CN117698808A - Large-scale heavy-load train group operation control method integrating longitudinal dynamics of trains - Google Patents
Large-scale heavy-load train group operation control method integrating longitudinal dynamics of trains Download PDFInfo
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Abstract
The invention discloses a large-scale reload train group operation control method for fusing longitudinal dynamics of trains. The method comprehensively considers the complexity of the running environment and the attribute of the heavy-duty train, builds a heavy-duty train running control model based on the longitudinal dynamics theory of the train, designs a group running controller by combining a distributed model predictive control method, provides a real-time simulation scheme of planning and control, forms a large-scale heavy-duty train group running control real-time simulation method integrating longitudinal dynamics, realizes the construction of a unified model of the longitudinal dynamics of the train and the train running control, and solves the main problems that the longitudinal dynamics model has poor real-time calculation capability and cannot meet the 0.2 second refresh rate of the existing train control system when being used for long-time grouping and multi-train running control in a large-scale scene.
Description
Technical Field
The invention belongs to the technical field of rail transit, and particularly relates to a large-scale heavy-load train group operation control method for fusing longitudinal dynamics of trains.
Background
The rapid increase of railway logistics transportation demands forcefully promotes the development of train operation control technology with shorter tracking distance and higher operation density represented by group operation, and simultaneously provides higher requirements for collision avoidance of an operation control layer and operation safety of a dynamics layer. The establishment of a reasonable and efficient simulation method suitable for the operation control of the heavy-load train group becomes an effective means for solving the key technical problem.
For the study, two key categories can be distinguished: short consist, high-dynamic drag ratio trains represented by high-speed rail, subway, etc., and long consist, low-dynamic drag ratio trains represented by heavy-duty trains.
The former are characterized by high mobility, low delay and good running line conditions, so they are also the main application targets of shorter distance tracking technologies such as virtual hitching, moving occlusion, etc. For the train, a train motion model based on single particles is generally adopted for operation control, and research hotspots are mainly focused on train-to-train communication delay, uncertain disturbance, system response delay and heterogeneous properties of the train. It should be noted that, since the performance of the train itself and the condition of the running line are good, various delays and disturbances can be limited to a small range, which makes linearization of the model and the control method feasible and reasonable.
The latter is mainly focused on control of a single train due to poor maneuverability (large number of unpowered trucks, long train length), large system response delay, complex running environment, and significant nonlinear characteristics, and current research includes optimizing air braking to reduce longitudinal impulse (Zhang and Zhuan, 2014; zhang et al., 2023), long ramp running control (Liu et al., 2021), etc., and the models are generally train longitudinal dynamics models, and important concerns are mainly air braking system response delay, train longitudinal impulse (coupler force), etc. Notably, the strong nonlinear characteristics of heavy-duty trains make it generally difficult to linearize their longitudinal dynamics models, and conventional single-particle-based train motion models do not reflect the objective fact that heavy-duty trains have significant longitudinal impulses. This has led to the very rare theoretical models, control methods, to date regarding shorter distance tracking techniques within a large group of heavy trains. Although Roscoe and Dick (2022) analyses the method of tracking control of heavy-duty trains in a mobile blocking mode based on the deep insight of the queuing technology of heavy-duty trucks, this is clearly inadequate, as it highlights only the large inertia of heavy-duty trains and the control method under ideal line conditions. It is still expected that more theoretical methods and application experience will be available to accelerate the construction of a theoretical system for shorter distance tracking of heavy-duty trains.
The basic requirement of safety protection during the shorter-distance tracking of the heavy-duty train promotes that we have to consider as many factors as possible such as time delay, disturbance and the like, which obviously promotes the complexity of the model and also increases the difficulty of train operation control. In general, a group includes a plurality of heavy-duty trains, which have high delay, strong nonlinearity, various communication modes, complex running environment and significant uncertain disturbance, and these factors determine that the train group running control model is a complex model with multiple parameters and high dimensionality, and it is not easy to respond in a short time and realize corresponding control actions. Whereas a fast response is a fundamental requirement of a train operation control device, such as chinese train operation control systems, currently widely employ a refresh rate of 0.2 seconds. How to improve the real-time computing capacity of the train group operation control model is a key link which must be considered in the design of a group operation scheme. At present, no research on the design of a heavy-load train operation control system based on train longitudinal dynamics is available, and the main reason is that a model is complex and difficult to convert, and real-time online optimization and control cannot be completed after the train longitudinal dynamics are introduced. Notably, central kunsland university in australia Wu et al (2023,2022) was first conducting a joint study of train operation control and train dynamics, which based on parallel computing architecture, presented a solution to short consist trains such as urban rail—validating the performance of a virtual articulated controller, test control system by means of train dynamics. Unfortunately, to date, we have not found a solution for using train system dynamics models directly forward for large scale re-loading train consist operation control.
