四轮独立线控电动汽车试验平台搭建与集成控制策略研究
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摘要
四轮独立线控电动汽车是一种全新的电动车辆形式，它继承了轮毂电机驱动电动汽车的所有优点，同时将车辆转向系统从传统机械转向机构中解放出来，形成一种全线控的四轮可独立驱动\独立制动\独立转向的先进车辆。与传统车辆相比，它具有更多的可控自由度，因此可以实现常规车辆无法完成的原地转向、斜行以及横向移动等特殊功能，极大的提高车辆的机动性能。同时，通过对整车动力学集成控制系统的设计，可以实现车轮转向、驱动、制动的协调控制，从而可以确保车辆每个轮胎具有最大附着裕度，提高整车的操纵稳定性。四轮独立线控电动汽车代表了未来车辆的发展方向，是目前车辆领域的研究热点之一。
本文依托国家自然科学基金项目“线控汽车底盘控制方法和关键技术研究”（编号：50775096）和“线控转向系统操纵杆及其双向控制方法研究”（编号：51105165），在已有相关研究成果基础上，针对四轮独立线控电动汽车的试验平台和驱动/制动/转向系统集成控制方法展开深入研究，包括四轮独立线控电动汽车整车架构和控制系统开发；四轮独立线控电动汽车底盘集成控制问题中所需状态信息的估算算法研究；四轮独立线控电动汽车驱动/制动/转向集成控制策略研究以及相关试验验证。论文研究工作的具体内容如下：
（1）基于全电控方案搭建了四轮独立线控电动汽车试验平台，并设计了全车控制系统和整车CAN通讯网络应用层协议。
本文首先对四轮独立线控电动汽车试验平台的功能需求进行分析，在此基础上，确定了平台整车结构设计方案，并据此开发出线控四轮独立电动车辆。车辆采用四个轮毂电机、四个电磁制动器和四个力矩电机分别作为整车的驱动、制动和转向系统的执行部件，所有子系统间通过CAN总线进行信息交互。基于整车结构方案，利用模块化设计方法对整车控制系统进行了设计与实现。设计过程中将其划分为传感器单元，整车控制单元以及底层执行单元三个子模块，各子模块在设计中，除了着重考虑其单元功能外，融入了容错控制思想，以提高整个系统可靠性。除此之外，本文还构建了整车的网络架构，并参照SAEJ1939协议建立了全车的CAN总线应用层通讯协议，以便于新的子系统融入整车网络，增强系统的可扩展性。
（2）结合四轮独立线控电动汽车的特点，研究并建立了基于Unscented卡尔曼滤波（简称UKF）理论的车辆状态估算算法。
为解决集成控制中车辆状态信息缺失的问题，本文展开了对四轮独立线控电动汽车状态估算方法的研究。首先建立了包含纵向、侧向和横摆在内的三自由度四轮车辆估算模型，同时结合纵向力计算模型和基于单轨模型的轮胎侧向力模块，根据四轮轮毂电机电动车轮可以精确获得各车轮转速和力矩的特点，设计了UKF状态估算器，并基于四轮电动车辆模型对其进行了仿真与分析。为减小侧向力估算误差和整车质量变化对估算精度的影响，本文对UKF状态估算器进行了改进，引入了HSRI轮胎模型和整车质量估算部分。改进后的估算算法利用四轮电动车辆模型进行了仿真验证。分析结果表明，估算算法提高了估算精度，同时作为状态量的整车质量也会迅速收敛到真实值。
（3）结合分层控制思想，建立了基于有约束模型预测控制算法的驱动/制动/转向集成控制策略。
本文结合四轮独立线控电动汽车四轮纵向力和侧向力独立可控的特点，基于分层控制思想设计了驱动/制动/转向的集成控制策略。集成控制策略划分为集成控制层和有约束控制分配层，单独对其进行设计。集成控制层以跟踪参考模型纵向车速、侧向车速及横摆角速度为目标，基于具有反馈校正特征的模型预测控制算法进行设计，优化计算车辆的整车控制力（矩）即纵向合力、侧向合力及附加横摆力矩，优化过程考虑执行器和路面附着条件引起的约束问题。控制分配层则以优化各车轮轮胎附着裕度为目标，对集成控制层输出的整车控制力（矩）进行四个车轮的优化分配，计算出各车轮的纵向驱动力和车轮转角。针对约束条件中存在的非线性约束问题，为简化计算求解过程，控制分配层分解为等式约束优化和不等式约束优化两个部分进行求解。利用四轮独立电动车辆模型进行的仿真验证结果来看，所开发的集成控制策略可以有效跟踪理想目标，同时各车轮具有近似相等的轮胎附着裕度。
（4）首先对开发的四轮独立线控电动汽车试验平台进行功能性验证，基于该平台对本文提出的状态估算算法和集成控制策略进行实车场地试验验证。
本文选取直线加速工况、四轮转向工况以及原地转向和蟹行两种特殊工况，对四轮独立线控电动汽车试验平台的功能性和可靠性进行验证。其中四轮转向工况可以综合检验驱动、转向及传感器系统的性能。试验效果和数据的分析结果表明，四轮独立线控电动汽车试验平台所有系统均工作正常。基于该试验平台，选取具有加速和大转角转向过程的复合工况对提出的状态估算算法进行了试验验证。试验结果表明，纵向车速估计值具有有较高的估算精度，而由于侧向车速很低，估算精度受噪声和模型误差影响更大一些。最后，对集成控制算法进行了多工况下实车验证。试验结果表明，集成控制算法很好的解决了试验平台直行跑偏的问题，在大转角工况下通过驱动力矩和车轮转角集成控制，可以较好的跟踪目标车速和横摆角速度。
通过本文的研究工作，主要取得如下创新成果：
（1）建立了基于全线控技术的四轮独立电动汽车试验平台。试验平台综合线控技术的优势，可以实现四轮转向，蟹行转向和原地转向等多种工作模式。整车控制系统架构采用模块化设计思想，着重考虑了系统的扩展性和可靠性。子系统的设计除了考虑其单元功能外，融入了自身的容错功能。
（2）针对四轮独立线控电动汽车四个车轮轮速和力矩可以精确获得的特点，建立了基于Unscented卡尔曼滤波理论的车辆状态估算算法。估算算法采用了HSRI轮胎模型作为轮胎侧向力估算单元，同时将质量作为状态量引入到状态估算过程中，有效的提高了估算精度，并实现了整车的质量参数估算。
（3）结合分层控制思想和四轮独立线控电动汽车四轮纵向力和侧向力独立可控的特征，建立了基于有约束模型预测控制的驱动/制动/转向集成控制策略。为减小模型误差对控制效果的影响，集成控制层基于反馈校正特征的模型预测控制进行设计，同时考虑了执行器和路面附着的约束。控制分配层以各轮胎负荷率最低为优化目标，优化分配四个车轮的纵向力和车轮转角。
Four-wheel-independent electric vehicle with x-by-wire is a new type of vehicle of whichall wheels can be driven, brake and steered independently. It inherits all the advantages of theelectric vehicle driven by in-wheel-motor, and its steering system be free from the traditionalmechanical steering mechanism. Compared with conventional vehicles, the electric vehicle hasmore controllable degrees of freedom, so the maneuverability be greatly improved bycompleting the special actions such as zero radius turning, oblique driving and crab. Through theintegrated design of the chassis control system, the steering, drive and braking system can becoordinated to ensure each tire of the vehicle having maximum adhesion margin, that enhancesthe vehicle stability greatly. Above all, Four-wheel-independence electric vehicle with x-by-wirerepresents the future direction of the development of electric vehicles, and it is one of the hotresearch fields of vehicles.
This Ph.D. dissertation is based on the project of National Natural Science Foundation ofChina named―Research on Control methods and Key Technology for X-By-Wire‖(No.50775096) and―Research on bilateral control methods of vehicle Steer-by-wire System byJoystick‖(No.51105165). Based on the summary of the relevant research results, the papermakes intensively studies on the research of the experiment platform of four-wheel-independentelectric vehicle with x-by-wire and its integrated chassis control strategy, including the research of the platform's framework and control systems, the estimation of the state information whichare used in the control and the integrated control strategy of the drive/braking/steering systems.The specific contents of each part are as follows:
(1) The framework of the experiment platform and the vehicle control system is studiedbased on full electronic control scheme, and the application layer protocols of the vehicle CANcommunication network is designed.
