WO2023126023A1 - 重载列车及其纵向动力学牵引运行优化控制系统、方法 - Google Patents

重载列车及其纵向动力学牵引运行优化控制系统、方法 Download PDF

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WO2023126023A1
WO2023126023A1 PCT/CN2023/078438 CN2023078438W WO2023126023A1 WO 2023126023 A1 WO2023126023 A1 WO 2023126023A1 CN 2023078438 W CN2023078438 W CN 2023078438W WO 2023126023 A1 WO2023126023 A1 WO 2023126023A1
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traction
train
heavy
force
electric braking
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PCT/CN2023/078438
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English (en)
French (fr)
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李蔚
王松旭
于永生
张文璐
吴建华
陈国忠
王凯
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中南大学
国家铁路局装备技术中心
大秦铁路股份有限公司科学技术研究所
长沙南睿轨道交通电气设备有限公司
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Publication of WO2023126023A1 publication Critical patent/WO2023126023A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B61L23/08—Control, warning or like safety means along the route or between vehicles or trains for controlling traffic in one direction only
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • B61L15/0018—Communication with or on the vehicle or train
    • B61L15/0027—Radio-based, e.g. using GSM-R

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  • the invention relates to a control technology for optimizing the dynamic performance of a long and heavy-duty combined train running on a complex line, in particular to a heavy-duty train and its longitudinal dynamic traction operation optimization control system and method.
  • the slave locomotive in the middle of the heavy-duty combined train follows the master locomotive at the head.
  • locomotive distributed power traction control can reduce the longitudinal force of the train and improve the running quality of the train. Suppress the deterioration of the longitudinal force under the specific working conditions of the train. Under abnormal faults, the locomotives in the train will have excessive forward and backward impulses, which will cause variations in the longitudinal force of the coupler, and seriously cause the train to separate.
  • the operation safety can only be guaranteed by formulating complex train control methods, which cannot fundamentally solve the problem.
  • the technical problem to be solved by the present invention is to provide a heavy-duty train and its longitudinal dynamics traction operation optimization control system and method to realize the speed regulation of heavy-duty combined trains, especially long-distance trains. Larger longitudinal impulse suppression that may occur when the PPI operating condition is switched.
  • a heavy-duty train longitudinal dynamic traction operation optimization control system including:
  • Motion dynamics model the input is the control command of the train, the optimization goal is to reduce the longitudinal impulse, and the expected traction/electric braking force is output;
  • the expert system uses the expected traction/electric braking force output by the motion dynamics model, the optimization output and the output of the feedback module as input, adjusts the expected traction/electric braking force, and feeds back the adjustment result to the motion dynamics module ;
  • a predictive model taking the expected traction force/electric braking force output by the expert system as input, setting an objective function, and predicting traction force/electrical braking force;
  • the optimization output and feedback module is used to adjust the traction force/electric braking force of the train according to the traction force/electric braking force predicted by the prediction model, and feed back the adjusted traction force/electric braking force and the coupler force monitored in real time to the expert system.
  • the invention realizes the operation adjustment of the heavy-duty combined train by adding the model prediction function to the locomotive wireless reconnection system.
  • the speed is high, especially when the long train is switched at the slope point, it may produce a large longitudinal impulse suppression, so as to overcome the major safety hazards that affect the safe and stable operation of the heavy-duty combined train.
  • the system of the present invention also includes: a data acquisition module, which is used to collect the traction vehicle type, traction formation mode, vehicle type difference data, traction characteristics, traction working conditions, power Braking conditions, line signals and traffic permit information, train speed; the input of the expert system also includes the data collected by the data acquisition module.
  • the objective function expression of prediction model setting is:
  • F r (k+i) is the expected traction force/electric braking force obtained by the expert system at k+i moment
  • F(k) is the actual traction force/electric braking force at time k;
  • ⁇ i is the flexibility coefficient calculated by the expert system according to the train load;
  • the traction/electrical braking force of expected double-connected locomotive can be quickly obtained and an optimized trajectory can be formed, and the longitudinal force balance between vehicles is the target optimization function.
  • the traction force/electrical braking force of each reconnected locomotive is adjusted in real time, which reduces the longitudinal interaction between train vehicles and ensures driving safety.
  • u(k+i-1) is the optimal control rate at time k+i-1.
  • the control time domain length M is set to 1 ⁇ M ⁇ p.
  • M selects the train running process between two signal machines as the control time domain
  • the length of the prediction time domain p selects the length of a block interval.
  • the prediction interval is limited, considering the low speed of heavy-duty trains, M is set to The calculation amount of the 1 ⁇ M ⁇ p system can be greatly reduced, ensuring the real-time performance and robustness of the coordinated control of heavy-duty combined trains.
  • the value range of ⁇ i is 0.2-0.6; the value range of r j is 0.3-0.5.
  • the system of the present invention also includes a characteristic feedback module, which is used to detect the longitudinal force of the vehicle, and classify the operating conditions of the train into normal operating conditions and abnormal operating conditions, and the output of the characteristic feedback module is connected to the expert system.
  • the system of the present invention also includes an abnormal condition constraint module, which is used to identify the abnormal running state of the train and perform safety protection; the output of the abnormal condition constraint module is connected to the expert system.
  • an abnormal condition constraint module which is used to identify the abnormal running state of the train and perform safety protection; the output of the abnormal condition constraint module is connected to the expert system.
  • the present invention also provides a wireless reconnection control unit of a locomotive, in which the above optimization control system is embedded.
  • the present invention also provides a reconnection train, which adopts the wireless reconnection control unit of locomotives.
