传感器故障重建使用强大的自适应性未知输入观察器
Qiang Huang1, Zhi-Wei Gao1, Yuanhong Liu1
1Research Centre for Digitalization and Intelligent Diagnosis to New Energies, College of Electrical and Information Engineering, Northeast Petroleum University, Daqing 163318, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
本研究引入了一个自适应的未知输入观察器,以准确地重建工业自动化中的传感器故障和系统状态. 该技术通过解决输入不确定性和非线性系统来增强监测和诊断.
科学领域:
- 控制系统工程 控制系统工程
- 检测和诊断故障的检测和诊断.
- 非线性系统分析 非线性系统分析
背景情况:
- 传感器故障会降低工业自动化性能.
- 输入的不确定性挑战了系统监控,诊断和控制.
- 准确的状态和故障估计对于稳健的系统运行至关重要.
研究的目的:
- 开发一种新的适应性未知输入观察器,用于同时进行传感器故障和系统状态重建.
- 为了应对输入不确定性和错误估计中的非线性动态所带来的挑战.
- 提高自动化系统中故障检测和诊断的稳定性和准确性.
主要方法:
- 使用一个未知输入的观察者来解干扰.
- 使用线性矩阵不等式 (LMI) 来减弱干扰.
- 应用适应性技术来追踪传感器故障.
- 将方法扩展到利普希茨非线性系统.
主要成果:
- 实现了传感器故障和系统状态的强大而准确的重建.
- 通过LMI优化,成功地减弱了未分离的干扰.
- 在飞机和机器人手臂模型上证明了有效性.
- 通过比较研究验证性能.
结论:
- 提出的强大的自适应故障重建技术有效地处理传感器故障和未知的输入不确定性.
- 该方法为复杂的工业自动化和非线性系统的故障诊断提供了可靠的方法.
- 经过验证的算法在系统监控和控制性能方面提供了显著的改进.
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