摘要
随着信息融合技术的广泛应用,其算法日趋成熟。线控汽车作为概念全新的车辆,其安全性尤为重要,必须根据当前的车辆状态和环境信息以及驾驶员意图控制决策车辆的行驶路径,以保证线控汽车的主动安全性。本文提出了利用信息融合中的卡尔曼滤波算法通过建模估算出车辆状态和路面附着系数,并应用到线控车转向控制策略中,从而为提高线控汽车的安全性奠定基础。
本文以三自由度车辆动力学模型为基础,利用线性、非线性轮胎动力学模型,分别建立了经典卡尔曼、扩展卡尔曼、双重扩展卡尔曼估计汽车状态和路面附着系数的递推估算模型,同时提出了基于车辆状态及反馈的线控汽车转向控制策略,并分别在CarSim车辆软件、自行设计的线控汽车仿真试验台和实车试验进行验证。结果表明,所提出的估算方法能准确估计车辆状态和路面参数。
本文的创新之处在于通过双重扩展卡尔曼算法,实时、准确地估算出车辆状态与路面参数的变化,并将其应用到线控汽车转向控制策略中,使线控汽车具有更高的主动安全性和易驾驶性。同时本文为研究的需要还设计了线控汽车仿真试验台。
Information fusion, an automated comprehensive information processing technology, has had significant development in the last decade. The technology provides more comprehensive, reliable and accurate signal source through the integration of real-time, redundant or complementary signals provided by multiple sensors, so as to guarantee the enhancement of system performance. On the other hand, along with the rapid development of electrical-electronic technology and the micro-processing technology,Steer–by-wire, as a new generation of automotive mechatronics control technology, has been increasingly attracted attentions from the world's major automobile manufacturers. One of the key technologies for practical real-time access of Steer–by-wire vehicle to motor sport is the motion state and road status information. As the number of vehicle states and road condition information (such as side slip Angle, road tire friction coefficient, etc.) are difficult and costly to obtain accurately. Researches on how to obtain accurate state of vehicle and road state estimation algorithm and put it into practical use for steer–by-wire vehicle have important theoretical and engineering significance.
Based on the steering control information fusion theory, this paper proposed to use a decoupled extended Kalman filtering technique to achieve steering control of steer–by-wire vehicle and accurate estimation the state of motion and road tire friction coefficient, which has been broadly tested by a test-bed. The main contribution of this paper are as follows:
1、steer–by-wire vehicle steering control Control Strategy
Steering performance is an important evaluation criterion of steer–by-wire vehicle performance, it directly affects the handling and stability capability of steer–by-wire vehicle. To ensure safe driving of vehicles, reducing traffic accidents plays an important role. Due to the cancellation of the fixed connection between the steering wheel and front wheel steering mechanism (ie, steering ratio is no longer a fixed value), steering ratio is changed to ensure the yaw rate gain G~r_(δ_(sw)) on the steering wheel angle to be always a fixed value. The steer–by-wire simulation test-bed test verification shows that when G~r_(δ_(sw)) is 0.5, the driver has the best subjective feeling, and can adapt to a variety of speed and driving conditions. Based on this theory, the yaw rate and lateral acceleration feedback steering control strategy are integrated to verify different road adhesion coefficient respectively. Testing results show that the use of fixed gain of yaw rate and yaw rate and lateral acceleration feedback control can improve the steer–by-wire vehicle control and stability, and reduce driver’s burden.
