四轮独立驱动电动汽车转向稳定控制

四轮驱动电动汽车在中高速转向行驶过程中,轮胎的非线性特性会使得汽车出现大摆动、侧滑、过度或不足转向等安全问题.针对可能出现的问题,提出了四轮驱动电动汽车转向稳定分层控制策略.上层横摆稳定控制器采用基于图表的滑模控制算法规划出使车辆转向稳定的附加横摆力矩.下层转矩优化分配控制器采用模型预测控制方法实现4个轮胎的转矩分配,...     

基于模糊控制的分布式电动车辆横摆力矩研究

为提高多轮分布式电驱动车辆在不同工况下的操纵稳定性,设计了一种基于直接横摆力矩控制的分层控制策略。上层以横摆角速度和质心侧偏角为控制变量,采用模糊控制进行目标运动状态跟踪,决策出所需要的横摆力矩。下层按设计的规则进行转矩分配。应用TruckSim和Matlab/Simulink建立车辆和控制器模型,分别在高、低附着等工况下进行联合仿真。仿真结果表明,设计的模糊控制方法能对车辆目标状态进行良好跟踪,相较于无控制状态能够提高车辆的操纵稳定性。     

汽车直接横摆力矩控制系统综述

汽车直接横摆力矩控制（direct yaw moment control,DYC）系统用于有效避免车辆遇到非预计危险时的侧向运动,以保证汽车运行的稳定性和驾驶的安全性。目前,DYC系统大多采用分层结构。文章首先从车辆状态估计与环境感知、直接横摆力矩控制器设计和力矩分配法这3个角度分析了汽车DYC系统架构,接着重点阐述了其对车速、车间距离、路面信息、横摆角速率以及车辆质心侧偏角等状态信息的获取与处理方法;然后介绍了上层控制器中车辆动力学参考模型、控制结构及不同变量控制的设计方法以及下层控制器中车辆横摆力矩的分配方式;最后总结了汽车直接横摆力矩控制系统现存的问题以及将来发展的方向。     

基于模糊控制的汽车直接横摆力矩研究

为防止汽车产生测滑,针对汽车直接横摆力矩控制,提出了横摆角速度与质心侧偏角联合控制的模糊控制方法.横摆力矩控制采用分层控制方法,设计了模糊控制器和规则制动力分配方法.模糊控制器根据期望值和车辆状态决策出所需的附加横摆力矩,并通过规则制动力分配方法进行主动差动制动实现.采用Matlab/Simulink与CarSim联合仿真对控制方法进行了仿真验证.结果表明:横摆角速度与质心侧偏角联合控制的横摆力矩模糊控制方法使汽车能够较好地跟踪期望,有效提高汽车极限工况下的行驶稳定性.     

分布式驱动电动车行驶稳定性控制建模与仿真

针对分布式驱动电动车的行驶稳定性控制问题,利用CarSim和MATLAB/Simulnk软件建立模型并搭建联合仿真平台。设计稳定性控制算法,包括横摆力矩控制、转矩协调控制和驱动防滑控制。以车辆横摆角速度和质心侧偏角作为控制变量,运用滑模变结构控制方法,设计横摆力矩控制器。通过转矩分配算法,对单个车轮施加驱动或制动力,产生稳定横摆力矩,并建立模糊控制器对车轮滑转率进行控制。在仿真平台完成了双移线工况的仿真,结果表明,该算法能提高车辆行驶稳定性。     

车辆主动转向和独立驱动集成控制

针对车辆行驶安全的主动控制问题,本文提出一种主动转向和4轮独立驱动的双层集成控制系统.针对所建立的8自由度汽车模型,利用上层控制结构的质心侧偏角—质心侧偏角速度β–■相平面算法,实现对车辆运动状态的确定;利用下层控制结构的滑模变结构和二次规划算法,实现主动转向和驱动力的协调,控制车辆的运行状态.在MATLAB/Simulink下,对集成控制器进行增幅正弦极限工况的仿真,结果显示该集成控制器可将车辆的横摆和侧滑控制在安全的区域内,明显提高极限工况下的车辆稳定性.     