In order to solve the serious problems of weak theoretical foundation, lack of calculation model and poor real-time capability of a heavy-load train group operation control system, research of a unified model construction technology of train longitudinal dynamics and train operation control is developed, complexity of a heavy-load train operation environment and its own attribute is comprehensively considered, a real-time simulation scheme of planning and control is provided, a heavy-load train operation control model is constructed based on the train longitudinal dynamics theory, a group operation controller is constructed in combination with a distributed model predictive control method, and a large-scale heavy-load train group operation control efficient simulation method integrating train system dynamics is formed.
Disclosure of Invention
In order to overcome the defects, a large-scale reloading train group operation control method integrating longitudinal dynamics of trains is provided, which can highlight the train-to-train communication relationship in shorter tracking distance operation modes such as group operation and the like, and provides technical support for large-scale reloading train group operation communication relationship simulation and communication-based virtual interaction relationship characterization between trains; the vehicle-to-vehicle communication delay and the system response delay under different communication modes are accurately quantized, key basis is provided for real-time calculation of the relative braking distance between trains, and actual characteristics of train group operation can be simulated more truly and accurately.
The technical scheme adopted by the invention for achieving the purpose is as follows: a large-scale reload train group operation control method for fusing longitudinal dynamics of trains is provided. The method comprises the following steps:
step 1: environmental awareness 1) static parameters: acquiring the type and the parameters of a group train, the parameters of line design, the parameters of a communication system and the information of a road network; 2) Dynamic time-varying parameters: weather conditions, manual temporary authorization information, preset dynamic compiling and decomposing actions and occurrence positions thereof;
step 2: modeling of communication systems in a train group: establishing a train-in-train communication model according to the communication system parameters acquired in the step 1;
step 3: modeling longitudinal dynamics of a train: according to the group train parameters obtained in the step 1, a longitudinal dynamics model of the heavy-duty train considering uncertain disturbance and time delay is established based on a basic theory of train system dynamics;
step 4: motion planning-train reachability domain planning: 4.1, calculating and determining the highest running speed of the train in a given line and under the current environment, so as to obtain the speed interval change characteristics; 4.2, determining the position of a corresponding traction/braking operation switching point according to the speed interval change characteristics, wherein the length of the speed interval is not less than twice the length of the train; 4.3, calculating and determining absolute braking distances of the trains at all positions in the current line and the current environment according to the highest running speed;
step 5: motion planning-intra-group train collaborative planning: 5.1, carrying the highest running speed obtained in the step 4 into a train system dynamics model of the step 3 one by one, calculating a time-mileage correspondence graph when each train passes through the line, establishing a plane rectangular coordinate system by one by taking mileage as a horizontal axis and time as a vertical axis, translating each train mileage-time curve along the time axis, and ensuring that the minimum value of disjoint two adjacent train mileage-time curves is the minimum departure time interval in a group, wherein the maximum departure time interval in the group is 5 minutes; 5.2, combining the type of the group train in the step 1 and the real-time absolute braking distance of the train in the step 4, and calculating and determining the relative braking distances of the trains in the group under different combined modes by taking the time interval of departure in the group as an independent variable; 5.3, on each train time-mileage graph, correcting the minimum departure time interval by taking the sum of the relative braking distance and the length of the front train as constraint, wherein the coordinate difference (namely the relative position) of the horizontal axis is not lower than the sum of the relative braking distance and the length of the front train; 5.4, re-optimizing to obtain the optimal running speed of each train in the group by taking the highest running speed of the step 4 as a boundary constraint condition and taking the shortest time of the line interval as a target;
step 6: motion planning-motion stability planning: smoothing the optimal speed obtained in the step 5, reducing longitudinal impulse of the train caused by locomotive operation, and obtaining maximum allowable acceleration and maximum allowable jerk by taking the car coupler force not exceeding the limit as constraint;
step 7: modeling a loading train group operation control system: the mileage, speed and operating force of each train are used as state vectors, the mileage and speed are used as output vectors, the train lumped control force is used as a control vector, and the train system dynamics model in the step 3 is converted into a discrete time state space to form a reloaded train group operation control system model;
step 8: the design of the operation controller of the heavy-load train group is as follows: taking the minimum departure time interval in the group of the step 5 as an initial value constraint, taking the operation control system model of the heavy-load train group of the step 7 as a dynamics constraint, taking the maximum allowable acceleration and the maximum allowable jerk in the step 6 as a locomotive operation constraint, taking the minimum relative braking distance tracking error and the optimal operation speed tracking error of each train in the step 5 as an objective function, taking the sum of three terms of two norms of the difference between the output vector predicted value and the expected value of the step 7, the two norms of the difference between the output vector predicted value and the assumed value and the two norms of the difference between the control vector and the operating force as a cost function, and designing a controller for each train in the group based on the model prediction control method;
step 9: speed determination before control: judging whether the optimal running speed calculated in the step 5 meets the speed limiting requirement of the current position, and if so, carrying out the step 10; if not, adjusting the speed to meet the temporary speed limiting requirement, and then performing step 10;
step 10: group operation control: evaluating whether the train can generate corresponding control force at the current speed, if so, distributing the speed to the controller in the step 8, performing train operation control, and calculating a real-time control instruction; if not, determining the speed consistent with the maximum effective output force of the locomotive in the current environment according to the traction/braking characteristic curve, then distributing the speed to the controller in the step 8, performing train operation control, and calculating a real-time control command.
