基于陀螺仪的车辆前轮转角测量方法研究

发布时间：2018-03-29 03:23

本文选题：嵌入式系统　切入点：陀螺仪　出处：《上海工程技术大学》2016年硕士论文

【摘要】：目前,智能车辆主要使用传统的角度传感器来作为敏感前轮转向角的器件。但是,车辆的转向过程十分复杂,既有车轮相对地面的转动,也有车轮相对地面的移动。因此,传统的角度传感器满足不了实际需求中高精度、实时性和智能化的要求。本文在对陀螺仪工作原理和数据处理方法研究的基础上,采用硅微陀螺仪对车辆前轮偏向角的实时动态数据进行测量,着重分析了陀螺仪原始信号的数据处理方法并进行了仿真实验。通过两种滤波方法的比较,选择扩展卡尔曼滤波对陀螺信号进行处理。最后,在实验平台上进行跑车实验,通过设计优良的扩展卡尔曼滤波方法实现偏角信号的精准处理。本论文的具体研究内容如下:1)首先介绍了陀螺仪的工作原理,包括利用高速旋转的刚体在外力矩作用下产生进动和利用科里奥利力产生科氏加速度两种基本原理。然后,简要介绍了陀螺仪的分类和基本特征。最后,介绍了惯性坐标系的分类及选取。在此基础之上,本文详细介绍了实验中采用的CRS03-02S型硅微陀螺仪的工作原理、动态方程和技术参数。2)应用基于时间序列的分析方法进行硅微陀螺仪CRS03-02S的漂移误差分析及建模,分别对陀螺仪采集的数据分别进行平稳性、正态性和独立性检验。在深入研究陀螺仪原始数据三种特性的基础上进行陀螺仪漂移误差模型建模,为后续的漂移误差补偿提供一个良好的数学模型。最后,着重研究如何消除陀螺仪的随机漂移误差。3)详细研究了卡尔曼滤波和扩展卡尔曼滤波这两种数据处理方法,深入探讨了卡尔曼滤波和扩展卡尔曼滤波两种滤波过程,并分别研究了这两种滤波方法的滤波性能,并进行仿真结果比较。通过比较分析,选择精度最高、实时性最好、测量最稳定的扩展卡尔曼滤波改进算法来作为本实验的跑车算法。4)进行小车跑车实验,通过陀螺仪来进行车辆前轮转角测量。介绍了实验小车平台和S3C2440A处理器的特征参数,在ARM平台上运用卡尔曼滤波和改进后的卡尔曼滤波方法对测量结果进行数据处理。最后,将通过卡尔曼滤波处理后得到的角度信息与运用扩展卡尔曼滤波处理后得到的角度信息进行比较分析,可以看出扩展卡尔曼滤波在处理陀螺仪测量数据中的优良性能。
[Abstract]:At present, the intelligent vehicle mainly uses the traditional angle sensor as the device of the sensitive front wheel steering angle. However, the steering process of the vehicle is very complicated, both the rotation of the wheel relative to the ground and the movement of the wheel relative to the ground. The traditional angle sensor can not meet the requirements of high precision, real time and intelligence in the actual demand. Based on the research of the working principle and data processing method of gyroscope, the traditional angle sensor can not meet the requirement of high precision, real time and intelligence. The real-time dynamic data of vehicle front wheel deviation angle are measured by silicon microgyroscope. The data processing method of the original signal of gyroscope is analyzed and the simulation experiment is carried out. The extended Kalman filter is selected to process the gyro signal. Finally, the sports car experiment is carried out on the experimental platform. The precise processing of deflection signal is realized by designing excellent extended Kalman filter method. The specific research contents of this paper are as follows: (1) the working principle of gyroscope is introduced firstly. It includes two basic principles: precession under the action of external torque of rigid body with high speed rotation and Coriolis force generating Coriolis acceleration. Then, the classification and basic characteristics of gyroscope are briefly introduced. The classification and selection of inertial coordinate system are introduced. On this basis, the working principle of CRS03-02S silicon micro gyroscope used in the experiment is introduced in detail. Dynamic equation and technical parameter. 2) the drift error analysis and modeling of silicon microgyroscope (CRS03-02S) are carried out based on time series analysis method, and the data collected by the gyroscope are stationary, respectively. Based on the in-depth study of the three characteristics of the original data of gyroscope, the model of gyroscope drift error model is established, which provides a good mathematical model for the subsequent compensation of drift error. This paper focuses on how to eliminate the random drift error of gyroscopes. (3) the two data processing methods of Kalman filter and extended Kalman filter are studied in detail, and the two filtering processes of Kalman filter and extended Kalman filter are discussed in depth. The filtering performance of the two filtering methods is studied, and the simulation results are compared. The most stable extended Kalman filter (EKF) improved algorithm is used as the roadster algorithm of this experiment to carry out the vehicle sports car experiment, and the gyroscope to measure the front wheel angle of the vehicle. The characteristic parameters of the experimental vehicle platform and S3C2440A processor are introduced. Using Kalman filter and improved Kalman filter on ARM platform to process the data of measurement results. Finally, The angle information obtained by Kalman filtering is compared with the angle information obtained by extended Kalman filter, and the excellent performance of extended Kalman filter in the processing of gyroscope measurement data can be seen.
【学位授予单位】：上海工程技术大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U463.6

【相似文献】