2、The application of information fusion on estimation algorithm of steer–by-wire vehicle status parameters and road tire friction coefficient
On steer–by-wire vehicle steering control strategy, various types of state parameters of vehicles and roads are needed to be estimated. This paper adopts information fusion technology,in using of Kalman filter theory for rapid simulation and estimation of these parameters. Using different vehicles model and tire model, the classical Kalman filter, extended Kalman filter, the decouple extended Kalman filter recursive estimation models are established and verified. Experimental results show that dynamic model based on three degrees of freedom, using the HSRI tire model of a decouple extended Kalman filter, not only accurately estimates the vehicle state parameters, but also estimates the road tire friction coefficient in real-time. Road tire friction coefficient is difficult to measure in practice, but it is essential to control steer–by-wire vehicle. When there are changes on road, steer–by-wire vehicle control strategies must be changed accordingly. In the decouple extended Kalman filter, two recursive state and parameter estimation model exists in parallel, while they are dependent on each other, and has real-time interaction correction to forecast information, which quickly converges towards estimated true value for simulation. The accurate estimation of decouple extended Kalman filter theory in the vehicle state and road information make it possible that some of the parameters can be estimated, which is proved to be difficult to obtain, and it also provides the necessary conditions for steer–by-wire vehicle steering control strategy. In the meantime, the validity and feasibility of this algorithm have been verified by steer–by-wire simulation and the real vehicle test-bed venue.
3、Steer–by-wire simulation test-bed software and hardware design
In this paper, a steer–by-wire simulation test-bed is proposed, which is composed of two parts, namely test-bed hardware and software. For software designation, real-time computing power steering models, data acquisition system and control the caller are proposed. For hardware designation, real steer–by-wire system, drivers operating systems, instrument show the simulated system are proposed. Steer–by-wire simulation test-bed is developed for the driver in order that a real operating environment is provided, in which the estimation results of parameters and state is more similar with the actual vehicle condition. This paper focuses on the simulation test-bed signal acquisition and control methods and drive control algorithms, including stalls, pedal displacement, ignition, steering, steering wheel angle etc; analog, digital acquisition card and timing card driver control algorithm preparation. From this research, a real driver, steer–by-wire system hardware and real-time dynamic model of the organic integration are achieved. The entire test-bed provides a person - Vehicle Hardware-in-loop simulation platform, on which experiments can be carried out without danger to any limits of environmental conditions. Sufficient preparatory works have also been done for the real vehicle field test
4. steer–by-wire vehicle status parameters and the road tire friction coefficient estimation algorithm validation
Using the theory of information fusion technology to model the simulation, this paper respectively controlled steer–by-wire simulation test-bed and give real vehicle field test validation. During the steer–by-wire simulation test-bed, we propose to use Carsim vehicle dynamics model and the matlab built under the Simulink simulation model state estimation, real-time interactive vehicle status information, the vehicle and road condition are then estimated. Test-bed experimental results show that the algorithm is able to precisely estimate the speed, centroid lateral angle, road tire friction coefficient and other state parameters. The validity and accuracy of the proposed estimation algorithms are also verified by field test using real vehicle data.
The novelty of this paper is that we propose to use decoupled extended Kalman filter theory to accurately estimate the vehicle state parameters and road tire friction coefficient, the proposed estimation algorithm is able to estimate the state parameters, including vehicle speed, lateral acceleration, yaw rate and the road tire friction coefficient etc. Estimation results are applied to steer–by-wire vehicle steering control feedback strategy to improve the active operation safety of steer–by-wire vehicle. This paper also designed a steer–by-wire simulation test-bed software and hardware equipment, which is proved to be a good platform for fugure research on steer–by-wire vehicle theory. We design a set of wheel speed signal acquisition methods, which is proved to able to accurately capture the vehicle speed signal. The proposed research achievements lay the foundation for the theoretical and practical development of steer-by-wire vehicle in the future.