基于分层控制策略的六轮滑移机器人横向稳定性控制

六轮野外机器人通常体积庞大,难以建立其动力学模型.采用传统的速度控制方法很难保证机器人的横向稳定性.为解决这一问题,开展基于分层控制策略的六轮滑移机器人横向稳定性控制研究.首先分析整车受力情况,建立六轮滑移机器人的动力学模型.其次,设计基于分层控制策略的动力学控制器,其中上层为基于改进趋近律的滑模控制器,实现对期望横摆角速度的跟踪;下层为基于附着率最优的转矩分配控制器,该控制器可以保证机器人行驶的横向稳定性.最后,在不同工况下进行仿真实验,并搭建实验平台进行实物测试.结果表明设计的控制器可以有效提高机器人的横向稳定性.     

双层预测控制中保证动态控制可行的稳态目标计算策略

在双层结构模型预测控制(Model predictive control, MPC)中, 稳态目标计算(Steady-state targets calculation, SSTC)层(上层)为动态控制(Dynamic control, DC)层(下层)提供操作变量、被控变量设定值和变量约束. 但是,上层可行域和下层吸引域间存在的不一致性可能使得上层给出的设定值无法实现. 本文为下层事先选取若干组放松的软约束, 并对每一组软约束都离线计算出相应的吸引域, 其中最大的一个吸引域包含稳态目标计算的可行域. 在控制过程中, 根据当前状态所属吸引域在线地决定在DC层采用的软约束组. 采用上述方法后, 对所有处于最大吸引域的初始状态, 在跟踪稳态目标的过程中, 下层优化问题都是可行的. 仿真算例证明了该方法的有效性.     

轮毂驱动电动车辆动力学稳定性滑模控制策略研究

为了提高轮毂驱动电动车辆的操纵稳定性,理论分析了车辆横摆角速度和质心侧偏角对于车辆稳定性的影响.设计了基于滑模变结构控制理论和直接横摆力矩控制的双层控制器.在Carsim中搭建了四轮轮毂电机驱动车辆仿真实验平台,并进行了Carsim/Simulink联合仿真,在标准换道工况下,分别验证了基于质心侧偏角的滑模变结构控制和基于横摆角速度的滑模变结构控制策略的效果,验证了双层车辆稳定性控制策略的有效性和稳定性.     

基于Stackelberg博弈的动态用户最优配流和信号控制

研究了动态用户最优配流与信号控制的组合问题.首先基于交通分配将交通流分配到合适的路网上由信号控制来适应这些交通流的思想,并由此建立了交通分配和信号控制的Stackelberg博弈模型,模型的上层是动态用户最优,下层是信号控制优化.然后,通过对模型离散化应用模拟退火算法进行求解.最后,对一个简单的交通网络进行仿真,仿真结果表明所提方法的有效性.     

MPC-based compensation control system for the yaw stability of distributed drive electric vehicle

Conventional yaw stability strategy of distributed drive electric vehicle (DDEV) is usually realised by torque distribution strategy. However, the instantaneous variations of four independent tyres slip ratio and the effect of disturbance have not been considered sufficiently. Therefore, it is difficult to realise the robustness of yaw stability for DDEV under various operating conditions. To solve this problem, a novel model predictive controller-based compensation control system (MPC-CCS) is proposed in this paper. The proposed MPC-CCS consists of two parts, an MPC based-feedback controller and a Kalman filter based-feedforward controller. In the feedback controller, a dual torque distribution scheme is adopted to obtain optimal torque values derived from the real-time signals of four independent tyres slip ratio, an MPC is designed to realise optimal torque values for vehicle yaw motion. In the feedforward controller, a Kalman filter is employed to attenuate the effect of the disturbance on yaw performance. In this way, the robustness of yaw stability for DDEV can be guaranteed by the proposed MPC-CCS. The proposed MPC-CCS is evaluated on eight degrees of freedom simulation platform and simulation results of different conditions show the effectiveness of the MPC-CCS.     

