TY - JOUR
T1 - Fault diagnosis of underwater vehicle based on improved continuous-discrete unscented Kalman filter
AU - Xu, Demin
AU - Liu, Fuqiang
AU - Zhang, Lichuan
AU - Cui, Rongxin
N1 - Publisher Copyright:
©, 2014, Northwestern Polytechnical University. All right reserved.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - To diagnose the faults of an underwater vehicle's continuous nonlinear system, we propose an improved continuous-discrete unscented Kalman filtering (CDUKF) algorithm. Firstly, according to the estimation mean values and covariance matrix of the state of the continuous nonlinear system and the parameters of its faults, we construct its unscented state matrices and forecast its unscented state array through integrating the unscented state differential functions. Then we obtain the estimation mean values and covariance matrix of its state and parameters through calculating the mean values and updating the estimation. Secondly, we fuse the faults of the actuator of the underwater vehicle into the continuous nonlinear system in the form of proportional coefficient or affixation parameter and estimate the parameters of the faults of the actuator through constructing respectively the state and the parameters of the CDUKF, thus diagnosing the faults of the actuator. Finally, to diagnose the faults of the actuator in simulating the docking of the underwater vehicle on a horizontal plane, we use the CDUKF algorithm to effectively estimate the faults of the underwater vehicle in their parameter form, thus verifying its feasibility and effectiveness.
AB - To diagnose the faults of an underwater vehicle's continuous nonlinear system, we propose an improved continuous-discrete unscented Kalman filtering (CDUKF) algorithm. Firstly, according to the estimation mean values and covariance matrix of the state of the continuous nonlinear system and the parameters of its faults, we construct its unscented state matrices and forecast its unscented state array through integrating the unscented state differential functions. Then we obtain the estimation mean values and covariance matrix of its state and parameters through calculating the mean values and updating the estimation. Secondly, we fuse the faults of the actuator of the underwater vehicle into the continuous nonlinear system in the form of proportional coefficient or affixation parameter and estimate the parameters of the faults of the actuator through constructing respectively the state and the parameters of the CDUKF, thus diagnosing the faults of the actuator. Finally, to diagnose the faults of the actuator in simulating the docking of the underwater vehicle on a horizontal plane, we use the CDUKF algorithm to effectively estimate the faults of the underwater vehicle in their parameter form, thus verifying its feasibility and effectiveness.
KW - Autonomous underwater vehicles
KW - Continuous-discrete unscented Kalman filtering (CDUKF)
KW - Extended Kalman filters
KW - Failure analysis
KW - Fault diagnosis
KW - Nonlinear control systems
KW - State estimation
KW - Unscented Kalman filter (UKF)
KW - White noise
UR - http://www.scopus.com/inward/record.url?scp=84929395663&partnerID=8YFLogxK
M3 - 文章
AN - SCOPUS:84929395663
SN - 1000-2758
VL - 32
SP - 756
EP - 760
JO - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
JF - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
IS - 5
ER -