  • the present invention also provides a method for optimal control of longitudinal dynamic traction operation of a heavy-duty train, comprising the following steps:
  • F r (k+i) is the expected traction force/electric braking force obtained at time k+i
  • the beneficial effects of the present invention are: according to the longitudinal dynamics of the locomotive reconnection traction heavy-duty combined train under the disturbance of the working condition of the slave locomotive, the present invention combines the longitudinal dynamics of the heavy-duty train traction operation under the line condition Based on the nonlinear relationship between dynamics and reconnection control, an expert system model is established to predict the dynamics variation and development law of train dynamic operation, and the future development trend is inferred according to the inertia of the train state to optimize the traction dynamic performance of the train, forming a combination of heavy-duty trains. Longitudinal dynamics quality optimization and reconnection constraint control and model predictive control methods for traction operation.
  • it can gradually transition to asynchronous control under different working conditions of the train, so as to optimize the dynamic performance of long-term combined trains and reduce the longitudinal impulse of heavy-duty trains.
  • the invention realizes the suppression of the large longitudinal impulse that may occur when the heavy-duty combined train is running, especially when the long train is switched over at the slope point, and overcomes such problems that affect the safe and stable operation of the heavy-duty combined train. major safety hazard.
  • the system of the invention can be adapted and refitted on the original locomotive wireless reconnection system, the effect is remarkable, the cost is low, and it is suitable for popularization and application in heavy-duty combined trains, thereby obtaining good economic benefits.
  • Fig. 1 is the structural diagram of the control system of the embodiment of the present invention.
  • Fig. 2 is a network topology structure diagram of the system and related equipment of the embodiment of the present invention.
  • Fig. 3 is a frame diagram of the core prediction model algorithm of the embodiment of the present invention.
  • Fig. 4 is a system hierarchical block diagram of an embodiment of the present invention.
  • Fig. 5 is a structural block diagram of an optimization control system according to an embodiment of the present invention.
  • the optimization goal of the longitudinal force balance during the operation of the combined train is realized, thereby achieving the optimization of the dynamics of the long combined train Performance, the purpose of reducing the longitudinal impulse of heavy-duty trains, including:
  • (1) Construct a traction and electrical braking cooperative processing system based on model prediction, which is mainly divided into three parts.
  • the first is the data input acquisition module, which communicates with the locomotive electrical interface and the vehicle network to collect the data required for model prediction. Data and conditions are collected and summarized;
  • the second is the core prediction module composed of an expert system and a dynamic model, which centrally processes input information and feedback data, imports it into the algorithm model described in the implementation mode (5) for calculation and derivation, and undergoes multiple iterations
  • the optimal forecast adjustment parameters after the optimized combination are obtained;
  • the third is the optimization output and feedback module, which adjusts the parameters of the traction/electric brake control optimization strategy derived from the forecast model and outputs it, and outputs the feedback locomotive traction force ⁇ electric brake force
  • Data such as the coupler force measurement value obtained by the external auxiliary coupler stress measuring device are imported into the core prediction module again, and the closed-loop adjustment and optimization is performed in combination with the algorithm model described in the implementation mode (5).
  • the second-level expert prediction model includes expert system [Document: Li Wei. Research and application of key technology of wireless reconnection synchronous control of heavy-haul locomotives [D].
  • the third layer is the feedback feature module, which is used as a data interface for closed-loop adjustment, and can collect the feedback data after optimization output into the core prediction model.
  • the relationship between the three-layer models is that the first-layer motion dynamics model and the third-layer feedback feature module simultaneously input condition parameters to the second-layer expert prediction model, and the collaborative optimization control parameters are derived from the second-layer expert prediction model.
  • the expert prediction model of the second layer feeds back the calculation results to the motion dynamics model of the first layer, and the expert prediction model of the second layer is the main calculation and decision-making layer.
  • ⁇ is the flexibility coefficient adjusted by the expert system
  • p is the length of the predicted time domain.
  • linear interpolation selects the range of ⁇ as 0.2 to 0.6
  • the length of prediction time domain p selects the length of a block interval, so as to ensure the real-time performance and robustness of the coordinated control of heavy-duty combined trains.
  • M is the control time domain, and the train operation process between two signal machines is selected as the control time domain, and 1 ⁇ M ⁇ p is satisfied at this time;
  • r j is the weighting coefficient, according to the expert system according to the train load (5000 tons to 30000 tons ) Linear interpolation select r range is 0.3 ⁇ 0.5.
  • the prediction error weighting coefficient is selected according to the ratio of the coupler force to the maximum bearing;
  • R diag[r 1 ,r 2 ,...,r p ], the weighted matrix of the control quantity, which limits the drastic change of the control increment, through Adjust the above r to achieve;
  • the feedback coefficient matrix is determined according to the state feedback structure of the locomotive electrical system and control system;
  • G [g 1 ,g 2 ,...,g p ] T , the impulse response coefficient, describes the dynamic output characteristics of the system within a finite number of sampling periods.
  • the above prediction algorithm module is imported into the algorithm framework diagram shown in Figure 3, and will form a prediction optimization control target value, which will be imported into the optimization output and feedback module to perform subsequent operations.
  • the slave device network structure can be divided into two levels, the lower layer is the collaborative control execution layer, which receives the vehicle through the electrical interface of the locomotive and the network communication interface.
  • the horizontal acceleration, vertical acceleration, longitudinal acceleration and other parameters are obtained through the dynamic detection equipment of the locomotive, and the parameters are transmitted to the locomotive wireless reconnection control unit, combined with the expert system for real-time analysis, and simultaneously output to the locomotive microcomputer control system and locomotive wireless reconnection synchronization
  • the control system combined with the operating conditions of the reconnection, realizes optimal control, and performs fault warning and safety guidance.
  • the optimization control method and system included in Embodiment 1 of the present invention can form a data input acquisition module 2, a core prediction module 3, an optimization output and feedback module 4 to construct a traction and control system based on model prediction.
