【摘要】：作為汽車主動(dòng)安全控制系統(tǒng)的關(guān)鍵技術(shù)之一,車輛縱向速度、質(zhì)心側(cè)偏角及橫擺角速度等車輛狀態(tài)參數(shù)的估計(jì)研究受到了越來(lái)越多的關(guān)注。本文研究分別基于整車動(dòng)力學(xué)模型及運(yùn)動(dòng)學(xué)模型的狀態(tài)參數(shù)估計(jì)方法,解決車輛狀態(tài)參數(shù)估計(jì)的實(shí)時(shí)性與精確性問(wèn)題�；趧�(dòng)力學(xué)模型的狀態(tài)參數(shù)估計(jì)研究,采用能充分發(fā)揮動(dòng)力學(xué)模型非線性模擬特性的非線性觀測(cè)技術(shù)對(duì)車輛縱向速度及質(zhì)心側(cè)偏角進(jìn)行估計(jì)。根據(jù)相關(guān)輪胎試驗(yàn)數(shù)據(jù)辨識(shí)出魔術(shù)公式輪胎模型參數(shù),以此為基礎(chǔ)結(jié)合車輛系統(tǒng)動(dòng)力學(xué)建立包括縱向、側(cè)向及橫擺運(yùn)動(dòng)的3自由度整車模型并與車輛動(dòng)力學(xué)仿真軟件Carsim進(jìn)行聯(lián)合仿真分析。根據(jù)所建立的整車動(dòng)力學(xué)模型設(shè)計(jì)非線性車輛狀態(tài)觀測(cè)器,所設(shè)計(jì)非線性觀測(cè)器由一個(gè)非線性子觀測(cè)器和一個(gè)修正模塊組成。非線性子觀測(cè)器采用固定增益形式保證了觀測(cè)器的實(shí)時(shí)性,修正模塊根據(jù)典型工況仿真結(jié)果調(diào)整增益參數(shù)保證估計(jì)的精度�；谶\(yùn)動(dòng)學(xué)模型的車輛狀態(tài)參數(shù)估計(jì)研究,采用能充分發(fā)揮運(yùn)動(dòng)學(xué)模型實(shí)時(shí)性好這一特點(diǎn)的卡爾曼濾波技術(shù)對(duì)車輛縱向速度進(jìn)行估計(jì)。對(duì)輪速傳感器及陀螺儀信號(hào)進(jìn)行濾波處理以降低測(cè)量噪聲。推導(dǎo)出車輪速度與車輛縱向速度關(guān)系,建立考慮縱向滑移率影響的運(yùn)動(dòng)學(xué)估計(jì)模型。根據(jù)估計(jì)模型應(yīng)用卡爾曼濾波技術(shù)建立車輛縱向速度估計(jì)卡爾曼濾波器組,并通過(guò)加權(quán)平均融合由縱向加速度積分得到縱向速度信息估計(jì)出車輛縱向速度值。典型工況下的聯(lián)合仿真結(jié)果表明,本文所提出的兩種車輛狀態(tài)參數(shù)估計(jì)方法具有較高的估計(jì)精度,能夠滿足車輛主動(dòng)安全控制系統(tǒng)要求。
[Abstract]:As one of the key technologies of vehicle active safety control system, more and more attention has been paid to the estimation of vehicle state parameters, such as vehicle longitudinal velocity, side deflection angle of mass center and yaw angle velocity. In this paper, the methods of state parameter estimation based on vehicle dynamics model and kinematics model are studied, respectively, to solve the real-time and accuracy problems of vehicle state parameter estimation. Based on the research of state parameter estimation of dynamic model, the vehicle longitudinal velocity and side deflection angle of mass center are estimated by using nonlinear observation technology which can give full play to the nonlinear simulation characteristics of dynamic model. Based on the related tire test data, the parameters of the magic formula tire model are identified, which are combined with the vehicle system dynamics to establish the longitudinal model. The 3 DOF vehicle model of lateral and yaw motion is simulated and analyzed jointly with the vehicle dynamics simulation software Carsim. According to the established vehicle dynamics model, a nonlinear vehicle state observer is designed. The designed nonlinear observer consists of a nonlinear sub-observer and a correction module. The nonlinear subobserver adopts the fixed gain form to ensure the real-time performance of the observer, and the correction module adjusts the gain parameters to ensure the accuracy of the estimation according to the simulation results of typical operating conditions. The research of vehicle state parameter estimation based on kinematics model and the Kalman filter technique which can give full play to the real time of kinematics model is used to estimate the longitudinal velocity of vehicle. The signal of wheel speed sensor and gyroscope are filtered to reduce the measurement noise. The relationship between wheel speed and vehicle longitudinal velocity is derived, and a kinematics estimation model considering the influence of longitudinal slip ratio is established. According to the estimation model, the Kalman filter bank of vehicle longitudinal velocity estimation is established by using Kalman filter technique, and the longitudinal velocity information is obtained from the longitudinal acceleration integral by the weighted average fusion. The results of joint simulation under typical operating conditions show that the two methods proposed in this paper have high estimation accuracy and can meet the requirements of vehicle active safety control system.
【學(xué)位授予單位】：武漢科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U463.6

【相似文獻(xiàn)】

相關(guān)期刊論文 前6條

1 趙林輝;劉志遠(yuǎn);陳虹;;一種車輛狀態(tài)的非線性估計(jì)方法研究[J];系統(tǒng)仿真學(xué)報(bào);2009年06期

2 郭文強(qiáng);付菊;張玉杰;侯勇嚴(yán);;貝葉斯網(wǎng)絡(luò)在車輛狀態(tài)遠(yuǎn)程故障診斷系統(tǒng)中的應(yīng)用[J];陜西科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年01期

3 王建鋒;李平;;基于多信息融合的車輛狀態(tài)參數(shù)估計(jì)[J];計(jì)算機(jī)仿真;2013年11期

4 趙林輝;劉志遠(yuǎn);陳虹;;一種車輛狀態(tài)滑模觀測(cè)器的設(shè)計(jì)方法[J];電機(jī)與控制學(xué)報(bào);2009年04期

5 楊志強(qiáng);譚_g;;車道保持系統(tǒng)的車輛狀態(tài)預(yù)測(cè)模型[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2008年10期

6 ;[J];;年期

相關(guān)碩士學(xué)位論文 前5條

1 黃皓;基于駕駛操作及車輛狀態(tài)的疲勞駕駛行為檢測(cè)研究[D];東南大學(xué);2016年

2 佘國(guó)芹;基于模型的車輛狀態(tài)參數(shù)估計(jì)研究[D];武漢科技大學(xué);2016年

3 金未平;獨(dú)立分量分析在車輛狀態(tài)監(jiān)測(cè)中的應(yīng)用研究[D];合肥工業(yè)大學(xué);2010年

4 屈肖蕾;基于轉(zhuǎn)向操作和車輛狀態(tài)的疲勞駕駛檢測(cè)方法研究[D];清華大學(xué);2012年

5 黃巍;基于CAN總線的車輛狀態(tài)信息顯示處理系統(tǒng)[D];大連理工大學(xué);2005年

，

本文編號(hào)：2321233