多维轮力传感器的静态解耦及信号去噪研究
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摘要
汽车运动是地面与车轮作用的结果,测量汽车行驶过程中车轮上各维载荷的变化,对汽车整车和各分系统研究有重要意义。车轮力的测量是基于多维轮力传感器(WFT)关键技术实现的,提高WFT的测量精度具有重要的理论研究意义和工程应用价值。以提高WFT的测量精度为目的,本文进行了WFT的静态解耦及信号去噪研究,完成的工作如下:
(1)在液压标定装置上完成了WFT静态标定试验。根据标定数据特点,提出了小波变换进行粗大误差和随机误差的同步剔除方法;并利用该方法对标定数据进行了滤波预处理,取得了很好的滤波效果。
(2)假设WFT为线性系统并对其进行显著性检验。针对标定WFT时不能真正意义上施加单维力的情况,提出了迭代法实现WFT的静态线性解耦,并进行了WFT耦合度分析。对实车道路试验数据的解耦表明:静态线性解耦消除了WFT系统线性耦合,提高了WFT的测量精度。
(3)为克服静态线性解耦的局限进行了静态非线性解耦研究。提出了基于GA-BP网络的WFT静态非线性解耦方法,根据标定样本数据进行了仿真试验,结果表明该方法具有可行性和有效性。该方法拓宽了解耦的思路。
(4)分析了WFT的信噪特征,确定小波变换作为WFT信号的去噪方法。用常规阈值法和改进的3σ准则阈值法分别对WFT信号进行了去噪,结果表明:阈值去噪法对WFT信号进行去噪能够取得很好的效果;在同样的小波基和分解层次下3σ准则阈值法的去噪效果优于常规阈值法。
The movement of automobile is caused by the mutual force effect between the ground and the wheels, and it is of great significance to measure the multi-axis wheel forces for researching automobile and its subsystems. The wheel forces are measured using a muti-axial wheel force transducer (WFT) as the key technique, and it is valuable in the theory research and engineering applications to improve the measurement precision of WFT. The paper studies static decoupling and signal denoising with the purpose of improving the measurement precision of WFT. The main research work is summarized as follows:
(1) The static calibration tests are finished using the hydraulic calibration device for WFT. According to the characteristics of the calibration data, the paper proposes the method using the wavelet transform to eliminate gross errors and random errors synchronously. Then filtering preprocessing is performed to the calibration data using the proposed method, and good filtering results are achieved.
(2) Assuming WFT is a linear system, the paper does significance tests to verify WFT system’s linear hypothesis. Considering single axis force cannot be put on WFT indeed during the calibration tests, the paper puts forward iterative method to realize linear decoupling, and then characteristics of crosscoupling are analyzed. Data obtained from actual driving conditions in the automobile road experiment is linearly decoupled, and the experimental results show that linear decoupling for WFT eliminates linear crosscoupling error and improves the measurement precision.
(3) To overcome the limitations of static linear decoupling, static nonlinear decoupling of WFT is studied. The paper proposes a static nonlinear decoupling method based on GA-BP artificial neural network. The emulation tests are done using the sample calibration data, and the emulation results indicate that the method is feasible and effective. The proposed method develops ideas of decoupling for WFT.
(4) Having analyzed the signal-to-noise characteristics of WFT, the paper adopts the wavelet transform as the denoising method. The conventional threshold denoising method and the ameliorated threshold denoising method based on the 3σrule are used in denoising WFT signals respectively. The experimental results show that obvious effect can be obtained using threshold denosing method for WFT signals, and the improved threshold method outperforms the conventional threshold method for WFT signals denoising under the same wavelet function and decomposed wavelet levels.