  • Electric brake co-processing module 1. The system is divided into three parts, the first part builds the data input acquisition module 2, the second part The second part builds the core prediction module 3, and the second part builds the optimization output and feedback module 4.
  • the relationship between the three parts is that the data input acquisition module 2 exports the collected locomotive operating conditions, line signals and marshalling-related data to the core prediction module 3, and the core prediction module 3 exports the result data to the optimization output and feedback module 4, optimizes output and feedback Module 4 exports optimization feedback data to core prediction module 3 .
  • the data input acquisition module 2 includes seven key imported data in three categories, including locomotive operating conditions, line signals and marshalling data.
  • the locomotive working condition data includes: traction working condition 9, electric braking working condition 10, speed 12; line signal includes: line signal and driving permit 11; marshalling data includes: traction vehicle 5, traction marshalling mode 6, model difference 7 and Traction features8.
  • These three types of data are imported into the core prediction module 3 after data integration and summary to generate algorithmic model conditions.
  • the core prediction module 3 includes a motion dynamics model 13 , an expert system 14 , a prediction model 17 , a feature feedback module 18 , exception constraints 19 and feedback features 20 .
  • the model prediction 17 is a model core module, which is composed of an adaptive dynamic adjustment module 15 and an evaluation module 16 .
  • the data imported by the data input acquisition module 2 first enters the motion dynamics model 13 to form a heavy-duty combined train model based on the imported data, and then imports the model into the expert system 14 .
  • the expert system 14 is based on the longitudinal dynamics of the locomotive double-coupled traction heavy-duty combined train under the condition of the disturbance of the working condition of the central subordinate locomotive, combined with the nonlinear relationship between the longitudinal dynamics of the heavy-duty train traction operation and the double-coupled control under the line conditions, The established system model for predicting the development law of dynamic variation of train dynamic operation.
  • the model exported by the expert system 14 will be imported into the model prediction 17.
  • the prediction model takes the balance of the longitudinal force during the operation of the combined train as the optimization goal, and exports the predicted optimized feature value results, and reversely imports them into the expert system 14 through the feature feedback module 18.
  • the model internal warning is carried out for the predicted abnormal situation, and the expert system 14 is reversely imported into the expert system 14 for internal correction through the abnormal situation constraint 19, so that the predicted optimized extra value is constantly approaching the optimal range.
  • the feedback feature module 20 will also collect the actually executed optimization output execution results and import them into the core prediction module to perform closed-loop adjustment control.
  • the optimization output and feedback module 4 includes a traction force ⁇ electric braking force parameter adjustment module 21 , an output traction force ⁇ electric braking force module 22 and a vehicle coupler force monitoring module 23 .
  • the optimized eigenvalues derived by the core prediction module 3 will be imported into the traction force ⁇ electric braking force parameter adjustment module 21 for dynamic matching of traction force ⁇ electric braking force, and form control commands to be transmitted to the output traction force ⁇ electric braking force module 22 to form the power distribution of the entire train .
  • the vehicle coupler force monitoring module 23 monitors the dynamic parameters of the coupler, and transmits the monitoring data to the feedback feature module 20 and the core prediction module 3 .
  • Embodiment 2 of the present invention includes the system equipment topology connection relationship of reconnected locomotives in two sections when the system is implemented on a locomotive, and the locomotive reconnected control unit 24 is embedded with traction and electric braking based on model prediction synergy Processing module 1, or transform the traction and electrical braking characteristic processing module in the wireless reconnection control unit of 24 locomotives into traction and electrical braking cooperative processing module 1 based on model prediction.
  • Embodiment 1 for an example of the invention of the traction and electric braking cooperative processing module 1 based on model prediction.
  • the traction and electric braking cooperative processing module 1 based on model prediction and the locomotive equipment perform information command transmission and data interaction through the communication interface module 28 . It includes obtaining line signals and driving permits through the communication interface module 28 and the train safety monitoring device LKJ 29, performing data transmission and exchange with the train network control and management system 30 through the communication interface module 28, and indirectly collecting and driving the electrical control interface signals of the locomotive.
  • the communication interface module 28 performs data transmission and exchange with the locomotive logic control unit 32 , performs data transmission and exchange with the brake control unit 33 through the communication interface module 28 , and performs data transmission and exchange with other third-party devices 34 through the communication interface module 28 .
  • the two cars realize the interaction of reconnection data through the train bus 35 .
  • the embodiment of the present invention constructs prediction model and algorithm in core prediction module 3, expects input F c (k) to import expert system 37, and expert system 37 combines the feedback value of output feedback prediction module 41 to calculate result
  • the value F r (k+j) is imported into the traction/electric braking coordination optimization control module 38 of the double-connected locomotive, and the output result value u(k+j) is transmitted to the locomotive electric drive system 39 for execution, and the train multi-particle dynamics model 40
  • the train-level dynamics deduction is carried out, and the resulting values F(k+j) and F m (k+j) derived from the two are combined to generate the parameter e(k+j).
  • the output feedback prediction module 41 will summarize the three parameters u(k+j), F m (k+j) and e(k+j), and output the feedback result value F r (k+j), which is provided to The traction/electric braking coordination optimization control module 38 of the double-connected locomotive performs feedback correction.
  • Embodiment 3 of the invention includes a two-layer control management layer constructed when implementing the traction and electric brake cooperative processing model based on model prediction on a long train. Taking the remote marshalling mode of one master and one slave as an example, the master car is at the head of the train, and the slave car is in the middle of the train. As the first layer of collaborative control decision-making layer (that is, level I), this layer is mainly responsible for the heavy-duty combined train.
