基于5G+智慧灯杆的区域交通协调优化控制系统功能分析与研究

随着国内城市化的稳步推进,智慧交通建设的理念逐渐得到了广泛的关注.在智慧交通的建设中,车载传感器因其载体局限性,对于规避道路拥堵、优化行车路线上无法起到关键性作用.车路协同的模式越来越受到科研人员的关注.智慧灯杆,因其可拓展性成了智慧交通建设中最佳的感知载体.杆件上可安装监控设施、广播、大屏幕,以及各类传感器、检测器等...     

虚拟轨道列车超螺旋滑模自适应导向控制

为提高虚拟轨道列车在参数不确定和未知外部扰动环境中自导向控制的鲁棒性能,针对列车运行中多输入多输出的过驱动控制问题,基于拉格朗日方程建立了多铰接列车的非线性导向控制模型,将等效轮胎侧偏力作为控制输入量;利用虚拟轨道离散点坐标与列车运行速度,建立了计算列车位置、速度与加速度的参考模型,设计了独立的列车导向控制器与纵向速度控制器;利用李雅普诺夫方法,基于传统滑模控制(SMC)和自适应超螺旋滑模(ASTSM)分别设计了2种列车导向控制器,利用轮胎逆模型计算了线控转向系统的转角控制量;建立了轮速分配模型,基于参考速度矢量,将列车纵向速度控制转换为每个轮毂电机的转速与电磁转矩控制;建立了7节编组列车的动力学仿真模型,通过变速和综合线路测试分析了轮毂电机转速和电磁转矩的响应过程,研究了车辆模块之间铰接作用力的分布规律,比较了SMC和ASTSM在参数不确定和未知外部扰动工况下的鲁棒性能。研究结果表明：建立的列车导向控制模型、运动参考模型与轮速分配模型是有效的;车辆模块的纵向速度跟踪误差小于1.5 km·h^-1,车轮转速跟踪误差率小于1%;与SMC相比,当存在未建模动态、50%负载变化与未知扰动时,提出的ASTSM具有更好的自适应鲁棒性能,使车轴中心位置偏差能在有限时间内收敛至0附近;在侧向力干扰下,ASTSM的车轴中心偏差均方根与最大值分别为10和42 mm,分别降低了82%和61%;ASTSM在曲线路段中无明显的稳态偏差,且车间铰接角能一致地收敛至稳态值,保证了虚拟轨道列车的运行稳定性。     

水下航行器三维航迹反演滑模跟踪控制

  目的  旨在研究非线性六自由度自主水下航行器（AUV）的轨迹跟踪问题。  方法  为此,设计三维轨迹变前视距离视线（LOS）导引律,利用李雅普诺夫稳定性判据证明此导引律的稳定性,使用设计的自适应S面控制算法对AUV的航向角、纵倾角和纵向速度进行控制。然后,以REMUS 100 AUV为例, 利用Matlab软件分别对AUV的航向角、纵倾角和纵向速度控制及其空间直线、曲线轨迹跟踪进行仿真,以验证所提导引律的有效性。  结果  结果表明：对于非线性控制而言,相比于传统的S面控制,自适应S面控制可降低控制参数整定的难度,抗干扰能力更强;而且,相比于传统的LOS导引律,所提导引律可使AUV更快地跟踪上参考轨迹。  结论  所设计的轨迹跟踪控制算法能够实现对AUV的三维轨迹跟踪,并改善AUV的操纵性。     

自主式水下机器人控制技术研究综述

自主对接是无人水下航行器（UUV）协同作业的关键,但受复杂环境和对象特性的影响,精准引导与对接难度很大。为了提高水下对接的准确性和鲁棒性,设计一种基于视觉引导的对接方案,并针对视觉解算和三维轨迹跟踪控制技术开展研究。

首先,结合任务和对象特性分析,设计基于视觉引导的总体对接方案。其次,设计YOLOv5神经网络完成水下对接站的目标检测,并基于EPnP算法实现对接站与UUV之间相对位姿关系的在线测量。接着,结合视觉解算结果,基于三维LOS制导、径向基函数神经网络（RBFNN）、终端滑模控制（TSMC）和李雅普诺夫理论,设计一种高效的三维鲁棒轨迹跟踪控制器。最后,通过数值仿真和水池试验验证该设计方案的有效性。

在水池试验中,视觉引导控制算法能够有效完成水下对接站的在线检测与相对定位,实现UUV的精准水下对接。

研究表明,所提出的视觉引导三维轨迹跟踪控制方案合理、高效,可为UUV水下对接奠定基础。

     

大学生职业发展与就业指导课程教学探讨

大学生职业发展与就业指导课程教学对大学生职业发展意义重大。文章从本门课程教学的性质与目标出发。就课程教学有关的问题提出笔者的观点和看法,与同行交流探讨。     

Quantifying the environmental and economic benefits of cooperation: A case study in temperature-controlled food logistics