The large-scale reloading train group operation control method for fusing the longitudinal dynamics of the trains comprises the following steps of: in step 1, the road network information includes station information, and switch information.
The large-scale reloading train group operation control method for fusing the longitudinal dynamics of the trains comprises the following steps of: in step 3, the uncertain disturbances are reflected in a gaussian function based on the weather condition data in step 1, and the time delays include an air brake system response time delay determined by an air brake system charge/discharge time based on the train type in step 1 and a car-to-car communication time delay obtained in step 2.
The large-scale reloading train group operation control method for fusing the longitudinal dynamics of the trains comprises the following steps of: in step 4.1, the current environment includes weather conditions and manual temporary authorization information.
The large-scale reloading train group operation control method for fusing the longitudinal dynamics of the trains comprises the following steps of: in step 4.1, the highest running speed of the train which can be achieved under the given line and the current environment is calculated and determined based on the vehicle-track coupling dynamics and the design specification of the heavy haul railway.
The large-scale reloading train group operation control method for fusing the longitudinal dynamics of the trains comprises the following steps of: speed smoothing methods include, but are not limited to, exponential, trapezoidal, cosine, polynomial, 7-segment S-shape, 7-segment modified S-shape, 15-segment S-shape, 31-segment S-shape smoothing methods.
The large-scale reloading train group operation control method for fusing the longitudinal dynamics of the trains comprises the following steps of: in step 6, the speed smoothing is performed exponentially: the optimal speed obtained in the smoothing step 5 is shown in the following formulas (1) - (3):
（1）
（2）
（3）
in the method, in the process of the invention,、/>、/>the method is characterized in that the method is known and respectively represents the current mileage coordinates, the maximum allowable acceleration and the maximum allowable jerk of the ith train; />The optimal speed of the ith train after smoothing is represented as the target amount; />、、/>、/>Is a coefficient of the smoothing equation.
The large-scale reloading train group operation control method for fusing the longitudinal dynamics of the trains comprises the following steps of: in step 8, the relative braking distance tracking error is calculated by the relative braking distances of the trains in the group in step 5 under different combination modes; and 5, calculating the optimal running speed tracking error of each train in the step by the optimal running speed of each train in the group.
The large-scale reloading train group operation control method for fusing the longitudinal dynamics of the trains comprises the following steps of: in step 9, the speed limit of the current position is obtained by the manual temporary authorization information in step 1
Compared with the prior art, the technical scheme of the invention has the following advantages/beneficial effects:
1. the method has the advantages that the train-to-train communication relations under shorter tracking distance operation modes such as group operation are highlighted, and technical support is provided for large-scale train group operation communication relation simulation and communication-based virtual interaction relation characterization between trains; the method accurately quantifies the train-to-train communication delay under different communication modes, provides key basis for real-time calculation of the relative braking distance between trains, and can simulate the actual characteristics of train group operation more truly and accurately.
2. The car coupler force real-time online calculation and evaluation is incorporated into a large-scale heavy-load train group operation control system for the first time, and the locomotive operation is restrained by the acceleration and jerk which do not exceed the maximum allowable value, so that the longitudinal impulse of the heavy-load train is further controlled, the risk of broken coupler when the heavy-load train group operates is effectively reduced, no additional car coupler force monitoring equipment is needed, and the equipment cost, the labor cost and the operation and maintenance cost are obviously reduced.
3. The control scheme has extremely strong robustness, and even if the train faces larger uncertain disturbance force, vehicle-to-vehicle communication delay and system response delay, the controller can still calculate and output corresponding control instructions, so that a more robust, more reliable and more accurate control method is provided for the operation control of the heavy-duty train group in a complex environment (severe communication conditions, rainfall/snowfall/strong wind and other complex meteorological conditions).
4. The real-time computing power is significantly enhanced. The system and the method overcome the fundamental requirement that the dynamic model of the train system cannot meet the 0.2 second refresh rate of the existing train control system when being used for the real-time calculation capability difference in the large-scale train operation control of long-marshalling and multi-train scenes.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a large scale re-loading train group operation control method of the present invention that fuses longitudinal dynamics of trains.
