基于ADAMS/Car的半挂汽车列车转向瞬态响应试验仿真分析

运用多体动力学软件ADAMS/Car建立某半挂汽车列车虚拟试验整车模型,参照汽车操纵稳定性试验方法对该半挂汽车列车进行转向瞬态响应试验仿真,分析得到了半挂汽车列车在不同速度下的侧向加速度和横摆角速度响应、转向瞬态响应试验的极限车速及半挂汽车列车安全运行的最大侧倾角,为半挂汽车列车的设计和使用提供理论依据.     

基于卡尔曼滤波的半挂汽车列车状态估计

准确获取车辆的状态信息是汽车主动安全控制研究的关键。文中主要研究半挂汽车列车稳定性控制中的状态估计问题,研究了基于线性四自由度(4-DOF)半挂汽车列车模型,利用卡尔曼滤波(KF)算法估计半挂汽车列车的横摆角速度与质心侧偏角;基于商用车动力学仿真标准软件TruckSim,通过标准双移线仿真工况进行验证,结果表明,在非高速急转向工况下,所提出的估计方案能较准确地跟踪汽车列车的状态信息,为半挂汽车列车主动安全控制奠定基础。     

基于LQR的重型半挂汽车列车稳定性控制策略

以半挂车的横摆角速度、侧向加速度、牵引车与半挂车的铰接角及铰接角速度为控制变量,建立了4自由度6轴重型半挂汽车列车的动力学参考模型,并应用ISO双移线工况对此模型进行验证。考虑到等速圆周稳态工况下行驶和地面附着系数等因素对汽车的限制,确定了控制变量横摆角速度和侧向加速度的参考值。采用基于线性二次型调节器(LQR)的最优控制策略及半挂汽车列车单侧制动方案,应用Trucksim软件对参考模型进行了鱼钩转向工况仿真分析。仿真结果表明:所提出的控制策略正确、有效,实现了提高半挂汽车列车稳定性的控制目标。     

基于主动制动的半挂汽车列车横摆稳定性控制

为研究半挂汽车列车在高速大转向等极限操作工况下的横摆稳定性控制问题,建立了14自由度的半挂汽车列车非线性仿真模型;提出了牵引车与半挂车独立直接横摆力矩控制的横摆稳定性控制方案,通过牵引车和半挂车车轮的合理选择和主动制动实现横摆控制;以跟踪参考模型的稳态横摆响应为目标,设计了PI横摆稳定性控制器,对牵引车和半挂车分别设计了目标制动车轮的选择决策规则。单移线操作仿真结果表明,基于主动制动的横摆力矩控制可有效改善极限工况下半挂汽车列车的横摆稳定性,牵引车与半挂车进行独立横摆控制可以减小制动车轮选择决策的复杂性,而获得较好的控制效果。     

双挂汽车列车操纵稳定性评价指标研究

为了确定双挂汽车操纵稳定性的评价指标并进行横向稳定性分析,在分析国外多挂汽车列车研究现状的基础上,分别总结了结构参数和使用参数以及各种主动控制策略对多挂汽车列车横向稳定性的影响。与中国汽车列车操纵稳定性评价方法相比,针对多挂汽车列车增加了后部放大系数(RWA)和轨迹偏移量(Off-tracking)2种横向稳定性评价指标;构建了横摆运动和侧向运动的双挂汽车列车动力学模型,仿真阶跃响应下各个车辆单元侧向速度、横摆角速度、侧向-横摆相轨迹、侧向加速度以及铰接角的变化,并分别计算以横摆角速度和侧向加速度为基准的RWA值,将计算结果与国外相关研究文献进行了比较。结果表明:当牵引车和一挂车的侧向速度最大值分别为1.15,0.89m·s~(-1)时,对应拖台和二挂车的侧向速度最大值分别为2.81,1.31m·s~(-1),证明其为双挂汽车列车发生失稳的主要影响因素;由横摆角速度、侧向加速度对应的RWA值分别为1.14和1.54可知,以侧向加速度为基准的RWA值更能反映车辆的后部放大状态;由牵引车与一挂车之间的铰接角为5.9°,拖台与二挂车之间的铰接角为9.6°,而一挂车与拖台之间的铰接角恢复到0可知,一挂车与拖台的链接形式比第5轮式的铰接形式更稳定,且恢复到稳定状态时间更短;研究结果可为双挂汽车列车操纵稳定性评价指标的确定及应用提供参考。     