  • Wireless reconnection group control and traction optimization control decision-making are composed of a locomotive reconnection control unit 24 and an antenna system 25 including a traction and electric brake cooperative processing module 1 based on model prediction, and the 26 cooperative control decision-making layers of the main vehicle are
  • the optimized control core of the whole train is characterized by remote reconnection management and optimal control of multiple locomotives (including up to 4 locomotives), realizing wireless reconnection marshalling of up to four locomotives and real-time transmission of internal reconnection information.
  • the second level is the vehicle cooperative control execution layer (ie level II), which consists of the communication interface module 28 of each locomotive, the train safety monitoring device LKJ 29, the train network control and management system 30, the locomotive electrical control interface 31, and the locomotive logic Control unit LCU 32, brake control unit 33, other third-party equipment 34, etc., through the locomotive network and electrical control
  • the 36 interface of the braking system realizes the traction/electric braking coordinated control inside the locomotive, executes and implements the optimization strategy, and at the same time matches the dynamic characteristics of the two locomotives to meet the requirements of dynamic balance.

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Abstract

本发明公开了一种重载列车及其纵向动力学牵引运行优化控制系统、方法，通过对机车无线重联系统增加模型预测功能，实现对重载组合列车运行调速时、尤其是长大列车在变坡点工况切换时可能会产生较大的纵向冲动抑制，克服这类影响重载组合列车安全平稳运行的重大安全隐患。在重载组合列车分布式动力编组模式下，通过模型预测重联机车相同工况下主从控机车牵引及再生制动力的差异需求，合理调整主从控机车牵引及再生制动的功率大小，并逐步过渡到列车不同工况下的异步控制，从而达到优化长大组合列车动力学性能，减小重载列车纵向冲动目的，保障列车的运行。

Description

重载列车及其纵向动力学牵引运行优化控制系统、方法 技术领域
本发明涉及长大重载组合列车在复杂线路运行时列车动力学性能优化的控制技术，特别是一种重载列车及其纵向动力学牵引运行优化控制系统、方法。
背景技术
重载组合列车牵引运行纵向动力学性能优化及其重联机车分布动力牵引匹配控制一直是重载牵引关键科学问题之一，重载组合列车的许多重大事故均与列车牵引运行中纵向力劣化关系密切，影响了列车运行的安全。
基于目前的机车无线重联牵引控制方法，重载组合列车中部的从控机车跟随位于头部的主控机车。通常机车分布动力牵引控制可降低列车的纵向作用力，提升列车的运行品质，但遇到特别是在线路变坡点、电力机车过分相以及故障时刻等情况下，采用分布动力重联控制有时不能抑制列车特定工况下的纵向力劣化，异常故障下将发生列车中机车车辆前后冲动过大引起车钩纵向力变异，严重的造成列车分离。目前只能通过制定复杂的列车操纵办法来保障运行安全，不能从根本上解决问题。
发明内容
本发明所要解决的技术问题是，针对现有技术不足，提供一种重载列车及其纵向动力学牵引运行优化控制系统、方法，实现对重载组合列车运行调速时、尤其是长大列车在变坡点工况切换时可能产生的较大的纵向冲动抑制。
为解决上述技术问题，本发明所采用的技术方案是：一种重载列车纵向动力学牵引运行优化控制系统，包括：
运动动力学模型，输入为列车的控制指令，以减小纵向冲动为优化目标，输出期望的牵引/电制动力；
专家系统，以所述运动动力学模型输出的期望的牵引/电制动力、优化输出及反馈模块的输出为输入，调整期望的牵引力/电制动力，并向所述运动动力学模块反馈调整结果；
预测模型，以所述专家系统输出的期望的牵引力/电制动力为输入，设定目标函数，预测牵引力/电制动力；