FIG. 2 is a graph of real-time computational power assessment test results under uncertain disturbances of the large-scale re-loading train group operation control method of the present invention incorporating longitudinal dynamics of trains.
FIG. 3 is a graph of real-time computing power assessment test results at a time delay of a train-to-train communication of the large-scale re-loading train group operation control method of the present invention incorporating longitudinal dynamics of a train.
Fig. 4 is a graph of real-time computing power evaluation test results of the system response delay of the large-scale re-loading train group operation control method of the present invention which fuses the longitudinal dynamics of the train.
FIG. 5 is a graph of coupler force profiles under different evaluation conditions of the large-scale re-loading train consist operation control method of the present invention incorporating train longitudinal dynamics.
Fig. 6 is a train operation diagram of embodiment 2 of the present invention.
Fig. 7 is a graph of the highest running speed achievable in a given line, current environment for a train of example 2 of the invention.
Fig. 8 is a diagram of a minimum departure time interval of embodiment 2 of the invention.
Fig. 9 is a graph showing the relative braking distance results for different combinations of patterns of trains in a consist according to example 2 of the present invention.
Fig. 10 is a schematic diagram showing the result of optimal running speeds of trains in the group according to embodiment 2 of the present invention.
Fig. 11 is a schematic diagram of the speed smoothing of embodiment 2 of the present invention.
Fig. 12 is a graph of the smooth front-rear velocity profile and the difference value thereof according to embodiment 2 of the present invention.
Fig. 13 is a time-mileage image when each train in the group passes through the route before and after speed adjustment according to embodiment 2 of the present invention.
Fig. 14 shows the train speed after adjustment according to embodiment 2 of the present invention.
FIG. 15 is a graph showing the relative distance between each train in a group (i.e., the distance between locomotive heads of two adjacent trains) during operation according to the speed in accordance with embodiment 2 of the present invention.
FIG. 16 is a graph showing the real-time control of locomotives in each train in a consist of example 2 of the present invention.
FIG. 17 is a control command and corresponding speed change when FT-2 exits the group in embodiment 2 of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention. Accordingly, the detailed description of the embodiments of the invention provided below is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus, once an item is defined in one figure, it may not be further defined and explained in the following figures.
Example 1:
as shown in fig. 1, a method for controlling the operation of a large-scale heavy-load train group which fuses the longitudinal dynamics of trains. The method comprises the following steps:
step 1: environmental awareness 1) static parameters: acquiring the type and parameters (quality, traction/braking characteristic curve, air braking system response delay and the like) of a group train, line design parameters (line plane and longitudinal section), communication system parameters (information sending rate, propagation rate, processing delay and queuing delay), and road network information (including station, station track and turnout information); 2) Dynamic time-varying parameters: weather conditions, manual temporary authorization information, preset dynamic compiling and decomposing actions and occurrence positions thereof;
step 2: modeling of communication systems in a train group: establishing a train-in-train communication model according to the communication system parameters acquired in the step 1;
step 3: modeling longitudinal dynamics of a train: according to the group train parameters obtained in the step 1, a longitudinal dynamics model of the heavy-duty train taking into account uncertain disturbance and time delay is established based on a basic theory of train system dynamics, the uncertain disturbance is reflected in a Gaussian function based on weather condition data in the step 1, the time delay comprises response time delay of an air brake system and communication time delay of a train-to-train, the response time delay of the air brake system is determined by charging/discharging time of the air brake system based on the train type in the step 1, and the communication time delay of the train-to-train is obtained in the step 2;
step 4: motion planning-train reachability domain planning:
4.1, calculating and determining the highest running speed of the train which can be achieved under a given line and a current environment (weather conditions and manual temporary authorization information), so as to obtain speed interval change characteristics; 4.2, determining the position of a corresponding traction/braking operation switching point according to the speed interval change characteristics, wherein the length of the speed interval is not less than twice the length of the train; 4.3, calculating and determining absolute braking distances of the trains at all positions in the current line and the current environment according to the highest running speed;