Firstly, the framework of the experiment platform is built according to the analysis of thefunctional requirements. In-wheel motors, electromagnetic brakes and torque motors are used asthe actuators of the drive/steering/braking systems respectively. All these subsystemsinterchanges information via CAN bus. Then based on the framework scheme, the vehiclecontrol system is designed by modular design method, including the sensor unit, the vehiclecontrol unit and the execution unit. During the design process, all these submodule not only havethe basic function, but also are considered the fault-tolerant control strategy to improve thereliability. Moreover, the vehicle CAN network architecture is built and the application layerprotocols is designed according to the SAEJ1939, which can enhance the scalability of thesystem.
(2) Based on the characteristics of the four-wheel-independent electric vehicle withx-by-wire, the method of vehicle state estimation using Unscented Kalman filter theory isstudied.
In order to solve the problem of lacking of vehicle status information in the integratedcontrol, the vehicle state estimation for four-wheel-independent electric vehicle with x-by-wireis studied. Firstly, the three degrees of freedom estimation model including longitudinal, lateraland yaw is set up. Combining this model with the calculation of longitudinal forces and lateraltire force based on the single-track model, the UKF state estimator is designed and verified inthe simulation environment. According to the results of the verification, the estimator isoptimized with HSRI tire model and the estimation of the total vehicle's mass, which can reducethe influence of the lateral force estimation errors and the quality changes to the state estimationaccuracy. The results of the simulation show that the algorithm has good estimation accuracy andthe estimated value of the vehicle's mass converge to the true value quickly.
(3) Combining with the hierarchical control thought, the integrated control strategy for thevehicle drive/braking/steering systems is designed using model predictive control algorithm withconstraint.
Combined with the hierarchical control thought, the integrated control strategy of thevehicle drive/braking/steering systems is designed, which have considerations of thecharacteristics of the electric vehicle four wheel longitudinal force and lateral forceindependently controllable. The control strategy is divided into the integrated control layer andthe constrained control allocation layer. Integrated control layer applying the model predictivecontrol algorithm with feedback correction features, optimizes the vehicle control force, that islongitudinal resultant force, lateral resultant force and additional yaw moment torque. It aims atkeeping track of the reference model longitudinal speed, lateral speed and yaw rate. The processof the optimization considered the constraint problems caused by the actuators and roadadhesion conditions. Control allocation layer set each wheel tire adhesion margin maximum asthe target to distribute the four wheel’s longitudinal driving force and wheel angle. In order tosolve the nonlinear constrained problem and simplify the calculation of the solution process,control allocation layer is divided into equality constrained optimization and inequalityconstrained optimization. The results of the simulation show that the control strategy caneffectively track the ideal goal; meanwhile, four tires have approximately equal adhesionmargin.
(4) The functional test for the established experiment platform is carried out. The designedstate estimator and integrated control strategy are all verified on the platform.
The conditions including straight-line acceleration, four wheel steering, zero radius turningand crab are chosen to verify the functionality and reliability of the experiment platform. Amongthem, the four wheel steering condition is used to comprehensively test the performance of thedriving, steering and the sensor system. The results show that all the subsystems work normally.Based on the platform, the proposed UKF state estimator is verified in straight-line accelerationand large lateral acceleration conditions. The results show that the estimated values were in agood agreement with the real data. Finally, the integrated control strategy is experimented underthe various conditions. The test results indicate that the strategy perfectly solve the problem of running deviation and can track the reference target greatly under the corning condition.
The study of this paper makes the following innovations:
(1) The experiment platform of four-wheel-independent electric vehicle with x-by-wire isbuilt. The platform with the advantage of x-by-wire technology, can realize multi-operationworking modes, such as four wheel steering, zero radius turning and crab. The vehicle controlsystem is designed by modular design method, and the system scalability and reliability areemphatically considered. All the submodule not only have the basic function, but also areconsidered the fault-tolerant control strategy to improve the reliability.
(2) According to the characteristics that the four wheel speed and torque can be obtainedaccurately, the state estimator applying UKF theory is designed for four-wheel-independentelectric vehicle with x-by-wire. The estimator uses HSRI tire model as the calculation of lateraltire force, and introduces the vehicle mass into the estimation process. These ways improve theestimation accuracy effectively.
(3) Combined with the hierarchical control thought, the integrated control strategy of thevehicle drive/braking/steering systems is designed, which have considerations of thecharacteristics of the electric vehicle four wheel longitudinal force and lateral forceindependently controllable. The strategy is divided into integrated control layer and controlallocation layer. In order to reduce the affection of the model error, the integrated control layerapplies model predictive control algorithm which enjoy feedback correction features, and theconstraints are introduced into its optimization process. Control allocation layer set each wheeltire adhesion margin maximum as the target to distribute the four wheel’s longitudinal drivingforce and wheel angle.
本文依托国家自然科学基金项目“线控汽车底盘控制方法和关键技术研究”（编号：50775096）和“线控转向系统操纵杆及其双向控制方法研究”（编号：51105165），在已有相关研究成果基础上，针对四轮独立线控电动汽车的试验平台和驱动/制动/转向系统集成控制方法展开深入研究，包括四轮独立线控电动汽车整车架构和控制系统开发；四轮独立线控电动汽车底盘集成控制问题中所需状态信息的估算算法研究；四轮独立线控电动汽车驱动/制动/转向集成控制策略研究以及相关试验验证。论文研究工作的具体内容如下：
（1）基于全电控方案搭建了四轮独立线控电动汽车试验平台，并设计了全车控制系统和整车CAN通讯网络应用层协议。
本文首先对四轮独立线控电动汽车试验平台的功能需求进行分析，在此基础上，确定了平台整车结构设计方案，并据此开发出线控四轮独立电动车辆。车辆采用四个轮毂电机、四个电磁制动器和四个力矩电机分别作为整车的驱动、制动和转向系统的执行部件，所有子系统间通过CAN总线进行信息交互。基于整车结构方案，利用模块化设计方法对整车控制系统进行了设计与实现。设计过程中将其划分为传感器单元，整车控制单元以及底层执行单元三个子模块，各子模块在设计中，除了着重考虑其单元功能外，融入了容错控制思想，以提高整个系统可靠性。除此之外，本文还构建了整车的网络架构，并参照SAEJ1939协议建立了全车的CAN总线应用层通讯协议，以便于新的子系统融入整车网络，增强系统的可扩展性。
（2）结合四轮独立线控电动汽车的特点，研究并建立了基于Unscented卡尔曼滤波（简称UKF）理论的车辆状态估算算法。
为解决集成控制中车辆状态信息缺失的问题，本文展开了对四轮独立线控电动汽车状态估算方法的研究。首先建立了包含纵向、侧向和横摆在内的三自由度四轮车辆估算模型，同时结合纵向力计算模型和基于单轨模型的轮胎侧向力模块，根据四轮轮毂电机电动车轮可以精确获得各车轮转速和力矩的特点，设计了UKF状态估算器，并基于四轮电动车辆模型对其进行了仿真与分析。为减小侧向力估算误差和整车质量变化对估算精度的影响，本文对UKF状态估算器进行了改进，引入了HSRI轮胎模型和整车质量估算部分。改进后的估算算法利用四轮电动车辆模型进行了仿真验证。分析结果表明，估算算法提高了估算精度，同时作为状态量的整车质量也会迅速收敛到真实值。
（3）结合分层控制思想，建立了基于有约束模型预测控制算法的驱动/制动/转向集成控制策略。
本文结合四轮独立线控电动汽车四轮纵向力和侧向力独立可控的特点，基于分层控制思想设计了驱动/制动/转向的集成控制策略。集成控制策略划分为集成控制层和有约束控制分配层，单独对其进行设计。集成控制层以跟踪参考模型纵向车速、侧向车速及横摆角速度为目标，基于具有反馈校正特征的模型预测控制算法进行设计，优化计算车辆的整车控制力（矩）即纵向合力、侧向合力及附加横摆力矩，优化过程考虑执行器和路面附着条件引起的约束问题。控制分配层则以优化各车轮轮胎附着裕度为目标，对集成控制层输出的整车控制力（矩）进行四个车轮的优化分配，计算出各车轮的纵向驱动力和车轮转角。针对约束条件中存在的非线性约束问题，为简化计算求解过程，控制分配层分解为等式约束优化和不等式约束优化两个部分进行求解。利用四轮独立电动车辆模型进行的仿真验证结果来看，所开发的集成控制策略可以有效跟踪理想目标，同时各车轮具有近似相等的轮胎附着裕度。
