基于置信规则库的捷联惯组误差系数预测方法

doi:10.3969/j.issn.1001-506X.2020.12.24

Abstract

Abstract:

The strapdown inertial measurement unit (SIMU) is the core of such large aircraft control systems as the rocket. Its structure is quite complicated, and the available error parameter samples are relatively few. As a kind of expert system, the belief rule base (BRB) can effectively address small sample information under uncertainty by aggregating monitoring data and expert knowledge. Thus, a BRB-based forecasting method for the error coefficient of the SIMU prediction is proposed. In order to make full use of the input information, the information transformation accuracy is improved and the information loss is reduced during the index transformation process, a new calculation method of matching degrees is proposed according to the characteristics of the impulse output of SIMU, which can improve the accuracy of the model. Finally, the effectiveness of the proposed method is verified by an example of a type of SIMU accelerometer.

Key words: strapdown inertial measurement unit (SIMU), error coefficient, belief rule base (BRB), expert knowledge, matching degree

CLC Number:

TP202

Xinhao DONG, Zhijie ZHOU, Youmin ZHANG, Zhichao FENG, You CAO. Forecasting method of the error coefficient for SIMU based on belief rule base[J]. Systems Engineering and Electronics, 2020, 42(12): 2867-2874.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Table 3

Table 4

Fig.4

Table 5

Fig.5

Table 6

References 27

1	WANG L , WU W Q , LI G , et al. Ring laser gyro G-sensitive misalignment calibration in linear vibration environments[J]. Sensors, 2018, 18 (2): 601- 609. doi: 10.3390/s18020601
2	解静, 肖龙旭, 任革学. 捷联惯测组合误差系数分离与补偿方法[J]. 系统工程与电子技术, 2011, 33 (12): 2716- 2720.
	XIE J , XIAO L X , REN G X . Error coefficients separation and compensation for SIMU[J]. Systems Engineering and Electro-nics, 2011, 33 (12): 2716- 2720.
3	SIAVASH H . Deep Kalman filter: simultaneous multi-sensor integration and modelling; a GNSS/IMU case study[J]. Sensors, 2018, 18 (5): 1316- 1325. doi: 10.3390/s18051316
4	于湘涛, 董卫华, 张兰, 等. 基于灰色最小二乘支持向量机的加速度计参数预测[J]. 中国惯性技术学报, 2013, 21 (6): 813- 816.
	YU X T , DONG W H , ZHANG L , et al. Prediction of accelero-meter pa-rameters based on grey least squares support vector machine[J]. Journal of Chinese Inertial Technology, 2013, 21 (6): 813- 816.
5	黄小凯, 陈云霞, 康锐. 加速度计参数变化建模方法[J]. 北京航空航天大学学报, 2010, 36 (10): 1229- 1233.
	HUANG X K , CHEN Y X , KANG R . Bias and scale factor drift modeling met-hods for accelerometers[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36 (10): 1229- 1233.
6	汪立新, 王建华, 李成. 捷联惯组误差系数的分形特征及插值算法[J]. 中国惯性技术学报, 2012, 20 (5): 515- 520.
	WANG L X , WANG J C , LI C . Fractal characteristic and interpolation method for SIMU error coefficients[J]. Journal of Chinese Inertial Technology, 2012, 20 (5): 515- 520.
7	YAN G M , YAN W S , XU D M . Application of simplified UKF in SINS initial alignment for large misalignment angles[J]. Journal of Chinese Inertial Technology, 2008, 16 (3): 253- 264.
8	FENG Y , NAVAL E D . An analysis of the maintenance mode of laser gyro strapdown inertial navigation system[J]. Instrumentation Technology, 2018, 11, 17- 19.
9	LEE H K , LEE J G , JEE G I . Calibration of measurement delay in global positioning system/strapdown inertial navigation system[J]. Journal of Guidance, Control, and Dynamics, 2002, 25 (2): 240- 247. doi: 10.2514/2.4904
10	YANG J B , LIU J , WANG J , et al. Belief rule-base inference methodology using the evidential reasoning approach-RIMER[J]. IEEE Trans.on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2006, 36 (2): 266- 285. doi: 10.1109/TSMCA.2005.851270
