一种改进PSO-ARMA半球谐振陀螺温度误差建模方法

吴宗收; 汪立新; 李新三; 李灿

doi:10.13700/j.bh.1001-5965.2020.0710

一种改进PSO-ARMA半球谐振陀螺温度误差建模方法

doi: 10.13700/j.bh.1001-5965.2020.0710

火箭军工程大学导弹工程学院, 西安 710025

基金项目:

陕西省自然科学基础研究计划 2020JQ-491

陕西省高校科协青年人才托举计划 20200109

详细信息

通讯作者:
汪立新，E-mail: 3115000134@mail2.gdut.edu.cn

中图分类号: V221⁺.3; TB553
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- 文章访问数: 250
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- PDF下载量: 27
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-23
- 录用日期: 2021-05-04
- 网络出版日期: 2022-06-20
- 整期出版日期: 2022-06-20

An improved PSO-ARMA method for temperature error modeling of hemispherical resonator gyroscope

School of Missile Engineering, Rocket Force Engineering University, Xi'an 710025, China

Funds:

Natural Science Basic Research Program of Shaanxi 2020JQ-491

Shaanxi Provincial Association for Science and Technology of Colleges and Universities Young Talents Lifting Program 20200109

More Information

Corresponding author: WANG Lixin, E-mail: 3115000134@mail2.gdut.edu.cn

摘要

摘要:
为减小半球谐振陀螺(HRG)在温度效应下产生的漂移，建立了温度漂移补偿模型，对与温度有关的确定性漂移进行了补偿。提出了一种改进PSO-ARMA建模方法，对不确定性漂移进行了补偿。改进的PSO-ARMA建模方法将惯性权值递减策略引入到反向学习粒子群优化(PSO)算法中，提高算法跳出局部、快速收敛的能力，在建模时利用改进的PSO算法对ARMA参数寻优，以提高模型的精度。利用半球谐振陀螺升温实验数据进行了检验，经该模型补偿后，陀螺输出精度可达0.07°/h，且较传统ARMA建模方法精度提高了一倍。
- 半球谐振陀螺(HRG) /
- ARMA /
- 粒子群优化(PSO)算法 /
- 反向学习 /
- 温度漂移补偿模型
Abstract:
To reduce the drift of hemispherical resonator gyroscope (HRG) caused by temperature effect, a temperature drift compensation model is established, which compensates the deterministic drift related to temperature. An improved PSO-ARMA modeling method is proposed to compensate the uncertain drift. This method introduces the decreasing inertia weight strategy into the opposition-based learning particle swarm optimization (PSO) algorithm, improving the algorithm's ability to jump out of the local and converge fast. In the modeling process, the improved PSO algorithm is used to optimize the ARMA parameters, thus improving the model accuracy. The experimental data of HRG temperature rise are used for verification. After the model compensation, the output accuracy of HRG can reach 0.07°/h, which is twice as high as that of traditional ARMA modeling method.
- hemispherical resonator gyroscope (HRG) /
- ARMA /
- particle swarm optimization (PSO) algorithm /
- opposition-based learning /
- temperature drift compensation model

HTML全文

图 1 HRG谐振子结构示意图

Figure 1. Simple structure diagram of HRG harmonic oscillator

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图 2 改进的PSO-ARMA流程

Figure 2. Flowchart of improved PSO-ARMA

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图 3 HRG输出数据变化曲线

Figure 3. Curve of HRG output data

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图 4 确定性部分补偿结果

Figure 4. Results of deterministic drift compensation

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图 5 ARMA总补偿结果

Figure 5. Overall resucts of ARMA compensation

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图 6 PSO-ARMA总补偿结果

Figure 6. Overall results of PSO-ARMA compensation

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图 7 实验验证曲线

Figure 7. Experimental verification curves

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图 8 两种算法寻优过程对比

Figure 8. Comparison of optimization process between two algorithms

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表 1 AIC(p，q)值

Table 1. Values of AIC(p, q)

q	p
q	1	2	3
1	-21.527	-21.834	-21.848
2	-21.744	-21.856	-21.833
3	-21.791	-21.853	-21.851

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