引文
[1] The Hands-off approach[J],Automotive engineer,2000 November:38-39
[2] X-By-Wire Safety Related Fault Tolerant Systems in Vehicle Project. No: BE 95/1329 Contract No: BRPR-CT95-0032
[3] Jun T.,Naohiro Y.,Shoichi S.,Shigenori T.,Effects of Steering System Characteristics on Control Performance from the Viewpoint of Steer-by-Wire System Design[C],SAE 1999-01-0821:139-152
[4]王菊,左建令.汽车线控转向技术的研发现状及发展前景[J].汽车研究与发展,2005(5):30-35
[5] Bert Breuer,Ulrich Eichhorn,Jurgen Roth.Measure of tire-road friction ahead of the car and inside the tire[C].Proceeding of AVEC,1992,p347-353
[6] H.Eric Tseng,Li Xu,and Davor Hrovat.Estimation of land vehicle roll and pitch angle[C].Proceeding of AVEC’06,AVEC060191
[7] Paul Yih,J. Christian Gerdes.Steer-by-Wire for Vehicle State Estimation and Control[C].AVEC04,p785-790
[8]刘国福,张屺,王跃科,周停停.ABS系统基于数据融合技术的车速估计方法[J].仪器仪表学报,2004年,25卷第4期增刊
[9]胡丹.基于双扩展卡尔曼滤波的汽车状态及路面附着系数估计算法研究[D].长春:吉林大学汽车学院,2009.
[10] Sasaki H,Nishimaki T.A side-slip angle estimation using neural network for a wheeled vehicle[J].SAE Paper 2000-01-0695
[11]李君,喻凡等.基于道路自动识别ABS模糊控制系统的研究[J].农业机械学报,2001,32(5):26-29
[12] Zhang J,Xu S,Rachid A.Robust sliding mode observer for automatic steering vehicles[C].IEEE Intelligent Transportation Systems Conference,Dearborn,USA, 2000:85-90
[13] Cherouat H,Braci M,Diop S.Vehicle velocity,side slip angles and yaw rateestimation[C].IEEE International Symposium on Industrial Electronics,2005:349-355
[14] Hiemer M,Vietinghoff A,Kiencke U. Determination of the vehicle body side slip angle with non-linear observer strategies[C].SAE Paper 2005-01-0400
[15] Fukada Y.Estimation of vehicle slip angle with combination method of model observer and direct integration[C].5 International Symposium on Advanced Vehicle Control,Nagoya,JAPAN,1998:201-205
[16] O’brien R, Kiriakids K.A comparison of H∞with kalman filtering in vehicle state and parameter identification[C].American Control Conference,Minneapolis,Minnesota,14-16 June,2006:3 954-3 959
[17] Medy Satria and Matthew C. Best.Comparison Between Kalman Filter and Robust Filter for Vehicle Handling Dynamics State Estimation[J].SAE technical paper series,2002-01-1185
[18]高振海,郑南宁,程洪.基于车辆动力学和Kalman滤波的汽车状态软测量[J].系统仿真学报,Jan.2004,Vol.16,No.1
[19] TA Wenzel,K J Burnham,M V Blundell,R A Williams.Approach to Vehicle State and Parameter Estimation using Extended Kalman Filtering[C].AVEC04,p725-730
[20] TA Wenzel,K J Burnham,M V Blundell and R A Williams.Dual extended Kalman filter for vehicle state and parameter estimation[J].Vehicle System Dynamics Vol.44,No.2,February 2006.153-171
[21]宗长富,胡丹,杨肖,潘钊,徐颖.基于扩展卡尔曼滤波的汽车行驶状态估计[J].吉林大学学报工学版,Vol.39,No.1,P7—11
[22]边明远.汽车主动安全性控制系统路况识别技术纵览[J].试验与研究,2002年第1期,p31-33
[23] Matthew C Best and Timothy J Gordon.Combined state and parameter estimation of vehicle handling dynamics[C].Loughborough University,UK,Proceeding of AVEC 2000,5th Int’l Symposium on Advanced Vehicle Control,August 22-24,2000,Ann Arbor,Michigan
[24] Matthew A Wilkin , David C Crolla , Martin C Levesley , and Warren JManning.Estimation of non-linear tire forces for a performance vehicle using an extended kalman filter[J].SAE,2004-01-3529
[25] St Germann M,Dai.A.Monitoring of friction coefficient between tire and road surface[C].IEEE 1994:613-618
[26] F. Gustafsson.Monitoring Tire-Road friction using the wheel slip[J].IEEE Control System, 1998: 42-49.