11	YANG J B , LIU J , XU D L , et al. Optimization models for training belief rule based systems[J]. IEEE Trans.on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2007, 37 (4): 569- 585. doi: 10.1109/TSMCA.2007.897606
12	YANG J B , WANG Y M , XU D L , et al. Belief rule-based methodology for mapping consumer preferences and setting product targets[J]. Expert Systems with Applications, 2012, 39 (5): 4749- 4759. doi: 10.1016/j.eswa.2011.09.105
13	HU G Y , ZHOU Z J , ZHANG B C , et al. A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm[J]. Applied Soft Computing, 2016, 48, 404- 418. doi: 10.1016/j.asoc.2016.05.046
14	CHANG L L , CHEN Y W , HAO Z Y , et al. Indirect disjunctive belief rule base modeling using limited conjunctive rules: two possible means[J]. International Journal of Approximate Reasoning, 2019, 108 (5): 1- 20.
15	ZHOU Z J , HU G Y , ZHANG B C , et al. A model for hidden behavior prediction of complex systems based on belief rule base and power set[J]. IEEE Trans.on Systems, Man, and Cybernetics: Systems, 2018, 44 (9): 1649- 1655.
16	YIN X J , WANG Z L , ZHANG B C , et al. A double layer BRB model for health prognostics in complex electromechanical system[J]. IEEE Access, 2017, 5, 23833- 23847. doi: 10.1109/ACCESS.2017.2766086
17	YANG J B , XU D L . Nonlinear information aggregation via evidential reasoning in multi attribute decision analysis under uncertainty[J]. IEEE Trans.on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2002, 32 (3): 376- 393. doi: 10.1109/TSMCA.2002.802809
18	于恩宁.激光捷联惯性测量组合故障诊断技术研究[D].哈尔滨:哈尔滨工业大学, 2018.
	YU E N. The research of fault diagnosis techology on laser gyro strapdown inertial measurement unit[D]. Harbin: Harbin Institute of Technology, 2018.
19	WANG J . Research on zero speed correction of laser gyro strapdown inertial navigation system[J]. Piezoelectrics & Acoustooptics, 2018, 40 (4): 626- 632.
20	LI G L , ZHOU Z J , HU C H , et al. A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base[J]. Safety Science, 2017, 93, 108- 120. doi: 10.1016/j.ssci.2016.11.011
21	ZHOU Z J , CHANG L L , HU C H , et al. A new BRB-ER-based model for assessing the lives of products using both failure data and expert knowledge[J]. IEEE Trans.on Systems, Man, and Cybernetics: Systems, 2016, 46 (11): 1529- 1543. doi: 10.1109/TSMC.2015.2504047
22	YANG J B . Rule and utility based evidential reasoning approach for multi-attribute decision analysis under uncertainties[J]. European Journal of Operational Research, 2001, 131 (1): 31- 61.
23	ZHAO F J , ZHOU Z J , HU C H , et al. A new evidential reasoning-based method for online safety assessment of complex systems[J]. IEEE Trans.on Systems, Man, and Cybernetics: Systems, 2018, 48, 954- 966. doi: 10.1109/TSMC.2016.2630800
24	YANG J B , XU D L . Evidential reasoning rule for evidence combination[J]. Artificial Intelligence, 2013, 205, 1- 29. doi: 10.1016/j.artint.2013.09.003
25	YANG J B , SINGH M G . An evidential reasoning approach for multiple-attribute decision making with uncertainty[J]. IEEE Trans.on Systems, Man, and Cybernetics, 1994, 24 (1): 1- 18.
26	FENG Z C , ZHOU Z J , HU C H , et al. Fault diagnosis based on belief rule base with considering attribute correlation[J]. IEEE Access, 2018, 6, 2055- 2067. doi: 10.1109/ACCESS.2017.2781365
27	HU G Y , ZHOU Z J , ZHANG B C , et al. A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm[J]. Applied Soft Computing, 2016, 48, 404- 418. doi: 10.1016/j.asoc.2016.05.046

