Analysis of Automatic Steering Control for Highway Vehicles with Look-down Lateral Reference Systems

In this paper, steering control for passenger cars on automated highways is analyzed, concentrating on look-down reference systems. Extension of earlier experimental results for low speed to highway speed is shown to be non-trivial. The limitations of pure output-feedback of lateral vehicle displacement from the road reference are examined under practical constraints and performance requirements like robustness, maximum lateral error and comfort. The in-depth system analysis directly leads to a new alternative design direction which allows to preserve look-ahead reference systems for highway speed automatic driving.     

Analysis of Automatic Steering Control for Highway Vehicles with Look-down Lateral Reference Systems

SUMMARY

In this paper, steering control for passenger cars on automated highways is analyzed, concentrating on look-down reference systems. Extension of earlier experimental results for low speed to highway speed is shown to be non-trivial. The limitations of pure output-feedback of lateral vehicle displacement from the road reference are examined under practical constraints and performance requirements like robustness, maximum lateral error and comfort. The in-depth system analysis directly leads to a new alternative design direction which allows to preserve look-ahead reference systems for highway speed automatic driving.     

识别驾驶人意图的车道偏离防避有界控制

为了防止车辆偏离车道导致交通事故的发生和避免车道偏离防避系统（Lane Departure Avoidance Systems,LDAS）对驾驶人行为不必要的干预,提出基于中心区操纵特性阈值法和基于D-S（Dempster-Shafer）证据理论的车辆偏离车道驾驶人意图识别准则,并运用CarSim/Simulink联合仿真对比2种识别准则的有效性。建立转向盘角速度为输入的车路模型,设计LDAS滑模转向控制器,基于预瞄点的侧向偏移量和横摆角速度设计LDAS的期望横摆角速度观测器,并与基于道路曲率和预瞄点侧向偏移量的期望横摆角速度的LDAS进行性能对比。运用相平面法确定保证LDAS车辆稳定性的前轮转向角最大值,并基于CarSim/LabVIEW RT硬件在环试验平台验证基于BP神经网络训练获得D-S证据理论的初始概率赋值的驾驶人意图决策算法的有效性。结果表明：所提出的识别准则能够及时识别车辆偏离车道时的驾驶人意图,为LDAS控制器介入赢得了宝贵的时间,所设计的期望横摆角速度观测器具有很好的稳定性,所提出的方法能够有效避免车辆偏离车道。     

面向车道变换的路径规划及模型预测轨迹跟踪

为解决智能车辆在车道变换过程中的路径规划和路径跟踪问题,首先,利用梯形加速度法设计了车道变换虚拟理想轨迹,该路径规划方法的适应性取决于车道变换时间、横向加速度及变化率等关键变量的约束条件,因而对各关键变量之间的数学关系进行了定量计算,并绘制了不同工况下的车道变换虚拟理想轨迹,用于分析各关键变量对路径规划的影响;其次,建立了线性离散的车辆动力学预测模型,综合分析了车辆模型的控制输入、状态变量以及道路结构参数等约束条件,构建了多约束模型预测控制（MMPC）系统用于车道变换路径跟踪,并基于Hildreth二次规划算法对其目标函数进行了求解,获得前轮转向角控制量,从而保证智能车辆在车道变换过程中的路径跟踪性能及操纵稳定性能;最后,利用MATLAB和Carsim软件对提出的多约束模型预测控制系统进行联合仿真,并构建单约束模型预测控制（SMPC）系统与其进行性能比较,分别对车道变换时间为3 s和6 s时的车道变换性能进行比较分析。结果表明：当车道变换时间为6 s时,2种控制系统都能较好地实现车道变换功能;当车道变换时间为3 s时,与SMPC控制系统相比较,MMPC控制系统能够在有效跟踪期望行驶路径的同时改善车辆的操纵稳定性,从而提高车辆在路径跟踪过程中的主动安全性能。     

A lateral driver model for vehicle–driver closed-loop simulation at the limits of handling

