R-dPSO算法及其在ATO控制策略中的应用

城市轨道交通列车自动运行中,通过调整列车的5种工况序列解决含有安全、准点、准时、舒适度和能耗等指标的多目标优化问题。根据轨道交通列车自动运行过程中涉及的动力学公式建立ATO目标速度曲线的数学模型。提出一种随机驱动的全局粒子群优化算法（R-dPSO）,用12个基准函数测试了R-dPSO算法的有效性。进而,利用SPSO算法、XEPSO算法和R-dPSO算法解决上述多目标优化问题。实验表明,只有R-dPSO算法的优化结果满足ATO控制策略的各个指标要求。     

基于遗传算法和粒子群优化的列车自动驾驶速度曲线优化方法

针对列车自动驾驶(ATO)过程中的精准停车、准时性、舒适性以及能耗问题,提出一种基于遗传算法与粒子群优化(GAPSO)算法结合的ATO速度曲线优化方法.首先,建立列车ATO运行多目标优化模型,将列车过分相区断电惰行纳入控制策略,并对运行控制策略进行分析;其次,对粒子群优化(PSO)算法进行改进,采用非线性动态惯性权重和...     

一种新的实时智能汽车轨迹规划方法

针对智能汽车的驾驶决策和轨迹规划问题, 将轨迹表示为轨迹曲线和加速度变化两部分, 以优化轨迹的行驶效率、安全性、舒适性和经济性为目标建立非线性规划模型. 基于序优化思想, 提出混合智能优化算法OODE, 分内、外两层分别优化加速度变化和轨迹曲线, 通过“粗糙” 评价轨迹曲线实现轨迹曲线的快速择优. 仿真结果表明, 所提出的方法能够处理包含多动态障碍物的复杂交通场景, 且具备实时应用能力, 模型的精度和求解速度均优于传统方法.

     

基于最优预见控制的高速列车速度控制器研究

针对高速列车自动驾驶(ATO)速度控制器的设计及性能问题,以列车运行过程中的安全性、舒适性、控制输入为约束条件,提出用最优预见算法设计控制器。该算法以列车动力学为基础,确定列车模型传递函数,进行极点配置之后使控制系统稳定;以列车模型为该算法的控制对象,将列车运行过程中的线路附加阻力和基本阻力作为干扰,实现列车ATO模式下目标速度的自动跟踪控制。选取京津城际北京南站至武清站间线路数据进行仿真验证,仿真结果表明该算法在降低列车运行能耗、提高旅客舒适性与列车运行准点率方面的有效性。     

基于改进遗传算法的列车运行曲线优化

传统遗传算法很早就在列车运行优化研究中得到了应用,但是由于种群中染色体进化方向的不确定性和局部搜索能力不足,导致收敛速度缓慢和求解质量低下。针对以上问题,本文提出一种改进型遗传算法,对列车运行曲线的生成进行研究。以列车运行能耗最小为优化目标,将行车安全、准点和精确停车等约束条件转化为惩罚函数,同时以工况序列为遗传个体进行求解,为加快种群收敛速度和提高解的质量,设计包含准点调整和局部搜索的种群进化方向引导机制。仿真结果表明,改进后的算法适用于多约束的列车运行优化问题,有效提升了收敛速度,优化结果相比于简单遗传算法和自适应遗传算法更加节能。     

Real time eco-driving of high speed trains by simulation-based dynamic multi-objective optimization