参考等级	VS	S	M	H	VH
参考值	3 278	3 400	3 520	4 200	6 600

参考等级	VS	S	M	H	VH
参考值	652	700	1 000	1 600	3 900

参考等级	S	M	L
参考值	2 151 835	2 151 920	2 152 049

序号	规则权重	属性		误差系数置信度分配
序号	规则权重	x₁	x₂	误差系数置信度分配
1	1	VS	VS	(0 0.1 0.9)
2	1	VS	S	(0 0.2 0.8)
3	1	VS	M	(0 0.3 0.7)
4	1	VS	H	(0 0.35 0.65)
5	1	VS	VH	(0 0.4 0.6)
6	1	S	VS	(1 0 0)
7	1	S	S	(0 0.5 0.5)
8	1	S	M	(0 0.6 0.4)
9	1	S	H	(0 0.7 0.3)
10	1	S	VH	(0 0.8 0.2)
11	1	M	VS	(0 0.9 0.1)
12	1	M	S	(0 1 0)
13	1	M	M	(0 0.5 0.5)
14	1	M	H	(0.1 0.9 0)
15	1	M	VH	(0.2 0.8 0)
16	1	H	VS	(0.25 0.75 0)
17	1	H	S	(0.3 0.7 0)
18	1	H	M	(0.4 0.6 0)
19	1	H	H	(0.5 0.5 0)
20	1	H	VH	(0.6 0.4 0)
21	1	VH	VS	(0.7 0.3 0)
22	1	VH	S	(0.8 0.2 0)
23	1	VH	M	(0.9 0.1 0)
24	1	VH	H	(0.95 0.05 0)
25	1	VH	VH	(1 0 0)
—	—	—	—	——

序号	规则权重	属性		误差系数置信度分配
序号	规则权重	x₁	x₂	误差系数置信度分配
1	0.61	VS	VS	(0 0 1)
2	0.99	VS	S	(0.1 0.9 0)
3	0.39	VS	M	(0.88 0.05 0.07)
4	0.42	VS	H	(0.75 0.20 0.05)
5	0.09	VS	VH	(0.21 0.29 0.50)
6	0	S	VS	(0.36 0.56 0.80)
7	0.17	S	S	(0.99 0 0.01)
8	0.56	S	M	(0.92 0.05 0.03)
9	0.48	S	H	(0.53 0.30 0.17)
10	0.96	S	VH	(0.28 0.40 0.32)
11	1	M	VS	(0.04 0.64 0.31)
12	0.08	M	S	(0.21 0.79 0)
13	0.81	M	M	(0.59 0.24 0.17)
14	0.44	M	H	(0.49 0.35 0.16)
15	0.2	M	VH	(0.52 0.48 0)
16	0.01	H	VS	(0.31 0.66 0.03)
17	0.71	H	S	(0.39 0.34 0.27)
18	0.97	H	M	(0.57 0.28 0.15)
19	0.51	H	H	(0.69 0.21 0.10)
20	0.82	H	VH	(0.93 0.06 0.01)
21	0.71	VH	VS	(0.97 0 0.03)
22	0.07	VH	S	(0.56 0.39 0.05)
23	0.56	VH	M	(0.37 0.30 0.33)
24	1	VH	H	(0.67 0.25 0.08)
25	0.94	VH	VH	(0.99 0.01 0)
—	—	—	—	——

Forecasting method of the error coefficient for SIMU based on belief rule base

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 27

Related Articles 7

Recommended Articles

Metrics

Comments

预测方法	MSE
基于传统BRB构建的误差系数预测模型	2.71×10^-5
基于改进BRB构建的误差系数预测模型	8.74×10^-6
优化后传统的BRB误差系数预测模型	1.54×10^-6
优化后改进的BRB误差系数模型	6.29×10^-7
BP神经网络	1.72×10^-6
模糊推理	2.21×10^-6

[1]	Jie LEI, Xiaobin XU, Xiaojian XU, Leilei CHANG. Concurrent fault diagnosis method based on belief rule base [J]. Systems Engineering and Electronics, 2020, 42(2): 497-505.
[2]	WANG Xiaoyan, SUN Jianbin, ZHAO Qingsong, CHANG Leilei, ZOU Zhigang. Belief rule base approach for capability satisfactory evaluation with incomplete information [J]. Systems Engineering and Electronics, 2019, 41(11): 2507-2513.
[3]	JIANG Wenqi, QI Chenchen. Research on similarity measure model of interval-valued intuitionistic fuzzy sets driven by feature matching [J]. Systems Engineering and Electronics, 2018, 40(11): 2505-.
[4]	DI Ruohai, GAO Xiaoguang, GUO Zhigao. Bayesian networks structure learning based on improved BIC scoring [J]. Systems Engineering and Electronics, 2017, 39(2): 437-444.
[5]	XIE Jing, XIAO Long-xu, REN Ge-xue. Error coefficients separation and compensation for SIMU [J]. Journal of Systems Engineering and Electronics, 2011, 33(12): 2716-2720.
[6]	HONG Bei，HU Chang-hua，JIANG Xue-peng. Error prediction of IGS based on subject and object information [J]. Journal of Systems Engineering and Electronics, 2010, 32(8): 1785-1788.
[7]	QIAN Shan, LI Peng-kui, ZHANG Shi-feng, CAI Hong. Study on modeling of MIMU/GPS navigation and time delay synchronization method of GPS [J]. Journal of Systems Engineering and Electronics, 2009, 31(6): 1432-1435.