This paper presents a lateral driver model for vehicle–driver closed-loop simulation at the limits of handling. An appropriate driver model can be used to evaluate the performance of vehicle chassis control systems via computer simulations before vehicle tests which incurs expenses especially at the limits of handling. The driver model consists of two parts. The first part is an upper-level controller employing force-based approach to reduce the number of unknown vehicle parameters. The feedforward part of the upper controller has been designed by using the centre of percussion. The feedback part aims to minimise ‘tangential error’, defined as the sum of body slip angle and yaw error, to match vehicle direction and road heading angle. The part is designed to regenerate an appropriate skid motion similar to that of a professional driver at the limits. The second part is a lower-level controller which converts the desired front lateral force to steering wheel angle. The lower-level controller also consists of feedforward and feedback parts. A two-degree-of-freedom bicycle model-based feedforward part provides nominal steering wheel angle, and the feedback part aims to eliminate unmodelled error. The performance of the lateral driver model has been investigated via computer simulations. It has been shown that the steering behaviours of the proposed driver model are quite close to those of a professional driver at the limits. Compared with the previously developed lateral driver models, the proposed lateral driver model shows good tracking performance at the limits of handling.     

基于自适应模型预测的智能汽车横向轨迹跟踪控制

当路面附着情况和车辆行驶状态不断变化时,基于恒定侧偏刚度的模型预测控制(MPC)不能考虑轮胎非线性特性的影响,难以保证车辆轨迹跟踪的适应性。为此,提出一种考虑轮胎侧向力计算误差的自适应模型预测控制(AMPC),以提高智能汽车在不确定工况下的轨迹跟踪性能。分析了路面附着系数和垂向载荷对轮胎侧向力的影响,基于平方根容积卡尔曼滤波(SCKF)算法,设计了利用侧向加速度和横摆角速度作为测量变量的前后轮胎侧向力估计器。利用轮胎侧向力线性计算值与估计值的差值计算得到侧偏刚度修正因子,设计了前后轮胎侧偏刚度的自适应修正准则,进而提出了一种基于时变修正刚度的AMPC控制方法。基于CarSim与MATLAB/Simulink联合仿真和硬件在环测试平台,对AMPC控制的有效性和实时性进行了验证。研究结果表明：在不同的路面附着情况和车辆行驶状态下,AMPC控制都能够降低横向位置偏差和航向角偏差,有效提高车辆的轨迹跟踪精度,其控制效果明显优于基于恒定侧偏刚度的标准MPC控制。尤其在低附着工况下,标准MPC控制会因为线性轮胎力的计算误差过大而导致车辆在轨迹跟踪时严重失稳,而AMPC控制通过估计轮胎力修正侧偏刚度依然能够保证车辆稳定有效的跟踪参考轨迹。所提出的AMPC控制在保证控制精度的同时具有良好的实时性,对智能汽车控制系统的设计与优化具有重要参考价值。     

Investigation of automatic path tracking using an extended Kalman filter

This paper proposes a lane marker recognition method that uses the steering angle in addition to image information. A Kalman filter was reconfigured regarding the yaw motion and lateral motion of lane markers, previously treated as a stochastic process, as the states of a vehicle model driven on the basis of the steering angle. Driving tests conducted with an actual vehicle verified that this method provides good tracking at the time of steering input and avoids misrecognition of lane marker candidate points in inclement weather.     

Drive control system design for stability and maneuverability of a 6WD/6WS vehicle

This paper describes a drive controller designed to improve the lateral vehicle stability and maneuverability of a 6-wheel drive / 6-wheel steering (6WD/6WS) vehicle. The drive controller consists of upper and lower level controllers. The upper level controller is based on sliding control theory and determines both front and middle steering angle, additional net yaw moment, and longitudinal net force according to the reference velocity and steering angle of a manual drive, remotely controlled, autonomous controller. The lower level controller takes the desired longitudinal net force, yaw moment, and tire force information as inputs and determines the additional front steering angle and distributed longitudinal tire force on each wheel. This controller is based on optimal distribution control and takes into consideration the friction circle related to the vertical tire force and friction coefficient acting on the road and tire. Distributed longitudinal/lateral tire forces are determined as proportion to the size of the friction circle according to changes in driving conditions. The response of the 6WD/6WS vehicle implemented with this drive controller has been evaluated via computer simulations conducted using the Matlab/Simulink dynamic model. Computer simulations of an open loop under turning conditions and a closed-loop driver model subjected to double lane change have been conducted to demonstrate the improved performance of the proposed drive controller over that of a conventional DYC.     