（4）首先对开发的四轮独立线控电动汽车试验平台进行功能性验证，基于该平台对本文提出的状态估算算法和集成控制策略进行实车场地试验验证。
本文选取直线加速工况、四轮转向工况以及原地转向和蟹行两种特殊工况，对四轮独立线控电动汽车试验平台的功能性和可靠性进行验证。其中四轮转向工况可以综合检验驱动、转向及传感器系统的性能。试验效果和数据的分析结果表明，四轮独立线控电动汽车试验平台所有系统均工作正常。基于该试验平台，选取具有加速和大转角转向过程的复合工况对提出的状态估算算法进行了试验验证。试验结果表明，纵向车速估计值具有有较高的估算精度，而由于侧向车速很低，估算精度受噪声和模型误差影响更大一些。最后，对集成控制算法进行了多工况下实车验证。试验结果表明，集成控制算法很好的解决了试验平台直行跑偏的问题，在大转角工况下通过驱动力矩和车轮转角集成控制，可以较好的跟踪目标车速和横摆角速度。
通过本文的研究工作，主要取得如下创新成果：
（1）建立了基于全线控技术的四轮独立电动汽车试验平台。试验平台综合线控技术的优势，可以实现四轮转向，蟹行转向和原地转向等多种工作模式。整车控制系统架构采用模块化设计思想，着重考虑了系统的扩展性和可靠性。子系统的设计除了考虑其单元功能外，融入了自身的容错功能。
（2）针对四轮独立线控电动汽车四个车轮轮速和力矩可以精确获得的特点，建立了基于Unscented卡尔曼滤波理论的车辆状态估算算法。估算算法采用了HSRI轮胎模型作为轮胎侧向力估算单元，同时将质量作为状态量引入到状态估算过程中，有效的提高了估算精度，并实现了整车的质量参数估算。
（3）结合分层控制思想和四轮独立线控电动汽车四轮纵向力和侧向力独立可控的特征，建立了基于有约束模型预测控制的驱动/制动/转向集成控制策略。为减小模型误差对控制效果的影响，集成控制层基于反馈校正特征的模型预测控制进行设计，同时考虑了执行器和路面附着的约束。控制分配层以各轮胎负荷率最低为优化目标，优化分配四个车轮的纵向力和车轮转角。
Four-wheel-independent electric vehicle with x-by-wire is a new type of vehicle of whichall wheels can be driven, brake and steered independently. It inherits all the advantages of theelectric vehicle driven by in-wheel-motor, and its steering system be free from the traditionalmechanical steering mechanism. Compared with conventional vehicles, the electric vehicle hasmore controllable degrees of freedom, so the maneuverability be greatly improved bycompleting the special actions such as zero radius turning, oblique driving and crab. Through theintegrated design of the chassis control system, the steering, drive and braking system can becoordinated to ensure each tire of the vehicle having maximum adhesion margin, that enhancesthe vehicle stability greatly. Above all, Four-wheel-independence electric vehicle with x-by-wirerepresents the future direction of the development of electric vehicles, and it is one of the hotresearch fields of vehicles.
This Ph.D. dissertation is based on the project of National Natural Science Foundation ofChina named―Research on Control methods and Key Technology for X-By-Wire‖(No.50775096) and―Research on bilateral control methods of vehicle Steer-by-wire System byJoystick‖(No.51105165). Based on the summary of the relevant research results, the papermakes intensively studies on the research of the experiment platform of four-wheel-independentelectric vehicle with x-by-wire and its integrated chassis control strategy, including the research of the platform's framework and control systems, the estimation of the state information whichare used in the control and the integrated control strategy of the drive/braking/steering systems.The specific contents of each part are as follows:
(1) The framework of the experiment platform and the vehicle control system is studiedbased on full electronic control scheme, and the application layer protocols of the vehicle CANcommunication network is designed.
Firstly, the framework of the experiment platform is built according to the analysis of thefunctional requirements. In-wheel motors, electromagnetic brakes and torque motors are used asthe actuators of the drive/steering/braking systems respectively. All these subsystemsinterchanges information via CAN bus. Then based on the framework scheme, the vehiclecontrol system is designed by modular design method, including the sensor unit, the vehiclecontrol unit and the execution unit. During the design process, all these submodule not only havethe basic function, but also are considered the fault-tolerant control strategy to improve thereliability. Moreover, the vehicle CAN network architecture is built and the application layerprotocols is designed according to the SAEJ1939, which can enhance the scalability of thesystem.
(2) Based on the characteristics of the four-wheel-independent electric vehicle withx-by-wire, the method of vehicle state estimation using Unscented Kalman filter theory isstudied.
In order to solve the problem of lacking of vehicle status information in the integratedcontrol, the vehicle state estimation for four-wheel-independent electric vehicle with x-by-wireis studied. Firstly, the three degrees of freedom estimation model including longitudinal, lateraland yaw is set up. Combining this model with the calculation of longitudinal forces and lateraltire force based on the single-track model, the UKF state estimator is designed and verified inthe simulation environment. According to the results of the verification, the estimator isoptimized with HSRI tire model and the estimation of the total vehicle's mass, which can reducethe influence of the lateral force estimation errors and the quality changes to the state estimationaccuracy. The results of the simulation show that the algorithm has good estimation accuracy andthe estimated value of the vehicle's mass converge to the true value quickly.
(3) Combining with the hierarchical control thought, the integrated control strategy for thevehicle drive/braking/steering systems is designed using model predictive control algorithm withconstraint.
Combined with the hierarchical control thought, the integrated control strategy of thevehicle drive/braking/steering systems is designed, which have considerations of thecharacteristics of the electric vehicle four wheel longitudinal force and lateral forceindependently controllable. The control strategy is divided into the integrated control layer andthe constrained control allocation layer. Integrated control layer applying the model predictivecontrol algorithm with feedback correction features, optimizes the vehicle control force, that islongitudinal resultant force, lateral resultant force and additional yaw moment torque. It aims atkeeping track of the reference model longitudinal speed, lateral speed and yaw rate. The processof the optimization considered the constraint problems caused by the actuators and roadadhesion conditions. Control allocation layer set each wheel tire adhesion margin maximum asthe target to distribute the four wheel’s longitudinal driving force and wheel angle. In order tosolve the nonlinear constrained problem and simplify the calculation of the solution process,control allocation layer is divided into equality constrained optimization and inequalityconstrained optimization. The results of the simulation show that the control strategy caneffectively track the ideal goal; meanwhile, four tires have approximately equal adhesionmargin.
(4) The functional test for the established experiment platform is carried out. The designedstate estimator and integrated control strategy are all verified on the platform.
The conditions including straight-line acceleration, four wheel steering, zero radius turningand crab are chosen to verify the functionality and reliability of the experiment platform. Amongthem, the four wheel steering condition is used to comprehensively test the performance of thedriving, steering and the sensor system. The results show that all the subsystems work normally.Based on the platform, the proposed UKF state estimator is verified in straight-line accelerationand large lateral acceleration conditions. The results show that the estimated values were in agood agreement with the real data. Finally, the integrated control strategy is experimented underthe various conditions. The test results indicate that the strategy perfectly solve the problem of running deviation and can track the reference target greatly under the corning condition.
