基于MATLAB铰接式车辆原地转向特性的研究

基于铰接式车辆原地转向特性,建立了铰接式车辆原地转向运动学模型,同时建立了装载机原地转向力学模型,分别对单桥驱动、双桥驱动和转向力学模型阻力矩进行了计算,得到了前、后桥轮荷。运用MATLAB软件对满载原地转向阻力矩和轮荷进行了计算和仿真,为铰接车辆转向系统设计和校验提供了依据。     

汽车驾驶员自适应模糊PID控制模型

汽车动力学控制系统具有强非线性特性,在人-车-路闭环系统中采用基于传递函数的传统方法难以建立精确的驾驶员模型.在"预瞄最优曲率模型"的基础上,对驾驶员校正环节采用模糊PID控制,对包括"魔术公式"轮胎模型在内的汽车模型,建立加速度反馈自适应模糊PID控制驾驶员模型.该模型通过模糊控制器在线实时调整PID的3个参数.仿真结果表明,所建立的自适应模糊PID控制驾驶员模型很好地描述了驾驶员的方向控制行为,为人-车-路闭环系统的进一步研究和智能车辆自动驾驶控制提供了可行的途径.     

An investigation on the integrated human driver model for closed-loop simulation of intelligent safety systems

This paper presents an integrated human driver model for a closed-loop simulation of the intelligent safety system. A lateral human driver model was developed to represent the steering behavior of a human driver using the finite preview optimal control method. A longitudinal human driver model represents a human driver’s throttle and brake control behavior relative to the preceding vehicle motion. It computes the desired acceleration and generates throttle/brake inputs to maintain vehicle-to-vehicle clearance at a desired level or to control vehicle speed. An integrated driver model was been developed using the longitudinal and lateral driver models to represent the behavior of a human driver in alternative driving situations, that is, vehicle following, lane following, emergency braking, and so on. Simulation studies were conducted using “Carsim” model which is validated using vehicle test data. Results showed that a human driver’s behaviors could be well represented by the integrated human driver model presented in this paper. Closed-loop simulations of a unified chassis control system with the integrated human driver model were conducted.     

基于人-车-路环境下汽车无级自动变速传动的智能控制

在分析人一车一路闭环系统各部分关系的基础上，研究驾驶员意图、道路行驶环境与汽车传动系统状态参数之间的内在联系，针对目前无级自动变速汽车只能单．纯反映汽车行驶状态的最佳燃油经济性和最佳动力性控制原则的局限性问题，建立基于驾驶员操纵推理和道路诊断的汽车无级自动变速智能控制策略，为开发智能化的无级自动变速汽车提供理论依据。     

车辆驾驶员辅助系统中基于运动模型的前车最优跟随控制

为解决驾驶员辅助系统在前车跟随控制中车距与车速误差不易同步收敛的问题,建立考虑主车对期望加速度动态响应的主车与前车运动模型,以对前车跟随控制提供模型参考。基于主车实际运动与理想前车跟随行驶间的偏差建立前车跟随误差模型,并用线性二次型调节器求解最优期望加速度控制序列,以作为前车跟随控制中主车运动参数控制的输出。为考虑在实车控制中车辆及环境参数发生变化的可能,在前车跟随误差模型中引入车距与车速误差累积项以使线性二次型调节器具有积分控制功能,以在实际车辆与理想模型发生偏差时仍可进行有效控制。仿真对比及实车试验证明,基于运动模型的前车跟随控制可使主车车距与车速误差同步收敛并有效减少主车达到稳态前车跟随行驶的时间,同时易于在实车控制中展开应用。     

一类基于轨迹预测的驾驶员方向控制模型

驾驶员方向控制模型在人-车-路闭环系统仿真、驾驶员辅助系统开发和智能汽车控制中具有重要作用。在假设驾驶员具有汽车轨迹预测能力的基础上,提出一类基于轨迹预测的驾驶员方向控制模型。分别假定汽车在将来一段时间内保持恒定的横摆角速度或横摆角加速度,并结合汽车状态参数预测汽车的行驶轨迹,采用期望式、增量式以及期望式与增量式集成的转角决策方法建立5种不同的驾驶员模型。在ve DYNA/Simulink联合仿真平台上对各驾驶员模型进行仿真试验,结果表明,增量式驾驶员模型表现出良好的路径跟踪精度和很强的鲁棒性,期望式模型的转向操纵更加平滑,而集成式模型则具备综合优势。在ve DYNA/Labview硬件在环实时试验台架上对所提出的驾驶员模型进行模拟试验,所得结论与仿真基本一致。     

基于灰色预测的驾驶员-汽车系统侧翻预测

为了保证高速行车安全性,研究了基于预瞄-预测模型的汽车侧翻评价指标估计问题,提出了一种基于预瞄-预测驾驶员模型的侧翻预测方法。以高重心汽车为研究对象,建立了三自由度汽车模型和预瞄驾驶员模型;基于预瞄-跟随理论和灰色预测理论,搭建了预瞄-预测驾驶员模型;根据汽车侧翻的机理提出了能反映侧翻情况的指标--横向载荷转移率,并在此基础上提出了汽车侧翻指标估计-预测模型;基于主动转向控制策略和汽车侧翻指标估计-预测模型构建了汽车侧翻预测系统,并进行了不同工况下的仿真分析,从而验证了侧翻预测系统的有效性。     

