无人驾驶城轨车辆运动分析与控制策略研究

发布时间:2018-05-25 04:18

  本文选题:无人驾驶 + 城轨车辆 ; 参考:《兰州交通大学》2014年博士论文


【摘要】:目前,轨道交通无人驾驶技术在国外发达国家的研究已日趋成熟,而我国无人驾驶技术的研究起步较晚,水平相对落后。由于我国大部分轨道交通列车的核心设备技术及运营模式是从国外引进的,存在“水土不服”的问题,例如,车辆在站停留时间不能根据客流量动态调整、行车间隔不智能,城轨车辆“常与地面失去联系”等,已在很大程度上影响了城轨车辆无人驾驶技术在我国的发展速度。存在这些问题的原因,主要是因为我国在城轨车辆无人驾驶“控制系统”方面还缺乏自主知识产权的核心技术,特别是在该领域相关基础理论研究方面严重滞后,成为制约我国城轨交通无人驾驶发展水平的主要瓶颈。针对我国实际国情,开展城轨车辆无人驾驶技术基础理论特别是控制策略方面的研究,对消除人为等不确定因素给城轨车辆运行的安全性、平稳性和高效性带来的潜在危害和不利影响,提高城轨交通系统无人驾驶车辆的自动化控制水平,具有十分重要的理论和现实意义。本文以无人驾驶城轨车辆为研究对象,以列车牵引运动学和动力学为基础,通过对车辆牵引运动、横向和垂向振动建模与分析,引入鲁棒控制理论对车辆振动进行控制分析与综合,建立符合无人驾驶城轨车辆混合优化控制策略,并对城轨车辆协同控制技术、以及如何实现多列车高效运行的控制目标进行了相关分析。其主要研究内容为:(1)以列车牵引运动学为基础,通过对无人驾驶城轨车辆在不同工况下的牵引运行过程进行受力分析,建立城轨列车牵引运动学模型,并给出列车运动学方程。(2)以列车动力学理论为基础,通过对运行列车动力学分析,建立车辆相关多自由度横向、垂向振动模型,利用Matlab对模型进行仿真分析,研究列车运行过程中影响其平稳性的相关因素。(3)为抑制列车运行过程中的不确定干扰对城轨车辆造成的影响,在建立车辆横向振动模型基础上,分析扰动等不确定因素对列车造成的影响,建立横向振动广义对象模型,引入鲁棒H∞控制理论对其进行控制分析与综合,并将控制前、后结果进行对比,验证引入鲁棒控制后控制效果的合理性和有效性。仿真结果表明,与被动悬架控制技术相比较,无人驾驶城轨列车在速度较高的情况下,引入鲁棒控制器后可明显抑制车辆的横向振动、提高列车运行的平稳性和舒适性。(4)在城轨列车多质点运动模型的基础上,通过对节能牵引控制策略和快速牵引控制策略的分析,针对城轨车辆的两种运行模式,建立了城轨车辆混合优化牵引控制策略,通过建模和仿真分析,寻找出牵引运行过程最佳决策量,实现牵引运行过程中能耗的最低和全程运行时间的最短。(5)通过对城轨车辆运营模式和无人驾驶技术特点的调研和分析,针对我国城轨交通客流量波动大的特点,提出了无人驾驶城轨车辆运营过程中分时段配置列车数量的思路,并以某条城轨线路上的列车为对象、以客流量为依据,利用排队论思想给出了分时段列车数量配置和优化的方法。在此基础上,建立了城轨车辆协同控制模型,通过采用实时信息采集方法来实现列车的等间隔协同控制。利用二次型最优控制理论来得到下一信息采集间隔时间内车辆行进的距离,并对列车协同控制中最优运行间隔进行了计算和讨论。利用物联网技术搭建多列车协同控制条件下实时交互技术框架,建立分层递阶控制模型,为通过列车牵引控制策略来实现多列车间动态协同控制的研究奠定基础。
[Abstract]:At present, the research on unmanned driving technology in rail transit has become more and more mature in developed countries. However, the research on pilotless technology in China started relatively late and the level is relatively backward. Because the core equipment technology and operation mode of most rail transit trains in our country are imported from abroad, there is a problem of "water and soil", for example, vehicles are in The stop time of the station can not be adjusted dynamically according to the passenger flow, the interval of driving is not intelligent, the urban rail vehicles "often lose contact with the ground" and so on, which has greatly influenced the development speed of the unmanned driving technology of urban rail vehicles in our country. The reasons for these problems are mainly because of the "control system" of the unmanned driving of urban rail vehicles in our country. It also lacks the core technology of independent intellectual property rights, especially in the field of basic theory research in this field, which has become the main bottleneck restricting the development level of urban rail traffic unmanned driving in our country. In view of the actual conditions of China, the research on the basic theory of unmanned driving technology of urban rail vehicles, especially the control strategy, is eliminated. It is of great theoretical and practical significance to improve the safety, stability and efficiency of urban rail vehicles and improve the level of automatic control of unmanned vehicles in urban rail transit system. And on the basis of dynamics, through the modeling and analysis of vehicle traction, lateral and vertical vibration, the robust control theory is introduced to control analysis and synthesis of vehicle vibration, and a hybrid optimization control strategy for unmanned urban rail vehicles is established, and the cooperative control technology for urban rail vehicles and how to control the efficient operation of multiple trains