CN111291856A - Subway train operation and control multi-objective optimization method and system - Google Patents
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Abstract
The invention provides a subway train operation and control multi-objective optimization method and system. In order to solve the optimization problem that a plurality of performance indexes such as energy conservation, punctuality, comfort and the like are required to be met simultaneously in the train operation process, the train operation control multi-target optimization model is established by taking train energy consumption, comfort and punctuality as optimization targets; and introducing a particle swarm optimization algorithm of a genetic evolution mechanism, adopting multi-target included angle cosine as an evaluation standard for solving the advantages and disadvantages, and simultaneously taking the fusion distance as a judgment condition for judging that the particle swarm is gathered in extreme value particles so as to obtain the optimal comprehensive performance index of the subway train in the operation process. A corresponding optimization system is designed according to the optimization method, and simulation tests are carried out under two different automatic driving scenes of the subway train. The test result shows that under the condition that the subway train and the operation line thereof are specified and the planned operation time is the same, the technical scheme of the invention has better optimizing performance, thereby being capable of obtaining better optimizing result.
Description
Technical Field
The invention relates to the technical field of subway train operation, in particular to a subway train operation and control multi-objective optimization method and system.
Background
The operation control optimization system in the train automatic driving system has good operation control optimization performance so as to provide a subway train target speed track which enables various optimization indexes such as comfort, energy conservation, punctual and accurate parking in the automatic driving operation process of the train to be optimized as much as possible. The important premise is to ensure that the operation and control comprehensive performance indexes of the subway train have good comfort, energy conservation and punctuality in the operation process.
With the vigorous development of subway transportation industry, subway traffic is favored by more and more people due to the characteristics of rapidness, convenience and comfort. However, the train operation process is an optimization problem that a plurality of performance indexes such as energy saving, punctuality and comfort need to be met simultaneously, and more than one or even infinite Pareto solutions which are not bad mutually exist in an optimization solution set. Therefore, an optimization solution obtained by optimizing a single optimization target cannot simultaneously optimize the comprehensive performance indexes of a plurality of performance indexes such as energy conservation, punctuality and comfort. In order to achieve the purpose that various optimization indexes of comfort, energy conservation, punctuality, accurate parking and the like in the automatic driving operation process of the subway train are optimized as much as possible, a subway train operation and control multi-objective optimization method and system with good operation and control optimization performance are required to be designed.
Disclosure of Invention
According to the technical problems, a multi-objective optimization method and system for operation and control of a subway train are provided. The invention establishes a multi-target optimization model for train operation control by taking train energy consumption, comfort level and punctuality as optimization targets, and provides a multi-target genetic particle swarm algorithm based on the cosine of an included angle and a fusion distance. Compared with a linear weighting method, the method provided by the invention has the advantages that the objective vector and target demand vector included angle cosine serving values are used as evaluation indexes for solving the advantages and disadvantages, and the problem of blind selection of subjective parameters is avoided. Compared with the Euclidean distance, the fusion distance can take account of the relativity and the independence of the multi-feature variables, and can accurately detect whether the particles have the phenomenon of individual aggregation at the end of iteration, thereby inhibiting the local convergence of the particles. Under the condition that the subway train and the operation line thereof are specified and the planned operation time is the same, the subway train operation and control multi-objective optimization method and the system have better optimization performance and can obtain better optimization results.
The technical means adopted by the invention are as follows:
a subway train operation and control multi-objective optimization method comprises the following steps:
s1, establishing a train operation and control multi-objective optimization model by taking train energy consumption, comfort and punctuality as optimization objectives;
s2, introducing a particle swarm optimization algorithm of a genetic evolution mechanism, and selecting a group of optimal train operation sequences as an optimal solution;
and S3, on the basis of the step S2, the multi-target included angle cosine is used as an evaluation standard for solving the advantages and disadvantages, and meanwhile, the fusion distance is used as a judgment condition for judging that the particle swarm is gathered in extreme value particles, so that the comprehensive performance index of the optimal subway train in the running process is obtained.
Further, the train operation and manipulation multi-objective optimization model established in the step S1 is as follows:
in the formula, KEThe energy consumption measurement index is represented,
KAa measure of the comfort level is represented,
KTthe punctuality measure is represented by a scale of punctuality,
wherein, aiAcceleration representing the ith condition, si represents displacement of the ith condition, RiRepresenting the resistance of the i-th condition; min represents the minimum value of the function f (u), and if the target vector of the multi-objective optimization problem is f (x) ═ f1(x),f2(x),…,fk(x) The specific optimization model can be expressed as:
where k is the number of optimization objectives, x is the decision variable, g (x) is the constraints of equality and inequality of x, and x "is the absolute optimal solution (Pareto optimal), if and only if any x 'in D, F (x") is better than F (x').