四轮驱动电动汽车稳定性预测控制器快速实现

为解决四轮驱动电动汽车在高速情况下易发生甩尾失控的安全性问题,针对整车和执行器间的动力学耦合、控制系统非线性、多变量、实时性等问题,本文采用集中式的控制策略,设计了一种车辆横摆稳定的快速非线性预测控制器,实现了整车横摆稳定和电机转矩分配的一体化控制.为了控制系统的实时实现,将非线性规划问题转化为代数方程组求解,通过解耦预测时域间方程组的耦合关系,实现时域间优化问题的并行求解,提高了控制器的计算速度.最后给出了控制器的硬件并行加速实验,完成了控制系统的硬件在环实验,实现了车辆横摆稳定系统的实时控制.实验结果表明该控制器不仅具有良好的控制性能,而且明显提升了系统实时性.     

后轴双电机扭矩矢量控制研究

车辆的横摆响应受到转向系统、悬架系统、制动系统及驱动系统影响,传统车辆主要以转向输入进行主动控制,随着线控底盘的发展,ESC、后轮转向、扭矩矢量等技术逐步参与到车辆横摆的主动控制中;相对于ESC以制动力差产生横摆力矩,扭矩矢量可在不降低总驱动力的前提下产生横摆力矩,不会引起车辆的制动效应;通过后轴双电机扭矩矢量控制（TVC）产生主动横摆力矩,旨在改善车辆横摆响应,TVC采用前馈与反馈结合控制,基于二自由度车辆模型、目标稳态增益K及横摆角速度-速度修正因子K1建立目标横摆角速度;利用车辆模型逆函数计算横摆力矩前馈值,PID计算横摆力矩反馈值,总横摆力矩转换得到左右车轮纵向力调整量;纵向力调整量与驱动力分量叠加获得左右轮总纵向力;左右轮驱动力过大时可能会受到滑移率、电机扭矩等限制,为保证横摆力矩偏差在要求范围内,需要根据限制情况对左右轮纵向力进行调整;通过仿真验证,TVC可明显改变车辆横摆响应     

Yaw Moment Control Strategy for Four Wheel Side Driven EV

When four wheel side driven EV travals in steering or changes lanes in high speed, the vehicle is easy to side-slip or flick due to the difference of wheel hub motor and a direct effect of vehicle nonlinear factors on vehicle yaw motion, which would affect vehicle handling and stability seriously. To solve this problem, a joint control strategy, combined with the linear programming algorithm and improved sliding mode algorithm, which combines the exponential reaching law and saturation function was proposed. Firstly, the vehicle dynamics model and the reference model according with the structure and driving characteristics of four wheel side driven EV were set up. Then, introduced the basic method of the improved sliding mode variable structure control and complete the sliding mode variable structure controller design basic on vehicle sideslip angle and yaw velocity.The controller accomplish optimal allocation of vehicle braking force through a linear programming algorithm, according to yaw moment produced by the vehicle motion state. Single lane driving simulation results show that the proposed control strategy can not only control vehicle sideslip angle and yaw velocity well, but also accomplish good controlling of the vehicle yaw moment, so as to significantly improve the handling and stability of vehicle.     

Disturbance Observer Based Control for Four Wheel Steering Vehicles With Model Reference

This paper presents a disturbance observer based control strategy for four wheel steering systems in order to improve vehicle handling stability. By combination of feedforward control and feedback control, the front and rear wheel steering angles are controlled simultaneously to follow both the desired sideslip angle and the yaw rate of the reference vehicle model. A nonlinear three degree-of-freedom four wheel steering vehicle model containing lateral, yaw and roll motions is built up, which also takes the dynamic effects of crosswind into consideration. The disturbance observer based control method is provided to cope with ignored nonlinear dynamics and to handle exogenous disturbances. Finally, a simulation experiment is carried out, which shows that the proposed four wheel steering vehicle can guarantee handling stability and present strong robustness against external disturbances.      

