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.     

Integrated vehicle and powertrain optimization for passenger vehicles with vehicle-infrastructure communication

This research proposes an optimal controller to improve fuel efficiency for a vehicle equipped with automatic transmission traveling on rolling terrain without the presence of a close preceding vehicle. Vehicle acceleration and transmission gear position are optimized simultaneously to achieve a better fuel efficiency. This research leverages the emerging Connected Vehicle technology and utilizes present and future information—such as real-time dynamic speed limit, vehicle speed, location and road topography—as optimization input. The optimal control is obtained using the Relaxed Pontryagin’s Minimum Principle. The benefit of the proposed optimal controller is significant compared to the regular cruise control and other eco-drive systems. It varies with the hill length, grade, and the number of available gear positions. It ranges from an increased fuel saving of 18–28% for vehicles with four-speed transmission and 25–45% for vehicles with six-speed transmission. The computational time for the optimization is 1.0–2.1 s for the four-speed vehicle and 1.8–3.9 s for the six-speed vehicle, given a 50 s optimization time horizon and 0.1 s time step. The proposed controller can potentially be used in real-time.     

Vehicle platoon formation using interpolating control: A laboratory experimental analysis

This work addresses the formation phase of automatic platooning. The objective is to optimally control the throttle of vehicles, with a given arbitrary initial condition, such that desired ground speed and inter-vehicular spacings are reached. The steering of the vehicles is also controlled, because the vehicles should track a desired path while forming the platoon. In order to address the platoon formation problem, a cooperative strategy is formed by constructing a discrete state space model which represents the dynamics of a set of n vehicles. Once this model is set, a control method known as Interpolating Control, which aims at regulating to the origin an uncertain and/or time-varying linear discrete-time system with state and control constraints, is utilized. The performance of this control method is evaluated and compared with other approaches such as Model Predictive Control (MPC).Simulations are conducted which suggest that the Interpolating Control approach can be seen as an alternative to optimization-based control schemes such as Model Predictive Control, especially for problems for which finding the optimal solution requires calculations, where the Interpolating Control approach can provide a straightforward sub-optimal solution.In the experimental part of this work, the control algorithms for the platoon formation and path tracking problems are combined, and tested in a laboratory environment, using three mobile robots equipped with wireless routers. Validation of the proposed models and control algorithms is achieved by successful experiments.     

煤炭采样系统自动控制研究

近年来由于对煤采样的要求提高和流量吨数的增大,煤采样设备已由原始采样机械发展成为多级采样系统,自动控制显得尤为重要。介绍了二级煤炭采样系统的自动控制原理及其采样系统的设计,指出采用该采样系统,可以大大提高采样工作效率,采样精度更准确,经济效益可明显提升。     

Prioritizing road extension projects with interdependent benefits under time constraint

Since transportation projects are costly and resources are limited, prioritizing or sequencing the projects is imperative. This study was inspired by a client who asked: “I have tens of approved road extension projects, but my financial resources are limited. I cannot construct all the projects simultaneously, so can you help me prioritize my projects?” To address this question, the benefits and costs of all the possible scenarios must be known. However, the impacts (or benefit) of road extension projects are highly interdependent, and in sizable cases cannot be specified thoroughly. We demonstrate that the problem is analogous to the Traveling Salesman Problem (TSP). Dynamic change in travel demand during construction is another aspect of the complexity of the problem. The literature is yet to provide efficient methods for large cases. To this end, we developed a heuristic methodology in which the variation of travel demand during the construction period is considered. We introduce a geometrical objective function for which a solution-finding policy based on “gradient maximization” is developed. To address the projects’ interdependency, a special neural network (NN) model was devised. We developed a search engine hybridized of Ant Colony and Genetic Algorithm to seek a solution to the TSP-like problem on the NN based on gradient maximization. The algorithm was calibrated and applied to real data from the city of Winnipeg, Canada, as well as two cases based on Sioux-Falls. The results were reliable and identification of the optimum solution was achievable within acceptable computational time.     

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.     

公路施工项目成本管理的探讨

对公路工程项目管理成本控制的原则进行分析。结合在滨德高速公路的施工项目,提出了成本控制措施。     

客车空调智能控制系统的研制

基于模糊控制技术的客车窄调智能控制系统以AT89C52为微处理器,对车内外的温度、车内湿度以及车内空气中二氧化碳含量进行自动巡回监测,将测量结果送入串行模数转换器TLC1543转换成数字量送入单片机,通过计算输出相应的控制信号控制各个风门电机、热水阀、加湿阀和空气净化器进行协调动作,将车内空气控制在人体感觉舒适的范围内。     

CTCS2+ATO列控车载设备测试平台设计

列车运行控制系统测试平台是列车运行控制系统研发、生产及调试的重要支撑。通过分析车载设备的测试需求,提出测试平台解决方案,并进行了设计实现。研究成果可为城际铁路列控系统的测试提供一定的参考。     

浅谈FAS双中心建设及设备维护

重点分析了西安局集团公司由2K数调主系统组网更新改造为FAS双中心网络的建设方法,并根据日常维护经验,列举了调度台通道故障处理、FASB简易故障判断及计表倒换试验、FASA和FASB系统与MSC之间的业务故障处理等。