[27] Dai.A . Model based calculation of friction curves between tire and road surface[C].IEEE 1995: 291-195
[28] Jin-Oh Hahn,Rajesh Rajamani.GPS-Based Real-Time Identification of Tire–Road Friction Coefficient[C].IEEE transaction on control systems technology,Vol.10,No.3,May 2002,p331-343
[29]宗长富,左建令,管欣等,汽车电子转向技术的发展与展望[J]。汽车技术,2003,1
[30]田红涛,杨明忠,汽车线控转向系统的应用和发展[J],机械制造,2008,46(6):53-55
[31]宗长富,麦莉,郭学立,汽车前轮电子转向系统[J],中国记协工程,2004,15(11)
[32]贾和平,钟绍华,汽车线控转向系统的技术探讨[J],中国汽车制造,2006(4):21-24
[33] Führer, T., Schedl A., The Steer-By-Wire Prototype Implementation: Realizing Time Triggered System Design, Fail Silence Behavior and Active Replication with Fault-Tolerance Support[C]. SAE Paper, 1999-01-0400
[34] Harter, W., Pfeiffer, W., Dominke, P., Ruck, G., Future Electrical Steering System: Realizations with Safety Requirements[C]. SAE Paper, 2000-01- 0822.
[35] Yih, P., Steer-By-Wire: Implications for Vehicle Handling and Safety. Dissertation for the degree of doctor of philosophy, Department of Mechanical Engineering, Stanford University. California, USA
[36] Takimoto, S., A Study of Driver’s Behavior in Turing a Curve[C]. Proc. of JSAE Spring Convention. No, 9732748
[37] Tajima, J., Yuhara, N., Takimoto, S., Kawai T., Shimizu, Y., Research on Effect ofSteering Characteristics on Control Performance of Driver-Vehicle System from a Viewpoint of Steer-by-Wire System Design[C]. Proc. of AVEC 1998, Nagoya, Japan.
[38] Tajima, J., Yuhara, N., Sano, S., Takimoto, S., Effects of Steering System Characteristics on Control Performance from the Viewpoint of Steer-by-Wire System Design[J]. SAE Paper, 1999-01-0821.
[39] Segawa, M., Nishizaki, K., Nakano, S., A Study of Vehicle Stability Control by Steer-by Wire System[C]. Proc. of AVEC 2000, Ann Arbor, Michigan.
[40] Segawa, M., Kimura, S., Kada, T., Nakano, S., A Study of Reactive Torque Control for Steer-by Wire System[C]. Proc. of AVEC 2002, Hiroshima, Japan.
[41] Chai, Y. W., Saitou, Y., Kano, Y., Abe, M., A Study on Effects of Derivative Parameters in a SBW System for Active Interface Vehicle on Driver-Vehicle System Performance[C]. Proc. of AVEC 2004, Arnhem, The Netherlands.
[42] Hiraoka, T., Nishihara, O., Kumamoto, H., Steering Reactive Torque Control for Steeri-By-Wire Vehicle[C]. Proc. of AVEC 2006, Taipei, Taiwan.
[43] Yao, Y. X., Vehicle Steer-by-Wire System Control[J]. SAE Paper 2006-01-1175.
[44] Cesiel, D., Gaunt, M. C., Daugherty, B., Development of a Steer-by-Wire System for the GM Sequel[J]. SAE Paper, 2006-0101173.
[45] Kaufmann, T., Millsap, S., Murray, B., Petrowski, J., Development Expierence with Steer-by-Wire[J]. SAE Paper, 2001-01-2479.