step 5: motion planning-intra-group train collaborative planning: 5.1, carrying the highest running speed obtained in the step 4 into a train system dynamics model of the step 3 one by one, calculating a time-mileage correspondence graph when each train passes through the line, establishing a plane rectangular coordinate system by one by taking mileage as a horizontal axis and time as a vertical axis, translating each train mileage-time curve along the time axis, and ensuring that the minimum value of disjoint two adjacent train mileage-time curves is the minimum departure time interval in a group, wherein the maximum departure time interval in the group is 5 minutes; 5.2, combining the type of the group train in the step 1 and the real-time absolute braking distance of the train in the step 4, and calculating and determining the relative braking distances (relative braking distance = rear absolute braking distance-front absolute braking distance) of the trains in the group under different combined modes by taking the departure time interval in the group as an independent variable; 5.3, on each train time-mileage graph, correcting the minimum departure time interval by taking the sum of the relative braking distance and the length of the front train as constraint, wherein the coordinate difference (namely the relative position) of the horizontal axis is not lower than the sum of the relative braking distance and the length of the front train; 5.4, re-optimizing to obtain the optimal running speed of each train in the group by taking the highest running speed of the step 4 as a boundary constraint condition and taking the shortest time of the line interval as a target;
step 6: motion planning-motion stability planning: and (3) smoothing the optimal speed obtained in the step (5) by taking acceleration and jerk as constraints, reducing longitudinal impulse of a train caused by locomotive operation, wherein the speed smoothing method comprises, but is not limited to, exponential, trapezoidal, cosine, polynomial, 7-segment S-shaped, 7-segment modified S-shaped, 15-segment S-shaped, 31-segment S-shaped and other smoothing methods, and performing speed smoothing specific operations according to the exponential type:
the optimal speed obtained in the smoothing step 5 reduces the longitudinal impulse of the train caused by locomotive operation, and the optimal speed obtained in the smoothing step 5 is smoothed according to the following formulas (1) - (3):
（1）
（2）
（3）
in the method, in the process of the invention,、/>、/>the method is characterized in that the method is known and respectively represents the current mileage coordinates, the maximum allowable acceleration and the maximum allowable jerk of the ith train; />The optimal speed of the ith train after smoothing is represented as the target amount; />、、/>、/>Coefficients that are smooth equations;
step 7: modeling a loading train group operation control system: the mileage, speed and operating force of each train are used as state vectors, the mileage and speed are used as output vectors, the train lumped control force is used as a control vector, and the train system dynamics model in the step 3 is converted into a discrete time state space to form a reloaded train group operation control system model;
step 8: the design of the operation controller of the heavy-load train group is as follows: taking the minimum departure time interval in the group in the step 5 as an initial value constraint, taking the model of the operation control system of the reloaded train group in the step 7 as a dynamic constraint, taking the maximum allowable acceleration and the maximum allowable jerk in the step 6 as locomotive operation constraint, taking the minimum relative braking distance tracking error and the optimal operation speed tracking error of each train as target functions (the relative braking distance tracking error is calculated by the relative braking distance of each train in different combination modes in the group in the step 5; the optimal operation speed tracking error of each train in the step 5 is calculated by the optimal operation speed of each train in the group), taking the sum of the two norms of the difference between the predicted value and the expected value of the output vector in the step 7, the two norms of the difference between the predicted value and the assumed value of the output vector and the two norms of the difference between the control vector and the operating force as cost functions, and designing a controller for each train in the group based on the model prediction control method;
step 9: speed determination before control: judging whether the optimal running speed calculated in the step 5 meets the speed limit requirement of the current position (the speed limit of the current position is acquired by the manual temporary authorization information in the step 1), and if so, carrying out the step 10; if not, adjusting the speed to meet the temporary speed limiting requirement, and then performing step 10;
step 10: group operation control: evaluating whether the train can generate corresponding control force at the current speed, if so, distributing the speed to the controller in the step 8, performing train operation control, and calculating a real-time control instruction; if not, determining the speed consistent with the maximum effective output force of the locomotive in the current environment according to the traction/braking characteristic curve, then distributing the speed to the controller in the step 8, performing train operation control, and calculating a real-time control command.
Example 2:
FIGS. 2-5 are graphs showing the actual refresh time of control commands for trains in the group under [ -18,18] kN uncertainty disturbance force, [0,6] s train-to-train communication delay and [0,10] s system response delay, and the coupler force distribution corresponding to each working condition, respectively, based on Monte Carlo testing. In fig. 2 to 5, UD: an uncertain disturbance force; T2T CD: vehicle-to-vehicle communication latency; SRD: system response delay; ele_loc: traction of an electric locomotive; die_loc: and (5) traction of the diesel locomotive.
PLF communication mode: the trains in the group can simultaneously acquire the information of the preceding trains and the pilot trains (the pilot trains refer to the first trains in the group);
PF communication mode: the trains in the group can only acquire the information of the trains in front of the trains.
In fig. 2-5, 37500 discrete parameters are input corresponding to each working condition during the monte carlo test, and are normally distributed.
