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NUIO based actuator fault detection for a UAV
ZHANG He, ZHONG Maiying
School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
Abstract:The actuator fault detection for an unmanned aerial vehicle (UAV) longitudinal system with unknown atmospheric disturbances and stochastic noise was studied. Based on introducing a nonlinear longitudinal model of the fixed UAV, a residual generation was designed by employing a nonlinear unknown input observer (NUIO) which is based on cubature Kalman filter (CKF). The unknown input observer structure was constructed to decouple the unknown disturbances from residual. At the same time, the CKF was applied to calculate the gain matrix to achieve the requirement of robustness to noise. Finally, the occurrence of fault can be detected based on chi-square test about the residual sequence. The simulation results show that the proposed method can decouple the unknown disturbances from residual effectively and achieve the fast and accurate actuator fault detection.
Key words: unmanned aerial vehicle (UAV)     fault detection     cubature Kalman filter (CKF)     nonlinear unknown input observer (NUIO)     chi-square test
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1 系统模型及问题描述 1.1 系统模型

1) 视飞行器为刚体,且质量为恒定.

2) 选取地面坐标系为惯性坐标系,不考虑离心加速度和哥氏加速度的影响.

3) 忽略地球曲率,将其视为平面.

4) 重力加速度不随高度变化而变化.

5) 机体坐标系的Oxz平面为飞行器的对称平面,即Ixz=Izy=0.

1.2 问题描述

1) 作动器正常工作时:bk=0.

2) 作动器发生恒偏差故障时:bk为偏差值.

2 作动器故障检测 2.1 残差产生器设计

1) 初始化:

2) 根据式(11),计算Hk+1.

3) 采用3阶容积规则获得如下的基本采样点和相应权值:

4) 时间更新:

Pk－1|k－1做Cholesky分解:

5) 量测更新:

Pk|k-1做Cholesky分解:

6) 状态更新:

k时刻的状态估计值为

k时刻的状态误差协方差估计值为

7) 残差产生:

2.2 残差评价

3 仿真分析

 参数 数值 参数 数值 m/kg 2 000 m5 -0.025 5 ρ/(kg·m-3) 0.037 1 x1 0.714 Sw/m2 39.02 x2 0.714 g/(m·s-2) 9.780 3 x3 0.068 2 Tx/N 4 864 x4 -0.089 6 Tz/N 212 z1 -7.509 5 c/m 1.98 z2 -0.237 5 m1 -0.789 9 z3 6.573 1 m2 -0.689 9 z4 -0.354 1 m3 -3.930 0 z5 -0.317 5 m4 -1.862 9

 图 1 无故障时系统的残差信号 Fig. 1 Residual signal of system in the fault free case

 图 2 Qkr=Qka情况下Jf(k)变化曲线与J0(k) Fig. 2 Evolution of the Jf(k) and J0(k) in the case of Qkr=Qka

 图 3 Qkr=10Qka情况下Jf(k)变化曲线与J0(k) Fig. 3 Evolution of the Jf(k) and J0(k)in the case of Qkr=10Qka

 离散周期/s 故障幅值 基于EKF的NUIO 基于CKF的NUIO 检测延时/s 可检测性 检测延时/s 可检测性 0.1 0.04 - × 2.1 √ 0.07 0.60 √ 0.5 √ 0.10 0.18 √ 0.1 √ 0.01 0.04 - × 2.0 √ 0.07 0.40 √ 0.3 √ 0.10 0.15 √ 0.1 √
4 结 论

1) 基于CKF的NUIO构造的残差收敛速度快,具有良好的干扰解耦性能.

2) 与基于EKF的NUIO故障检测方法相比,在噪声统计特性准确已知的条件下,该方法在故障发生时产生的评价函数幅值更大,具有更好的故障检测性能.

3) 在过程噪声统计特性不准确的情况下,该方法能够快速、准确地检测出故障,而基于EKF的NUIO故障检测方法失效.

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#### 文章信息

ZHANG He, ZHONG Maiying

NUIO based actuator fault detection for a UAV

Journal of Beijing University of Aeronautics and Astronsutics, 2015, 41(7): 1300-1306.
http://dx.doi.org/10.13700/j.bh.1001-5965.2014.0522