The study of this paper makes the following innovations:
(1) The experiment platform of four-wheel-independent electric vehicle with x-by-wire isbuilt. The platform with the advantage of x-by-wire technology, can realize multi-operationworking modes, such as four wheel steering, zero radius turning and crab. The vehicle controlsystem is designed by modular design method, and the system scalability and reliability areemphatically considered. All the submodule not only have the basic function, but also areconsidered the fault-tolerant control strategy to improve the reliability.
(2) According to the characteristics that the four wheel speed and torque can be obtainedaccurately, the state estimator applying UKF theory is designed for four-wheel-independentelectric vehicle with x-by-wire. The estimator uses HSRI tire model as the calculation of lateraltire force, and introduces the vehicle mass into the estimation process. These ways improve theestimation accuracy effectively.
(3) Combined with the hierarchical control thought, the integrated control strategy of thevehicle drive/braking/steering systems is designed, which have considerations of thecharacteristics of the electric vehicle four wheel longitudinal force and lateral forceindependently controllable. The strategy is divided into integrated control layer and controlallocation layer. In order to reduce the affection of the model error, the integrated control layerapplies model predictive control algorithm which enjoy feedback correction features, and theconstraints are introduced into its optimization process. Control allocation layer set each wheeltire adhesion margin maximum as the target to distribute the four wheel’s longitudinal drivingforce and wheel angle.
引文
[1] Satoshi Murata. Vehicle Dynamics Innovation with In-wheel Motor[C]. SAE paper.2011-39-7204.
[2]陈清泉,孙逢春,祝嘉光.现代电动汽车技术[M].北京:北京理工大学出版社.2002
[3] Watts A, Vallance A, Whitehead A, et al. The technology and economics of in-wheel motors[J].SAE International Journal of Passenger Cars-Electric and Electrical Systems.2010,2(3):37-54.
[4] Hori Yoichi. Future vehicle driven by electricity and control-Research on four-wheel-motored"UOT Electric March II"[C]. IEEE Transaction on Industrial Electronics.2004,5(51).
[5] Huihuan QIAN, Tin Lun LAM, Weimin LI, Chenggang XIA, Yangsheng XU. System and Designof an Omni-directional Vehicle[C]. Proceedings of the2008IEEE International Conference onRobotics and Biomimetics.2009:389-394.
[6]姜炜,余卓平,张立军.汽车底盘集成控制综述[J].汽车工程.2007.29(5):420-425.
[7] Satoshi Murata. Innovation by In-Wheel-Motor Drive Unit[C]. Keynote Presentation, AVEC10.2010.
[8] Strange vehicles: Toyota i-Unit[OL]. http://www.diseno-art.com/encyclopedia/strange_vehicles/Toyota_i-unit.html.
[9] Concept Cars: Toyota Fine-X[OL].http://www.diseno-art.com/encyclopedia/concept_cars/toyota_fine-x.html
[10] All-wheel drive using new type of in-wheel motor[OL]. http:/www.mitsubishi-motors.com/en/orporate/pressrelease/orporate/detail1321.html
[11] Colt EV test car uses in-wheel motors&lithium-ion batteries[OL]. http://www.mitsubishi-motors.com/en/corporate/pressrelease/corporate/detail1269.html2005
[12]何磊.基于FlexRay总线的线控转向系统双电机控制方法研究[D].长春:吉林大学.2011.
[13] Smart-wheel Technology[OL]. http://www.smartwheel.com.au/
[14] Min Wan Choi, Jun Seok Park, Bong Soo Lee. Man Hyung Lee. The performance of independentfour wheels steering vehicle(4WS) applied Ackerman geometry[C]. International Conference onControl, Automation and Systems.2008.197-202.
[15] Sigvard Zetterstr m. Electromechanical Steering, Suspension, Drive and Brake Modules[C].Vehicular Technology Conference.2002, Vol.4:1856-1863.
[16] Rahman K.M., Patel N.R. etc. Application of Direct-Drive Wheel Motor for Fuel Cell Electric andHybrid Electric Vehicle Propulsion System[C]. IEEE Transactions on Industry Applications.2006,Vol.42:1185-1192.
[17] Sakai Shin-Ichiro, Sado Hideo, Hori, Yoichi. Novel skid detection method without vehicle chassisspeed for electric vehicle[C]. JSAE review.2000,Vol.21:503-510.
[18] Cong Geng, Lotf Mostefai, Mouloud. Ect. Direct yaw-moment control of an in-wheel-motoredelectric vehicle based on body slip angle fuzzy observer[C]. IEEE Transactions on IndustryApplications.2009, Vol.56:1411–1419.
[19] Nagai, Masao. The Perspective of Research for Enhancing Active Safety Based on AdvancedControl Technology[J]. Vehicle System Dynamics.2007,5(45):413-431.
[20] Shimizu Hiroshi, Emoto Kikuo, Kawakami Kiyomoto. Development of High-performance ElectricVehicle―Eliica‖[J]. Koyo Engineering Journal English Edition.2006, Vol.26:13-19.
[21] Mokhiamar O, Abe M. Effects of An Optimum Cooperative Chassis Control From The View Pointsof Tire Workload[J]. JSAE.2004,3(35):215-221.
[22] Ando Naoki, Fujimoto Hiroshi.. Yaw-rate Control for Electric Vehicle with Active Front/RearSteering and Driving/Braking Force Distribution of Rear Wheels[C]. The11th IEEE InternationalWorkshop on Advanced Motion Control.2010:726-731.
[23]余卓平,左建令,陈慧.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.2007,2(29):141-145.
[24]余卓平,张立军,熊璐.四驱电动车经济性改善的最优转矩分配控制[J].同济大学学报.2005,10(33):1355-1361.
[25] Lu Xiong, Zhuping Yu and Yufeng Meng. Vehicle Dynamic Control for a4In-Wheel-Motored EVbased on Identification of Tire Cornering Stiffness[C]. AVEC10.2010:861-866.
[26] Huihuan Qian, Tinlun Lam, Weimin Li, etc. System and Design of an Omni-directional Vehicle[C].Proceeding of the2008IEEE on Robotics and Biomimetics.2009:21-26.
[27]杨福广.4WID/4WIS电动车辆防滑与横摆稳定性控制研究[D].烟台:山东大学,2010.
[28]邹广才,罗禹贡,李克强.四轮独立电驱动车辆全轮纵向力优化分配方法[J].清华大学学报(自然科学版).2009,5(49):111-115.
[29]王博,罗禹贡等.基于控制分配的四轮独立电驱动车辆驱动力分配算法[J].汽车工程.2010,2(32):128-132.
[30]王庆年,张缓缓,靳立强.四轮独立驱动电动车转向驱动的转矩协调控制[J].吉林大学学报(工学版).2007,5(37):985-989.
[31]王庆年,王军年,靳立强.用于电动轮驱动汽车的差动助力转向[J].吉林大学学报(工学版).2009,1(39):1-6.
[32]赵艳娥,张建武.基于滑模控制的四轮驱动电动汽车稳定性控制[J].上海交通大学学报.2009,10:1526-1530.
[33]马雷,刘晶等.四轮独立驱动电动汽车驱动力最优控制方法[J].汽车工程.2010,12(32):1057-1062.
[34]明守政,田浩.电动轮独立驱动的汽车的R-v控制策略[J].系统仿真学报.2009,24(21):7883-7890.
[35]李信梅.基于磁阻转矩的高功率密度永磁轮毂电机的设计研究[D].哈尔滨:哈尔滨工业大学,2008.
[36]余卓平,高晓杰.车辆行驶过程中的状态估计问题综述[J].机械工程学报.2009,5(45):20-33.
[37]赵林辉.车辆状态和路面附着系数的非线性估计方法研究[D].哈尔滨:哈尔滨工业大学,2009.
[38] Song K, Uchanski M., Hedrick K. Vehicle Speed Estimation Using Accelerometer and Wheel SpeedMeasurements[C].SAE Paper.2002-01-2229.
[39] Jiang F, Gao Z. An Adaptive Nonlinear Filter Approach to the Vehicle Velocity Estimation forABS[C]. IEEE International Conference on Control Applications.2000:490-495.
[40] Daiss A, kniecke U. Estimation of vehicle speed fuzzy-estimation in comparison with kalman-filtering[C]. Proceedings of the4th IEEE Conference on Control Applications.1995:281-284.
