智能汽车自动紧急转向避撞的跟踪控制方法对比研究

为了提高智能汽车的主动安全性,提出3种不同的自动紧急转向避撞跟踪控制方法。首先建立汽车避撞简化模型,对制动、转向及两者相结合的3种不同避撞方式进行对比分析。其次,为深入研究汽车避撞过程中的实际响应,建立包含转向、制动及悬架3个子系统耦合特性的底盘18自由度统一动力学模型,并进行相关试验验证。随后构建智能汽车自动紧急转向避撞控制框架,对五次多项式参考路径和七次多项式参考路径的横摆角速度和横摆角加速度进行对比分析。接着以线性2自由度转向动力学模型为参考对象,对最优控制四轮转向、最优控制前轮转向、前馈与反馈控制相结合的前轮转向3种不同的跟踪控制系统分别进行设计。最后,以汽车底盘18自由度统一动力学模型为研究对象,对上述3种避撞控制系统进行仿真试验对比分析。研究结果表明：与制动避撞相比而言,转向避撞所需的纵向距离有较大降低,随着车速的增加和路面附着系数的越低,效果越明显;七次多项式参考路径比五次多项式参考路径的避撞过渡过程更为平缓,当实际车速与控制器所用车速不一致时,前者避撞性能表现更优;最优四轮转向控制系统在高、低2种不同附着路面都具有较好的避撞效果,最优前轮转向控制系统次之,而前馈与反馈相结合的前轮转向控制系统在低附着路面上则表现出严重的失稳。     

铁路接触网最优主动控制研究

研究目的:本文为研究主动控制对接触网性能的影响,建立了接触网有限元模型,应用Ansys软件计算模型的整体质量及刚度矩阵数据,并利用KMExtract程序分离出整体质量、整体刚度矩阵,据此建立接触网系统动力学方程。为便于系统仿真及控制器设计,使用模态降阶法对系统降阶并将其转化为状态方程形式,而后为系统设计LQR控制器,使用simulink对实施主动控制前后的接触网系统进行动态仿真分析。研究结论:研究表明:(1)对接触网实施主动控制能有效改善其振动特性,接触网振动的最大值、最小值有较大降低,幅度达90%以上,均方差也有较大下降,幅度达70%以上,系统性能得到了极大提升。(2)在通过改变弓网自身参数的方法改善弓网性能遇到瓶颈时,接触网的主动控制或半主动控制将成为改善弓网性能的重要手段。     

弹性阻尼耦合轮对动力学特性分析

耦合轮对左右车轮间是通过耦合度可变的耦合器连接的,既不完全固结,也不可相对独立旋转,因此其动力学性能也有别于二者。现建立弹性阻尼耦合轮对(EDCW)车辆的动力学模型,系统地分析了其直线稳定性和曲线通过性能。研究发现,选择适当的耦合度时,全部轮对均为EDCW的车辆系统动力学性能居于传统轮对和独立旋转车轮车辆系统之间。在直线上的临界速度小于独立旋转车轮而大于传统轮对,在曲线上的导向性能劣于传统轮对而优于独立旋转车轮,其直线上临界速度的提高是以曲线上导向能力的下降为前提的。研制一种具有主动控制性能的耦合器,使其在高速时具有小耦合度,在低速和通过曲线时具有大耦合度,可以很好地满足当今铁路发展的需求。     

基于灰色评价理论的自适应最优路径选择

充分考虑了驾驶员在路径选择中的不同要求,依据层次分析法和灰色评价理论建立了一种根据驾驶员的偏好自适应选择最优路径的方法,并设计了相应的决策支持系统,为智能交通流诱导系统的进一步发展铺平了道路。     

高速公路路面养护管理系统开发和研究

在考虑当前路况的基础上，又考虑路龄的使用性能预测模型，预测的结果更为快速准确。探讨高等级公路道路养护管理决策系统和经济分析方法，提出了改进优劣系数法，该方法充分利用了专家经验知识，减小了决策误差。开发了高速公路路面养护管理辅助决策系统，建立高速公路基本数据库，由系统自动进行路面使用性能评价分析，并利用专家知识系统得出推荐养护方案，系统投入运行后反响良好。该养护管理系统的建立和实施进一步提高和改善了江苏省高等级公路的使用管理和养护管理模式，为高等级公路早日实现网级化系统管理奠定基础。     

连续刚构桥双薄壁墩参数优化研究

考虑侧向风压产生扭转及温度变化产生纵向变形的作用，通过对双薄壁墩连续刚构桥的受力分析，探讨了双薄壁墩设计的影响因素，以非线性规划为基础，应用罚函数法对双薄壁墩参数进行优化研究。     

小型铣刨机车体升降油缸动态过程的分析

通过对小型铣刨机车体升降系统的简化,建立了液压缸运动速度与流量的传递函数,并且在系统参数静态设计方法的基础上,考虑了系统参数对其动态特性的影响.     

