基于FlexRay总线的线控转向系统通信网络

给出了一种汽车线控转向系统通信网络设计.首先对线控转向系统总体方案进行设计,重点介绍了线控转向系统对通信网络的要求;之后设计了基于FlexRay总线的线控转向系统通信网络,包括FlexRay通信网络设计、各节点通信任务分配以及节点初始化;最后进行了通信实验,结果表明所设计的通信网络安全可靠性好,满足汽车线控转向系统通信要求.     

线控转向汽车传感器的容错控制

以基于最优滤波理论的自适应渐消Kalman滤波技术的状态估计器为基础,提出了一种新的线控转向汽车主要传感器的容错控制方法,并进行了硬件在环仿真验证。结果表明:该方法可以应用于线控转向汽车传感器的容错控制,能够有效提高线控转向汽车传感器的可靠性和安全性。     

Robust Control for Steer-by-Wire Vehicles

The design and analysis of steer-by-wire systems at the actuation and operational level is explored. At the actuation level, robust force feedback control using inverse disturbance observer structure and active observer algorithm is applied to enhance the robustness vs non-modelled dynamics and uncertain driver bio-impedance. At the operational level, the robustness aspects vs parameter uncertainties in vehicle dynamics and driver bio-impedance are issued and for a given target coupling dynamics between driver and vehicle the design task is converted to a model-matching problem. H_∞ techniques and active observer algorithms are used to design the steer-by-wire controller. Robustness issues at both levels are covered by mapping stability bounds in the space of physical uncertain parameters.Naim Bajçinca has been working as a researcher at German Aerospace Center (DLR) in Oberpfaffenhofen since 1998. He has graduated studies on Physics (1994) and Electrical Engineering (1995) at the University of Prishtina. His main interests include methods of robust and nonlinear control, model-reference control, uncertain time-delay systems with applications on haptics, active vehicle steering and master-slave systems.Rui Cortesão received the B.Sc. in Electrical Engineering., M.Sc. in Systems and Automation and Ph.D. in Control and Instrumentation from the University of Coimbra in 1994, 1997, and 2003, respectively. He has been visiting researcher at DLR for more than two years (1998–2003), working on compliant motion control, data fusion and steer-by-wire. In 2002 he was visiting researcher at Stanford University, working on haptic manipulation. He is Assistant Professor at the Electrical Engineering Department of the University of Coimbra since 2003 and researcher of the Institute for Systems and Robotics (ISR-Coimbra) since 1994. His research interest include data fusion, control, fuzzy systems, neural networks and robotics.Markus Hauschild received his Diploma in Mechatronics from Munich University of Applied Sciences in 2002. He joined the DLR Institute of Robotics and Mechatronics in 2001 working on control strategies for Harmonic Drive gears. His research interests include human-machine-interfaces, x-by-wire, and modeling of biomedical systems. In 2003 he was visiting researcher at National Yunlin University of Science and Technology, Taiwan, developing iterative learning control for compensation of periodic disturbances. In the European ENACTIVE network of excellence he is the DLR coordinator for the “actuators and sensors for haptic interfaces” activities. Presently he is a PhD student at University of Southern California in the Department of Biomedical Engineering.     

带有干扰观测器的线控转向系统复合自适应神经网络控制

考虑车辆线控转向(SbW)系统存在不确定动态特性以及外界干扰影响.本文提出一种带有干扰观测器的复合自适应神经网络实现SbW系统的精确建模与稳定控制.首先,利用神经网络在线逼近系统不确定动态,避免控制器设计中使用到系统模型的先验知识.然后,结合系统的跟踪误差与建模误差提出一种新的复合自适应学习率来更新神经网络的权值,从而加快跟踪误差的收敛速度.最后通过设计干扰观测器补偿系统受到摩擦力矩、回正力矩与神经网络逼近误差的影响,提高了系统的抗干扰能力.李雅普诺夫稳定性理论证明了闭环系统的跟踪误差信号一致最终有界.数值仿真与硬件在环实验结果验证了该控制方法的有效性和优越性.     

FlexRay总线架构下线控转向系统可靠性分析

文章在研究FlexRay总线理论基础上,对FlexRay总线式线控转向系统的可靠性进行研究,给出了具有容错结构的线控转向系统的方案。在对系统可靠性分析中采用FEMA的分析方法,有效的分析系统中的隐患;采用硬件冗余的方案设计出双通道冗余型的线控系统,大大提高FlexRay线控转向系统的可靠性要求。     

线控转向汽车传动比智能控制策略的研究

以线控转向汽车的传动比控制为重点进行研究,提出了传动比智能控制策略,旨在进一步改善系统的转向性能。针对转向系统的不确定性、非线性的特点,在原有基于模型控制方法的基础上,研究了模糊控制和模糊神经网络在传动比控制中的应用;首先利用模糊控制技术设计传动比控制器,然后研究了模糊神经网络的结构和算法,利用模糊神经网络对理想传动比控制进行改进,实现了输入变量隶属函数中心值和宽度的在线调节,进而对传动比进行优化;从仿真结果可以看出基于模糊神经网络的控制方法比单模糊控制扩大了传动比变化的车速范围,更加符合理想传动比的要求,表明基于模糊神经网络控制方法更具有可行性和有效性。     

Nonlinear adaptive optimal control for vehicle handling improvement through steer-by-wire system简

A control algorithm for improving vehicle handling was proposed by applying right angle to the steering wheel, based on the nonlinear adaptive optimal control （NAOC）. A nonlinear 4-DOF model was initially developed, then it was simplified to a 2-DOF model with reasonable assumptions to design observer and optimal controllers. Then a simplified model was developed for steering system. The numerical simulations were carried out using vehicle parameters for standard maneuvers in dry and wet road conditions. Moreover, the hardware in the loop method was implemented to prove the controller ability in realistic conditions. Simulation results obviously show the effectiveness of NAOC on vehicle handling and reveal that the proposed controller can significantly improve vehicle handling during severe maneuvers.     

线性转向车辆自动角控转向器研究

介绍了一种用于线性转向车辆自动角控转向器装置,旨在准确检测方向盘的旋转角度,能为驾驶员提供可调、合适的路感,且能在不同的工况下改变工作模式、调整方向盘完成全转向的转动圈数。     

线控转向汽车直流电机的故障诊断与容错控制

针对线控转向汽车容错控制研究的需要,建立了线控转向系统的直流电机模型.根据电机模型,用最小二乘法离线辨识相应的参数值,再以自适应渐消卡尔曼滤波技术为基础,在线估计相应参数,通过二者的残差,实现电机的故障诊断与容错控制.并在以上的研究基础上,对提出的故障诊断与容错控制方法进行了实车试验验证,结果表明:该方法可应用于线控转...     

汽车线控四轮转向控制策略

基于转向传动比随汽车速度和方向盘转角而变化,提出了前轮控制、侧滑率反馈控制和侧滑率及加速度反馈控制三种前轮线控转向的稳定性控制策略,并进行了驾驶模拟器试验评价。结合后轮主动转向研究了分别采用前馈控制方式和反馈控制方式的线控四轮转向系统的转向控制策略,并将其与前轮线控转向和传统四轮转向系统进行了比较。仿真和模拟器试验验证了线控四轮转向系统能有效提高汽车的操纵稳定性。