自适应神经模糊推理的方向驾驶员模型研究

以方向驾驶员模型为研究对象,讲解了自适应神经模糊推理系统的原理和结构,并基于自适应神经模糊推理系统建立了一种两输入单输出的方向驾驶员模型.输入变量是道路参考线到预瞄点的横向偏差和道路参考线与车辆X轴之间面积偏差,输出是车辆方向盘转角.首先,通过车辆动力学仿真软件Carsim获取车辆的仿真数据.其次,自适应神经模糊推理系...     

纯电动轿车制动能量回收系统研究

针对某款量产纯电动轿车进行制动能量回收系统及控制研究。基于ABS开发回馈效率最优的制动能量回收系统。在满足ECE制动法规要求并尽量提高安全性和驾驶员感觉的前提下,提出一种最大制动能量回收控制策略,并利用Cruise软件对控制策略的有效性进行验证。在底盘测功机上测试制动能量回收系统效率,试验结果表明可实现ECE工况下整车续驶里程提升10%以上。     

预瞄优化神经网络驾驶员模型

根据预瞄跟随理论并结合真实驾驶员的学习目标、生理限制与不断优化的行为特性,建立了预瞄优化神经网络驾驶员模型。利用2自由度车辆模型进行了汽车在方向控制下的仿真计算。仿真结果表明,预瞄优化神经网络驾驶员模型与实际合格驾驶员行为非常相似,按照该理论建立的驾驶员模型可以应用于驾驶员-汽车闭环系统的研究和自动驾驶等智能车辆的控制。     

Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds

The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation(CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control(MC)problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control(NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control(TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lanechange(DLC) test results show that by using the optimal predictive controller, the root-mean-square(RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration(OPA) driver model with the non-preview speed-tracking method. Additionally,the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive-by-wire vehicle with better lane-keeping and collision-avoidance capabilities during autonomous driving.     

视觉导航智能车辆横向运动的自适应预瞄控制

车道保持系统中车辆横向运动控制应模拟驾驶员的横向操纵行为,驾驶员根据前方道路曲率及车辆速度适时调节预瞄距离,以获得理想的路径跟踪性能。首先,以车辆二自由度动力学模型及车辆道路几何位置关系为基础,建立车-路横向动力学模型。其次,基于单点预瞄最优曲率模型设计侧向加速度PD跟踪控制器,联立车-路横向动力学模型构建横向控制闭环系统,分析预瞄距离、车速、道路曲率的变化对系统响应的影响。最后,设计模糊控制器对预瞄距离进行模糊选择以提高车辆横向控制精度和减小侧向加速度,采用遗传算法对模糊规则进行优化以使横向控制系统性能达到最优。试验表明,相对固定预瞄控制方法,自适应预瞄减小了车辆侧向加速度,且道路跟踪的方向偏差和距离偏差均得到减小。     

基于遗传算法的汽车紧急避让控制与优化

考虑轮胎侧偏特性的非线性,在MATLAB/Simulink中建立了非线性两自由度汽车紧急避让转向模型,并通过实车试验验证了模型的正确性。基于汽车操纵稳定性评价和单神经元自适应PID控制理论,得到了汽车紧急避让控制算法,实现了车辆以不同车速跟踪给定单移线,且结果良好。在此基础上,利用多岛遗传算法进行控制参数优化设计,以实现系统的最优控制。对控制参数优化前后的汽车以相同速度跟踪给定路径进行仿真计算。结果表明：以最优控制参数进行计算的路径跟踪精度更高,具有良好的鲁棒性和自适应性。     

一种汽车列车结构及其路径跟踪控制方法

为解决现有城市交通拥挤问题,满足城市居民生活及工作的乘车需求,综合轨道车辆单程运输量大和传统铰接车辆基础建设成本低的特点,提出一种多铰接式汽车列车。该汽车列车可灵活编组,具有大容量、单程运输效率高、因结构纵向对称且采用轮速差速运动控制、行驶灵活度高等优点。研究适用于该车型的路径跟踪控制方法,采用非时间因素的控制策略,设计汽车列车各轴轮速控制律,并构建李雅普诺夫函数,采用李雅普诺夫直接法证明路径跟踪偏差逐渐递减,即汽车列车能够沿目标路径行驶,且该方法不受车厢数量限制,可使汽车列车灵活编组。最后,建立多铰接式汽车列车路径跟踪仿真模型,分别对直线路径、圆弧路径、正弦路径进行跟踪控制,仿真结果证明采用非时间参考的跟踪控制方法,能够控制列车对多种路径跟踪。     

