多模型自校准Kalman滤波方法
Multiple-model self-calibration Kalman filter method
杨海峰 1王金娜 2王宇翔3
作者信息
- 1. 工业和信息化部 高新技术司,北京 100804
- 2. 西安交通大学 能源与动力工程学院,西安 710049
- 3. 华中科技大学 机械科学与工程学院,武汉 430074
- 折叠
摘要
基于自校准Kalman滤波方法和多模型估计理论,针对工程实际中未知输入(如突风、故障和未知系统误差等)对系统状态方程的影响问题,提出了一种多模型自校准Kalman滤波方法.该方法同时采用自校准Kalman滤波和标准Kalman滤波进行运算,并根据贝叶斯定理自动分配两种方法滤波值的权重,通过加权融合得到最终的滤波结果.与自校准Kalman滤波方法相比,多模型自校准Kalman滤波方法既能有效地补偿非零未知输入的影响,又明显改善了系统在未知输入为零时的滤波精度,大量数值仿真结果表明该方法精度提升可达10％以上,具有更强的适应性和鲁棒性.
Abstract
Based on the self-calibration Kalman filter(SKF)and the multiple-model estimation(MME),considering the influence of unknown inputs(such as gusts,faults,unknown system errors,etc.)on the system state equation in Engineering,the multiple-model self-calibration Kalman filter(MSKF)was proposed.According to the Bayes'theorem,this filtering method used the SKF and the standard Kalman filter(KF)whose weights were assigned automatically to obtain the final filtering result through weight-average way.Compared with the SKF,the MSKF can not only effectively compensate the effects of non-zero unknown inputs,but also improve the estimation accuracy when unknown inputs were zero.A large number of simulation results showed that accuracy can be improved by more than 10％,using the proposed method.In summary,the MSKF has stronger adaptability and robustness.
关键词
自校准滤波/多模型估计/Kalman滤波/未知输入/故障诊断Key words
self-calibration filter/multiple-model estimation/Kalman filter/unknown input/fault diagnosis引用本文复制引用
基金项目
国家自然科学基金面上项目(61972021)
出版年
2024
NETL
NSTL
万方数据