间接式频率法轮胎压力监测系统的关键技术研究
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摘要
随着汽车消费市场的日益升温,汽车的安全性能备受消费者的关注。主要用于汽车在行驶时实时地对轮胎气压进行自动监测的轮胎压力监测系统(Tire Pressure Monitoring System, TPMS)是车辆安全行驶的有效保障措施,对提高行车安全具有重要的意义,因此具有广阔的应用前景。基于频率估计实现间接式TPMS系统在具体实现过程中包括一系列的关键技术:轮速信号的误差处理,涉及异点剔除、消除齿轮误差、齿轮误差匹配等;非均匀采样信号的重建过程,即如何高效、快速、不失真地恢复出原始信号;共振频率的提取方法等。
论文对上面关键技术进行了较为深入地研究,具有创新性的工作主要包括以下几个部分:
(1)分析了胎压对轮胎的影响;利用扭转和垂直振动方向的弹簧-阻尼模型建立了连续时间轮胎模型和离散时间扭转振动模型;推导了轮胎共振频率与模型参数之间的对应关系。
(2)描述了轮速传感器的特性,轮速信号的测量及误差产生的原因。研究了异点剔除方法,提出了基于弹性BP神经网络分析轮速传感器误差及实现误差匹配的方法,并根据轮速信号的特点构造了BP神经网络,最终实现了对轮速信号的修正。仿真和实验结果表明,该方法使误差精度达到2×10-4,能够有效地消除传感器误差,提高轮速信号的精度。
(3)比较分析了信号重建的几种方法,包括插值重建,基函数扩展重建等。针对非均匀采样信号的特点,应用自适应权值梯度法(Adaptive Weights Preconditioned Conjugate Gradient method,AWPCG)对信号进行重建。首先,将整个信号的重建过程转化为对Toeplitz方程组的求解;然后在此基础上加入了修正权值,重新得到Toeplitz矩阵;最后采用预条件共轭梯度法对Toeplitz方程组进行求解。理论和仿真结果表明该方法精度高、时延短,能够很好地完成对非均匀采样信号的重建;通过AWPCG方法重建的信号与真实值之间的相对误差不超过0.01。
(4)针对轮速非均匀采样信号插值算法复杂,插值过程耗时较长的问题,提出了应用非均匀小波变换直接对初步修正后未插值轮速数据处理的提取共振频率的方法。仿真结果表明插值后采用均匀采样的小波变换结果稍优于未插值直接用修正后数据采用非均匀采样小波变换结果,而非均匀采样小波变换提取信号频率在耗时和节省系统内存资源方面较插值后应用均匀采样的小波变换有明显优势。
论文利用道路实验采集的不同胎压状态下的轮速信号对弹性BP神经网络分析传感器误差及误差匹配算法、自适应权值梯度法实现非均匀采样信号的重建算法和提取共振频率的AR模型参数法和非均匀小波变换法进行了验证。试验结果表明了论文中提出的各种算法的有效性和可行性。
论文中TPMS系统算法的研究对汽车轮胎安全系统的进一步发展和完善具有一定的参考价值和现实意义。
As the automobile consumption market develops increasingly, consumers care the safety performance of vehicles extraordinarily. The Tire Pressure Monitoring System (TPMS) which is mainly used to monitor the tire pressure during driving in real-time is an effective measure for guaranteeing safe driving. TPMS has crucial significance for improving the safety of vehicles, so it has wide application foreground. The implementation process of indirect TPMS based on frequency estimation includes a series of key technologies: the error process of wheel speed signal which involves singular point rejecting, attenuating toothed wheel errors and errors match; reconstruction of non-uniform sampling signal, which means how to effectively reconstruct the original signal without distortion; resonance frequency estimation and so on.
The key technologies mentioned above have been investigated, and the main creative achievements include:
(1) The tire pressure’s impact on tire has been analyzed; the continuous time tire model and discrete time torsional vibration model have been established by using the spring-damper model of vertical and torsional direction; the function relation between resonance frequency and model parameters has been deduced.
(2) The characteristic of wheel speed sensor, the measurement and error source of wheel speed were investigated. How to reject the singular point has been described. Method of attenuating wheel speed sensor errors and error matching based on Resilient Back Propagation Neural Network has been presented. The corresponding neural network has been designed and the wheel speed correction has been realized finally. The results of simulation and experiment show that the method can make the errors reduce to 2×10-4, which attenuates sensor errors effectively and improves the accuracy greatly.
(3) Means for signal reconstruction have been surveyed comparatively, comprising interpolation, basis expansion and so on. AWPCG (Adaptive Weights Preconditioned Conjugate Gradient method) has been presented to reconstruct the non-uniform sampling signal in allusion to its characteristic. Firstly, Reconstruction process is transformed into solving Toeplitz formulation; then new Toeplitz matrix can be obtained by adding adaptive weights and resolved by utilizing preconditioned conjugate gradient method ultimately. Simulation results show that AWPCG algorithm can reconstruct non-uniform sampling signal precisely without time delay and that the absolute error is not larger than 0.5rad/s and the relative error is less than 0.01.
(4) To lessen the complexity of the non-uniform sampling interpolation algorithm and shorten the spend time of the interpolation process, this dissertation presents the ways to estimate the resonance frequency by means of dealing with the adjusted data but not interpolated using non-uniform wavelet transform. The simulation results show that the estimated resolution utilizing interpolated data and uniform sampling wavelet transform is higher than that utilizing not interpolated data and non-uniform sampling wavelet transform, while the latter has the obvious benefit on saving dealing time and system memory resource.