(1)在液压标定装置上完成了WFT静态标定试验。根据标定数据特点,提出了小波变换进行粗大误差和随机误差的同步剔除方法;并利用该方法对标定数据进行了滤波预处理,取得了很好的滤波效果。
(2)假设WFT为线性系统并对其进行显著性检验。针对标定WFT时不能真正意义上施加单维力的情况,提出了迭代法实现WFT的静态线性解耦,并进行了WFT耦合度分析。对实车道路试验数据的解耦表明:静态线性解耦消除了WFT系统线性耦合,提高了WFT的测量精度。
(3)为克服静态线性解耦的局限进行了静态非线性解耦研究。提出了基于GA-BP网络的WFT静态非线性解耦方法,根据标定样本数据进行了仿真试验,结果表明该方法具有可行性和有效性。该方法拓宽了解耦的思路。
(4)分析了WFT的信噪特征,确定小波变换作为WFT信号的去噪方法。用常规阈值法和改进的3σ准则阈值法分别对WFT信号进行了去噪,结果表明:阈值去噪法对WFT信号进行去噪能够取得很好的效果;在同样的小波基和分解层次下3σ准则阈值法的去噪效果优于常规阈值法。
The movement of automobile is caused by the mutual force effect between the ground and the wheels, and it is of great significance to measure the multi-axis wheel forces for researching automobile and its subsystems. The wheel forces are measured using a muti-axial wheel force transducer (WFT) as the key technique, and it is valuable in the theory research and engineering applications to improve the measurement precision of WFT. The paper studies static decoupling and signal denoising with the purpose of improving the measurement precision of WFT. The main research work is summarized as follows:
(1) The static calibration tests are finished using the hydraulic calibration device for WFT. According to the characteristics of the calibration data, the paper proposes the method using the wavelet transform to eliminate gross errors and random errors synchronously. Then filtering preprocessing is performed to the calibration data using the proposed method, and good filtering results are achieved.
(2) Assuming WFT is a linear system, the paper does significance tests to verify WFT system’s linear hypothesis. Considering single axis force cannot be put on WFT indeed during the calibration tests, the paper puts forward iterative method to realize linear decoupling, and then characteristics of crosscoupling are analyzed. Data obtained from actual driving conditions in the automobile road experiment is linearly decoupled, and the experimental results show that linear decoupling for WFT eliminates linear crosscoupling error and improves the measurement precision.
(3) To overcome the limitations of static linear decoupling, static nonlinear decoupling of WFT is studied. The paper proposes a static nonlinear decoupling method based on GA-BP artificial neural network. The emulation tests are done using the sample calibration data, and the emulation results indicate that the method is feasible and effective. The proposed method develops ideas of decoupling for WFT.
(4) Having analyzed the signal-to-noise characteristics of WFT, the paper adopts the wavelet transform as the denoising method. The conventional threshold denoising method and the ameliorated threshold denoising method based on the 3σrule are used in denoising WFT signals respectively. The experimental results show that obvious effect can be obtained using threshold denosing method for WFT signals, and the improved threshold method outperforms the conventional threshold method for WFT signals denoising under the same wavelet function and decomposed wavelet levels.
引文
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[6] 张小龙.辆主动安全性能道路试验系统及评价方法研究:[博士学位论文],东南大学,南京:2006
[7] 宋国民,翟羽健,周晓晶.基于模糊推理的多维传感器解耦算法研究.工业仪表与自动化装置,2001;(1):3-5
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[9] 宋国民.多分力车轮力传感器研究及其在汽车道路试验中的应用:[博士学位论文],东南大学,南京:2001
[10] 姜力,刘宏,蔡鹤皋 等.基于神经网络的多维力传感器静态解耦的研究.中国机械工程,2002;13(24):2100-2103
[11] 姜力,刘宏,蔡鹤皋 等.多维力/力矩传感器静态解耦的研究.仪器仪表学报,2004;25(3):284-287
[12] 宋国民,翟羽健,周晓晶.基于模糊推理的多维传感器解耦算法研究.工业仪表与自动化装置,2001;(1):3-5
[13] Alessandra Flammini, Daniele Marioli, Andrea Taroni. Application of an optimal look-up table to sensor data processing. IEEE Transactions on Instrument and Measurement, 1999; 48(4): 813-816
[14] 徐科军,李成.多维腕力传感器静态解耦的研究.合肥工业大学学报(自然科学版),1999;22(2):1-6
[15] 王国泰,易秀芳,王理丽.六维力传感器发展中的几个关键问题.机器人,1997;19(6): 474-478
[16] 全子一,周利清,门爱东 编著.数字信号处理基础.北京:北京邮电大学出版社,2002
[18] 邓自立 著.卡尔曼滤波与维纳滤波:现代时间序列分析方法.哈尔滨:哈尔滨工业大学出版社,2001
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[20] 林逸榕.基于小波变换的腕力传感器信号消噪:[硕士学位论文],合肥工业大学,合肥:2000
[21] 张文春 主编.汽车理论.北京:机械工业出版社,2005:9-10
[22] 秦文虎.基于多维轮力测量的车辆 ABS 评价系统研究与工程实现:[博士学位论文],东南大学,南京:2005
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