[46] Verschuren, R.M.A.F., Duringhof, H.M.., Design of a Steer-by-Wire Prototype[C].SAE paper, 2006-01-1497
[47]罗石,商高高,苏青祖。线控转向系统转向盘力回馈控制模型的研究[J]。汽车工程,2006,10(28):914-917
[48]于蕾艳,线控转向系统的转向盘力反馈控制策略研究[J],计算机仿真,2009(1):264-266,279
[49]于蕾艳,林逸,施国标,线控转向系统的主动转向控制策略研究[J],计算机仿真,2009,25(5):233-235,343
[50]施国标,于蕾艳,林逸,四轮线控转向横摆角速度反馈控制策略研究[J],系统仿真学报2008,20(2):506-508
[51] Ying Xu , Chang-fu Zong , Hsiao-hsiang Na, Lei Liu,Investigations on Control Algorithm of Steady-State Cornering and Control Strategy for Dynamical Correction in Steer-by-Wire System[J],Journal of Zhejiang University,2009,10(6):900-907
[52]王凤朝,黄树采,韩朝超,多传感器信息融合及其新技术研究[J],航空计算技术,2009,39(1):102-106
[53]何友[1]薛培信[3]王国宏[1,2],一种新的信息融合功能模型,海军航空工程学院学报,2008,23(3):241-244,248
[54]王莉于盛林严仰光,多传感器信息融合结构及其实现[J],仪器仪表学报,2001(z1)
[55]汪文元,张晓囡,朱林,徐兴杰,信息融合流模型研究[J],计算机仿真,2009(2):118-122
[56]韩崇昭,朱洪艳,多传感信息融合与自动化[J],自动化学报,2002(s1) [128] SHI Hang,YAN Liping,LIU Baosheng,ZHU Jihong,A Sequential Asynchronous Multirate Multisensor Data Fusion Algorithm for State Estimation[J],电子学报:英文版,2008,17(4):630-632
[57]蒋碧鸿,苏毅康,信息融合在状态检测中的应用[J],上海电力学院学报,2009,25(3):264-266
[58]龚延成,刘平方,陆克久,基于信息融合技术的汽车故障模式识别系统[J],军事交通学院学报,2009,11(2):35-39
[59] Feiyu Lian; Qing Li; Maixia Fu; Yuan Zhang,A Study on a General Model of Information Fusion Networks[C] , Computational Intelligence and Industrial Application, 2008,12(1):579-584
[60] Wei-Guo Tong; Bao-Shu Li; Xiu-Zhang Jin,A Study on Model of Multisensor Information Fusion and its Application[C],Machine Learning and Cybernetics,2006,8(13):3073-3077
[61]潘泉,于昕,程咏梅,张洪才.信息融合理论的基本方法与进展[J],自动化学报,2003,29(4)
[62]宋新民,王格芳,冯锡智,基于信息融合技术的复杂系统故障诊断研究[J],仪器仪表学报,2006 27(z2)
[63]康耀红,数据融合理论与应用[M],西安电子科技大学出版社,1997
[64]何友,王国宏,多传感器信息融合及应用[M],电子工业出版社,2001
[65]杨万海,多传感器数据融合及其应用[M],西安:西安电子科技大学出版社,2004年
[66] E.Waltz, J. Llinas. Multi-sensor Data Fusion[M]. Artech House, Norwood, Massachusets, 1990.
[67] L Wald. An European Proposal for terms of reference in data fusion[J]. International Archives of Photogrammetry and Remote Sensing. 1998,7:651~654
[68] Nandhakumar N, Aggarwal J. K. Physics-based Integration of Multiple Sensing Modalitiesfor Scene Interpretation. 1997,85(1):147~163
[69] Kalman,R.E.A New Approach to Linear Filtering and Prediction Problems[J],Transaction of the ASME-Journal of Basic Engineering.1960(3):35-45
[70] Greg Welch and Gary Bishop.Introduction to Kalman filter[J].Department of Computer Science,University of North Carolina at Chapel Hill3,Chapel Hill,NC 27599-3175
[71] Greg Welch and Gary Bishop,Department of Computer Science University of North Carolina at Chapel Hill ,http://www.cs.unc.edu/~welch/kalman/media/pdf/kalman_intro_chinese.pdf
[72] Brown,R.G. and P.Y.C. Hwang.Introduction to Random Signals and Applied Kalman Fitering[M],Second Edition,John Wiley & Sons,Inc
[73] Grewal , Mohinder S , and Angus P Andrews . Kalman Filtering Theory and Practice.Upper Saddle River,NJ USA,Prentice Hall
[74] Maybeck, Peter S. Stochastic Models, Estimation, andControl[M], Volume 1, Academic Press, Inc.1979
[75] Sorenson, H. W. Least-Squares estimation: fromGauss to Kalman[C], IEEE Spectrum, 1970(7):63-68
[76]库索夫科夫,控制系统的最优滤波和辨识方法[M].北京:国防工业出版社,1984
[77]秦永元.张洪钺,汪叔华.卡尔曼滤波与组合导航原理[M].西北工业大学出版社,1998.