优化输出及反馈模块，用于根据预测模型预测的牵引力/电制动力调整列车的牵引力/电制动力，将调整后的牵引力/电制动力及实时监测的车钩力反馈至专家系统。
本发明通过对机车无线重联系统增加模型预测功能，实现对重载组合列车运行调 速时、尤其是长大列车在变坡点工况切换时可能会产生较大的纵向冲动抑制，克服这类影响重载组合列车安全平稳运行的重大安全隐患。
为进一步优化控制效果，本发明的系统还包括：数据采集模块，用于采集重载组合列车分布式动力编组模式下牵引车辆类型、牵引编组方式、车型差异数据、牵引特征、牵引工况、电制动工况、线路信号及行车许可信息、列车速度；所述专家系统的输入还包括所述数据采集模块采集的数据。
本发明中，预测模型设定的目标函数表达式为：
Figure PCTCN2023078438-ftappb-I100001
其中，Fr(k+i)为所述专家系统在k+i时刻获得的期望的牵引力/电制动力，表达式为：Fr(k+i)＝(1-αi)Fr(k)+αiF(k)；F(k)为k时刻实际的牵引力/电制动力；αi为专家系统按列车载重计算得到的柔性系数；rj为加权系数，u(k)＝(G1 TQG1+R)TGTQ[Fr(k+1)-G2u(k-1)-He(k)]，Q＝diag[q1,q2,…,qp]，q1,q2,…,qp为预测误差加权系数；R＝diag[r1,r2,…,rp]，r1,r2,…,rp为控制量加权矩阵；H＝[h1,h2,…,hp]T，h1,h2,…,hp为反馈系数矩阵；G＝[g1,g2,…,gp]T，g1,g2,…,gp为脉冲响应系数矩阵，G1为预测未来状况的脉冲系数矩阵，G2为过去已知状况的脉冲系数矩阵；M为控制时域长度；p为预测时域长度；e(k)为k时刻的预测误差；Fp(k)＝Fm(k)+He(k)；e(k)＝F(k)-Fm(k)；Fm(k)为k时刻的预测输出。
本发明中，通过模型预测，同时克服了模型预测在线计算量大的缺点，可以快速得到期望重联机车牵引/电气制动力并以此形成优化轨迹，以车辆间纵向力均衡为目标优化函数，经模型预测优化控制，实时调整重联各机车的牵引力/电气制动力，降低了列车车辆间纵向作用，确保了行车安全。
预测模型在k+i时刻预测的牵引力/电制动力Fm(k+i)表达式为：
Figure PCTCN2023078438-ftappb-I100002
u(k+i-1)为k+i-1时刻的优化控制率。
控制时域长度M设置为1<M<p。其中，M选取两个信号机之间的列车运行过程作为控制时域，预测时域长度p选取一个闭塞区间的长度，虽然预测区间有限，但考虑到重载列车运行速度不高，M设置为1<M<p系统的计算量可以极大减少，保证重载组合列车协同控制的实时性及控制的鲁棒性。
αi取值范围为0.2～0.6；rj取值范围为0.3～0.5。根据专家系统按列车载重(5000吨～30000吨)线性插值选取，减小了计算量，同时可以动态反映与列车纵向力直接相关的列车牵引载重因素。本发明的系统还包括特征反馈模块，用于进行车辆纵向力的检测，并将列车的运行状况分为正常运行状况及异常运行状况，所述特征反馈模块的输出接入所述专家系统。
本发明的系统还包括异常情况约束模块，用于识别列车异常运行状态并进行安全防护；所述异常情况约束模块的输出接入所述专家系统。
作为一个发明构思，本发明还提供了一种机车无线重联控制单元，其内嵌入有上述优化控制系统。
作为一个发明构思，本发明还提供了一种重联列车，其采用上述机车无线重联控制单元。
作为一个发明构思，本发明还提供了一种重载列车纵向动力学牵引运行优化控制方法，包括以下步骤：
设定以下目标函数：
Figure PCTCN2023078438-ftappb-I100003
其中，Fr(k+i)为k+i时刻获得的期望的牵引力/电制动力，表达式为：Fr(k+i)＝(1-αi)Fr(k)+αiF(k)；F(k)为k时刻实际的牵引力/电制动力；αi为按列车载重计算得到的柔性系数；rj为加权系数，
Figure PCTCN2023078438-ftappb-I100004
Q＝diag[q1,q2,…,qp]，q1,q2,…,qp为预测误差加权系数；R＝diag[r1,r2,…,rp]，r1,r2,…,rp为控制量加权矩阵；H＝[h1,h2,…,hp]T，h1,h2,…,hp为反馈系数矩阵；G＝[g1,g2,…,gp]T，g1,g2,…,gp为 脉冲响应系数矩阵，G1为预测未来状况的脉冲系数矩阵，G2为过去已知状况的脉冲系数矩阵；M为控制时域长度；p为预测时域长度；e(k)为k时刻的预测误差；Fp(k)＝Fm(k)+He(k)；e(k)＝F(k)-Fm(k)；Fm(k)为k时刻的预测输出；
根据k+i时刻预测的牵引力/电制动力调整列车的牵引力/电制动力。
与现有技术相比，本发明所具有的有益效果为：本发明根据从控机车工况扰动下机车重联牵引重载组合列车的纵向动力学情况，结合线路条件下重载列车牵引运行纵向动力学与重联控制的非线性关系，建立预测列车动态运行动力学变异发展规律专家系统模型，根据列车状态的惯性来推断未来的发展趋势来优化列车牵引动力学性能，形成重载组合列车的牵引运行纵向动力学品质优化与重联约束控制及模型预测控制方法。以组合列车运行时纵向作用力均衡为优化目标，实时基于模型预测重联机车相同工况下主从控机车牵引及再生制动力的差异需求，进而合理且安全调整主从控机车牵引及再生制动的功率大小，并根据运行情况可逐步过渡到列车不同工况下的异步控制，从而达到优化长大组合列车动力学性能，减小重载列车纵向冲动目的。本发明实现了对重载组合列车运行调速时、尤其是长大列车在变坡点工况切换时可能会产生较大的纵向冲动抑制，克服了这类影响重载组合列车安全平稳运行的重大安全隐患。本发明的系统可在原机车无线重联系统上进行适应改装，效果显著，成本较低，适合在重载组合列车中推广应用，从而获得良好的经济效益。