(1) Fig. 2 shows that: under the action of uncertain disturbance force of [ (18, 18] kN), the method can stably output control instructions, and the average refresh time of the instructions is only required to be 0.04s and is far lower than the refresh rate requirement of the existing train control system of 0.20 s;
(2) Fig. 3 shows that: the method can stably output control instructions under the communication delay of [0,6] s vehicle-to-vehicle (6 s is the maximum communication delay allowed by a group operation control system), and the average refreshing time of the instructions is respectively 0.11s, 0.17s and is lower than the refreshing rate requirement of the existing train control system of 0.20s in PLF and PF vehicle-to-vehicle communication modes;
(3) Fig. 4 shows that: under the response delay of the [0,10] s system, the method can stably output control instructions no matter the train towed by the electric locomotive or the train towed by the diesel locomotive, and the average refreshing time of the instructions is respectively 0.14s and 0.17s and is lower than the refreshing rate requirement of the existing train control system of 0.20 s;
(4) Fig. 5 shows that: thanks to the real-time computational evaluation of the coupler forces and the reasonable constraints of locomotive maneuvering, even if the train is subjected to large uncertain disturbance forces, car-to-car communication delays and system response delays, the coupler forces are well below the safety limit of 2250 kN.
As shown in fig. 6, a detailed application process of the method is described by taking a certain actual in-service heavy-duty railway line and train allocation situation in China as an example.
Step 1: environmental awareness 1) static parameters: the single group comprises 7 trains which are respectively pulled by 3 columns of HXN3 and 4 columns of SS4B, the truck is C80, 5000 tons are taken as basic units, and the train numbers are LT-0, FT-1, FT-2, FT-3, FT-4, FT-5 and FT-6; the full length of the line is 68.5km, which comprises 8 stations and 7 line sections; the intra-group train-to-train communication mode is PF (i.e., trains in the group can only acquire information of trains in front of the group); the train group needs to pass through a No. 12 turnout in a 3 rd station, and the speed limit is 45km/h; 2) Dynamic time-varying parameters: after the group first trains get off, rainfall starts, the rail surfaces are wet, wind exists and gradually increases; since the rail surface becomes increasingly moist, the anti-slip measures are started from the 4 th station, and in addition, the speed limit is temporarily limited by 50 km/h when passing the fifth station; a dynamic group editing occurs AT station 7 where FT-2 exits the group and train AT-1, pulled by the diesel locomotive, joins the group after FT-4.
Step 2: modeling of communication systems in a train group: according to the communication system parameters obtained in the step 1, a train-train communication model in a train group is established, and during modeling, the transmission delay, the propagation delay, the information processing and the queuing delay are mainly considered;
step 3: kinetic modeling: according to the train parameters obtained in the step 1, a longitudinal dynamics model of the heavy-duty train is established, wherein the longitudinal dynamics model considers uncertain disturbance and time delay, and the uncertain disturbance mainly simulates the wind load in the step 1 and is characterized by a Gaussian function; the rail surface state is reflected as the actual use coefficient of traction/braking force; the time delay comprises a system response time delay and a vehicle-to-vehicle communication time delay, the former is mainly determined by the air brake system charging/discharging time based on the train type in the step 1, and the latter is the sum of the transmission time delay, the propagation time delay, the information processing and the queuing time delay in the step 2;
step 4: motion planning-train reachable domain planning: (1) the highest operation speed which can be achieved by the train under the given line and the current environment (weather conditions and manual temporary authorization information) is calculated and determined based on the vehicle-rail coupling dynamics and the design specification of the heavy haul railway, and the result is shown in fig. 6; (2) determining the position of a corresponding traction/braking operation switching point according to the speed interval change characteristics, wherein the speed interval mileage is doubled by the length of the train, and the result is shown in fig. 6; (3) the real-time braking distance of the train at each position in the current line and the current environment is calculated and determined according to the highest running speed, and the result is shown in fig. 7.
Step 5: motion planning-intra-group train collaborative planning: (1) taking the highest running speed obtained in the step 4 into a train system dynamics model of the step 3, calculating a mileage-time corresponding relation graph when each train passes through the line, uniformly taking mileage as a horizontal axis (starting point position is recorded as 0) and time as a vertical axis (starting point position is recorded as 0) by each train, establishing a plane rectangular coordinate system, translating each train mileage-time curve along the time axis, and taking a minimum value capable of ensuring that two adjacent train mileage-time curves are disjoint as a minimum departure time interval in a group, wherein the maximum departure time interval in the group takes 5 minutes, and the schematic diagram is shown in fig. 8; (2) combining the type of the group train in the step 1 and the real-time absolute braking distance of the train in the step 4, calculating and determining the relative braking distances of the trains in the group under different combined modes by taking the departure time interval in the group as an independent variable, wherein the result is shown in figure 9; (3) on each train mileage-time graph, the coordinate difference of the horizontal axis is not lower than the relative braking distance to be used as the constraint correction minimum departure time interval; (4) re-optimizing to obtain the optimal running speed of each train in the group by taking the highest running speed in the step 4 as a boundary constraint condition and taking the shortest time of the line interval as a target, wherein the result is shown in fig. 10;
step 6: motion planning-motion stability planning: smoothing the optimal speed shown in FIG. 10 in step 5 according to the following formulas (1) - (3) in order to reduce the longitudinal impulse of the train caused by locomotive maneuvers;
（1）
（2）
（3）
in the method, in the process of the invention,、/>、/>the method is characterized in that the method is known and respectively represents the current mileage coordinates, the maximum allowable acceleration and the maximum allowable jerk of the ith train; />The optimal speed of the ith train after smoothing is represented as the target amount; />、/>、、/>For the coefficients of the smoothing equation, the equation is represented by +.>，/>，，/>Four points are calculated. The smoothed front-rear speed curve and the difference thereof are shown in fig. 12.