[78]蒋志凯.数字滤波与卡尔曼滤波[M].中国科学技术出版社,1993.3
[79]杨卫,李艳,基于振动传感器的车辆信号采集与分析[J],传感器与微系统,2009,28(5):22-24,27
[80]林海军滕召胜迟海易钊邬蓉蓉。基于多传感器信息融合的汽车衡误差补偿[J]。仪器仪表学报,2009(6):1245-1250。
[81]徐宁寿,随机信号轨迹与系统控制[M],北京:北京工业大学出版社,2001
[82]庄继德,汽车轮胎学[M],北京:北京理工大学出版社,1996
[83] Sage A P,Husa G W,Adaptive filtering with unknown prior statistics[C],Proceedings of Joint Automatic Control Conferece,1969:760-769
[84]徐倜凡,高晓杰.车辆纵向速度估算算法发展现状综述[J].上海汽车,2007年6月
[85]付梦印,邓志红,Kalman滤波理论及其在导航系统中的应用[M],北京:科学出版社,2003
[86]张常云,自适应滤波方法研究[J],北京:航空学报,1998.12
[87]吕伟杰刘鲁源,多模型自适应控制理论的研究[J],仪器仪表学报, 2001(z1):440-441,444
[88]安部正人(著),陈辛波(译).汽车的运动和操纵[M].机械工业出版社,1998,p. 61-66
[89]孙春玲韩加蓬张锋,基于模型的轮胎参数估计方法[J],山东理工大学学报,2007,21(4):100-102
[90]郭孔辉,汽车操纵动力学[M],长春:吉林科学技术出版社,1991
[91]曹登庆,含不确定参数的车辆动力学模型横向稳定性分析[J],西南交通大学学报,1999,34(3):253-258
[92]吴植民,汽车构造[M],北京:人民交通出版社,1993
[93] Guang rong Chen and Charles K。Chui。A modified adaptive Kalman Filter for Real-Time Applications[J]。IEEE Transactions on Aerospace and Electronics System,1991,27(1):149-154
[94]王瑞平,陈序三,KALMAN滤波的实现和自适应处理[J],核电子学与探测技术,2001.11
[95]刘国庆,卡尔曼滤波器的自适应算法[J],南京:南京化工学院学报,1995.1
[96]冉陈键,顾磊,邓自立,相关观测融合Kalman估值器及其全局最优性[J],控制理论与应用,2009,26(2):174-178
[97] Massaki Wada Kang Sup Yoon Hideki Hashimoto, high accuracy road vehicle state estimation using Extended Kalman Filter [C], 2000 IEEE Intellignet Transportation System Conference Proceedings Dearborn(MI),USA.2000,10(3)
[98]靳立强,王庆年等.四轮独立驱动电动汽车动力学控制系统仿真.吉林大学学报(工学版)2004年34(04):547-553
[99]刘学军,张金换等.汽车侧面碰撞模型的非线性动力学参数辨识[J].清华大学学报(自然科学版),1999,39(2):106-109.