Eco-driving is a traffic operation measure that may lead to important energy savings in high speed railway systems. Eco-driving optimization has been applied offline in the design of commercial services. However, the benefits of the efficient driving can also be applied on-line in the regulation stage to recover train delays or in general, to adapt the driving to the changing conditions in the line. In this paper the train regulation problem is stated as a dynamic multi-objective optimization model to take advantage in real time of accurate results provided by detailed train simulation. If the simulation model is realistic, the railway operator will be confident on the fulfillment of punctuality requirements. The aim of the optimization model is to find the Pareto front of the possible speed profiles and update it during the train travel. It continuously calculates a set of optimal speed profiles and, when necessary, one of them is used to substitute the nominal driving. The new speed profile is energy efficient under the changing conditions of the problem. The dynamic multi-objective optimization algorithms DNSGA-II and DMOPSO combined with a detailed simulation model are applied to solve this problem. The performance of the dynamic algorithms has been analyzed in a case study using real data from a Spanish high speed line. The results show that dynamic algorithms are faster tracking the Pareto front changes than their static versions. In addition, the chosen algorithms have been compared with the typical delay recovery strategy of drivers showing that DMOPSO provides 7.8% of energy savings.     

An Advanced Real-Time Train Dispatching System for Minimizing the Propagation of Delays in a Dispatching Area Under Severe Disturbances

In highly utilized rail networks, as in the Netherlands, conflicts and subsequent train delays propagate considerably in time and space during operations. In order to realistically forecast and minimize delay propagation, there is a need to extend short-term traffic planning up to several hours. On the other hand, as the magnitude of the time horizon increases the problem becomes computationally intractable and hard to tackle. In this paper, we decompose a long time horizon into tractable intervals to be solved in cascade with the objective of improving punctuality. We use the ROMA dispatching system to pro-actively detect and globally solve conflicts on each time interval. The future evolution of railway traffic is predicted on the basis of the actual track occupation, the Dutch signaling system and dynamic train characteristics. Extensive computational tests are carried out on the railway dispatching area between Utrecht and Den Bosch.     

Intelligent driving methods based on expert knowledge and online optimization for high-speed trains

In the engineering control practice of High-Speed Train (HST), the traditional automatic driving method increases the energy consumption and impairs the intelligence of train operation. Different from previous studies, we propose the intelligent driving methods (IDMs), including expert knowledge system and online optimization algorithms, to achieve the multi-objective (safety, punctuality, energy efficient, passengers’ riding comfort, and so on) control of HST. First, we establish the expert knowledge system based on the driving data and control rules of excellent drivers. Then, in order to enhance the adaptability and real-time performance of proposed IDMs, two online optimization algorithms, including exact online programming driving (EOPD) and inexact online programming driving (IOPD), are developed by improved gradient descent and stochastic meta-decent method to update the controller’s output online. Finally, using the field data collected from Beijing-Shanghai High-Speed Railway, the proposed IDMs are verified under the real speed-limit conditions. The simulation results show that EOPD and IOPD can achieve better performances than automatic driving method based on ATO, Fuzzy PID controller and traditional multi-objective optimization method, especially in passengers’ riding comfort and energy-consumption. Furthermore, as the step size is selected with wide randomness in the updating process, IOPD has more operating mode switching times than EOPD but its punctuality is better.     

基于滚动时域优化的无人飞行器轨迹规划

给出了寻求无人飞行器的最优轨迹的一种方法,其问题描述为使飞行器从初始状态飞行到目标状态,同时避免撞到障碍物。基于混合整数规划的滚动时域优化方法用来求解飞行器的轨迹规划问题。给出的仿真结果显示此方法的有效性以及在复杂环境下的可实时计算性。     

基于滚动时域的无人机动态航迹规划

针对带有动力学约束的多旋翼无人机航迹规划问题,提出了一种基于滚动时域控制和快速粒子群优化(RHC-FPSO)方法。该方法引入了基于VORONOI图的代价图方法说明从航迹端点到达目标点的距离估计。根据滚动时域和人工势场法的思想,将路径规划问题转化为优化问题,以最小距离和其他性能指标为代价函数。设计评价函数准则,按照评价准则使用变权重粒子群优化算法求解。针对无人机靠近危险区飞行的问题,将斥力场引入到代价函数中,提升其安全性。仿真实验结果显示,使用文中方法可以有效地在满足约束条件下穿过障碍物区域,以及在复杂环境下可以动态计算。     