[41] Farrely J, Wellstead P. Estimation of Vehicle Lateral Velocity[C]. IEEE International Conference onControl Application.1996:552-558.
[42] Cherouat H, Braci M, Diop S. A side-slip angle estimation using neural network for a wheeledvehicle[C]. SAE Paper,2000-01-0695.
[43] Matthew C Best, Timothy J Gordon. Combined state and parameter estimation of vehicle handlingdynamics[C]. AVEC2000.2000:429-436.
[44] T A Wenzel，K J Burnham，M V Blundell and R A Williams．Dual extended Kalman filter forvehicle state and parameter estimation[J]. Vehicle System Dynamics.2006,2(44):153-171.
[45] T A Wenzel, K J Burnham, M V Blundell, R A Williams. Approach to Vehicle State and ParameterEstimation using Extended Kalman Filtering[C]. AVEC04.2004:725-730.
[46]王仁广,刘昭度,齐志权,马岳峰.基于自适应卡尔曼滤波算法确定汽车参考车速[J].农业机械学报.2006,4(37):9-11.
[47]齐志权.基于汽车ABS/ASR/ACC集成化系统的ABS参考车速确定方法的研究[J].汽车工程.2003,25(6):617-620.
[48]余卓平,左建令,陈慧.基于四轮驱动电动车的路面识别及驱动防滑研究[J].汽车工程.2007,2(29):141-145.
[49]徐倜凡.四轮轮边驱动电动车纵向车速估算算法研究[D].上海:同济大学,2008.
[50]林棻,赵又群.基于双重扩展自适应卡尔曼滤波的汽车状态和参数估计[J].中国机械工程学报.2009,6(20):750-755.
[51]时艳茹.基于UKF滤波的汽车纵向和侧向速度估计算法研究[D].长春:吉林大学,2011.
[52]宗长富,胡丹,杨肖,潘钊.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).2009,1(39):7-11.
[53]宗长富,潘钊,胡丹.基于扩展卡尔曼滤波的信息融合技术在车辆状态估计中的应用[J].机械工程学报.2009,10(45):272-277.
[54]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,2009.
[55] Shinsuke Sato, Hideo Inoue, Masaaki Tabta.. Integrated chassis control system for improved vehicledynamics[C]. AVEC92.1992:413-418.
[56] Yoshiyuki Yasui, Hiroyuki Kodama.. Electronic Stability Control (ESC) Coordinated With ElectricPower Steering (EPS)[C]. SAE Paper,2006-05-0506.
[57] Junjie He, D A Crolla, M C Levesley, W J Manning. Coordination of active steering，driving andbraking for integrated vehicle dynamics control[J]. Proc. IMechE, Part D: J. Automobile,Engineering.2006,1(220):1401-1421.
[58] Tin Lun Lam, Yangsheng Xu, Guoqing Xu. Traction Force Distribution on Omni-directional FourWheel Independent Drive Electric Vehicle[C].2009IEEE International Conference on Robotics andAutomation.2009:3724-3729.
[59] Tin Lun Lam, Huihuan Qian, Yangsheng Xu. Omni-directional Steer-by-Wire Interface forFour-Wheel Independent Steering Vehicle[C]. IEEE/ASME Transaction on Mechatronics.2010,15(3):1083-4435.
[60] Edward J, Bedner Jr.. A Supervisory Control to Manage Brakes and Four-Wheel-Steer Systems[C].SAE Paper,2004-01-1059.
[61] Semmler S J, Rieth P E. Global chassis control-the networked chassis[C]. SAE Paper,2006-01-1954.
[62] Chandrasekaran Rengaraj, David Crolla. Integrated Chassis Control to Improve Vehicle HandlingDynamics Performance[C]. SAE Paper,2011-01-0958.
[63] Willy Klier, Martin Kieren, Wolfgang Schr der. Integrated Safety Concept and Design of a VehicleDynamics Management System[C]. SAE Paper,2007-01-0842.
[64] Youseok Kou, Wanil Kim, SangHo Yoon, JeongWoo Lee, Dongshin Kim. Integration ChassisControl (ICC) Systems of Mando[C]. SAE Paper,2004-01-2044.
[65] Nagai M, Shino M, Gao F. Study on integrated control of active front steer angle and direct yawmoment[J]. JSAE Review.2002,23(3):309-315.
[66] M J L Boada, B L Boada, A Mu oz, V Díaz. Integrated control of front-wheel steering and frontbraking forces on the basis of fuzzy logic[J]. Proceedings of the Institution of Mechanical Engineers,Part D: Journal of Automobile Engineering.2006,3(220):253-267.
[67] Klaus Webers, Rainer Busch. Ford Integrated Vehicle Dynamics Control Realization[J]. Research&Advanced Engineering.2003.
[68] Junmin Wang, Raul G, Longoria. Coordinated Vehicle Dynamics Control with ControlDistribution[C].2006American Control Conference.2006:5348-5353.
[69] Ali Roshanbin, Mahyar Naraghi. Vehicle Integrated Control-An Adaptive Optimal Approach toDistribution of Tire Forces[C].2008IEEE International Conference on Networking.2008:885-890.
[70] Ossama Mokhiamar, Masato Abe. How the four wheels should share forces in an optimumcooperative chassis control[J]. Control Engineering Practice.2006,3(14):295-304.
[71] Eiichi Ono, Yoshikazu Hattori, Yuji Muragishi, K. Koibuchi. Vehicle dynamics integrated controlfor four-wheel-distributed steering and four-wheel-distributed traction/braking systems[J]. VehicleSystem Dynamics.2006,2(44):139-151.
[72] Johannes Tjonnas, Tor A. Johansen. Stabilization of Automotive Vehicles Using Active Steeringand Adaptive Brake Control Allocation[C]. IEEE transactions on control systems technology.2010,3(18):545-558.
[73] S Yim，W Cho，J Yoon，K Yi. Optimum distribution of yaw moment for unified chassis control withlimitations on the active front steering angle[J]. International Journal of Automotive Technology.2010,5(11):665-672.
[74] Johan Andreasson，Christian Knobel. On Road Vehicle Motion Control-striving towards synergy[C].AVEC06.2006.
[75] Aleksander Hac, David Doman, Michael Oppenheimer. Unified Control of Brake-and Steer-by-WireSystems Using Optimal Control Allocation Methods[C]. SAE Paper,2006-01-0924.
[76]武建勇,唐厚君,欧阳涛,许秀香,刘伟.基于AFS与DYC集成控制提高车辆操纵稳定性的研究[J].系统仿真学报.2009,5(21):1227-1232.
[77]余卓平,高晓杰,张立军.用于车辆稳定性控制的直接横摆力矩及车轮变滑移率联合控制研究[J].汽车工程.2006,9(28):844-848.
[78]裴晓飞,刘绍度.车辆纵向动力学集成控制系统研究[J].汽车工程.2012,2(34):99-102.
[79]杨福广,阮久宏,李贻斌,等.4WID-4WIS车辆横摆运动AFS+ARS+DYC模糊控制[J].农业机械学报.2011,10(42):6-12.
[80]杨福广.4WID/4WIS电动车辆防滑与稳定性控制研究[D].济南:山东大学,2010.
[81]李道飞,喻凡.基于最优轮胎力分配的车辆动力学集成控制[J].上海交通大学学报.2008,6(42):887-891.
[82]李道飞.基于轮胎力最优分配的车辆动力学集成控制研究[D].上海:上海交通大学,2008.
[83]宗长富,陈国迎,梁赫奇,田承伟.基于模型预测控制的汽车底盘协调控制策略[J].农业机械学报.2011,2(42):1-7.
[84]梁赫奇.基于模型预测控制的底盘分层集成控制算法研究[D].长春:吉林大学,2011.
[85]郑宏宇,宗长富,王祥汽车线控转向系统路感模拟方法[J].农业机械学报.2011,2(42):18-22.
[86]候永涛.汽车电磁制动器设计关键技术研究及集成平台构建[D].镇江:江苏大学,2008.
[87] NI PXIe-8133User Manual and Specifications. PXI Express.2010.
