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论文:2014,Vol:32,Issue(5):756-760 |
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引用本文： |
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徐德民, 刘富樯, 张立川, 崔荣鑫. 基于改进连续-离散无迹卡尔曼滤波的水下航行器故障诊断[J]. 西北工业大学 |
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Xu Demin, Liu Fuqiang, Zhang Lichuan, Cui Rongxin. Fault Diagnosis of Underwater Vehicle Based on ImprovedContinuous-Discrete Unscented Kalman Filter[J]. Northwestern polytechnical university |
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| 基于改进连续-离散无迹卡尔曼滤波的水下航行器故障诊断 |
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| 徐德民, 刘富樯, 张立川, 崔荣鑫 |
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| 西北工业大学 航海学院, 陕西 西安 710072 |
| 摘要: |
| 针对连续非线性系统的参数估计问题,提出了改进的连续-离散无迹卡尔曼滤波算法。该算法结合系统状态和参数的估计均值和协方差阵,通过构建控制系统的无迹状态矩阵,并对无迹状态函数积分获得预测无迹状态阵,再经过均值解算和估计更新,获得参数的估计值。然后,针对水下航行器连续非线性控制系统的故障诊断问题,将水下航行器执行机构的故障,以比例系数和附加参数的形式表达在控制系统的状态空间方程中,通过采用改进的连续-离散无迹卡尔曼滤波算法,估计故障数据,实现执行机构的故障诊断。最后,在水下航行器回坞仿真实验中,采用该算法有效估计出执行机构故障,验证了算法的可行性和有效性。 |
| 关键词:
水下航行器
故障诊断
扩展卡尔曼滤波
无迹卡尔曼滤波
连续-离散无迹卡尔曼滤波
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| Fault Diagnosis of Underwater Vehicle Based on ImprovedContinuous-Discrete Unscented Kalman Filter |
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| Xu Demin, Liu Fuqiang, Zhang Lichuan, Cui Rongxin |
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| School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| To diagnose the faults of an underwater vehicle's continuous nonlinear system,we propose an improvedcontinuous-discrete unscented Kalman filtering (CDUKF) algorithm.Firstly,according to the estimation mean val-ues and covariance matrix of the state of the continuous nonlinear system and the parameters of its faults,we con-struct its unscented state matrices and forecast its unscented state array through integrating the unscented state dif-ferential functions.Then we obtain the estimation mean values and covariance matrix of its state and parametersthrough calculating the mean values and updating the estimation.Secondly,we fuse the faults of the actuator of theunderwater vehicle into the continuous nonlinear system in the form of proportional coefficient or affixation parameterand estimate the parameters of the faults of the actuator through constructing respectively the state and the parame-ters of the CDUKF,thus diagnosing the faults of the actuator.Finally,to diagnose the faults of the actuator in sim-ulating the docking of the underwater vehicle on a horizontal plane,we use the CDUKF algorithm to effectively esti-mate the faults of the underwater vehicle in their parameter form,thus verifying its feasibility and effectiveness. |
| Key words:
autonomous underwater vehicles
failure analysis
extended Kalman filters
unscented Kalman filter(UKF)
nonlinear control systems
state estimation
white noise
fault diagnosis
continuous-discreteunscented Kalman filtering (CDUKF)
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| 收稿日期: 2014-04-01
修回日期:
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| DOI: |
| 基金项目: 国家自然科学基金(51379176、51209174)资助 |
| 通讯作者:
Email： |
| 作者简介: 徐德民(1937-)，中国工程院院士，西北工业大学教授、博士生导师，主要从事水中兵器科学与技术和控制理论与工程应用研究。
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参考文献: |
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