Methods to reduce dimensionality and identify candidate solutions in multi-objective signal timing problems

Adjusting traffic signal timings is a practical way for agencies to manage urban traffic without the need for significant infrastructure investments. Signal timings are generally selected to minimize the total control delay vehicles experience at an intersection, particularly when the intersection is isolated or undersaturated. However, in practice, there are many other potential objectives that might be considered in signal timing design, including: total passenger delay, pedestrian delays, delay inequity among competing movements, total number of stopping maneuvers, among others. These objectives do not tend to share the same relationships with signal timing plans and some of these objectives may be in direct conflict. The research proposes the use of a new multi-objective optimization (MOO) visualization technique—the mosaic plot—to easily quantify and identify significant tradeoffs between competing objectives using the set of Pareto optimal solutions that are normally provided by MOO algorithms. Using this tool, methods are also proposed to identify and remove potentially redundant or unnecessary objectives that do not have any significant tradeoffs with others in an effort to reduce problem dimensionality. Since MOO procedures will still be needed if more than one objective remains and MOO algorithms generally provide a set of candidate solutions instead of a single final solution, two methods are proposed to rank the set of Pareto optimal solutions based on how well they balance between the competing objectives to provide a final recommendation. These methods rely on converting the objectives to dimensionless values based on the optimal value for each specific objectives, which allows for direct comparison between and weighting of each. The proposed methods are demonstrated using a simple numerical example of an undersaturated intersection where all objectives can be analytically obtained. However, they can be readily applied to other signal timing problems where objectives can be obtained using simulation outputs to help identify the signal timing plan that provides the most reasonable tradeoff between competing objectives.     

Nonlinear pricing in linear cities with elastic demands

Nonlinear road pricing charges each traveler based on his/her trip’s corresponding particular attribute level. In order to help authorities in designing road pricing systems at a strategic level, this paper attempts to address two fundamental questions: (i) what is the value of pricing’s nonlinearity for mitigating traffic congestion? (ii) if a nonlinear toll function is implemented, should it be convex, concave or other shape? Specifically, we consider distance-based pricing in linear cities. For linear monocentric cities with heterogeneous travelers, we show that the system optimal distance-based pricing indeed exhibits nonlinearity. It is proved that: (i) the cost-based system optimal toll function is monotonically increasing and concave with respect to the traveled distance; (ii) the time-based system optimal toll function always exists and is unique. If the initial proportion of each traveler group is invariant along a corridor and the demand function is of exponential type, then the time-based system optimal toll function enables the travelers, living further away from a city center, to face a lower toll level per unit distance. For a linear polycentric city, we demonstrate: (i) there always exists the system optimal differentiated (in terms of city centers) toll functions; (ii) it is highly possible that the system optimal non-differentiated toll function does not exist. Hence, we further propose an optimal toll design model, prove the Lipschitz continuity of its objective and adopt a global-optimization algorithm to solve it.     

Distributed MPC for cooperative highway driving and energy-economy validation via microscopic simulations

Traffic congestion and energy issues have set a high bar for current ground transportation systems. With advances in vehicular communication technologies, collaborations of connected vehicles have becoming a fundamental block to build automated highway transportation systems of high efficiency. This paper presents a distributed optimal control scheme that takes into account macroscopic traffic management and microscopic vehicle dynamics to achieve efficiently cooperative highway driving. Critical traffic information beyond the scope of human perception is obtained from connected vehicles downstream to establish necessary traffic management mitigating congestion. With backpropagating traffic management advice, a connected vehicle having an adjustment intention exchanges control-oriented information with immediately connected neighbors to establish potential cooperation consensus, and to generate cooperative control actions. To achieve this goal, a distributed model predictive control (DMPC) scheme is developed accounting for driving safety and efficiency. By coupling the states of collaborators in the optimization index, connected vehicles achieve fundamental highway maneuvers cooperatively and optimally. The performance of the distributed control scheme and the energy-saving potential of conducting such cooperation are tested in a mixed highway traffic environment by the means of microscopic simulations.