Algorithms for wheel speed sensor errors’elimination and match through Resilient BP Neural Network by using wheel speed signal under different pressures, non-uniform sampling signal’reconstruction by AWPCG and resonance frequency estimation by AR model parametric way and non-uniform sampling wavelet transform way have been validated. The results show that the algorithms proposed in the dissertation are effective and feasible.
The researches on the algorithms mentioned above in TPMS have reference values and practical meanings for developing and perfecting vehicle safety further.
论文对上面关键技术进行了较为深入地研究,具有创新性的工作主要包括以下几个部分:
(1)分析了胎压对轮胎的影响;利用扭转和垂直振动方向的弹簧-阻尼模型建立了连续时间轮胎模型和离散时间扭转振动模型;推导了轮胎共振频率与模型参数之间的对应关系。
(2)描述了轮速传感器的特性,轮速信号的测量及误差产生的原因。研究了异点剔除方法,提出了基于弹性BP神经网络分析轮速传感器误差及实现误差匹配的方法,并根据轮速信号的特点构造了BP神经网络,最终实现了对轮速信号的修正。仿真和实验结果表明,该方法使误差精度达到2×10-4,能够有效地消除传感器误差,提高轮速信号的精度。
(3)比较分析了信号重建的几种方法,包括插值重建,基函数扩展重建等。针对非均匀采样信号的特点,应用自适应权值梯度法(Adaptive Weights Preconditioned Conjugate Gradient method,AWPCG)对信号进行重建。首先,将整个信号的重建过程转化为对Toeplitz方程组的求解;然后在此基础上加入了修正权值,重新得到Toeplitz矩阵;最后采用预条件共轭梯度法对Toeplitz方程组进行求解。理论和仿真结果表明该方法精度高、时延短,能够很好地完成对非均匀采样信号的重建;通过AWPCG方法重建的信号与真实值之间的相对误差不超过0.01。
(4)针对轮速非均匀采样信号插值算法复杂,插值过程耗时较长的问题,提出了应用非均匀小波变换直接对初步修正后未插值轮速数据处理的提取共振频率的方法。仿真结果表明插值后采用均匀采样的小波变换结果稍优于未插值直接用修正后数据采用非均匀采样小波变换结果,而非均匀采样小波变换提取信号频率在耗时和节省系统内存资源方面较插值后应用均匀采样的小波变换有明显优势。
论文利用道路实验采集的不同胎压状态下的轮速信号对弹性BP神经网络分析传感器误差及误差匹配算法、自适应权值梯度法实现非均匀采样信号的重建算法和提取共振频率的AR模型参数法和非均匀小波变换法进行了验证。试验结果表明了论文中提出的各种算法的有效性和可行性。
论文中TPMS系统算法的研究对汽车轮胎安全系统的进一步发展和完善具有一定的参考价值和现实意义。
As the automobile consumption market develops increasingly, consumers care the safety performance of vehicles extraordinarily. The Tire Pressure Monitoring System (TPMS) which is mainly used to monitor the tire pressure during driving in real-time is an effective measure for guaranteeing safe driving. TPMS has crucial significance for improving the safety of vehicles, so it has wide application foreground. The implementation process of indirect TPMS based on frequency estimation includes a series of key technologies: the error process of wheel speed signal which involves singular point rejecting, attenuating toothed wheel errors and errors match; reconstruction of non-uniform sampling signal, which means how to effectively reconstruct the original signal without distortion; resonance frequency estimation and so on.
The key technologies mentioned above have been investigated, and the main creative achievements include:
(1) The tire pressure’s impact on tire has been analyzed; the continuous time tire model and discrete time torsional vibration model have been established by using the spring-damper model of vertical and torsional direction; the function relation between resonance frequency and model parameters has been deduced.
(2) The characteristic of wheel speed sensor, the measurement and error source of wheel speed were investigated. How to reject the singular point has been described. Method of attenuating wheel speed sensor errors and error matching based on Resilient Back Propagation Neural Network has been presented. The corresponding neural network has been designed and the wheel speed correction has been realized finally. The results of simulation and experiment show that the method can make the errors reduce to 2×10-4, which attenuates sensor errors effectively and improves the accuracy greatly.
(3) Means for signal reconstruction have been surveyed comparatively, comprising interpolation, basis expansion and so on. AWPCG (Adaptive Weights Preconditioned Conjugate Gradient method) has been presented to reconstruct the non-uniform sampling signal in allusion to its characteristic. Firstly, Reconstruction process is transformed into solving Toeplitz formulation; then new Toeplitz matrix can be obtained by adding adaptive weights and resolved by utilizing preconditioned conjugate gradient method ultimately. Simulation results show that AWPCG algorithm can reconstruct non-uniform sampling signal precisely without time delay and that the absolute error is not larger than 0.5rad/s and the relative error is less than 0.01.
(4) To lessen the complexity of the non-uniform sampling interpolation algorithm and shorten the spend time of the interpolation process, this dissertation presents the ways to estimate the resonance frequency by means of dealing with the adjusted data but not interpolated using non-uniform wavelet transform. The simulation results show that the estimated resolution utilizing interpolated data and uniform sampling wavelet transform is higher than that utilizing not interpolated data and non-uniform sampling wavelet transform, while the latter has the obvious benefit on saving dealing time and system memory resource.
Algorithms for wheel speed sensor errors’elimination and match through Resilient BP Neural Network by using wheel speed signal under different pressures, non-uniform sampling signal’reconstruction by AWPCG and resonance frequency estimation by AR model parametric way and non-uniform sampling wavelet transform way have been validated. The results show that the algorithms proposed in the dissertation are effective and feasible.
The researches on the algorithms mentioned above in TPMS have reference values and practical meanings for developing and perfecting vehicle safety further.
引文
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