[100]陈一虹,颜东,张洪钺,应卡尔曼滤波实时性研究及其应用[J],北京:北京航空航天大学学报,1996.4
[101]刘兰波,基于EKF的车辆组合导航滤波方法研究[J],科技论坛,2005.9
[102]邹卫军,薄煜明,差分线性化EKF滤波方法研究[J],计算机工程与应用,2009,45(9):64-66
[103] Wenzel,T.A.,Burnham,K.J.,Blundell,M.V.,Williams,R.A.,Dual extended Kalman filter for vehicle state and parameter estimation[J]. Veh. Syst. Dynam.,44(2):153-171
[104]宗长富,郑宏宇,徐颖,基于信息融合技术的汽车状态估算法[J],吉林大学学报(工学版),2008,38(2):1-4
[105] Larua R. Ray,Nonlinear state and tire force estimation for advanced vehicle control[J],IEEE Transaction on Control System Technology,1995,3(1):117-124
[106] Ray Laura.R.Nonlinear tire force estimation and road friction identification simulation and experiments[J].Automatic 1977,33(10):1819-1833
[107] Laura R.Ray . Experimental determination of tire forces and road friction[C].Proceedings of the American Control Conference,1998,p1843-1847
[108] Christopher R. Carlson and J. Christian Gerdes , Nonlinear Estimation of Longitudinal Tire Slip Under Several Driving Conditions[C],Proceedings of the American Control Conference Denver, Colorado ,2003,6(4)
[109] Behzad Samadi, Reza Kazemi, Kamaleddin Y. Nikravesh and Mansour Kabganian, Real-time estimation of vehicle state and tire-road friction forces[C],Proceedings of the American Control Conference Arlington.
[110] Rabhi, N.K. M”Sirdi and A.Elhajjaji.Estimation of Contact Forces and Tire Road Friction[C].2007 Mediterranean Conference on Control and Automation,T18-007,p1-6
[111] W.R. Pasterkamp and H.B. Pacejka . The Tyre as a Sensor to Estimate Friction[J].Vehicle System Dynamics, 27 (1997), pp. 409-422
[112]徐延海.基于轮速的路面状态识别方法[J].农业机械学报.2007年第38卷第3期
[113]沈勇,徐维庆.基于数据融合技术的道路特征实验识别[J].车辆与动力技术.2004年第4期
[114]余卓平,左建令.基于四轮轮边驱动电动车的路面附着系数估算方法[J].汽车工程2007年(第29卷)第2期
[115]刘长生.汽车轮胎与公路路面附着系数的研究[J].公路,2006(5)
[116] John T.Tielking,Naveen K.Mital.A Comparative Evaluation of Five Traction Tire Models[R].Interim Document 6,Jan.1974,Highway Safety Research Institute,University of Michigan
[117] Susanne,Dirk Jegminat,How to Do Hardware-in-the-loop Simulation Right[J],SAE,2005-01-1657
[118] Mayasa Segawa,Masayasu Higashi,Preliminary Study Concerning the Analysis of Steering Feeling using Hardware-in-the–loop Simulator[C],AVEC060157
[119] Jeha Ryu,HeeSoo Kim,Virtual Environment for Developing Electronic Power Steering Steer-by-Wire System[J],IEEE,1999
[120] Tong-Jin Park,Se-Wook Oh,The Design of a Controller for the Steer-by-Wire System Using the Hardware-in-the-Loop-Simulation System[C],SAE,2002-01-1596
[121] Tim Kaufmann,Scott Millsap,Brian Murray,Jim Petrowski,Development Experience with Steer-by-wire[C],SAE,2001-01-2479
[122] S.Feick and M。Padit,M。Zimmer and R。Uhler,Steer-by-Wire as a Mechatronic Implementation[C],SAE,200-01-082
[123] Syed Nabi,Mahesh Balike,An Overview of Hardware-in-the-loop Testing System at Viston[C]。SAE,2004-01-1240
[124]刘军,穆桂脂,罗石,高建立,谢文磊,线控转向硬件在环系统设计[J],机械设计与制造,2009(3):232-234
[125]田承伟,线控转向硬件在环实验台开发及控制算法研究[D].长春:吉林大学汽车学院,2006.
