基于状态反馈的线控转向系统控制策略研究

研究汽车转向稳定性控制问题.针对线控转向系统的可变转向特性对汽车操纵稳定性的影响,为保证行驶安全,建立基于车辆横摆角速度与稳态横摆角速度之差(反馈系数F1)以及车辆侧向加速度与理想侧向加速度之差(反馈系数F2)反馈的驾驶员模型.结合整车模型和综合评价指标函数,分别对反馈系数F1(F2=0)、F2(F1 =0)和F1、F2进行优化,对线控转向系统的行驶状态进行仿真并与无反馈控制的驾驶员模型进行了比较.仿真试验结果表明:基于侧向加速度与横摆角速度共同反馈的方法,对前轮转角进行补偿,可较好的控制车辆的侧向位移、侧向加速度、横摆角速度和质心侧偏角,提高车道跟踪性能、方向稳定性能、降低驾驶员的转向负担,提高汽车的操纵稳定性.     

横摆力矩和主动前轮转向结合的车辆横向稳定性模糊控制仿真

提出一种基于横摆力矩和主动前轮转向相结合的车辆横向稳定性控制方法,以横摆角速度和侧偏角为控制目标,利用前馈补偿和模糊控制产生横摆力矩和附加的前轮转角,通过控制制动力的分配以及对转向角的修正,使车辆转向行驶时的横摆角速度和侧偏角很好地跟踪参考模型.对转向轮阶跃输入和正弦输入两种工况分别进行了仿真研究,采用横摆力矩和主动前轮转向相结合控制方法,车辆转向时的瞬态及稳态响应优于单独的横摆力矩控制,表明该方法能有效地控制车辆横摆角速度和侧偏角,提高车辆转向时的横向稳定性,同时能有效地减轻驾驶员操纵负担.     

基于电动助力转向的汽车防侧翻预警系统研究

为了使驾驶员能主动感知车辆侧倾状态、防止车辆侧翻,提出了一种新型基于电动助力转向的防侧翻预警系统;利用横向载荷转移率的门限值作为防侧翻预警控制的触发条件,采用助力电机电流补偿控制,当车辆侧倾较大,即横向载荷转移率超过门限值时,预警控制系统使驾驶员操纵转向盘力矩增大,结合侧翻报警灯工作,提醒驾驶员车辆正处于危险工况;MATLAB/simulink中的仿真分析验证了防侧翻预警控制器能有效的提高车辆的操稳性。     

基于最优控制的ANN驾驶员模型与仿真分析

在分析驾驶员行为特性和行为操纵的基础上 ,根据预测跟随理论 ,建立了驾驶员预测控制神经网络 (ANN)模型 ;提出了用最优控制方法确定ANN模型参数的计算方法 ,采用遗传算法 (GA)进行全局优化保证参数的收敛 .对飞机俯仰角操纵进行仿真计算 .结果表明 ,所建立的驾驶员模型考虑了系统的非线性因素 ,实现了多输入多输出功能 ,具有智能特点 .     

驾驶员多源信息融合协同仿真算法研究

交通仿真是交通控制与管理方案评价和优化的重要实验研究手段。传统的微观交通仿真模型,特别是刻画驾驶员行为的车辆跟驰模型,未能综合考虑交通环境中信息刺激的多源性和驾驶员任务集聚、协调反应的行为过程。文章利用Bayes方法描述驾驶员在复杂行驶环境中多源信息的融合过程,确定驾驶员任务集聚后对车辆应采取的驾驶行为。模型验证表明:交通仿真过程中,在车辆跟驰模型实施之前,利用Bayes算法模型化驾驶员在多源信息刺激下任务集聚、协同反应的过程是行之有效的。     

通信拓扑切换下车辆队列分布式模型预测控制

考虑通信拓扑切换下异质非线性车辆队列系统协同控制问题,提出一种能够保证车辆队列稳定和弦稳定的分布式模型预测控制策略.先结合车辆队列动态通信拓扑切换过程,构建与时间相关的图函数,再利用邻居车辆状态信息描述平均协同代价函数,并将其引入局部滚动时域优化控制问题.进一步,应用平均停留时间概念和切换系统Lyapunov稳定性理论...     