[88] Controller Area Network (CAN) Interfaces. National Instruments.2012
[89]张文广,周孙锋,司利增.基于LabVIEW的汽车动力性仿真研究[J].汽车实用技术.2011,11-12:31-34.
[90]郝建树.基于DSP的电磁制动器控制系统研究与设计[D].秦皇岛:燕山大学,2011.
[91] Tae-Sung Kim, Byoung-Gun Park, Dong-Myung Lee. A New Approach to Sensorless ControlMethod for Brushless DC Motors[J]. International Journal of Control, Automation, and Systems.2008,4(6):477-487.
[92] Chaari L, Masmoudi N, Kamoun L. Electronic control in electric vehicle based on CAN network[J].Systems, Man and Cybernetics.2002,(7):6-9.
[93]邬宽明. CAN总线原理与应用系统设计[M].北京:北京航空航天大学出版社,1996.
[94] SAE J1939Standard. J1939/71-Vehicle Application Layer. SAE.1997
[95]阚宗挺.车辆控制网络及其在车辆电控系统中的研究与应用[D].杭州:浙江大学,2010.
[96] Greg Welch, Gary Bishop. An Introduction to the Kalman Filter [J]. SIGGRAPH.2001, Course8.
[97]张鹏.基于卡尔曼滤波的航空发动机故障诊断技术研究[D].南京:南京航空航天大学.2008.
[98] Julier S J. The scaled unscented transformation[C]. Proc of American Control Conf.2002:4555-4559.
[99] Zhang Chuan-bin, Tian Wei-feng, Jin Zhi-hua. Application of Unscented Kalman Filter in StateEstimation for Land Vehicle Navigation System[J]. Journal of System Simulation.2005,17(6):1456-1458.
[100]余志生.汽车理论(第四版)[M].北京:机械工业出版社.2006.
[101] Yoshiki Fukad. Slip-Angle Estimation for Vehicle Stability Control [J]. Vehicle System Dynamics.1999,32(4-5):375-388.
[102]宗长富,刘经文,郑宏宇.4WID/4WIS电动车建模和特殊工况仿真[J].汽车工程.2011,10(33):829-833.
[103]刘经文.四轮独立电动车驱动/转向/制动稳定性集成控制算法研究[D].长春:吉林大学,2012.
[104] John T.Tielking, Naveen K.Mital. A Comparative Evaluation of Five Traction Tire Models[R]．Interim Document6, Jan.1974, Highway Safety Research Institute, University of Michigan.
[105] Dugoff H. et al. An analysis of tire traction properties and their influence on vehicle dynamicperformance[C]. SAE paper,700377.
[106]喻凡,林逸.汽车系统动力学[M].北京:机械工业出版社.2005.
[107]郭孔辉.汽车操纵动力学原理[M].南京:江苏科学技术出版社.2011.
[108]杜峰.基于线控技术的四轮主动转向汽车控制策略仿真研究[D].西安:长安大学.2009.
[109] Wongun Kim, Kyongsu Yi, Juyong Kang. Development of Driving Control System Based onOptimal Distribution for a6WD/6WS Vehicle[C]. SAE Paper,2010-01-0091.
[110] Evren Ozatay, Samim Y. Unlusoy, A. Murat Yildirim. Enhancement of Vehicle Handling usingFour Wheel Steering Control Strategy[C]. SAE Paper,2006-01-0942.
[111] Vahraz Nikzad Siahkalroudi, M. Naraghi. Model Reference Tracking Control of a4WS VehicleUsing Single and Dual Steering Strategies[C]. SAE Paper,2002-01-1590.
[112] Vahraz Nikzad Siahkalroudi, M. Naraghi. A Comparison between Zero Steady State Compensatorsand Optimal Control Regulators in a4WS vehicle[C]. SAE Paper,2002-01-1591.
[113]马建军.过驱动系统控制分配理论及其应用[D].长沙:国防科学技术大学,2009.
[114] Liuping Wang, PhD. Model Predictive Control System Design and Implementation UsingMATLAB[M]. Springer.2009.
[115]席裕庚.预测控制[M].北京:国防工业出版社.1993.
[116]王博.四轮独立电驱动车辆实验平台及驱动力控制系统研究[D].北京:清华大学,2009.
[117] E. ono, Y. hattori, Y. muragishi, et al. Vehicle dynamics integrated control for four wheel distributedsteering and four wheel distributed traction/braking systems[J]. Vehicle System Dynamics.2006,2(44):139-151.
[118] SHIN-ICHIRO SAKAI, HIDEO SADO, YOICHI HORI. Dynamic Driving/Braking ForceDistribution in Electric Vechicles with Independently Driven Four Wheels[J]. Electrical Engineeringin Japan.2002,1(138):79-89.
[2]陈清泉,孙逢春,祝嘉光.现代电动汽车技术[M].北京:北京理工大学出版社.2002
[3] Watts A, Vallance A, Whitehead A, et al. The technology and economics of in-wheel motors[J].SAE International Journal of Passenger Cars-Electric and Electrical Systems.2010,2(3):37-54.
[4] Hori Yoichi. Future vehicle driven by electricity and control-Research on four-wheel-motored"UOT Electric March II"[C]. IEEE Transaction on Industrial Electronics.2004,5(51).
[5] Huihuan QIAN, Tin Lun LAM, Weimin LI, Chenggang XIA, Yangsheng XU. System and Designof an Omni-directional Vehicle[C]. Proceedings of the2008IEEE International Conference onRobotics and Biomimetics.2009:389-394.
[6]姜炜,余卓平,张立军.汽车底盘集成控制综述[J].汽车工程.2007.29(5):420-425.
[7] Satoshi Murata. Innovation by In-Wheel-Motor Drive Unit[C]. Keynote Presentation, AVEC10.2010.
[8] Strange vehicles: Toyota i-Unit[OL]. http://www.diseno-art.com/encyclopedia/strange_vehicles/Toyota_i-unit.html.
[9] Concept Cars: Toyota Fine-X[OL].http://www.diseno-art.com/encyclopedia/concept_cars/toyota_fine-x.html
[10] All-wheel drive using new type of in-wheel motor[OL]. http:/www.mitsubishi-motors.com/en/orporate/pressrelease/orporate/detail1321.html
[11] Colt EV test car uses in-wheel motors&lithium-ion batteries[OL]. http://www.mitsubishi-motors.com/en/corporate/pressrelease/corporate/detail1269.html2005
[12]何磊.基于FlexRay总线的线控转向系统双电机控制方法研究[D].长春:吉林大学.2011.
[13] Smart-wheel Technology[OL]. http://www.smartwheel.com.au/
[14] Min Wan Choi, Jun Seok Park, Bong Soo Lee. Man Hyung Lee. The performance of independentfour wheels steering vehicle(4WS) applied Ackerman geometry[C]. International Conference onControl, Automation and Systems.2008.197-202.
[15] Sigvard Zetterstr m. Electromechanical Steering, Suspension, Drive and Brake Modules[C].Vehicular Technology Conference.2002, Vol.4:1856-1863.
[16] Rahman K.M., Patel N.R. etc. Application of Direct-Drive Wheel Motor for Fuel Cell Electric andHybrid Electric Vehicle Propulsion System[C]. IEEE Transactions on Industry Applications.2006,Vol.42:1185-1192.
[17] Sakai Shin-Ichiro, Sado Hideo, Hori, Yoichi. Novel skid detection method without vehicle chassisspeed for electric vehicle[C]. JSAE review.2000,Vol.21:503-510.
[18] Cong Geng, Lotf Mostefai, Mouloud. Ect. Direct yaw-moment control of an in-wheel-motoredelectric vehicle based on body slip angle fuzzy observer[C]. IEEE Transactions on IndustryApplications.2009, Vol.56:1411–1419.
[19] Nagai, Masao. The Perspective of Research for Enhancing Active Safety Based on AdvancedControl Technology[J]. Vehicle System Dynamics.2007,5(45):413-431.
[20] Shimizu Hiroshi, Emoto Kikuo, Kawakami Kiyomoto. Development of High-performance ElectricVehicle―Eliica‖[J]. Koyo Engineering Journal English Edition.2006, Vol.26:13-19.