Step 7: modeling a loading train group operation control system: the mileage, speed and operating force of each train are used as state vectors, the mileage and speed are used as output vectors, the train lumped control force is used as a control vector, and the train system dynamics model in the step 3 is converted into a discrete time state space to form a reloaded train group operation control model;
step 8: the design of the operation controller of the heavy-load train group is as follows: taking the minimum departure time interval in the group of the step 5 as an initial value constraint, taking the discrete time state space model of the step 7 as a dynamics constraint, taking the acceleration and the jerk in the step 6 as a locomotive operation constraint, taking the tracking error of the relative braking distance (obtained by the step 5) and the optimal running speed of each train (obtained by the step 5) as an objective function, taking the sum of three terms of two norms of the difference between the output vector predicted value and the expected value, two norms of the difference between the output vector predicted value and the assumed value and two norms of the difference between the control vector and the operation force as a cost function, and designing a controller for each train in the group based on a model prediction control method.
Step 9: speed determination before control: and (5) judging whether the optimal running speed calculated in the step (5) meets the requirement of the speed limit of the current position (obtained by the manual temporary authorization information in the step (1)). If so, go to step 10; if not, the speed is adjusted to meet the temporary speed limit requirement, and then step 10 is performed. The results are shown in FIGS. 13-15.
FIG. 13 is a mileage-time image of each train in the consist passing through the route before and after speed adjustment; FIG. 14 is an adjusted train speed; fig. 15 is a graph showing the relative distance between trains (i.e., the distance between locomotive heads of two adjacent trains) within a consist when operating at that speed.
Step 10: group operation control: and evaluating whether the train can generate corresponding control force at the current speed. If the speed is feasible, distributing the speed to the controller in the step 8, performing train operation control, and calculating a real-time control instruction; if not, determining the speed consistent with the maximum effective output force of the locomotive in the current environment according to the traction/braking characteristic curve, then distributing the speed to the controller in the step 8, performing train operation control, and calculating a real-time control command. In this case, the real-time control command is shown in fig. 16-17.
FIG. 16 is a real-time control of locomotives in the consist and FIG. 17 is a control command when FT-2 exits the consist.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that the above-mentioned preferred embodiment should not be construed as limiting the invention, and the scope of the invention should be defined by the appended claims. It will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the spirit and scope of the invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (9)
1. The large-scale reloading train group operation control method integrating the longitudinal dynamics of the trains is characterized by comprising the following steps:
step 1: environmental awareness 1) static parameters: acquiring the type and the parameters of a group train, the parameters of line design, the parameters of a communication system and the information of a road network; 2) Dynamic time-varying parameters: weather conditions, manual temporary authorization information, preset dynamic compiling and decomposing actions and occurrence positions thereof;
step 2: modeling of communication systems in a train group: establishing a train-in-train communication model according to the communication system parameters acquired in the step 1;
step 3: modeling longitudinal dynamics of a train: according to the group train parameters obtained in the step 1, a longitudinal dynamics model of the heavy-duty train considering uncertain disturbance and time delay is established based on a basic theory of train system dynamics;
step 4: motion planning-train reachability domain planning: 4.1, calculating and determining the highest running speed of the train in a given line and under the current environment, so as to obtain the speed interval change characteristics; 4.2, determining the position of a corresponding traction/braking operation switching point according to the speed interval change characteristics, wherein the length of the speed interval is not less than twice the length of the train; 4.3, calculating and determining absolute braking distances of the trains at all positions in the current line and the current environment according to the highest running speed;
step 5: motion planning-intra-group train collaborative planning: 5.1, carrying the highest running speed obtained in the step 4 into a train system dynamics model of the step 3 one by one, calculating a time-mileage correspondence graph when each train passes through the line, establishing a plane rectangular coordinate system by one by taking mileage as a horizontal axis and time as a vertical axis, translating each train mileage-time curve along the time axis, and ensuring that the minimum value of disjoint two adjacent train mileage-time curves is the minimum departure time interval in a group, wherein the maximum departure time interval in the group is 5 minutes; 5.2, combining the type of the group train in the step 1 and the real-time absolute braking distance of the train in the step 4, and calculating and determining the relative braking distances of the trains in the group under different combined modes by taking the time interval of departure in the group as an independent variable; 5.3, on each train time-mileage graph, correcting the minimum departure time interval by taking the sum of the relative braking distance and the length of the front train as constraint, wherein the coordinate difference of the horizontal axis is not lower than the sum of the relative braking distance and the length of the front train; 5.4, re-optimizing to obtain the optimal running speed of each train in the group by taking the highest running speed of the step 4 as a boundary constraint condition and taking the shortest time of the line interval as a target;
