遗传算法优化线控转向系统角传动比的研究

线控转向系统取消了转向盘与转向轮的机械连接,可以根据车况灵活改变角传动比以提高汽车的操纵稳定性.首先建立了线控转向系统的人-车-路闭环动力学模型,包括道路模型、驾驶员模型、二自由度整车模型.然后研究了包括轨道误差总方差、方向误差指标、驾驶员转向负担三项指标的开环总方差.最后利用遗传算法,以开环总方差为适应度函数,对不同车速下的传动比进行优化.结果表明,各车速下的传动比使得开环总方差较小,提高了车道跟踪性能、方向稳定性能.降低了驾驶员的转向负荷,从而提高汽车的操纵稳定性.     

线控转向系统的转向盘力反馈控制策略研究

线控转向系统取消了转向盘和转向轮之间的机械连接,因而可以根据车况主动提供路感,提高车辆的操纵稳定性.首先研究了转向刚度力矩、由侧向加速度确定的转向盘回正力矩、由轮胎回正力矩确定的转向盘回正力矩、转向阻尼力矩等进行转向盘力反馈的四种力矩.然后,研究了四种组合的转向盘力反馈控制策略及其进行双纽线试验、蛇行试验的性能.结果表明,调整各种力反馈方案的相应参数可以获得低速时的转向轻便性和高速时的良好路感.     

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.     

汽车线控转向系统的建模与仿真研究

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影响稳定性,为保证行驶安全性,通过分析线控转向系统的工作原理和结构特点,建立线控转向系统各组成部分的数学模型,结合驾驶员模型和整车模型,对线控转向系统的可变角传递特性和力传递特性进行仿真。仿真结果表明:SBW系统能够获得与传统转向系统同样清晰的路感,并且SBW系统能够保持稳定的转向灵敏度,从而改善了汽车的稳定性,为保证行驶的安全提供了依据。     

电动助力转向系统控制策略及其仿真研究

针对EPS电动助力转向系统低速时转向轻便性不够和高速时转向路感不强的特点,提出了一种结合相位超前补偿、惯性补偿电流和阻尼补偿电流等的复合控制策略.通过深入研究电动助力转向系统(EPS)的结构、工作原理及性能要求,建立了转矩传感器、输入转向轴、电机及输出轴的动力学模型,设计了助力系统复合控制策略,并将所设计的复合控制器在EPS台架系统中进行了多组仿真和试验.通过对试验数据和曲线进行详细分析,结果表明:提出的控制策略能够较好的实现EPS系统操纵性能要求,提高了系统的助力跟踪精度,满足了低速时转向轻便性和高速时转向路感强的要求,且全车速范围内操纵稳定性好,具有一定的理论价值和实用价值.     

线控转向系统的主动转向控制策略研究

线控转向系统取消了转向盘和转向轮之间的机械连接,可以对前轮进行主动转向控制以增强操纵稳定性和主动安全性.通过使前轮线控转向系统的期望横摆角速度跟踪稳态质心侧偏角为0的四轮转向车辆的横摆角速度,设计线控转向系统的变传动比,主动控制前轮转角.通过时域响应、转向增益、开环总方差等指标对其进行了性能分析.结果表明:采用提出的主动转向控制策略时稳态质心侧偏角大大降低,开环总方差大大降低,从而提高了汽车的操纵稳定性.     

基于模糊控制策略的EPS设计与实现

介绍电动助力转向（Electric Power Steering,EPS）系统的工作原理,给出EPS的初步数学模型,参照EPS传统的PID控制方式,分析电动助力转向系统采用模糊控制的可行性,最后给出了基于模糊控制的EPS设计方法及其在工程中的具体实现。实践结果表明,在EPS控制系统中采用模糊控制可以使系统具有较强的鲁棒性,在提高系统的操纵轻便性的同时,能保证驾驶员获得充分的路感,从而获得较为理想的助力特性。     

基于电动助力转向系统控制算法的研究

为了达到对转向系统控制转向轻便又不失路感的目的,通过对汽车电动助力转向系统进行力学分析,建立数学模型,提出一种模糊控制与PD控制并行的控制算法。在大转矩时采用模糊PD控制,动态响应好,转向轻便,在小转矩是采用PD控制,控制精度高,不失路感。通过MATLAB进行仿真。研究结果表明,通过采用提出的控制策略即提高了系统的控制精度,又减小了系统的超调,缩短了调整时间,能获较理想的转向助力效果和良好的跟随性能。     

基于模糊免疫PID的EPS路感研究*

针对电动助力转向系统存在系统模型不确定性和各种干扰。以驾驶员获得良好的路感和系统具有较小的力矩波动为控制目标。根据生物免疫系统在干扰和不确定性环境中具有较强鲁棒性和自适应性,借鉴其反馈响应过程的调节规律,提出了一种模糊免疫PID控制策略。仿真结果表明,这种控制器抗干扰能力强,具有较快的响应速度,可有效抑制模型参数不确定、路面冲击等所引起的各种干扰和噪声。     

基于磁流变液的线控转向力反馈装置探究

传统的汽车转向系统方向盘与车轮之间采用的是机械连接:即通过多个万向联轴器使转向角度达到使用要求,但这种传统转向装置最大的问题在于它的转向传动比是确定不变的,转向范围窄。此外,该机械结构与多个部件连接,增加了车辆的重量,降低了驾驶舒适度。如果转向装置不使用机械连接,而采用电气连接,则驾驶员首先控制方向盘转换为电信号,然后将电缆介质传递给控制单元,控制单元则基于相应的算法。指令的控制转移到执行机构,最终执行机制是对应的。由于没有机械连接,轮胎对路面力的感知不能直接传递给驾驶员,因此提供合适的道路仿真是电气连接转向系统的主要方面,绝大多数线控转向机构采用。电机作为执行器执行力反馈和模拟道路感。但如果力反馈失败,电机拖动方向盘事故的方向,这将造成隐患,因此有必要使用被动或主动地理控制的系统来模拟路感。     