考虑侧倾的无人车NMPC轨迹跟踪控制

针对高速行驶工况下,无人车转弯时的侧倾易导致车辆模型非线性程度增加,引起轨迹跟踪精度下降和状态失稳的问题,设计一种考虑车辆侧倾因素,基于非线性模型预测控制(NMPC)的无人车轨迹跟踪控制器.根据拉格朗日分析力学和车辆运动学,考虑车辆侧倾几何学和载荷转移效应,建立考虑侧倾因素的非线性车辆模型,包括车体动力学模型和修正的“Magic Formula”轮胎模型;基于此车辆模型,构建非线性模型预测控制器(NMPC)的预测模型,并设定控制器的线性、非线性约束,以保证车辆的运动状态处于稳定区域内.在Carsim和Simulink联合仿真平台上,验证车辆高速蛇形工况和双移线工况下的轨迹跟踪控制效果,仿真结果显示,所设计的控制器可有效改善高速弯道工况下的跟踪精度和车辆状态稳定性.     

基于模型预测控制的车道保持控制策略及对比研究

基于模型预测控制（MPC）方法,设计了自适应MPC和非线性MPC的两种车道保持辅助控制策略。在Matlab/Simulink平台下,搭建了车辆驾驶员仿真环境,对两种控制策略下的LKA系统性能进行了对比研究。仿真结果表明,两种控制策略均可以主动帮助驾驶员及时调整前轮转向,并具有良好的稳定性。另外在加速度执行和速度追踪方面,基于非线性MPC的控制策略表现得更为平稳,可提供更佳的乘坐舒适性。     

考虑车辆横向主动安全的智能驾驶员模型

结合智能车面临的横向安全问题, 设计了一种具有横向安全性的智能驾驶员模型. 该系统由转向控制、速度控制和决策规划三个模块组成. 该系统的主要作用包括: 一是通过在转向控制中加入主要约束提高车辆在转向过程中的横向稳定性, 减小车辆发生侧滑、侧倾、侧偏等风险; 二是在换道场景下, 决策规划单元合理分析交通环境中的车间距并计算出驶入临近车道的速度和轨迹, 使智能车实现安全换道. CarSim/Simulink仿真结果表明, 该智能驾驶员系统提高了车辆行驶的横向安全性.     

通信随机时滞条件下基于分布式模型预测的AUV编队控制

考虑水声通信随机时滞条件下AUV编队协同控制问题,提出一种基于分布式模型预测的AUV编队控制方法.首先,通过所设计的随机时滞通信同步策略,将异步状态信息转换为同步状态信息;然后,结合虚拟轨迹、状态预测、控制约束以及编队内AUV状态信息描述协同编队代价函数,将其引入局部滚动时域优化,实现编队控制目标,并利用李雅普诺夫理论验证编队控制器的稳定性;最后,将所提出方法与现有编队控制方法进行对比仿真,仿真结果验证了其有效性.     

Steady-state drifting stabilization of RWD vehicles

A control scheme to stabilize rear-wheel-drive (RWD) vehicles with respect to high-sideslip cornering (drifting) steady-states using coordinated steering and drive torque control inputs is presented in this paper. The choice of coordinated control inputs is motivated by the observed data collected during the execution of drifting maneuvers by an expert driver. In addition, the steering and drive torque input variables directly correlate to a human driver's steering wheel and throttle commands. The control design is based on a comprehensive vehicle model with realistic tire force and drive-train characteristics, and validated in a high-fidelity simulation environment.     