附图说明
图1为本发明实施例控制系统结构图；
图2为本发明实施例系统与相关设备网络拓扑结构图；
图3为本发明实施例核心预测模型算法构架图；
图4为本发明实施例系统层次框图；
图5为本发明实施例优化控制系统结构框图。
具体实施方式
本发明实施例通过在机车无线重联控制单元内嵌入模型预测的牵引及电气制动协同处理模块，来实现对组合列车运行时纵向作用力均衡的优化目标，从而达到优化长大组合列车动力学性能，减小重载列车纵向冲动目的，包括：
(1)构建一种基于模型预测的牵引及电气制动协同处理系统，主要分为3大部分，第1为数据输入采集模块，通过与机车电气接口及车辆网络通信，将模型预测所需要的 数据及条件进行采集汇总；第2为专家系统和动力学模型组成的核心预测模块，集中处理输入信息和反馈数据，导入实施方式(5)所描述的算法模型中进行计算推导，经过多次迭代后得到优化组合后最优的预测调整参数；第3为优化输出及反馈模块，将预测模型导出的牵引/电气制动控制优化策略进行参数调整后输出，并将反馈的机车牵引力\电制动力及外部辅助车钩应力测量装置获取的车钩力测量值等数据再次导入核心预测模块，结合实施方式(5)所描述的算法模型进行闭环调整优化。
(2)构建一种基于模型预测的牵引及电气制动协同处理模型的核心预测模块，包括3层模型结构，第1为运动动力学模型，该模型主要处理基于长大重载货运列车纵向动力学的计算和处理[文献：耿志修编著.大秦铁路重载运输技术[M].北京:中国铁道出版社,2009.]，以减小纵向冲动为优化目标，其导出的优化结果作为第2层专家预测模型的输入条件。第2层专家预测模型包含专家系统[文献：李蔚.重载列车机车无线重联同步控制关键技术研究与应用[D].中南大学,2012.]、模型预测、特征反馈和异常情况约束[文献：李蔚,陈特放,陈春阳,成庶.列车电气系统分布式多传感器异常检测理论研究[J].铁道学报,2010,32(5):70～76.]四个核心模块，形成包括预测判断、特性反馈和异常处理的核心预测模型。第3层为反馈特征模块，该模块作为闭环调整的数据接口，可将优化输出后的反馈数据采集入核心预测模型。三层模型的关联关系为第一层运动动力学模型和第三层反馈特征模块同时向第二层专家预测模型正向输入条件参数，由第二层的专家预测模型导出协同优化控制参数，同时第二层的专家预测模型向第一层运动动力学模型反馈计算结果，第二层的专家预测模型为主要计算及决策层。
(3)构建一种基于模型预测的牵引及电气制动协同处理模型需要导入7种类型的数据源，包括：牵引车辆、牵引编组方式、机车车型差异、牵引特性、牵引工况、电制动工况、线路信号及行车许可，这7类数据是经过长期重载组合货运列车无线重联运行数据经验积累，评价出的关键数据参数，其主要分为机车工况、线路信号以及编组运行三大类别，对重载组合列车动力学优化控制有至关重要的影响。
(4)构建一种基于模型预测的牵引及电气制动协同处理模型，需要对输出的优化参数进行闭环控制调节，核心预测模型导出的参数，结合驾驶控制策略通过机车电气接口和网络控制接口输出牵引力\电制动力的调整，并最终形成全列车的动力控制，系统通过车辆车钩力的反馈模块，将纵向力的数据反馈给反馈特征模块，从而实现输出结果和反馈信息的数据闭环路径。
(5)根据已有的列车多质点动力学模型[文献：耿志修编著.大秦铁路重载运输技 术[M].北京:中国铁道出版社,2009.]，结合最优控制理论构建一种基于模型预测的牵引及电气制动协同处理算法构架，以使预测导出的机车牵引/电气制动力输出值F(k)(实际输出)符合动力学优化控制的目标，见图3所示。
①针对模型预测在线计算量大的缺点，在本发明实施例中，结合模型预测理论[文献：陈虹著.模型预测控制[M].北京：科学出版设，2021年。李国勇，杨丽娟编著.神经·模糊·预测控制及其MATLAB实现(第4版)[M].北京：电子工业出版社，2018年。]，将期望输出Fc(k)输入至专家系统直接得到期望牵引/电气制动力Fr(k+i)：
Fr(k+i)＝(1-αi)Fr(k)+αiF(k)；
其中，α为专家系统通过调整柔性系数，p为预测时域长度，此时，依据预测长度分别为α1,α2,α3,...,αi，根据专家系统按列车载重(5000吨～30000吨)线性插值选取α范围为0.2～0.6，预测时域长度p选取一个闭塞区间的长度，这样以保证重载组合列车协同控制的实时性及控制的鲁棒性。
②重联机车牵引/电制动协同优化控制率，其中优化性能以重载组合列车纵向力均衡为目标，目标函数指标如下确定：
Figure PCTCN2023078438-ftappb-I100005
其中，M为控制时域，选取两个信号机之间列车运行过程作为控制时域，此时满足1<M<p；rj为加权系数，根据专家系统按列车载重(5000吨～30000吨)线性插值选取r范围为0.3～0.5。
模型预测优化控制率：
u(k)＝(G1 TQG1+R)TGTQ[Fr(k+1)-G2u(k-1)-He(k)]，
其中，Q＝diag[q1,q2,…,qp]，预测误差加权系数，根据车钩力与最大承受的比值来选取；
R＝diag[r1,r2,…,rp]，控制量加权矩阵，限制控制增量的剧烈变化，通过 调整上述r来实现；
H＝[h1,h2,…,hp]T，反馈系数矩阵，根据机车电气系统及控制系统的状态反馈结构来具体确定；
G＝[g1,g2,…,gp]T，脉冲响应系数，描述系统在有限个采样周期内的动态输出特征。
③预测误差：
e(k)＝F(k)-Fm(k)
④反馈预测：
Fp(k)＝Fm(k)+He(k)
⑤输出预测：
Figure PCTCN2023078438-ftappb-I100006
以上预测算法模块导入图3所示的算法构架图中，将形成预测优化控制目标值，该目标值将导入优化输出及反馈模块执行后续操作。