电动助力转向系统扭矩信号采集系统设计

电动助力转向系统是安装在汽车上的智能转向机构,通过其控制器获取方向盘输入扭矩的实时信号,以及车速信号等表征汽车运行状态的参数,确定助力电机所提供的助力转矩的大小和方向,辅助驾驶员进行转弯,同时保证汽车的操纵稳定性和安全性。为了保证助力的准确性和快速性,需要对方向盘输入扭矩信号进行检测,针对扭矩信号的采样要求,设计的扭矩信号采集系统主控芯片采用DSP,联合高精度4通道同步采样A/D转换芯片AD7864,可实现对扭矩信号的采样。     

基于遗传算法的电动助力转向系统鲁棒H∞控制

针对电动助力转向系统(EPS)中存在的模型不确定性和路面干扰问题, 提出了基于遗传算法的鲁棒H∞控制方法. 构建了EPS系统数学模型, 以驾驶员获得较小的干扰波动和卓越的鲁棒性为控制目标, 运用鲁棒H∞方法极小化干扰问题, 将系统设计中加权函数的选取表示成多目标问题, 用遗传算法对其优化求解, 得到鲁棒控制器. 分析了受到路面干扰时, 方向盘把持转矩的响应情况. 仿真结果表明, 遗传优化后的EPS鲁棒控制器有效地增强了系统的鲁棒稳定性, 提高了系统的抗干扰能力, 使驾驶员获得满意的路感, 提高了行驶安全性.     

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.     

Multidisciplinary hybrid hierarchical collaborative optimization of electric wheel vehicle chassis integrated system based on driver’s feel

The optimization design of chassis integrated system mainly involves steering, suspension and brake subsystems, which is essentially a multidisciplinary design optimization. This paper mainly researches the multidisciplinary optimization of the chassis integrated system for the electric wheel vehicle, from the view of ensuring a favorable feel for the driver. The dynamic models of differential steering system, brake system, active suspension system and vehicle are established. Then, taking the coupling relationship of the chassis subsystems into account, this paper proposes an evaluating index of driver’s ride comfort (Drc), which is composed of the steering road feel, brake feel and suspension ride comfort. In order to determine the weight coefficient in the quantization formula of Drc, the technique for order preference by similarity to ideal solution (TOPSIS) method is used to overcome the subjectivity in the selection. Based on these, a multidisciplinary hybrid hierarchical collaborative optimization (HHCO) method is proposed on the basis of the collaborative optimization (CO), which consists of a system level coordinator and a coupling analyzer to solve the problem of poor convergence and the low efficiency of CO method. The optimization results show that the proposed HHCO method has excellent computational efficiency and better convergence compared with the CO method, which can further improve the steering road feel and the drive ride comfort, on the premise of ensuring the brake feel and suspension ride comfort.     

线控转向系统动力学模型的研究

准确简单的线控转向系统动力学模型是研究线控转向的各种控制策略和参数匹配的基础.建立了线控转向系统的人-车-路闭环动力学模型,包括道路模型、驾驶员模型、控制器模型、转向盘总成模型、整车模型(包括车身模型、轮胎模型)及前轮转向总成模型等.基于以上模型,研究了驾驶员模型参数:驾驶员的补偿转向增益和认知时间延迟引起的死区时间变化对实际侧向偏移到目标侧向偏移的传递函数频率响应的影响.结果表明,选择不同的参数对线控转向系统的操纵稳定性影响较大,应合理选择相关参数.     

Motion Control of a 6WD/6WS wheeled platform with in-wheel motors to improve its maneuverability

Multi-axle driving mobile platform that are favored in special environments require high driving performance, steering performance, and stability. Among these, six wheel drive and six wheel steering vehicles hereinafter called 6WD/6WS, gain structural safety by distributing the load and reducing the pitching motion during rapid acceleration and braking. 6WD/6WS mobile platforms are favorable for military use, particularly in off-road operations because of their high maneuverability and mobility on extreme terrains and obstacles. 6WD vehicles that use in-wheel motors can generate independent wheel torque without a need for additional hardware. Conventional vehicles, however, cannot generate an opposite driving force on wheels on both sides. In an independent steering and driving system six-wheel vehicles show better performance than conventional vehicles. This paper discusses the improvement of the cornering performance and maneuverability of 6WD/6WS mobile platform using independent wheel torque and independent steering on each wheel. 6WD/6WS vehicles fundamentally have satisfactory maneuverability under low speed, and sufficient stability at high speed. Consequently, there should be a control strategy for improving their cornering performance using the optimum tire forces that satisfy the driver’s command and minimize energy consumption. From the driver’s commands (i.e., the steering angle and accelerator/brake pedal stroke), the desired yaw moment with virtual steering, desired lateral force, and desired longitudinal force are obtained. These three values are distributed to each wheel as torque and steering angle, based on the optimum tire force distribution method. The optimum tire force distribution method finds the longitudinal/lateral tire forces of each wheel that minimize cost function, which is the sum of the normalized tire forces. This paper describes a 6WS/6WD vehicle with improved cornering performance and the results are validated through TruckSim simulations.

     

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