半挂汽车列车转弯制动试验方法研究

转弯制动性能对半挂汽车列车的制动安全有重大影响,针对我国目前半挂汽车列车转弯制动试验标准缺失,传统的单车试验设备不适用于半挂汽车列车等问题,本文中从试验系统搭建、系统安装方式和试验方法与评价等方面对半挂汽车列车转弯制动试验进行了探索性研究。结果表明:半挂汽车列车转弯制动过程中侧向加速度、制动减速度、横摆角速度和牵引车的俯仰角随车速的升高而增加。建议半挂汽车列车转弯制动试验中车速不超过30km/h,转弯半径不小于25m。     

基于横摆角速度的汽车ESP系统的仿真研究

横摆角速度是控制汽车横向稳定性的主要控制变量.基于横摆角速度对汽车ESF,系统进行仿真研究,首先在建立ADAMS/CAR中建立汽车整车模型,然后建立汽车理想二自由度参考模型,设计横摆角速度反馈控制PID控制器.仿真结果表明,基于横摆角速度控制的ESP系统控制的汽车明显改善汽车横向稳定性,提高了汽车行驶安全性能.     

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。     

合理选择设计参数提高半挂汽车列车的制动稳定性

通过对半挂汽车列车制动时的动力学分析，推导出半挂汽车列车产生折叠和甩尾的动力学方程式，对影响半挂汽车列车制动稳定性的有关因素进行分析。     

基于模糊控制的车辆动力学稳定性控制研究

运用模糊控制原理设计了汽车横摆角速度和质心侧偏角联合反馈控制的模糊控制器,使汽车实际的横摆角速度和质心侧偏角与名义值的差值保持在一定的稳定范围,保证汽车的稳定性,并使用Matlab/Simulink软件对这一控制模型进行仿真,验证控制算法的有效性;以dSPASE软件为基础,设计了车辆动力学稳定性控制硬件在环模拟试验平台...     

基于模型预测控制的半挂汽车主动防侧倾控制方法

提出了一种半挂汽车主动防侧倾控制方法。搭建了一个七自由度动力学模型和一个三自由度参考模型;用无迹Kalman滤波的方法,来估计车辆的横向载荷转移率;确定优化目标,运用模型预测控制(MPC)理论进行最优化求解,得到各车轴的主动防侧倾力矩;在Simulink/Trucksim联合仿真环境中,进行仿真对比与分析。结果表明:在本文的MPC控制器和PID控制器作用下,半挂汽车各状态量皆收敛,且横向载荷转移率保持在0.7以内;相比于PID控制,MPC控制所需的防侧倾力矩更小更均衡,各状态量变化也更加平稳。因此,本MPC控制器在提升半挂汽车侧倾稳定性的同时具有较好的鲁棒性。     

Nonlinear PI front and rear steering control in four wheel steering vehicles

Vehicle steering dynamics show resonances, which depend on the longitudinal speed, unstable equilibrium points and limited stability regions depending on the constant steering wheel angle, longitudinal speed and car parameters.

The main contribution of this paper is to show that a combined decentralized proportional active front steering control and proportional-integral active rear steering control from the yaw rate tracking error can assign the eigenvalues of the linearised single track steering dynamics, without lateral speed measurements, using a standard single track car model with nonlinear tire characteristics and a non-linear first-order reference model for the yaw rate dynamics driven by the driver steering wheel input. By choosing a suitable nonlinear reference model it is shown that the responses to driver step inputs tend to zero (or reduced) lateral speed for any value of longitudinal speed: in this case the resulting controlled vehicle static gain from driver input to yaw rate differs from the uncontrolled one at higher speed. The closed loop system shows the advantages of both active front and rear steering control: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres, enlarged stability regions, reduced lateral speed and improved manoeuvrability; in addition comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced.