(6)构建一种基于模型预测的牵引及电气制动协同处理模型，见图2，从设备网络结构可分为2层级，下层为协同控制执行层，通过机车电气接口和网络通信接口接收机车工况、线路信号和编组运行数据，并将经过机车网络控制系统的协议解析和机车逻辑控制单元处理后的电气控制指令导出给机车无线重联控制单元执行实施重载组合列车的优化控制；上层为协同控制决策层，各车辆设备管理层数据通过协同控制决策层整合之后，通过模型导入，经过预测算法计算，经由无线通信及协议处理模块进行列车级数据传送，以实现列车级协同决策控制。通过机车动力学动态检测设备得到水平加速度，垂直加速度，纵向加速度等参数，将参数传输到机车无线重联控制单元，结合专家系统进行实时分析，同时输出到机车微机控制系统及机车无线重联同步控制系统，结合重联运行工况来实现优化控制，并进行故障预警、安全导向等。
实施例1
如图1及图5所示，本发明实施例1包括的优化控制方法和系统，可形成包括数据输入采集模块2、核心预测模块3、优化输出及反馈模块4构建出基于模型预测的牵引及电气制动协同处理模块1。系统分为三部分，第一部分构建数据输入采集模块2，第 二部分构建核心预测模块3，第二部分构建优化输出及反馈模块4。三部分的关联关系是数据输入采集模块2向核心预测模块3导出采集的机车工况、线路信号和编组相关的数据，核心预测模块3向优化输出及反馈模块4导出结果数据，优化输出及反馈模块4向核心预测模块3导出优化反馈数据。
数据输入采集模块2包括三大类共7种关键导入数据，这三大类数据包括机车工况、线路信号和编组数据。其中机车工况数据包括：牵引工况9、电制动工况10、速度12；线路信号包括：线路信号及行车许可11；编组数据包括：牵引车辆5、牵引编组方式6、车型差异7和牵引特征8。这三类数据通过数据整合汇总之后导入核心预测模块3进行算法模型条件的生成。
核心预测模块3包括运动动力学模型13、专家系统14、预测模型17、特征反馈模块18、异常情况约束19和反馈特征20。其中模型预测17是模型核心模块，由自适应动态调整模块15和评估模块16组成。由数据输入采集模块2导入的数据首先进入运动动力学模型13，形成基于导入数据的重载组合列车模型，再将此模型导入专家系统14中。专家系统14是以中部从控机车工况扰动的情况下机车重联牵引重载组合列车的纵向动力学情况，结合线路条件下重载列车牵引运行纵向动力学与重联控制的非线性关系，建立的预测列车动态运行动力学变异发展规律的系统模型。专家系统14导出的模型会导入模型预测17中，预测模型以组合列车运行时纵向作用力均衡为优化目标，将预测出的优化特征值结果导出，并通过特征反馈模块18反向导入专家系统14进行内部修正，同时对预测出的异常情况进行模型内部预警，通过异常情况约束19反向导入专家系统14进行内部修正，这样不断的使得预测的优化特增值趋近于最优范围。同时反馈特征模块20也会将实际执行的优化输出执行的结果采集并导入核心预测模块中，进行闭环调整控制。
优化输出及反馈模块4包括牵引力\电制动力参数调整模块21、输出牵引力\电制动力模块22和车辆车钩力监测模块23。核心预测模块3导出的优化特征值将导入牵引力\电制动力参数调整模块21进行牵引力\电制动力的动态匹配，并形成控制指令传输到输出牵引力\电制动力模块22形成整列车的动力分配。与此同时，车辆车钩力监测模块23会监测车钩的动力学参数，将监测数据传输给反馈特征模块20和核心预测模块3。
实施例2
如图2所示，本发明实施例2包括系统在机车上实施时的两节内重联机车的系统设备拓扑连接关系，在机车重联控制单元24内嵌基于模型预测的牵引及电气制动协同处 理模块1，或者将24机车无线重联控制单元内的牵引及电气制动特性处理模块变换为基于模型预测的牵引及电气制动协同处理模块1。基于模型预测的牵引及电气制动协同处理模块1其发明示例详见实施例1。
基于模型预测的牵引及电气制动协同处理模块1与机车设备通过通信接口模块28进行信息指令传输和数据交互。包括通过通信接口模块28与列车安全监控装置LKJ 29获取线路信号及行车许可，通过通信接口模块28与列车网络控制与管理系统30进行数据传输交换，并间接采集和驱动机车电气控制接口信号，通过通信接口模块28与机车逻辑控制单元32进行数据传输交换，通过通信接口模块28与制动控制单元33进行数据传输交换，通过通信接口模块28与其他第三方设备34进行数据传输交换。两节车通过列车总线35实现重联数据的交互。
实施例3
如图3所示，本发明实施例在核心预测模块3中构建了预测模型及算法，期望输入Fc(k)导入专家系统37，专家系统37结合输出反馈预测模块41的反馈值将计算结果值Fr(k+j)导入重联机车牵引/电制动协调优化控制模块38，输出的结果值u(k+j)传输至机车电传动系统39执行，同时列车多质点动力学模型40进行列车级动力学推演，两者导出的结果值F(k+j)和Fm(k+j)结合生成参数e(k+j)。输出反馈预测模块41会将u(k+j)、Fm(k+j)和e(k+j)这是三个参数进行汇总，输出反馈结果值Fr(k+j)，提供给重联机车牵引/电制动协调优化控制模块38进行反馈修正。
实施例4
如图4所示，发明实施例3包括在长大列车上实施基于模型预测的牵引及电气制动协同处理模型时构建的两层控制管理层。以一主一从的远程编组模式为例，主车在列车头部，从车在列车中部，作为第1层协同控制决策层(即第Ⅰ级)，该层级的主要负责重载组合列车的无线重联编组控制及牵引优化控制决策，由包含有基于模型预测的牵引及电气制动协同处理模块1的机车重联控制单元24和25天线系统构成，由主车的26协同控制决策层是整列车的优化控制核心，其特征为多机(最多包括4台机车)的远程重联管理与优化控制，实现最多四台机车的无线重联编组及内部重联信息实时传输。第2级为车辆协同控制执行层层(即第Ⅱ级)，由每台机车的通信接口模块28、列车安全监控装置LKJ 29、列车网络控制与管理系统30、机车电气控制接口31、机车逻辑控制单元LCU 32、制动控制单元33、其他第三方设备34等组成，通过与机车网络及电气控 制系统36接口，实现机车内部的牵引\电制动协同控制，对优化策略执行并实施，同时对两节车的动力特性进行匹配，满足动力均衡的要求。

