容错的立方卡尔曼波器用于工程估计控制系统
IEEE transactions on cybernetics
|April 30, 2024
概括
这项研究引入了一种容错的立方卡尔曼波器 (CKF),以改善在有异常传感器测量的非线性系统中的状态估计. 增强过器通过纠正测量噪声来保持性能,确保在实际应用中可靠性.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 非线性系统分析 非线性系统分析
背景情况:
- 立方卡尔曼波器 (CKF) 对于非线性系统是有效的,但与异常的传感器测量有困难.
- 异常测量可以改变测量噪声共变率,降低过器性能,并可能导致故障.
- 容错过器处理异常测量,但理论分析是有限的.
研究的目的:
- 建立一个测量噪声模型并分析噪声偏差大小.
- 为具有异常测量系统开发和分析耐故障的立方卡尔曼波器 (CKF).
- 提出一个改进的故障检测算法,解决CKF采样近似偏差.
主要方法:
- 建立了测量噪声模型并分析了噪声偏差.
- 在异常测量下使用三个平均平方误差 (MSE) 分析过性能.
- 开发并评估了耐故障的CKF,具有用于创新纠正的色因子.
- 提出了一种改进的故障检测算法.
主要成果:
- 耐故障的CKF在异常测量下,与标准CKF相比,显示出更好的性能.
- 使用中小企业的分析显示了容错方法在保持准确状态估计方面的有效性.
- 拟议的故障检测算法减轻了CKF固有的采样近似偏差.
结论:
- 耐故障的CKF为在面临异常测量的非线性系统中进行状态估计提供了强大的解决方案.
- 理论分析和数值示例验证了耐故障CCF的增强性能和可靠性.
- 该研究有助于在工程中实际应用耐故障过技术.
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