step 6: motion planning-motion stability planning: smoothing the optimal speed obtained in the step 5, reducing longitudinal impulse of the train caused by locomotive operation, and obtaining maximum allowable acceleration and maximum allowable jerk by taking the car coupler force not exceeding the limit as constraint;
step 7: modeling a loading train group operation control system: the mileage, speed and operating force of each train are used as state vectors, the mileage and speed are used as output vectors, the train lumped control force is used as a control vector, and the train system dynamics model in the step 3 is converted into a discrete time state space to form a reloaded train group operation control system model;
step 8: the design of the operation controller of the heavy-load train group is as follows: taking the minimum departure time interval in the group of the step 5 as an initial value constraint, taking the operation control system model of the heavy-load train group of the step 7 as a dynamics constraint, taking the maximum allowable acceleration and the maximum allowable jerk in the step 6 as a locomotive operation constraint, taking the minimum relative braking distance tracking error and the optimal operation speed tracking error of each train in the step 5 as an objective function, taking the sum of three terms of two norms of the difference between the output vector predicted value and the expected value of the step 7, the two norms of the difference between the output vector predicted value and the assumed value and the two norms of the difference between the control vector and the operating force as a cost function, and designing a controller for each train in the group based on the model prediction control method;
step 9: speed determination before control: judging whether the optimal running speed calculated in the step 5 meets the speed limiting requirement of the current position, and if so, carrying out the step 10; if not, adjusting the speed to meet the temporary speed limiting requirement, and then performing step 10;
step 10: group operation control: evaluating whether the train can generate corresponding control force at the current speed, if so, distributing the speed to the controller in the step 8, performing train operation control, and calculating a real-time control instruction; if not, determining the speed consistent with the maximum effective output force of the locomotive in the current environment according to the traction/braking characteristic curve, then distributing the speed to the controller in the step 8, performing train operation control, and calculating a real-time control command.
2. The method for controlling the operation of a large-scale reloading train group with the longitudinal dynamics of trains fused according to claim 1, wherein in the step 1, the road network information comprises station, track and switch information.
3. The method of mass-reload train group operation control with fusion of longitudinal train dynamics according to claim 1, characterized in that in step 3, the uncertain disturbances are reflected in gaussian function based on the weather condition data in step 1, the time delays include an air brake system response time delay determined by an air brake system charge/discharge time based on the train type in step 1 and a train-to-train communication time delay obtained in step 2.
4. The method for controlling the operation of a large-scale reload train group with fusion of longitudinal dynamics of trains according to claim 1, wherein in step 4.1, the current environment includes weather conditions and manual temporary authorization information.
5. The method for controlling the operation of a large-scale heavy-duty train group with the longitudinal dynamics of trains fused according to claim 1, wherein in step 4.1, the highest operation speed of the trains which can be achieved in a given line and in the current environment is determined based on the vehicle-track coupling dynamics and the heavy-duty railway design specification calculation.
6. The method for controlling the operation of a large-scale heavy-duty train group with the longitudinal dynamics of the train fused according to claim 1, wherein in the step 6, the speed smoothing method comprises, but is not limited to, exponential, trapezoidal, cosine, polynomial, 7-segment S-type, 7-segment modified S-type, 15-segment S-type and 31-segment S-type smoothing methods.
7. The method for controlling the operation of a large-scale reload train group with merging longitudinal dynamics of trains according to claim 6, wherein the speed smoothing is performed exponentially: the optimal speed obtained in the smoothing step 5 is shown in the following formulas (1) - (3):
（1）
（2）
（3）
in the method, in the process of the invention,、/>、/>the method is characterized in that the method is known and respectively represents the current mileage coordinates, the maximum allowable acceleration and the maximum allowable jerk of the ith train; />The optimal speed of the ith train after smoothing is represented as the target amount; />、/>、、/>Is a coefficient of the smoothing equation.
8. The method for controlling the operation of a large-scale heavy-duty train group with the longitudinal dynamics of trains fused according to claim 1, wherein in the step 8, the relative braking distance tracking error is calculated by the relative braking distances of the trains in the group in the step 5 under different combination modes; and 5, calculating the optimal running speed tracking error of each train in the step by the optimal running speed of each train in the group.
9. The method for controlling the operation of a large-scale reload train group with the longitudinal dynamics of trains fused according to claim 1, wherein in step 9, the speed limit of the current position is obtained by the manual temporary authorization information in step 1.
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