基于随机动态规划的混合动力履带车辆能量管理策略

混合动力履带车辆采用发动机—发电机组和电池组混合供电,必须设计满足车辆动力性和燃油经济性约束的能量管理策略。针对串联式混合动力履带车辆,提出一种基于随机动态规划的能量管理策略设计方法。以实车行驶试验数据为目标工况,将驾驶员功率需求抽象为随车速变化的马尔科夫过程。建立发动机—发电机组、电池组以及直流母线功率平衡动态模型。以目标工况中燃油消耗及电池最终荷电状态的偏差作为车辆的优化控制成本函数,建立车辆能量管理最优控制问题。采用策略迭代法求解以发动机转速、电池组荷电状态、车速和驾驶员功率需求为输入、发动机电子节气门为输出的最优控制策略。所得控制策略通过基于前向车辆模型的仿真以及行驶试验验证。结果表明,相对于原发动机多点控制策略,所得最优控制在满足目标工况同时,燃油经济性明显提高。     

The articulated vehicle dynamic analysis using the AWS (All Wheel Steering) ECU (Electronic Control Unit) test

An AWS (all-wheel-steering) system is applied to the articulated vehicle to satisfy the required steering performance. AWS ECU (electronic control unit) controls the hydraulic actuator according to vehicle driving environment, such as driver steering angle, articulating angle, and vehicle velocity. In this paper, the test platform devloped for the AWS ECU black box test in an HIL( hardware in the loop) environment is explained. Using the developed test platform, the control algorithm of the AWS ECU can be evaluated under the virtual driving condition of the articulated vehicle. Also, the maneuver of the vehicle is investigated by using the developed AWS ECU test. This paper was presented at the 4th Asian Conference on Multibody Dynamics(ACMD2008), Jeju, Korea, August 20–23, 2008. Sooho Lee received a B.S. degree in Mechanical Engineering from Ajou University in 2003. He then went on to receive his M.S. degree from Ajou University in 2005. Mr. Lee is currently a Ph.D student in the School of Mechanical Engineering at Ajou University in Suwon, Korea. His research interests are in the area of dynamics, vehicle dynamics, control and HILS ( hardware in the loop simulation).     

基于集中质量的铰接式自卸车平顺性仿真研究

根据振动力学以及多刚体系统动力学原理,分析了某铰接式自卸车的振动模型,同时进行合理的简化,建立了集中质量的虚拟样机模型,并在虚拟样机软件ADAMS上进行仿真。通过与试验的对比,也验证了仿真方法可行,仿真模型可靠。因此可用该模型计算随机激励下铰接式自卸车的响应,并进行整车动力学分析和悬架系统参数优化设计。     

三轴汽车前后轮转向时的侧向动力学控制

基于三轴汽车前后轮转向时的闭环控制确定性模型 ,探讨了该汽车在典型的组合路面行驶过程中后轮的最优转向控制规律 ,通过引入状态反馈 ,改善了整车的转向特性。将实际汽车的前后轮胎侧偏刚度及外界干扰视为有界的不确定性参数 ,采用模型跟踪变结构控制方法 ,使得不确定的实际汽车模型能够很好地跟踪确定性的最优理论模型。仿真结果表明该方法的可行性 ,控制系统能够有效地克服参数摄动及外界干扰对系统稳定性的影响     

人-车闭环中电动助力转向系统的基本特性分析

建立详细的四自由度电动助力转向(EPS)系统机械电气模型,考虑Fiala轮胎模型和二自由度整车模型,引入以转向盘输入角为反馈信号的驾驶员模型,构建装备EPS系统的人-车非线性闭环系统模型。采用模糊控制器确定目标电流并通过PID控制和脉冲宽度调制(PWM)实现其跟踪控制,构建电流闭环控制,并采用基于非线性控制设计(NCD)的单纯形自寻优法对参数进行优化。通过仿真计算,以转向盘输入转矩以及整车的横摆角速度、侧向加速度为指标,研究分析了EPS系统在人-车非线性闭环系统中的基本特性。     

Minimum Time Overtaking Problem of Vehicle Handling Inverse Dynamics Based on Two Kinds of Safe Distances

Overtaking accidents caused by improper operations performed by a driver occur frequently. However, most stud?ies on overtaking safety have neglected research into driver control input. A novel method is proposed to obtain the driver control input during the overtaking process. Meanwhile, to improve the safety of overtaking, two types of safe distances, and the time of the overtaking are considered. Path constraints are established when considering the two types of safe distances. An optimal control model is established to solve the minimum time maneuver under multiple constraints. Using the Gauss pseudospectral method, the optimal control problem is converted into a nonlinear pro?gramming problem, which is then solved through sequential quadratic programming(SQP). In addition, the e ective?ness of the proposed method is verified based on the results of a Carsim simulation. The simulation results show that by adopting an inverse dynamics method to solve the manipulation problem of the vehicle's minimum overtaking time, the manipulation capability of a vehicle in completing an overtaking safely within the minimum time can be obtained. This method can provide a reference for research into the active safety of manned and unmanned vehicles.