车路协同下的无信号交叉口车辆通行调度策略

无信号交叉口车辆通行调度问题是智能交通领域的研究重点,由于车辆通行顺序决策问题的解空间随着车辆数增加而指数级增长,在保证实时性的同时找到较优通行顺序成为无信号交叉口通行调度的一大问题。针对该问题提出一种基于自适应蒙特卡罗树搜索算法的无信号交叉口车辆通行调度方法,采用分层式框架,上层集中式顺序决策,下层分布式轨迹规划。首先,建立基于冲突点的交叉口模型,将网联车加入到待搜索队列中,根据交叉口中的车辆通行特点设计通行顺序的蒙特卡罗树搜索流程,以总通行时间为指标建立评价函数,然后针对不同交通情景设计自适应探索系数及其他超参数,使算法在求解不同车辆数时以及搜索的不同时期保持最佳状态。轨迹规划环节以加速度二范数为目标函数,以速度、加速度以及始终点位置等为约束,建立最优控制命题求解车辆轨迹。最后,进行实验,结果表明该算法相较于其他算法在数值仿真和微缩平台实验中最大优化幅度分别达到33.42%和38.04%,为无信号交叉口车辆通行调度提供了一个有效解决方案。     

Two-stage on-board optimization of merging velocity planning with energy management for HEVs

This paper proposes a two-stage hierarchy control system with model predictive control (MPC) for connected parallel HEVs with available traffic information. In the first stage, a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging. After formulating the merging problem into a nonlinear optimization problem, a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road, respectively. The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage. In the second stage, an energy management strategy (EMS) for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency. The formulated nonlinear optimization problem is solved by sequential quadratic programming (SQP) method under the same receding horizon. The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.     

Real-time energy management strategy based on predictive cruise control for hybrid electric vehicles

With the help of traffic information of the connected environment, an energy management strategy (EMS) is proposed based on preceding vehicle speed prediction, host vehicle speed planning, and dynamic programming (DP) with PI correction to improve the fuel economy of connected hybrid electric vehicles (HEVs). A conditional linear Gaussian (CLG) model for estimating the future speed of the preceding vehicle is established and trained by utilizing historical data. Based on the predicted information of the preceding vehicle and traffic light status, the speed curve of the host vehicle can ensure that the vehicle follows safety and complies with traffic rules simultaneously as planned. The real-time power allocation is composed of offline optimization results of DP and the real-time PI correction items according to the actual operation of the engine. The effectiveness of the control strategy is verified by the simulation system of HEVs in the interconnected environment established by E-COSM 2021 on the MATLAB/Simulink and CarMaker platforms.     

基于钩缓约束的重载列车驾驶过程优化

重载列车是一种由上百甚至几百节车厢组成的动力集中式大载重系统, 其牵引力/制动力需通过车钩相继传递给车厢, 存在明显的非线性和大滞后性. 现有的人工驾驶模式, 司机难以考虑车厢之间的钩缓约束, 易引起车钩断裂和脱轨; 且运行性能与司机的操纵经验密切相关, 存在耗电大, 无法按照列车运行图正点运行等问题. 本文针对此关键问题, 以实现重载列车安全、正点、节能运行为目标, 开展其驾驶过程运行优化研究. 分析列车钩缓系统受力原理, 基于其特性曲线, 采用翟方法构造重载列车钩缓模型及整车纵向动力学模型; 据此, 考虑钩缓约束运用多目标自适应遗传算法, 结合实际运行线路(限速、坡道、曲线率等)约束条件设定列车理想的运行速度目标曲线; 最后, 采用改进广义预测控制器设计重载列车驾驶过程优化控制方法, 跟踪理想速度目标曲线安全、正点、低能耗运行. 基于大秦线上HXD1型重载列车实际数据的仿真结果表明本文所设计的理想目标速度曲线优化方法可以较好地改善列车运行中的安全, 正点和节能等关键性指标, 运行优化控制能保证列车精确跟踪理想速度目标曲线, 实现其驾驶过程优化运行.     