Automation-driver cooperative driving in presence of undetected obstacles

The work presented in this paper describes and discusses the principles of a haptic shared control between a human driver and an Electronic copilot (E-copilot) for a vehicle. The aim of the sharing control is to allow the driver to momentarily take control over the E-copilot without deactivating it nor being constrained, in order to deal with a specific situation such as avoiding an obstacle that has not been detected by the E-copilot. As the E-copilot acts simultaneously on the steering system with the driver, both have to be aware of one another's actions, which means bi-directional communication is essential. In this work, to achieve this goal, we consider the haptical interactions through the steering wheel. The torque applied by the driver on the steering system is used by the E-copilot to take into account the driver's actions while the E-copilot assistance torque is felt by the driver and used by him to understand the system's behavior. This low communication level strongly improves the cooperation between the driver and the E-copilot.The system takes into account the drivers actions thanks to a driver lane keeping model that is added to the road vehicle one in the controller synthesis step. This allows to introduce driver's interaction control variables in such a way that the E-copilot can consider conflicting objectives between the driver and the lane keeping task, and thus handle them.In order to highlight the assets of the approach, a comparison of the behaviors of a simple lane keeping E-copilot to that of a cooperative proposed here is given at the end of this paper. This comparison is achieved through computer simulations and experimental tests with a human driver carried out in the SHERPA-LAMIH interactive dynamic driving simulator. The results of these tests confirm the improvement of the level of cooperation between the human driver and the E-copilot and show that the cooperative E-copilot gives more authority to the human driver especially in hazardous situations.     

A method for fatigue detection based on Driver's steering wheel grip

We propose a simple method to measure a driver's fatigue state by detecting the driver's grip force on the steering wheel while driving. We tested the grip force of 36 drivers on the steering wheel in conscious states (Alert) and fatigue states under actual road driving conditions. Using the Stanford sleepiness scale (SSS), we divided drivers into Alert Group A, fatigue Group A, and fatigue Group B. During 20-min real-road driving trials, we measured the steering wheel grip force, electroencephalogram index (R = (α + θ)/β), and blink frequency of each driver synchronously. We found that ΔF, the difference between the maximum/minimum grip force and the standard deviation of the grip force, σF, for each driver, strongly correlated with the driver's fatigue state. In the fatigue state, both ΔF and σF increased significantly. We examined these force indices using analysis of variance (ANOVA) and validated them against the R-value, blink frequency, and the driver's self-reported fatigue state. Using the grip force in fatigue detection, our method can achieve an overall recognition rate of 86.6% and an individual recognition rate of 88.3%. These results indicate that this method can effectively detect a driver's fatigue state during actual road driving. This new method has several advantages, such as a high signal-to-noise ratio, simple data collection, and no influence on daily driving. Thus, our proposed method may provide a theoretical foundation for the development of fatigue-detecting steering wheels     

基于UIO的EPS系统状态反馈最优控制

准确地获知电动助力转向（Electric powering steering,EPS）系统阻力矩是提高行车安全的一个重要因素.针对车辆转向过程中,由不同附着路面上EPS 系统所需辅助力矩与转向路感之间的差别而可能导致的误操纵问题,本文基于2自由度整车动力学的EPS系统模型,结合轮胎特性,以轮胎侧偏角和理想路面附着系数为输入,通过设计非线性观测器估计当前路面的附着系数,以获取EPS系统阻力矩;进而,根据EPS 系统模型,运用未知输入观测器（Unknown input observer,UIO）估算方向盘输入转矩,并基于EPS系统状态反馈以实现对EPS系统的无传感器最优控制.最后,对基于永磁同步电机（Permanent magnet synchronous motor,PMSM）的EPS系统进行仿真实验分析.结果表明： 在以电机q轴电流闭环误差最小为指标函数情形下,本设计的方向盘回正残留角从25°降到0°,能有效抑制系统外界干扰,提高了转向时人-车系统的鲁棒性.     

Estimation method for the consciousness level while driving vehicles

Recently, the driver's attention while driving a vehicle has to be taken seriously in a modernized society. Although some studies of attention while driving are being conducted now, the character of human activity is complicated for estimating attention while driving a vehicle. In the present study, the driver's attention was studied by driving performance and meandering of the vehicle. Two sets of drivers were used to compare with higher and lower states of consciousness. For driving performance, the degree of steering and the degree of acceleration were measured. For meandering, the shoulder line on the road was detected by a CCD camera to calculate the coordinates of the vehicles. These three values showed the dynamical degree of the driver's attention. The results show that the meandering values and the degree of steering values correlated with the degree of attention of the driver, and these results can be applied to make an alert system for drivers during decreased consciousness or concentration in order to realize a safe society for our modern roadways.     