[126]徐颖,赵旗,刘磊,远程多台计算机同步定时系统的设计与实现,吉林大学学报(理学版),2009(6),P553~557
[127]凌澄,PC总线工业控制系统精粹[M],北京:清华大学出版社,1998,第1版
[128]傅志中,鲜海滢,陈友林,基于PCI总线的高速数据采集设备驱动开发[J],计算机应用,2009,29(2):577-579
[129]潘浩,李洪彪,张朝晖,一种基于RS485总线的远程数据通信系统[J] ,仪器仪表学报,2003,24(4):863-864
[130]浦泓,Windows NT设备驱动程序的中断处理[J],上海大学学报(自然科学版),1999(5):P69-72
[131]周德明,高档微型计算机[M],北京:清华大学出版社,1989
[132]徐颖,汽车驾驶模拟器的实时数据采集与控制系统[D].长春:吉林大学计算机科学与技术学院,2002.
[133] David A.solomon,Windows 2000技术内幕[M],北京大学出版社,2001第1版
[134]杨炳元,Windows操作系统下实时数据采集的实现[J],计算机工程,2000,26(7):153-155
[135]康伟,Windows下实时数据采集的实现[J],计算机应用研究,2001(3):105-106
[106]陈湘波,Windows下实时数据采集程序的探讨[J],微型机与应用,2001(5):51-53
[136] Chris Cant,Windows WDM设备驱动程序开发指南[M],机械工业出版社,2000,第1版.
[137]邓洪峰,基于WDM驱动程序的开发[J],南昌航空大学学报(自然科学版),2007,21(2):105-108
[138]钟钟,基于Windows NT的设备驱动程序开发[J],声学与电子工程,2002,第3期,P35-39。
[139]陈喜,Windows设备驱动程序结构及兼容性探讨[J],海军工程学院学报, 1999,第4期, P69-72
[140]游南琳,用DDK开发Windows NT下的设备驱动程序[J],软件天地,1999,第8期,P8-11
[141]鞠阳,Windows下设计硬件设备驱动程序的方法[J],计算计系统应用, 1998(7):51-52
[142] Peter G.Viscarola,新智工作室译,Windows NT与Windows 2000设备驱动程序开发[M],北京:电子工业出版社,2000
[143]孙守阁等,Windows设备驱动程序技术内幕[M],北京:清华大学出版社,2000
[144]陈坚,Visual C++网络高级编程[M],北京:人民邮电出版社,2001第1版
[145]谭浩强,C程序设计[M],北京:清华大学出版社,1991,第1版
[146]张小跃,张春熹,光纤陀螺/GPS组合导航系统中的信息融合技术[J],北京航空航天大学学报,2008,34(4):422-425
[147]李静,李幼德,赵健,宋大凤.四轮驱动汽车牵引力控制系统研究[J] .吉林大学学报:工学版,2003 ,33 (4) :1-6
[148]褚艺斌廖文良陈文芗,汽车电子控制系统驱动保护电路的分析与设计[J],中国仪器仪表,2005(9):107-109
[149]徐颖,刘磊,赵旗,ABS实时四轮轮速信号采集系统[J],吉林大学学报(理学版),2009,47(5):977-980
[150]白韶红,集成霍尔传感器的发展[J].自动化仪表2003,24(3) :1-7
[151]周云山,于秀敏编著,汽车电控系统理论与设计,北京理工大学出版社,,1999
[152]高阳,李静,赵健,苑绍志等,全时四轮驱动汽车驱动轮牵引力综合控制策略[J],吉林大学学报:工学版,2009,39(2):296-299
[153]童诗白,华成英等,模拟电子技术基础[M],高等教育出版社,2004(第三版)
[154]徐颖,高越,詹军.轮速信号采集系统[P],中国专利,200810050234.1,2008-06-11.
[155]张集乐,鲁植雄. ABS轮速传感器及其信号处理[J] .机械与电子, 2007,12:48-50