【作者】 李涛；

【导师】 周兵；

【作者基本信息】 湖南大学 ， 机械工程， 2021， 硕士

【摘要】 本文所研究的混合动力半挂列车是在现有普通半挂列车的基础上,为提高燃油消耗率,降低尾气排放和环境污染,在挂车部位增加电驱动来提高车辆运载能力和经济效益的背景下发展起来的。其具有与普通半挂列车一样的大质量、长尺寸、高质心和复杂鞍座耦合关系的结构特点,易导致列车发生折叠和侧翻等失稳情况,在挂车增加了控制不当的动力后,更易失稳。本文在所搭建车辆动力学模型的基础上,通过控制算法,实现合理匹配挂车的驱动/制动力矩,同时保证其具有防侧翻等失稳能力,提高整车稳定性和安全性的目的;其次介于控制算法所需的要一些状态参量无法直接测量获得或测量成本太高,需要结合车辆动力学原理,通过算法估计。因此本文以混合动力半挂列车为研究对象,对车辆状态估计、力矩匹配和稳定性控制展开研究,具体完成以下工作:(1)建立同时考虑牵引车和挂车的纵向、侧向、横摆和侧倾运动,6个轮胎的旋转和转向轮的转向运动,共计14个自由度的车辆数学模型,并与Truck Sim汽车动力学仿真软件联合仿真验证所建模型的可靠性。(2)针对半挂车辆状态估计过程中测量噪声不确定、累计误差影响严重、初值敏感等问题,提出一种适用于半挂列车铰接角、车速等多个状态量估计的双自适应无迹卡尔曼滤波算法(FFUKF)。基于半挂汽车非线性动力学模型,通过测量的轮速与车辆加速度等信息,首先利用模糊控制自适应调整滑移率容差,综合判断每个车轮的稳定状态,通过轮速估算出一种车速;与此同时,模糊控制自适应调整测量噪声,利用无迹卡尔曼算法,估计出铰接角和另一种车速;然后通过卡尔曼滤波算法融合两种方法估计的结果,实现车辆的纵向、侧向速度、横摆角速度和挂车与牵引车铰接角的实时估计。最后在Simulink/Truck Sim联合仿真环境下进行多工况仿真实验,验证所提出的双自适应无迹卡尔曼估计算法(FFUKF)有较强的适应性、稳定性和鲁棒性,其相比普通模糊自适应无迹卡尔曼(FUKF)有更高的估计精度,能有效的克服累计误差,即便在估计初始值不准和有ABS控制输入的情况,仍可以较精确地对车速和铰接角进行实时估计。(3)介绍了两种定量化的侧翻指标和侧翻预警时间的原理与流程;然后在半挂列车动力学模型和FFUKF估计器提供的横摆角速度、侧倾角和纵向铰接力等状态参数的支持下,设计一个既可合理匹配挂车的驱动力矩,又具有稳定性控制功能的控制器。其由稳定性、制动和驱动三个MPC(Model predictive control)子控制器构成,其中横摆MPC通过增加挂车附加差动力矩的方式,分别缩小实际侧倾角/横摆角速度与对应期望值的差,避免车辆侧滑、摆振和侧翻失稳;制动MPC通过增加附加制动力矩的方式防止列车发生折叠失稳;驱动MPC控制挂车增加合适的车轮驱动力矩,提高整车的运输能力。最后通过与Truck Sim联合仿真,验证本控制器不仅可以合理匹配混合动力半挂列车的动力,明确电驱动力矩的边界输入,而且有效的提高了车辆抗侧滑、摆振、侧翻和折叠的能力,具有响应时间短、稳定性控制效果好的优点。更多还原

【Abstract】 The hybrid semi-trailer train studied in this paper is developed on the basis of existing ordinary semi-trailer trains,in order to improve fuel consumption rate,reduce exhaust emissions and environmental pollution,and increase electric drive in trailer parts to improve vehicle carrying capacity and economic benefits.It has the same structural characteristics of large mass,long size,high center of mass and complex saddle coupling relationship as the ordinary semi-trailer train,which easily leads to the instability of the train,such as folding and rollover,etc.,and it is more likely to lose stability after the improper control power is added to the trailer.In this paper,based on the vehicle dynamics model,through the control algorithm,the driving/braking torque of the trailer can be reasonably matched,and at the same time,the stability and safety of the whole vehicle can be improved by ensuring its ability to prevent rollover and other instability;Some state parameters required by the control algorithm can not be directly measured or the measurement cost is too high,which needs to be estimated by the algorithm combined with the vehicle dynamics principle.Therefore,this paper takes the hybrid semi-trailer train as the research object,studies the vehicle state estimation,torque matching and stability control,and completes the following work:(1)a vehicle mathematical model with 14 degrees of freedom is established,which takes into account the longitudinal,lateral,yaw and roll motions of tractor and trailer,the rotation of six tires and the steering motion of steering wheels,and the reliability of the model is verified by joint simulation with Truck Sim automobile dynamics simulation software.(2)Aiming at the problems of uncertain measurement noise,serious influence of accumulated error and sensitive initial value in the process of semi-trailer state estimation,a double adaptive unscented Kalman filter algorithm(FFUKF)is proposed,which is suitable for estimating several state variables such as articulation angle and vehicle speed of semi-trailer trains.Based on the nonlinear dynamic model of semi-trailer car,through the measured wheel speed and vehicle acceleration information,firstly,fuzzy control is used to adaptively adjust the slip rate tolerance,comprehensively judge the stable state of each wheel,and estimate a vehicle speed through the wheel speed;At the same time,fuzzy control adaptively adjusts the measurement noise,and estimates the hinge angle and another vehicle speed by using unscented Kalman algorithm;Then,the Kalman filter algorithm is used to fuse the estimation results of the two methods,so as to realize the real-time estimation of the longitudinal and lateral velocity,yaw rate and the articulation angle between trailer and tractor.At last,the multi-condition simulation experiment is carried out in Simulink/Truck Sim co-simulation environment,which proves that the proposed double adaptive unscented Kalman estimation algorithm(FFUKF)has strong adaptability,stability and robustness,has higher estimation accuracy than the ordinary fuzzy adaptive unscented Kalman(FUKF),and can effectively overcome the cumulative error.even if the initial estimation value is inaccurate and ABS control input is available,the vehicle speed and hinge angle can still be accurately estimated in real time.(3)The principle and process of two quantitative rollover indicators and rollover warning time are introduced.Then,supported by the semi-trailer dynamic model and the yaw rate,roll angle,longitudinal hinge force and other state parameters provided by FFUKF estimator,a controller which can reasonably match the driving torque of the trailer and has stability control function is designed.It consists of three MPC sub-controllers: stability,braking and driving.The yaw MPC(Model predictive control)reduces the difference between the actual roll angle/yaw rate and the expected value by increasing the additional differential torque of the trailer,and avoids the vehicle side slip,shimmy and rollover instability.Brake MPC prevents the train from folding instability by increasing additional braking torque;Driving MPC controls trailer to increase proper wheel driving torque and improve the transportation capacity of the whole vehicle.Finally,through the joint simulation with Truck Sim,it is verified that the controller can not only reasonably match the power of the hybrid semi-trailer train and define the boundary input of the electric driving torque,but also effectively improve the anti-sideslip,shimmy,rollover and folding ability of the vehicle,and has the advantages of short response time and good stability control effect.更多还原