Vehicle stability control based on driver’s emergency alignment intention recognition

In this work, the reference model modification strategy for vehicle stability control based on driver's intention recognition under emergent obstacle avoidance situation was proposed. First the conflicts between the driver's emergency alignment (EA) intention and vehicle response characteristics were analyzed in critical emergent obstacle avoidance situation. Second combining steering wheel angle and its speed, the driver's EA intention was recognized. The reference model modification strategy based on steering operation index (SOI) was presented. Then a LQR model following controller with tire cornering stiffness adaption was used to generate direct yaw moment for tracking modified reference yaw rate and reference sideslip angle. Finally based on the four-in-wheel-motor-drive (FIWMD) electric vehicles (EV), double lane change and slalom tests were conducted to compare the results using modified reference model with the results using normal reference model. The experimental tests have proved the effectiveness of the reference model modification strategy based on driver's intention recognition.     

基于驾驶人转向意图的双电机驱动电动汽车稳定性控制策略

为了使双电机驱动电动车在车辆稳定性控制过程中能够精确解读驾驶意图,使车辆实际行驶状态与驾驶意图期望的车辆行驶状态尽可能相符合,提出一种基于驾驶人意图辨识的稳定性控制策略.利用基于支持向量机递归特征消除(SVM-RFE)得到的特征参数构建基于长短期记忆(LSTM)模型的驾驶人转向意图辨识模型;基于转向意图识别结果,以方向...     

智能汽车轨迹跟踪与横摆稳定性协同控制

为了提高四轮独立驱动智能电动汽车在变曲率弯道下的轨迹跟踪精度和横摆稳定性,提出了一种模型预测控制与直接横摆力矩控制协同的综合控制方法。建立了横纵向耦合的车辆动力学模型,采用2阶龙格库塔离散法保证了离散模型的精度,并基于简化的2自由度动力学模型推导了车辆横摆稳定性约束,设计了非线性模型预测控制器;利用直接横摆力矩控制能够改变车辆横摆角速度和航向角的特点,考虑模型预测控制器的预测状态、控制量以及跟踪误差,设计了协同控制规则。仿真结果表明,协同控制方法解决了考虑横摆稳定性约束的模型预测控制器中存在的稳定性约束与控制精度相矛盾的问题,并补偿了模型预测控制器没有可行解时对横摆稳定性的约束,同时提高了智能汽车的轨迹跟踪精度和横摆稳定性。     

基于可拓切换控制方法的智能车辆车道保持系统研究（双语出版）

针对道路曲率变化范围较大时，智能车辆在大曲率道路工况车道保持控制精度低的问题，提出一种基于可拓切换控制理论的智能车辆车道保持控制系统，该车道保持系统由上层可拓控制器和下层控制器两部分组成。在上层可拓控制器中，通过车道线检测得到车辆相对于道路的位置信息和道路曲率信息。根据可拓集合理论，选取预瞄点处横向位置偏差和前方道路曲率值作为可拓集合的特征值并划分可拓集合，求解关联函数，并根据关联函数值将车辆-道路系统状态分为经典域、可拓域和非域。在下层控制器中，在经典域采用基于横向位置偏差和航向偏差的PID反馈控制器，在可拓域中采用基于前方道路曲率的PID前馈-反馈控制器，非域中车辆-道路系统处于完全失控状态，采取紧急制动。2种仿真工况结果表明：相比于单一PID反馈控制，提出的车道保持控制系统，有效抑制了在大曲率道路下的跟踪误差值，提高了智能驾驶汽车在时变曲率的道路工况下车道保持控制精度和工况适应性。     