Inefficient road transportation causes unnecessary costs and polluting emissions. This problem is even more severe in refrigerated transportation, in which temperature control is used to guarantee the quality of the products. Organizing logistics cooperatively can help decrease both the environmental and the economic impacts. In Joint Route Planning (JRP) cooperation, suppliers and customers jointly optimize routing decisions so that cost and emissions are minimized. Vendor Managed Inventory (VMI) cooperation extends JRP cooperation by optimizing routing and inventory planning decisions simultaneously. However, in addition to their economic advantages, VMI and JRP may also yield environmental benefits. To test this assertion, we perform a case study on cooperation between a number of supermarket chains in the Netherlands. The data of this case study are analyzed to quantify both the economic and environmental benefits of implementing cooperation via JRP and VMI, using vehicle routing and an inventory routing models. We found that JRP cooperation can substantially reduce cost and emissions compared with uncooperative routing. In addition, VMI cooperation can further reduce cost and emissions, but minimizing cost and minimizing emissions no longer result in the same solution and there is a trade-off to be made.     

A day-to-day route flow evolution process towards the mixed equilibria

This study investigates a travelers’ day-to-day route flow evolution process under a predefined market penetration of advanced traveler information system (ATIS). It is assumed that some travelers equipped with ATIS will follow the deterministic user equilibrium route choice behavior due to the complete traffic information provided by ATIS, while the other travelers unequipped with ATIS will follow the stochastic user equilibrium route choice behavior. The interaction between these two groups of travelers will result in a mixed equilibrium state. We first propose a discrete day-to-day route flow adjustment process for this mixed equilibrium behavior by specifying the travelers’ route adjustment principle and adjustment ratio. The convergence of the proposed day-to-day flow dynamic model to the mixed equilibrium state is then rigorously demonstrated under certain assumptions upon route adjustment principle and adjustment ratio. In addition, without affecting the convergence of the proposed day-to-day flow dynamic model, the assumption concerning the adjustment ratio is further relaxed, thus making the proposed model more appealing in practice. Finally, numerical experiments are conducted to illustrate and evaluate the performance of the proposed day-to-day flow dynamic model.     

Traffic guidance–perimeter control coupled method for the congestion in a macro network

Macroscopic fundamental diagram (MFD) describes the macro relationship between a network vehicle density and a network space mean flow, without requiring the mastery of complex origin to destination data. Thus, MFD provides an opportunity for the macro control of urban road network. However, most of the existing MFD control methods ignore the active role of traffic guidance in solving congestion problems. This study presents a traffic guidance–perimeter control coupled (TGPCC) method to improve the performance of macroscopic traffic networks. The method considers the optimal cumulative volume of a network as the goal and establishes a programming function according to the network equilibrium rule of traffic flow amongst multiple MFD sub-regions, which regards the minimum delay of network, as the objective. The Logit model for the compliance rate of driver route guidance is established by the stated preference survey. Moreover, the perimeter control (PC) method is proposed for adjusting the phase split of intersections. Finally, three schemes, namely, the TGPCC, PC and the method without PC and guidance are tested on a network with four well-defined MFD sub-regions. Results show that the TGPCC addresses the issue of congestion and decreases the total delay accordingly.     

基于强化学习的城轨信息发布策略研究

通过信息发布影响乘客选择行为进而改变路网客流分布,是从需求侧缓解拥堵问题的重要手段之一.本文提出基于强化学习的城市轨道交通信息发布策略生成方法,根据路网各区间客流满载率提取系统状态,再根据系统状态在学习器生成由各OD推荐路径组成的信息发布动作,对乘客进行信息发布;通过发布信息后路网系统状态变化,评估获得实施信息发布动作的奖励值.依托城市轨道交通客流分布动态仿真系统,使用 Q- learning 算法进行训练,获得最优信息发布策略.以实际路网为例进行算例验证,通过对比有无信息发布情景得到,在有信息发布情景下路网客流拥堵情况得到了较大缓解.     

Modeling absolute and relative cost differences in stochastic user equilibrium problem

This paper aims to develop a hybrid closed-form route choice model and the corresponding stochastic user equilibrium (SUE) to alleviate the drawbacks of both Logit and Weibit models by simultaneously considering absolute cost difference and relative cost difference in travelers’ route choice decisions. The model development is based on an observation that the issues of absolute and relative cost differences are analogous to the negative exponential and power impedance functions of the trip distribution gravity model. Some theoretical properties of the hybrid model are also examined, such as the probability relationship among the three models, independence from irrelevant alternatives, and direct and indirect elasticities. To consider the congestion effect, we provide a unified modeling framework to formulate the Logit, Weibit and hybrid SUE models with the same entropy maximization objective but with different total cost constraint specifications representing the modelers’ knowledge of the system. With this, there are two ways to interpret the dual variable associated with the cost constraint: shadow price representing the marginal change in the entropy level to a marginal change in the total cost, and dispersion/shape parameter representing the travelers’ perceptions of travel costs. To further consider the route overlapping effect, a path-size factor is incorporated into the hybrid SUE model. Numerical examples are also provided to illustrate the capability of the hybrid model in handling both absolute and relative cost differences as well as the route overlapping problem in travelers’ route choice decisions.