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

Fault Diagnosis of Underwater Vehicle Based on Improved Continuous-Discrete Unscented Kalman Filter

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。

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 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 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 parame-ters of the CDUKF, thus diagnosing the faults of the actuator. Finally, to diagnose the faults of the actuator in simu-lating 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.