适应卡尔曼过用于在线光谱测量中补偿外部影响
Daniel Sbarbaro1, Tor Arne Johansen2, Jorge Yañez3
1Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepcion, Concepción 4070371, Chile.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了自适应卡尔曼波器 (AKF) 和减少顺序的AKF (rAKF),通过纠正环境干扰来提高工业环境中的实时光谱传感精度.
科学领域:
- 分析化学 分析化学
- 工艺工程是过程工程.
- 控制系统 控制系统
背景情况:
- 工业光谱传感精度受到温度,压力和粒子大小变化的影响.
- 传统方法无法考虑这些动态的环境影响,导致不准确的度估计.
研究的目的:
- 开发用于实时光谱传感的综合建模框架.
- 引入自适应卡尔曼波器 (AKF) 和减少顺序的AKF (rAKF) 来纠正测量扭曲.
主要方法:
- 开发了一个离散时间过程模型与基于物理的光谱传感器模型相结合.
- 应用AKF使用实时可观测性分析来适应不断变化的条件.
- 引入了用于有限可观测性的场景的rAKF,优化了计算效率.
主要成果:
- AKF和rAKF在扩展卡尔曼波器等常规方法上表现出卓越的性能.
- 过器有效地纠正了由外部因素引起的测量扭曲.
- 模拟证实了对噪声和初始参数变化的稳定性.
结论:
- 拟议的AKF和rAKF提高了光谱传感器测量的可靠性.
- 这些方法可以为工业监测和控制提供更精确的实时度估计.
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