Real-time driving danger-level prediction

This paper introduces a driving danger-level prediction system that uses multiple sensor inputs and statistical modeling to predict the driving risk. Three types of features were collected for the research, specifically the vehicle dynamic parameter, the driver's physiological data and the driver's behavior feature. To model the temporal patterns that lead to safe/dangerous driving state, several sequential supervised learning algorithms were evaluated in the paper, including hidden Markov model, conditional random field and reinforcement learning. Experimental results showed that using reinforcement learning based method with the vehicle dynamic parameters feature outperforms the rest algorithms, and adding the other two features could further improve the prediction accuracy. Based on the result, a live driving danger-level prediction prototype system was developed. Compared to many previous researches that focused on monitoring the driver's vigilance level to infer the possibility of potential driving risk, our live system is non-intrusive to the driver, and hence it is very desirable for driving danger prevention applications. Subjective on-line user study of our prototype system gave promising results.     

The prediction of car driver size and position to enhance safety in crashes

The positions which car drivers adopt when driving will depend on their anthropometric characteristics, the range and type of adjustment available from the vehicle package and their preferred driving posture. The design and testing of systems to protect occupants in car crashes assumes that the size and position of the driver is ‘normal’ or ‘average’, although there is some accommodation for adjustability. If, however, the occupant protection system had information on the driver's chosen seat position, on whether the driver was particularly large or small and on whether the driver was sitting close to or further from the steering wheel, in a crash the system could tailor its performance and enhance the protection offered. This study investigated whether it was possible to predict the physical characteristics of the driver and the driver's position in relation to the steering wheel, from data that could be collected by sensors in the seat and seat mounting. In order to do this, anthropometric characteristics of drivers and their usual seated position in their own vehicle were measured and analyses were undertaken to identify whether there were any relationships between the driver-related and the vehicle-related measures. The results showed that it was possible to predict drivers' head and chest positions relative to injury-producing features of the vehicle such as the steering wheel (and hence the airbag) and to predict some physical dimensions of drivers.     

基于高斯眼白模型的疲劳驾驶检测

目的 为解决疲劳驾驶检测中人眼状态识别的难点,提出一种基于眼白分割的疲劳检测方法。方法 首先对获取图像进行人脸检测,利用眼白在Cb-Cr上良好的聚类性,基于YCbCr颜色空间建立高斯眼白分割模型;然后在人脸区域图像内做眼白分割,计算眼白面积;最后将眼白面积作为人眼开度指标,结合PERCLOS（percentage of eyelid closure over the pupil over time）判定人的疲劳状态。结果 选取10个短视频进行采帧分析,实验结果表明,高斯眼白分割模型能有效分离眼白,并识别人眼开合状态,准确率可达96.77%。结论 在良好光线条件下,本文方法能取得不错的分割效果;本文所提出的以眼白面积作为判定人眼开度的指标,能准确地判定人的疲劳状态。实验结果证明了该方法的有效性,值得今后做更深入的研究。     

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.     

循环球电动助力转向系统控制及补偿策略

考虑循环球式转向系统内多因素的影响,设计循环球式电动助力系统的控制及补偿策略,建立循环球式电动助力转向系统模型,设计电流助力曲线,采用模糊PID控制方法,实现电机的实时控制;为了获得更好的助力力矩,补偿系统内损失,基于LuGre摩擦模型,通过观测到的系统参数,建立摩擦状态观测器,得到摩擦补偿叠加电流。使用Matlab/Simulink与CarSim的联合仿真验证控制系统;通过对增加摩擦补偿策略前后的对比分析,可知所设计的电动助力转向电流控制系统能综合车辆行驶时的摩擦、车速和转向盘转角等信息,由助力执行电机产生适当的助力,更准确地实现驾驶员的驾驶意图,使得回正过程更加平稳。