A review on motorcycle and rider modelling for steering control

The paper is a review of the state of knowledge and understanding of steering control in motorcycles and of the existing rider models. Motorcycles are well known to have specific instability characteristics, which can detrimentally affect the rider's control, and as such a suitable review of these characteristics is covered in the first instance. Next, early models which mostly treat riding as a regulatory task are considered. A rider applies control based on sensory information available to him/her, predominantly from visual perception of a target path. The review therefore extends to cover also the knowledge and research findings into aspects of road preview control. Here, some more emphasis is placed on recent applications of optimal control and model predictive control to the riding task and the motorcycle–rider interaction. The review concludes with some open questions which naturally present a scope for further study.     

A driver-adaptive stability control strategy for sport utility vehicles

Conventional vehicle stability control (VSC) systems are designed for average drivers. For a driver with a good driving skill, the VSC systems may be redundant; for a driver with a poor driving skill, the VSC intervention may be inadequate. To increase safety of sport utility vehicles (SUVs), this paper proposes a novel driver-adaptive VSC (DAVSC) strategy based on scaling the target yaw rate commanded by the driver. The DAVSC system is adaptive to drivers’ driving skills. More control effort would be exerted for drivers with poor driving skills, and vice versa. A sliding mode control (SMC)-based differential braking (DB) controller is designed using a three degrees of freedom (DOF) yaw-plane model. An eight DOF nonlinear yaw-roll model is used to simulate the SUV dynamics. Two driver models, namely longitudinal and lateral, are used to ‘drive’ the virtual SUV. By integrating the virtual SUV, the DB controller, and the driver models, the performance of the DAVSC system is investigated. The simulations demonstrate the effectiveness of the DAVSC strategy.     

单轨车辆驾驶员单点预瞄模型的参数优化

使用ADAMS软件建立摩托车的模型,并利用MATLAB软件建立驾驶员模型,构成驾驶员-车辆模型。采用MATLAB和ADAMS协同仿真,得出单移线和双移线工况实际行驶轨迹和手把力矩、侧倾力矩、转向角、侧倾角、横向加速度等。建立了摩托车移线工况的性能综合评价指标,并用此指标对摩托车在不同运动状况下的性能进行评价。以驾驶员模型反馈系数为设计变量,采用正交设计方法优选出一组参数。     

跨既有繁忙电气化铁路预应力混凝土连续箱梁单点顶推技术研究

由于本公路桥具有大悬臂、截面不对称、顶推就位后浇筑横向连接段、导梁与腹板连接受力复杂、纠偏难度大等特点,为使预应力混凝土连续箱梁能顺利跨越既有电气化铁路,在常规顶推施工经验的基础上对顶推方案进行改进与创新,采用大吨位单点顶推施工工艺,并提出一套"顶拉结合"的新型纠偏方式。实践证明,该顶推工艺操作简单、适用性强。通过对该技术的总结与研究,为类似跨线桥(跨铁路、公路等)工程提供借鉴和示范。     

基于前馈补偿的路网规划方法

交通需求与交通供给之间的正反馈效应，使得路网规划方案难以适应交通需求的变化。在研究交通需求与路网容量关系的基础上，提出了采用前馈的手段，把交通需求的变化预载到规划模型中，规划模型是以建设成本、建成后的运营成本和未满足的交通需求所造成的机会损失成本之和的年平均值最小为目标，并让总成本具有时间性来克服规划方案与未来实际情况之间的偏差，达到对当前的规划工作采取前馈控制，使规划方案兼顾未来的需求和规模上的控制，在一定程度上克服交通需求的不确定性。     

基于轴距预瞄的汽车半主动悬架控制及试验

为了研究轴距预瞄控制技术对半主动悬架系统性能的影响, 构建了基于轴距预瞄的1/2车辆半主动悬架综合模型, 结合神经网络和PID控制理论, 提出了单神经元自适应PID控制算法, 设计了轴距预瞄半主动悬架单神经元PID控制系统, 进行了仿真计算。为研究单神经元自适应PID控制策略的有效性及其控制系统的可靠性, 同时进行了基于轴距预瞄的实车道路试验。研究结果表明: 与被动悬架系统相比, 在车速为50km.h^-1时, 车身质心垂直加速度的峰值和标准差分别减少了20.91%和19.11%;车速为60km.h^-1时, 分别减少了24.42%和26.85%, 并且俯仰角加速度也有一定程度降低, 较好改善了车辆的行驶平顺性。