[21] Mokhiamar O, Abe M. Effects of An Optimum Cooperative Chassis Control From The View Pointsof Tire Workload[J]. JSAE.2004,3(35):215-221.
[22] Ando Naoki, Fujimoto Hiroshi.. Yaw-rate Control for Electric Vehicle with Active Front/RearSteering and Driving/Braking Force Distribution of Rear Wheels[C]. The11th IEEE InternationalWorkshop on Advanced Motion Control.2010:726-731.
[23]余卓平,左建令,陈慧.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.2007,2(29):141-145.
[24]余卓平,张立军,熊璐.四驱电动车经济性改善的最优转矩分配控制[J].同济大学学报.2005,10(33):1355-1361.
[25] Lu Xiong, Zhuping Yu and Yufeng Meng. Vehicle Dynamic Control for a4In-Wheel-Motored EVbased on Identification of Tire Cornering Stiffness[C]. AVEC10.2010:861-866.
[26] Huihuan Qian, Tinlun Lam, Weimin Li, etc. System and Design of an Omni-directional Vehicle[C].Proceeding of the2008IEEE on Robotics and Biomimetics.2009:21-26.
[27]杨福广.4WID/4WIS电动车辆防滑与横摆稳定性控制研究[D].烟台:山东大学,2010.
[28]邹广才,罗禹贡,李克强.四轮独立电驱动车辆全轮纵向力优化分配方法[J].清华大学学报(自然科学版).2009,5(49):111-115.
[29]王博,罗禹贡等.基于控制分配的四轮独立电驱动车辆驱动力分配算法[J].汽车工程.2010,2(32):128-132.
[30]王庆年,张缓缓,靳立强.四轮独立驱动电动车转向驱动的转矩协调控制[J].吉林大学学报(工学版).2007,5(37):985-989.
[31]王庆年,王军年,靳立强.用于电动轮驱动汽车的差动助力转向[J].吉林大学学报(工学版).2009,1(39):1-6.
[32]赵艳娥,张建武.基于滑模控制的四轮驱动电动汽车稳定性控制[J].上海交通大学学报.2009,10:1526-1530.
[33]马雷,刘晶等.四轮独立驱动电动汽车驱动力最优控制方法[J].汽车工程.2010,12(32):1057-1062.
[34]明守政,田浩.电动轮独立驱动的汽车的R-v控制策略[J].系统仿真学报.2009,24(21):7883-7890.
[35]李信梅.基于磁阻转矩的高功率密度永磁轮毂电机的设计研究[D].哈尔滨:哈尔滨工业大学,2008.
[36]余卓平,高晓杰.车辆行驶过程中的状态估计问题综述[J].机械工程学报.2009,5(45):20-33.
[37]赵林辉.车辆状态和路面附着系数的非线性估计方法研究[D].哈尔滨:哈尔滨工业大学,2009.
[38] Song K, Uchanski M., Hedrick K. Vehicle Speed Estimation Using Accelerometer and Wheel SpeedMeasurements[C].SAE Paper.2002-01-2229.
[39] Jiang F, Gao Z. An Adaptive Nonlinear Filter Approach to the Vehicle Velocity Estimation forABS[C]. IEEE International Conference on Control Applications.2000:490-495.
[40] Daiss A, kniecke U. Estimation of vehicle speed fuzzy-estimation in comparison with kalman-filtering[C]. Proceedings of the4th IEEE Conference on Control Applications.1995:281-284.
[41] Farrely J, Wellstead P. Estimation of Vehicle Lateral Velocity[C]. IEEE International Conference onControl Application.1996:552-558.
[42] Cherouat H, Braci M, Diop S. A side-slip angle estimation using neural network for a wheeledvehicle[C]. SAE Paper,2000-01-0695.
[43] Matthew C Best, Timothy J Gordon. Combined state and parameter estimation of vehicle handlingdynamics[C]. AVEC2000.2000:429-436.
[44] T A Wenzel，K J Burnham，M V Blundell and R A Williams．Dual extended Kalman filter forvehicle state and parameter estimation[J]. Vehicle System Dynamics.2006,2(44):153-171.
[45] T A Wenzel, K J Burnham, M V Blundell, R A Williams. Approach to Vehicle State and ParameterEstimation using Extended Kalman Filtering[C]. AVEC04.2004:725-730.
[46]王仁广,刘昭度,齐志权,马岳峰.基于自适应卡尔曼滤波算法确定汽车参考车速[J].农业机械学报.2006,4(37):9-11.
[47]齐志权.基于汽车ABS/ASR/ACC集成化系统的ABS参考车速确定方法的研究[J].汽车工程.2003,25(6):617-620.
[48]余卓平,左建令,陈慧.基于四轮驱动电动车的路面识别及驱动防滑研究[J].汽车工程.2007,2(29):141-145.
[49]徐倜凡.四轮轮边驱动电动车纵向车速估算算法研究[D].上海:同济大学,2008.
[50]林棻,赵又群.基于双重扩展自适应卡尔曼滤波的汽车状态和参数估计[J].中国机械工程学报.2009,6(20):750-755.
[51]时艳茹.基于UKF滤波的汽车纵向和侧向速度估计算法研究[D].长春:吉林大学,2011.
[52]宗长富,胡丹,杨肖,潘钊.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).2009,1(39):7-11.
[53]宗长富,潘钊,胡丹.基于扩展卡尔曼滤波的信息融合技术在车辆状态估计中的应用[J].机械工程学报.2009,10(45):272-277.
[54]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,2009.
[55] Shinsuke Sato, Hideo Inoue, Masaaki Tabta.. Integrated chassis control system for improved vehicledynamics[C]. AVEC92.1992:413-418.
[56] Yoshiyuki Yasui, Hiroyuki Kodama.. Electronic Stability Control (ESC) Coordinated With ElectricPower Steering (EPS)[C]. SAE Paper,2006-05-0506.
[57] Junjie He, D A Crolla, M C Levesley, W J Manning. Coordination of active steering，driving andbraking for integrated vehicle dynamics control[J]. Proc. IMechE, Part D: J. Automobile,Engineering.2006,1(220):1401-1421.
[58] Tin Lun Lam, Yangsheng Xu, Guoqing Xu. Traction Force Distribution on Omni-directional FourWheel Independent Drive Electric Vehicle[C].2009IEEE International Conference on Robotics andAutomation.2009:3724-3729.
[59] Tin Lun Lam, Huihuan Qian, Yangsheng Xu. Omni-directional Steer-by-Wire Interface forFour-Wheel Independent Steering Vehicle[C]. IEEE/ASME Transaction on Mechatronics.2010,15(3):1083-4435.
[60] Edward J, Bedner Jr.. A Supervisory Control to Manage Brakes and Four-Wheel-Steer Systems[C].SAE Paper,2004-01-1059.
[61] Semmler S J, Rieth P E. Global chassis control-the networked chassis[C]. SAE Paper,2006-01-1954.
[62] Chandrasekaran Rengaraj, David Crolla. Integrated Chassis Control to Improve Vehicle HandlingDynamics Performance[C]. SAE Paper,2011-01-0958.
[63] Willy Klier, Martin Kieren, Wolfgang Schr der. Integrated Safety Concept and Design of a VehicleDynamics Management System[C]. SAE Paper,2007-01-0842.
[64] Youseok Kou, Wanil Kim, SangHo Yoon, JeongWoo Lee, Dongshin Kim. Integration ChassisControl (ICC) Systems of Mando[C]. SAE Paper,2004-01-2044.
[65] Nagai M, Shino M, Gao F. Study on integrated control of active front steer angle and direct yawmoment[J]. JSAE Review.2002,23(3):309-315.
[66] M J L Boada, B L Boada, A Mu oz, V Díaz. Integrated control of front-wheel steering and frontbraking forces on the basis of fuzzy logic[J]. Proceedings of the Institution of Mechanical Engineers,Part D: Journal of Automobile Engineering.2006,3(220):253-267.
[67] Klaus Webers, Rainer Busch. Ford Integrated Vehicle Dynamics Control Realization[J]. Research&Advanced Engineering.2003.