is also established. The main research contents are as follows: (1) on the basis of the train traction kinematics, through the force analysis of the unmanned urban rail vehicles in different working conditions, the traction kinematics model of the urban rail trains is established and the train movement learning equation is given. (2) the train dynamics theory is based on the theory of train dynamics. To analyze the dynamic train dynamics, establish the vehicle related multi degree of freedom lateral and vertical vibration model, make use of Matlab to simulate the model, and study the related factors that affect the stability of the train during the train operation. (3) in order to restrain the influence of the uncertain interference on the urban rail vehicles during the train operation, the lateral vibration of the vehicle is established. On the basis of the model, the influence of the uncertain factors such as disturbance to the train is analyzed, the generalized object model of lateral vibration is established, and the robust H control theory is introduced to control analysis and synthesis, and the results are compared before and after the control. The validity and validity of the control effect after the introduction of the robust control are verified. Compared with the dynamic suspension control technology, under the condition of high speed, the unmanned urban rail train can obviously restrain the lateral vibration of the vehicle and improve the smoothness and comfort of the train. (4) on the basis of the multi particle motion model of the urban rail train, the energy saving traction control strategy and the fast traction control strategy are passed through. In view of the two operating modes of urban rail vehicles, a hybrid optimization traction control strategy for urban rail vehicles is established. Through modeling and simulation analysis, the optimal decision of the traction operation process is found. The minimum energy consumption and the shortest run time in the traction operation are achieved. (5) through the operation mode of urban rail vehicles and unmanned driving. According to the investigation and analysis of the technical characteristics, in view of the characteristics of the large passenger flow fluctuation in urban rail traffic in China, the train of thought of configuring the number of trains during the operation process of unmanned urban rail vehicles is put forward. The train number on a city rail line is taken as the object. Based on the passenger flow, the train number allocation and optimization are given by the queue theory. On this basis, the cooperative control model of urban rail vehicles is set up, and the coordinated control of the train is realized by using the real-time information collection method. The two times optimal control theory is used to get the moving distance of the vehicle in the next information collection interval, and the optimal operation interval in the coordinated control of the train is calculated. By using the technology of Internet of things to build a real-time interactive technology framework for multi train cooperative control, a hierarchical hierarchical control model is established, which lays the foundation for the study of dynamic cooperative control among multiple trains through train traction control strategy.
【学位授予单位】:兰州交通大学
【学位级别】:博士
【学位授予年份】:2014
【分类号】:U270.1

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