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-toinfrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industriallevel HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.     

基于云网格集成调度的防拥堵车辆路径规划算法

在道路交通路网中,车辆拥堵问题是流量与路网结构之间相互作用的一个复杂动态过程,通过车辆路径规划,实现对路网网格集成调度,从而提高路网通行吞吐量。传统方法采用并行微观交通动态负载平衡预测算法实现车辆拥堵调度和车辆路径规划,不能准确判断路面上的车辆密度,路径规划效益不好。提出一种基于云网格集成调度的防拥堵车辆路径规划算法,即构建基于Small-World模型的云网格路网模型,采用RFID标签信息进行路况信息采集,实现交通网络拥堵评估信息特征的提取,采用固有模态函数加权平均求得各车道的车辆拥塞状态函数,对所有车道内车辆密度取统计平均可获得簇内的车辆密度。设计交通路网拥堵检测算法来对当前个体道路信息进行一维邻域搜索,从而实现车辆路径规划控制目标函数最佳寻优。通过动态博弈的方式求得车辆防拥堵路径的近似最优轨迹,实现路径规划算法的改进。仿真结果表明,该算法能准确规划车辆路径,实现最优路径控制,从而提高严重拥堵路段的车流速度和路网吞吐性能,性能优越。     

基于强化学习的挖掘机时间最优轨迹规划

针对挖掘机的自主作业场景,提出基于强化学习的时间最优轨迹规划方法.首先,搭建仿真环境用于产生数据,以动臂、斗杆和铲斗关节的角度、角速度为状态观测变量,以各关节的角加速度值为动作信息,通过状态观测信息实现仿真环境与自主学习算法的交互;然后,设计以动臂、斗杆和铲斗关节运动是否超出允许范围、完成任务 总时间和目标相对距离为奖励函数对策略网络参数进行训练;最后,利用改进的近端策略优化算法(proximal policy optimization, PPO)实现挖掘机的时间最优轨迹规划.与此同时,与不同连续动作空间的强化学习算法进行对比,实验结果表明:所提出优化算法效率更高,收敛速度更快,作业轨迹更平滑,可有效避免各关节受到较大冲击,有助于挖掘机高效、平稳地作业.     

Real-time energy management for electric arc furnace operation

Electric arc furnaces are used extensively in the steel industry for steel production. Development of energy savings strategies for the highly energy-intensive batch process is extremely challenging due to the complexity of the process and lack of measurements due to the harsh operating conditions. Here we introduce a new energy management approach that effectively curtails the energy cost in real-time through the implementation of economically optimal operating decisions. An economics- oriented shrinking horizon nonlinear model predictive control (NMPC) algorithm that exploits time-varying electricity prices is coupled with a multi-rate moving horizon estimator (MHE) to form an integrated decision- making framework. With a detailed first-principles dynamic model functioning at the core, the multi-variable interactions and plant variations are successfully incorporated into the control strategy to achieve reliable performance. We also present a novel initialization scheme for obtaining fast on-line solutions of the economic NMPC and multi-rate MHE dynamic optimization problems. Using this initialization algorithm, we show that the optimal input decisions are obtained with sufficient computational speed for real-time implementation. The energy usage optimization results indicate a significant reduction in the operating cost and peak electricity demand compared to the case where the electricity price profile is not updated.     

基于闭塞区间的高速列车运行时间与节能协同优化方法

提出了一种高速列车运行时间与节能协同优化方法.针对由动态调度层、优化控制层、跟踪控制层组成的列车运行控制与动态调度一体化结构,设计了面向动态调度层和优化控制层的列车运行时间调整策略和节能速度位置曲线.基于高速铁路闭塞区间,建立了列车区间模型和列车速度曲线节能优化模型.利用模型预测控制方法对列车区间运行时间进行调整,优化列车总延误时间;根据调整后的区间运行时间设计列车运行优化速度位置曲线,减少列车运行能耗.仿真算例验证了设计的运行时间与节能协同优化策略的有效性.