[68] Junmin Wang, Raul G, Longoria. Coordinated Vehicle Dynamics Control with ControlDistribution[C].2006American Control Conference.2006:5348-5353.
[69] Ali Roshanbin, Mahyar Naraghi. Vehicle Integrated Control-An Adaptive Optimal Approach toDistribution of Tire Forces[C].2008IEEE International Conference on Networking.2008:885-890.
[70] Ossama Mokhiamar, Masato Abe. How the four wheels should share forces in an optimumcooperative chassis control[J]. Control Engineering Practice.2006,3(14):295-304.
[71] Eiichi Ono, Yoshikazu Hattori, Yuji Muragishi, K. Koibuchi. Vehicle dynamics integrated controlfor four-wheel-distributed steering and four-wheel-distributed traction/braking systems[J]. VehicleSystem Dynamics.2006,2(44):139-151.
[72] Johannes Tjonnas, Tor A. Johansen. Stabilization of Automotive Vehicles Using Active Steeringand Adaptive Brake Control Allocation[C]. IEEE transactions on control systems technology.2010,3(18):545-558.
[73] S Yim，W Cho，J Yoon，K Yi. Optimum distribution of yaw moment for unified chassis control withlimitations on the active front steering angle[J]. International Journal of Automotive Technology.2010,5(11):665-672.
[74] Johan Andreasson，Christian Knobel. On Road Vehicle Motion Control-striving towards synergy[C].AVEC06.2006.
[75] Aleksander Hac, David Doman, Michael Oppenheimer. Unified Control of Brake-and Steer-by-WireSystems Using Optimal Control Allocation Methods[C]. SAE Paper,2006-01-0924.
[76]武建勇,唐厚君,欧阳涛,许秀香,刘伟.基于AFS与DYC集成控制提高车辆操纵稳定性的研究[J].系统仿真学报.2009,5(21):1227-1232.
[77]余卓平,高晓杰,张立军.用于车辆稳定性控制的直接横摆力矩及车轮变滑移率联合控制研究[J].汽车工程.2006,9(28):844-848.
[78]裴晓飞,刘绍度.车辆纵向动力学集成控制系统研究[J].汽车工程.2012,2(34):99-102.
[79]杨福广,阮久宏,李贻斌,等.4WID-4WIS车辆横摆运动AFS+ARS+DYC模糊控制[J].农业机械学报.2011,10(42):6-12.
[80]杨福广.4WID/4WIS电动车辆防滑与稳定性控制研究[D].济南:山东大学,2010.
[81]李道飞,喻凡.基于最优轮胎力分配的车辆动力学集成控制[J].上海交通大学学报.2008,6(42):887-891.
[82]李道飞.基于轮胎力最优分配的车辆动力学集成控制研究[D].上海:上海交通大学,2008.
[83]宗长富,陈国迎,梁赫奇,田承伟.基于模型预测控制的汽车底盘协调控制策略[J].农业机械学报.2011,2(42):1-7.
[84]梁赫奇.基于模型预测控制的底盘分层集成控制算法研究[D].长春:吉林大学,2011.
[85]郑宏宇,宗长富,王祥汽车线控转向系统路感模拟方法[J].农业机械学报.2011,2(42):18-22.
[86]候永涛.汽车电磁制动器设计关键技术研究及集成平台构建[D].镇江:江苏大学,2008.
[87] NI PXIe-8133User Manual and Specifications. PXI Express.2010.
[88] Controller Area Network (CAN) Interfaces. National Instruments.2012
[89]张文广,周孙锋,司利增.基于LabVIEW的汽车动力性仿真研究[J].汽车实用技术.2011,11-12:31-34.
[90]郝建树.基于DSP的电磁制动器控制系统研究与设计[D].秦皇岛:燕山大学,2011.
[91] Tae-Sung Kim, Byoung-Gun Park, Dong-Myung Lee. A New Approach to Sensorless ControlMethod for Brushless DC Motors[J]. International Journal of Control, Automation, and Systems.2008,4(6):477-487.
[92] Chaari L, Masmoudi N, Kamoun L. Electronic control in electric vehicle based on CAN network[J].Systems, Man and Cybernetics.2002,(7):6-9.
[93]邬宽明. CAN总线原理与应用系统设计[M].北京:北京航空航天大学出版社,1996.
[94] SAE J1939Standard. J1939/71-Vehicle Application Layer. SAE.1997
[95]阚宗挺.车辆控制网络及其在车辆电控系统中的研究与应用[D].杭州:浙江大学,2010.
[96] Greg Welch, Gary Bishop. An Introduction to the Kalman Filter [J]. SIGGRAPH.2001, Course8.
[97]张鹏.基于卡尔曼滤波的航空发动机故障诊断技术研究[D].南京:南京航空航天大学.2008.
[98] Julier S J. The scaled unscented transformation[C]. Proc of American Control Conf.2002:4555-4559.
[99] Zhang Chuan-bin, Tian Wei-feng, Jin Zhi-hua. Application of Unscented Kalman Filter in StateEstimation for Land Vehicle Navigation System[J]. Journal of System Simulation.2005,17(6):1456-1458.
[100]余志生.汽车理论(第四版)[M].北京:机械工业出版社.2006.
[101] Yoshiki Fukad. Slip-Angle Estimation for Vehicle Stability Control [J]. Vehicle System Dynamics.1999,32(4-5):375-388.
[102]宗长富,刘经文,郑宏宇.4WID/4WIS电动车建模和特殊工况仿真[J].汽车工程.2011,10(33):829-833.
[103]刘经文.四轮独立电动车驱动/转向/制动稳定性集成控制算法研究[D].长春:吉林大学,2012.
[104] John T.Tielking, Naveen K.Mital. A Comparative Evaluation of Five Traction Tire Models[R]．Interim Document6, Jan.1974, Highway Safety Research Institute, University of Michigan.
[105] Dugoff H. et al. An analysis of tire traction properties and their influence on vehicle dynamicperformance[C]. SAE paper,700377.
[106]喻凡,林逸.汽车系统动力学[M].北京:机械工业出版社.2005.
[107]郭孔辉.汽车操纵动力学原理[M].南京:江苏科学技术出版社.2011.
[108]杜峰.基于线控技术的四轮主动转向汽车控制策略仿真研究[D].西安:长安大学.2009.
[109] Wongun Kim, Kyongsu Yi, Juyong Kang. Development of Driving Control System Based onOptimal Distribution for a6WD/6WS Vehicle[C]. SAE Paper,2010-01-0091.
[110] Evren Ozatay, Samim Y. Unlusoy, A. Murat Yildirim. Enhancement of Vehicle Handling usingFour Wheel Steering Control Strategy[C]. SAE Paper,2006-01-0942.
[111] Vahraz Nikzad Siahkalroudi, M. Naraghi. Model Reference Tracking Control of a4WS VehicleUsing Single and Dual Steering Strategies[C]. SAE Paper,2002-01-1590.
[112] Vahraz Nikzad Siahkalroudi, M. Naraghi. A Comparison between Zero Steady State Compensatorsand Optimal Control Regulators in a4WS vehicle[C]. SAE Paper,2002-01-1591.
[113]马建军.过驱动系统控制分配理论及其应用[D].长沙:国防科学技术大学,2009.
[114] Liuping Wang, PhD. Model Predictive Control System Design and Implementation UsingMATLAB[M]. Springer.2009.
[115]席裕庚.预测控制[M].北京:国防工业出版社.1993.
[116]王博.四轮独立电驱动车辆实验平台及驱动力控制系统研究[D].北京:清华大学,2009.
[117] E. ono, Y. hattori, Y. muragishi, et al. Vehicle dynamics integrated control for four wheel distributedsteering and four wheel distributed traction/braking systems[J]. Vehicle System Dynamics.2006,2(44):139-151.
[118] SHIN-ICHIRO SAKAI, HIDEO SADO, YOICHI HORI. Dynamic Driving/Braking ForceDistribution in Electric Vechicles with Independently Driven Four Wheels[J]. Electrical Engineeringin Japan.2002,1(138):79-89.