水下机器人执行器的高斯粒子滤波故障诊断方法

Actuator Fault Diagnosis of Automatic Underwater Vehicle Using Gaussian Particle Filter

针对智能水下机器人的执行器故障,提出了基于高斯粒子滤波的故障诊断方法.引入控制力(矩)损失参数表示故障,根据6自由度空间运动方程建立执行器故障模型;运用改进的高斯粒子滤波器对参数和运动状态进行联合估计;使用修正的贝叶斯算法检测故障,采用滑动窗口法估计故障的幅值;进行仿真实验并与真实海洋实验数据测试验证.结果表明,该方法能够快速检测故障,且故障幅值的估计精度较高.

For autonomous underwater vehicle（AUV） actuator faults,a fault diagnosis method based on Gaussian particle filter was proposed.First,parameters were introduced to express the control action loss of each degree caused by actuator failures and a fault model was built based on the equation of motion in six-degrees of freedom to describe the actuator fault.Secondly,these arguments were estimated in company with the motion states by using the modified Gaussian particle filter.Thirdly,those parameters were used to detect faults using modified Bayesian and estimate the amplitude of the failure with sliding window.Finally,simulation was conducted and some data from sea experiment were analyzed to test the algorithm.The results show that the proposed methods can detect and diagnose faults fast and accurately.