发的控制系统为和潜水解压室提供了有效和适应性的解决方案.
- 基于模型的规划和容错策略提高了高压作业的安全性和可靠性.
- 这项研究有助于人类占用高压系统的故障操作性能.
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