基于车辆动力学模型的换道轨迹规划研究

针对自动驾驶车辆换道轨迹规划时的操纵稳定性问题，基于CarSim/Simulink仿真平台建立了车辆动力学模型，构建了轨迹规划系统框架，通过轨迹信息后处理并提出了目标函数设计，进行了横向控制序列采样以保证车辆的稳定与极限性能，完成了算法对轨迹的综合评价选优。随后开展了仿真试验，对比分析了轨迹跟踪控制系统下的实际轨迹、最优规划方法所规划的换道轨迹。仿真结果表明，该轨迹规划系统框架及算法模型能有效提高车辆的操纵稳定性，可实现冰雪路面等极端工况下自动驾驶车辆换道轨迹规划。     

FSAC赛车轨迹跟踪协同控制策略研究

针对中国大学生方程式赛车 （FSAC） 在比赛中横向-纵向协同控制的轨迹跟踪精度和稳定性问题，根据现代控制理论和经典控制理论提出一种以纵向速度为结合点的线性二次控制器 （LQR） 和比例-积分-微分算法 （PID） 的横纵向协同控制策略，并根据赛车相对参考轨迹的位置设计了一种协同控制器。建立二自由度车辆动力学模型，基于该模型设计了横向LQR位置跟踪控制器和纵向PID速度跟踪控制器。所设计的控制策略在CarSim和Simulink搭建的循迹工况联合仿真场景下进行仿真验证，仿真结果为纵向位置偏差小于0.07 m，横向位置偏差小于0.03 m。对控制算法进行实车验证，结果表明，该策略有效提高了赛车的轨迹跟踪精度和行驶稳定性。     

基于人机共驾的车道保持辅助控制系统研究（双语出版）

车道保持控制系统是汽车安全辅助驾驶的重要组成部分，可有效提高汽车主动安全性、避免车辆无意识地偏离本车道。目前，大部分车道保持控制系统在工作时将驾驶人的操作视为外界干扰，没有考虑人机共驾阶段下驾驶人与控制系统的控制权分配问题，易造成人机冲突、影响驾驶人的驾驶感受。论文兼顾驾驶人与辅助控制系统各自优势，基于人机共驾技术对车道保持控制系统进行研究。构建基于安全行驶区域与最晚预警边界相结合的车道偏离决策模型，在保证其预警精度的同时降低计算复杂性，根据车辆行驶状态和路面附着系数动态调整预警阈值；研究串级MPC-PID控制策略实现对车辆横向位置的控制，将最优问题转化为二次规划求得目标前轮转角，利用PID算法完成对目标前轮转角的跟踪；引入共驾系数对车辆的控制权进行分配，研究共驾系数分配模型，以车辆状态误差和驾驶人转向力矩作为模糊控制的输入变量、共驾系数作为输出变量，降低辅助控制系统与驾驶人之间的冲突；最后，利用CarSim与Simulink联合仿真对所研究的控制策略进行仿真验证，结果表明共驾系数能够根据驾驶人的操作和车辆运行状态的变化实现动态调整，辅助控制力矩与驾驶人输入力矩变化趋势相同，在保留驾驶人一定操作的基础下可避免车辆偏离车道、降低人机冲突。     

Multi-axle vehicle dynamics stability control algorithm with all independent drive wheel

The stability driving characteristic and the tire wear of 8-axle vehicle with 16-independent driving wheels are discussed in this paper. The lateral stability of 8-axle vehicle can be improved by the direct yaw moment which is generated by the 16 independent driving wheels. The hierarchical controller is designed to determine the required yaw torque and driving force of each wheel. The upper level controller uses feed-forward and feed-backward control theory to obtain the required yaw torque. The fuzzification weight ratio of two control objective is built in the upper level controller to regulate the vehicle yaw and lateral motions. The rule-based yaw moment distribution strategy and the driving force adjustment based on the safety of vehicle are proposed in the lower level controller. The influence of rear steering angle is considered in the distribution of driving force of the wheel. Simulation results of a vehicle double lane change show the stability of 8-axle vehicle under the proposed control algorithm. The wear rate of tire is calculated by the interaction force between the tire and ground. The wear of tire is different from each other for the vehicle with the stability controller or not.     