For the designed control law a robustness analysis is presented with respect to system failures, driver step inputs and critical car parameters such as mass, moment of inertia and front and rear cornering stiffness coefficients. Several simulations are carried out on a higher order experimentally validated nonlinear dynamical model to confirm the analysis and to explore the robustness with respect to unmodelled dynamics.     

Nonlinear PI front and rear steering control in four wheel steering vehicles

Vehicle steering dynamics show resonances, which depend on the longitudinal speed, unstable equilibrium points and limited stability regions depending on the constant steering wheel angle, longitudinal speed and car parameters.

The main contribution of this paper is to show that a combined decentralized proportional active front steering control and proportional-integral active rear steering control from the yaw rate tracking error can assign the eigenvalues of the linearised single track steering dynamics, without lateral speed measurements, using a standard single track car model with nonlinear tire characteristics and a non-linear first-order reference model for the yaw rate dynamics driven by the driver steering wheel input. By choosing a suitable nonlinear reference model it is shown that the responses to driver step inputs tend to zero (or reduced) lateral speed for any value of longitudinal speed: in this case the resulting controlled vehicle static gain from driver input to yaw rate differs from the uncontrolled one at higher speed. The closed loop system shows the advantages of both active front and rear steering control: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres, enlarged stability regions, reduced lateral speed and improved manoeuvrability; in addition comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced.

For the designed control law a robustness analysis is presented with respect to system failures, driver step inputs and critical car parameters such as mass, moment of inertia and front and rear cornering stiffness coefficients. Several simulations are carried out on a higher order experimentally validated nonlinear dynamical model to confirm the analysis and to explore the robustness with respect to unmodelled dynamics.     

基于MPC的分布式驱动越野车辆原地转向控制研究

针对轮毂电机分布式驱动越野车辆在狭小空间快速机动的需求,设计了一种分层结构的原地转向控制策略。基于动力学原理分析了各轮载荷、附着条件对原地转向横摆速度的影响机理,并搭建原地转向运动学模型,上层采用模型预测控制算法设计原地转向理想轨迹以及期望的横摆角速度,开发基于PI滑模控制的横摆运动跟踪算法,通过补偿转向横摆力矩以提高方向角控制的鲁棒性和稳定性,下层以最优轮胎利用率为目标,设计二次规划算法优化分配各轮附加横摆力矩。dSPACE硬件在环测试结果表明,所提出的控制算法可在保证稳定性的前提下实现原地转向,大幅提高了车辆的转向机动性,在方向盘动态输入仿真中,车辆最大转弯半径为0.157 m,转向中心的最大偏移量为3.610 m;同时,驾驶员能对转向过程进行闭环控制,实现了原地转向过程中横摆速度的实时调节。     

Yaw rate and side-slip control considering vehicle longitudinal dynamics

Most conventional vehicle stability controllers operate on the basis of many simplifying assumptions, such as a small steering wheel angle, constant longitudinal velocity and a small side-slip angle. This paper presents a new approach for controlling the yaw rate and side-slip of a vehicle without neglecting its longitudinal dynamics and without making simplifying assumptions about its motion. A sliding-mode controller is used to develop a differential braking controller for tracking a desired vehicle yaw rate for a given steering wheel angle, while keeping the vehicle’s side-slip angle as small as possible. The trade-off that exists between yaw rate and side-slip control is described. Conventional and proposed algorithms are presented, and the effectiveness of the proposed controller is investigated using a seven-degree-of-freedom vehicle dynamics model. The simulation results demonstrate that the proposed controller is more effective than the conventional one.     

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.     

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.     

基于NMPC的智能汽车纵横向综合轨迹跟踪控制

本文中针对大曲率转弯工况下,智能汽车纵横向动力学特性的耦合和动力学约束导致轨迹跟踪精度和稳定性下降的问题,提出一种基于非线性模型预测控制(NMPC)的纵横向综合轨迹跟踪控制方法,通过NMPC和障碍函数法(BM)的有效结合,提高了跟踪精度,改善了行驶稳定性.首先建立四轮驱动-前轮转向智能汽车动力学模型和轨迹跟踪模型,采用...