Integrated vehicle dynamics management for distributed‐drive electric vehicles with active front steering using adaptive neural approaches against unknown nonlinearity

This paper proposes a new integrated vehicle dynamics management for enhancing the yaw stability and wheel slip regulation of the distributed‐drive electric vehicle with active front steering. To cope with the unknown nonlinear tire dynamics with uncertain disturbances in integrated control problem of vehicle dynamics, a neuro‐adaptive predictive control is therefore proposed for multiobjective coordination of constrained systems with unknown nonlinearity. Unknown nonlinearity with unmodeled dynamics is modeled using a random projection neural network via adaptive machine learning, where a new adaptation law is designed in premise of Lyapunov stability. Given the computational efficiency, a neurodynamic method is extended to solve the constrained programming problem with unknown nonlinearity. To test the performance of the proposed control method, simulations were conducted using a validated vehicle model. Simulation results show that the proposed neuro‐adaptive predictive controller outperforms the classical model predictive controller in tracking nominal wheel slip ratio, desired vehicle yaw rate and sideslip angle, showing its significance in vehicle yaw stability enhancement and wheels slip regulation.     

Controller design for vehicle stability enhancement

A Vehicle Dynamics Control (VDC) system is developed for tracking desired vehicle behavior. The cascade structure of control system consists of yaw moment major controller and wheel slip minor controller. The Linear Quadratic Regulator (LQR) theory is exploited for yaw moment controller and the sliding mode theory is applied for wheel slip controller design. The use of yaw moment control was investigated by regulating the wheel slip ratio for improving handling and stability of vehicle. The performance of the control system is evaluated under various emergency maneuvers and road conditions through pure computer simulations and Hardware In-the-Loop Simulation (HILS) system. The results indicate the proposed system can significantly improve vehicle stability for active safety.     

An intelligent approach to the lateral forces usage in controlling the vehicle yaw rate

A direct yaw moment control system (DYC) is designed to improve the handling and stability of a four‐wheel‐drive electric vehicle. The main task of this paper is to use the lateral forces in the process of optimally controlling vehicle stability. This is performed by defining a variable optimum region for the slip ratio of each wheel. A hierarchical structure is selected to design the control system. The higher‐level control system controls the yaw rate of the vehicle based on the fuzzy logic technique. The lower‐level control system, installed in each wheel, maintains the slip ratio of the same wheel within an optimum region using the fuzzy logic technique. This optimum region for each wheel is continuously modified based on the impact of the lateral force on the generated control yaw moment and the friction coefficient of the road. Therefore, an algorithm for estimation of the friction coefficient is proposed. Computer simulations are carried out to investigate the effectiveness of the proposed method. This is accomplished by comparison of the results of control methods with a fixed slip ratio region and the results of the proposed method with a variable slip ratio region in some maneuvers. The robustness of the proposed controller against hard braking and noise contamination, as well as the effect of steering wheel angle amplitude, is verified. The simulation results show that the influence of the proposed method on enhancing vehicle performance is significant. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

线控转向系统力反馈的研究

线控转向系统取消了转向盘与转向轮的机械连接,所以必须通过电机向驾驶员实时反馈路感,从而使驾驶员感知车辆行驶状态和路面状况.首先建立了包括驾驶员在内的转向盘力反馈模型.提出的路感控制策略包括上层控制策略和下层控制策略.上层控制策略中转向盘回正力矩建模为扭杆弹簧施加的回复力矩,与转向盘转角成线性;下层控制策略对电机电流进行比例积分控制.最后研究了不同驾驶员模型比例系数,积分系数和电流比例积分控制的比例系数,积分系数对转向盘转角跟踪性能的影响.结果表明,遗传算法优化得到的这四个参数,可使得驾驶员较好跟踪转向盘转角,路感电机电流较好跟踪目标电流,实现较好的力反馈.