基于蚁狮算法的UKF车辆状态参数估计器

为了准确获取分布式驱动电动汽车状态参数信息，满足车辆稳定性控制系统的需求，提出一种基于蚁狮算法的无迹卡尔曼滤波状态参数估计器。针对无迹卡尔曼滤波（UKF）过程中噪声协方差矩阵的不确定性，采用蚁狮优化算法（ALO）对其进行寻优，并引入奇异值分解（SVD）的方法来维持噪声协方差矩阵的正定性，此外，基于指数加权最小二乘法对车辆侧偏刚度进行辨识并将其作为状态参数估计器输入。基于MATLAB/Simulink和CarSim联合仿真平台，建立分布式驱动电动汽车参数估计模型，分别进行双移线工况和正弦迟滞工况仿真，并基于A&D5435快速原型开发平台进行双移线工况实车试验。仿真与试验结果表明：相比于SVDUKF算法估计结果，双移线仿真工况下，基于ALO-SVDUKF算法估计得到的质心侧偏角和横摆角速度的均方根误差分别降低了55.7%、30.7%，正弦迟滞仿真工况下，均方根误差分别降低了58.1%、85.1%，且在车辆处于极限失稳状态时仍能维持较好的估计效果；双移线试验工况下，横摆角速度的估计值与实际测量值之间的均方根误差仅为0.938 4（°）·s^-1；提出的基于ALO-SVDUKF算法的分布式驱动电动汽车状态参数估计器能够有效提高质心侧偏角和横摆角速度的估计精度，可为车辆稳定性控制提供精确的状态信息。     

改进的有限时间最优预瞄横向控制器设计

提出了有限时间最优预瞄横向控制算法,根据高速公路车道保持系统实际控制要求,使用了同时考虑车辆当前偏差、预瞄点偏差和控制变量的有限时间二次型性能指标函数。运用最优跟踪算法并进行预瞄距离内曲率恒定的假设,使得控制器参数可以离线求解,保证了车辆控制的实时性。通过仿真及实车试验验证,该控制算法具有较好的跟踪效果。     

基于三自由度的智能汽车模糊滑模横向运动控制研究

针对智能汽车运动过程中存在的车身姿态变化问题以及运动控制精度问题，设计了一种基于非线性3自由度动力学模型的模糊滑模横向运动控制器。建立了包括侧倾运动的3自由度动力学模型，进行了模型线性化；对基于线性化处理后的动力学模型进行了滑模控制器设计，通过控制前轮转角实现了路径跟踪横向控制，并引入了模糊控制提高控制效果，本控制系统能够在跟踪过程中对车身姿态变化进行观察。仿真结果表明，搭建的基于3自由度动力学模型的模糊滑模控制器能够在考虑侧倾运动的基础上，实现路径跟踪，且构建的模糊滑模控制系统相较于传统滑模控制其横向偏差与方向偏差分别降低了7.28%和1.50%，同时模糊控制也减弱了滑模控制固有的抖振影响。     

Vehicle Handling Improvement by Active Steering

Summary This paper first analyses some stability aspects of vehicle lateral motion, then a coprime factors and linear fractional transformations (LFT) based feedforward and feedback H 8 control for vehicle handling improvement is presented. The control synthesis procedure uses a linear vehicle model which includes the yaw motion and disturbance input with speed and road adhesion variations. The synthesis procedure allows the separate processing of the driver reference signal and robust stabilization problem or disturbance rejection. The control action is applied as an additional steering angle, by combination of the driver input and feedback of the yaw rate. The synthesized controller is tested for different speeds and road conditions on a nonlinear model in both disturbance rejection and driver imposed yaw reference tracking maneuvers.