Claims (13)

  1. 一种重载列车纵向动力学牵引运行优化控制系统，其特征在于，包括：
    运动动力学模型，输入为列车的控制指令，以减小纵向冲动为优化目标，输出期望的牵引/电制动力；
    专家系统，以所述运动动力学模型输出的期望的牵引/电制动力、优化输出及反馈模块的输出为输入，调整期望的牵引力/电制动力，并向所述运动动力学模块反馈调整结果；
    预测模型，以所述专家系统输出的期望的牵引力/电制动力为输入，设定目标函数，预测牵引力/电制动力；
    预测模型设定的目标函数表达式为：
    Figure PCTCN2023078438-ftappb-I200001
    其中，Fr(k+i)为所述专家系统在k+i时刻获得的期望的牵引力/电制动力，表达式为：Fr(k+i)＝(1-αi)Fr(k)+αiF(k)；F(k)为k时刻实际的牵引力/电制动力；αi为专家系统按列车载重计算得到的柔性系数；rj为加权系数，
    Figure PCTCN2023078438-ftappb-I200002
    Q＝diag[q1,q2,…,qp]，q1,q2,…,qp为预测误差加权系数；R＝diag[r1,r2,…,rp]，r1,r2,…,rp为控制量加权矩阵；H＝[h1,h2,…,hp]T，h1,h2,…,hp为反馈系数矩阵；G＝[g1,g2,…,gp]T，g1,g2,…,gp为脉冲响应系数矩阵，G1为预测未来状况的脉冲系数矩阵，G2为过去已知状况的脉冲系数矩阵；M为控制时域长度；p为预测时域长度；e(k)为k时刻的预测误差；Fp(k)＝Fm(k)+He(k)；e(k)＝F(k)-Fm(k)；Fm(k)为k时刻的预测输出；
    优化输出及反馈模块，用于根据预测模型预测的牵引力/电制动力调整列车的牵引力/电制动力，将调整后的牵引力/电制动力及实时监测的车钩力反馈至专家系统。
  2. 根据权利要求1所述的重载列车纵向动力学牵引运行优化控制系统，其特征在于，还包括：数据采集模块，用于采集重载组合列车分布式动力编组模式下牵引车辆类型、牵引编组方式、车型差异数据、牵引特征、牵引工况、电制动工况、线路信号及行车许可信息、列车速度；所述专家系统的输入还包括所述数据采集模块采集的数据。
  3. 根据权利要求1所述的重载列车纵向动力学牵引运行优化控制系统，其特征在于，预测模型在k+i时刻预测的牵引力/电制动力Fm(k+i)表达式为：
    Figure PCTCN2023078438-ftappb-I200003
    u(k+i-1)为k+i-1时刻的优化控制率。
  4. 根据权利要求1所述的重载列车纵向动力学牵引运行优化控制系统，其特征在于，控制时域长度M设置为1<M<p。
  5. 根据权利要求1所述的重载列车纵向动力学牵引运行优化控制系统，其特征在于，αi取值范围为0.2～0.6；rj取值范围为0.3～0.5。
  6. 根据权利要求1～5之一所述的重载列车纵向动力学牵引运行优化控制系统，其特征在于，还包括特征反馈模块，用于进行车辆纵向力的检测，并将列车的运行状况分为正常运行状况及异常运行状况，所述特征反馈模块的输出接入所述专家系统。
  7. 根据权利要求1～5之一所述的重载列车纵向动力学牵引运行优化控制系统，其特征在于，还包括异常情况约束模块，用于识别列车异常运行状态并进行安全防护；所述异常情况约束模块的输出接入所述专家系统。
  8. 一种机车无线重联控制单元，其特征在于，其内嵌入有权利要求1～7之一所述的优化控制系统。
  9. 一种重联列车，其特征在于，其采用权利要求8所述的机车无线重联控制单元。
  10. 一种重载列车纵向动力学牵引运行优化控制方法，其特征在于，包括以下步骤：
    设定以下目标函数：
    Figure PCTCN2023078438-ftappb-I200004
    其中，Fr(k+i)为k+i时刻获得的期望的牵引力/电制动力，表达式为：Fr(k+i)＝(1-αi)Fr(k)+αiF(k)；F(k)为k时刻实际的牵引力/电制动力；αi为按列车载重计算得到的柔性系数；rj为加权系数，
    Figure PCTCN2023078438-ftappb-I200005
    Q＝diag[q1,q2,…,qp]，q1,q2,…,qp为预测误差加权系数；R＝diag[r1,r2,…,rp]，r1,r2,…,rp为控制量加权矩阵；H＝[h1,h2,…,hp]T，h1,h2,…,hp为反馈系数矩阵；G＝[g1,g2,…,gp]T，g1,g2,…,gp为脉冲响应系数矩阵，G1为预测未来状况的脉冲系数矩阵，G2为过去已知状况的脉冲系数矩阵；M为控制时域长度；p为预测时域长度；e(k)为k时刻的预测误差；Fp(k)＝Fm(k)+He(k)；e(k)＝F(k)-Fm(k)；Fm(k)为k时刻的预测输出；
    根据k+i时刻预测的牵引力/电制动力调整列车的牵引力/电制动力。
  11. 根据权利要求10所述的方法，其特征在于，k+i时刻预测的牵引力/电制动力Fm(k+i)表达式为：
    Figure PCTCN2023078438-ftappb-I200006
    u(k+i-1)为k+i-1时刻的优化控制率。
  12. 根据权利要求10所述的方法，其特征在于，控制时域长度M设置为1<M<p。
  13. 根据权利要求10所述的方法，其特征在于，αi取值范围为0.2～0.6；rj取值范围为0.3～0.5。
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