Further, the step S3 includes the following steps:
s31, initialization:
initializing the speed and position of a population, setting the maximum iteration number d, giving acceleration constants c1 and c2 and a weight coefficient omega, initializing the optimal position of an individual particle, and initializing the optimal position of a population of particles;
s32, calculating the cosine value of the multi-target included angle of each particle according to the following formula;
in the formula, γ represents the cosine of the included angle, (T, C) represents the dot product of the solution target vector T and the target demand vector C, | a | | | represents the modular length of the vector a, | represents the numerical multiplication, TiAnd ciA normalized value representing the ith optimization objective of the solution objective vector T and the objective demand vector C;
s33, obtaining an individual extreme value and a global extreme value of the particle swarm by sequencing;
s34, updating the speed and the position of each particle by adopting the updating formula of the speed and the position of the particle; the updated formula of the velocity and position of the particle is as follows:
wherein i belongs to [1,2]Denotes the number of the particle, t ∈ [1, 2.,. D]The t-th dimension of the particle is represented, d represents the number of iterations, c1 and c2 are acceleration constants, rand is a random real number of the interval (0,1), omega is a weight coefficient, and a position vector is represented as
The velocity vector is expressed as
The optimal position of each particle is recorded as
The optimal position of the population is recorded as
S35, calculating the fusion distance, wherein the calculation formula of the fusion distance is as follows:
dMix＝ω×MD(X,Y)+(1-ω)×ED(X,Y)
in the formula (d)MixDenotes the fusion distance, MD denotes the Mahalanobis distance, CYA matrix of correlation coefficients representing a sample set Y, n representing the number of samples in the Y sample set, Yi(i ═ 1, …, n) denotes samples in the sample set Y, and ρ denotes a correlation coefficient;
s36, judging whether the particle swarm is aggregated in an individual extreme value by adopting the fusion distance calculated in the step S35, and if not, turning to the step S37; if the particles are aggregated, selecting, crossing and mutating corresponding particles by adopting a genetic algorithm, and then turning to step S37;
s37, judging whether the maximum iteration number d is reached, if not, adding 1 to the iteration number, and returning to the step S33; otherwise, outputting the comprehensive performance index of the operation process of the optimal subway train.
Further, the formula for determining whether the particle groups are clustered in the individual extremum in step S36 is as follows:
in the formula (I), the compound is shown in the specification,
the method comprises the steps of representing a sample space formed by extreme values of particle individuals, representing the distance from a sample A to the sample space B by Dis | A-B | representing a threshold value, and adopting a fusion distance for Dis.
The invention also provides a subway train operation and manipulation multi-objective optimization system, which comprises the following components: a lower control loop and an upper optimization loop;
the lower-layer control loop comprises a sensor, a conditioning circuit, a controller, a traction transmission link, a main circuit breaker, a train line data storage unit and an intermediate link circuit which are electrically connected;
the upper-layer optimization loop comprises a network connecting line, a transmitter, a signal processor, an optimizer, a main circuit breaker, a train line data storage unit and an intermediate link simulation circuit which are electrically connected;
the system has the functions of off-line optimization and on-line optimization at the same time; the subway train operation and control multi-target optimization method is applied to an off-line optimization function of the system, is embedded in an off-line optimization function module area of an optimizer core chip, does not need actual train tracking control during off-line optimization, and at the moment, a lower-layer control loop and an intermediate link circuit are isolated and replaced by a simulation circuit; during on-line optimization, the lower control loop and the intermediate link circuit are started to implement actual train tracking control.
Further, the off-line optimization is used for optimizing the obtained operation sequence of the comprehensive performance index of the train operation process to the maximum extent based on off-line data when the train stops operating; and the online optimization is used for adjusting the operation sequence obtained by offline optimization in combination with the current control condition when the train operates online so as to obtain the optimal operation optimization effect of actual tracking control.
Further, the optimizer is used for providing the controller with a corresponding target speed track;
the controller is used for controlling the traction transmission link to track and control the target speed track;
the train line data storage unit is used for providing necessary line and train related data, conditions and parameters for subway train operation optimization and tracking control;
the sensor, the network connecting line and the transmitter respectively collect corresponding data for the controller and the optimizer to implement tracking control and operation optimization calculation;
the conditioning circuit and the signal processor are respectively used for conditioning and processing the electric signals collected by the sensor, the network signals collected by the network connecting line and the transmitter into signals which can be accepted by the core chips of the controller and the optimizer;
the traction transmission link is driven by a traction motor to apply power to the subway train so as to brake or traction the subway train, and electric energy of the traction transmission link is transmitted from a power grid by an intermediate link circuit;
the main circuit breaker is used for switching on and switching off a circuit and protecting a system.
Compared with the prior art, the invention has the following advantages:
1. the invention adopts the cosine of the included angle of multiple targets as the evaluation index, and can effectively avoid the problem that part of parameters need to be subjectively determined in the evaluation index.
2. The invention adopts the fusion distance, and can consider the relevance and the independence of decision variables, thereby being capable of more accurately detecting whether the particle aggregation phenomenon exists or not and better inhibiting the local convergence of the particle swarm algorithm.
Based on the reason, the invention can be widely popularized in the fields of subway train operation and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram illustrating the cosine of an angle between a target vector and a target demand vector in the method of the present invention.
FIG. 3 is a system layout of the present invention.
Fig. 4 is a schematic diagram of a ramp and a speed limit curve (connected with a 12-gauge subway line) provided by the embodiment of the present invention.
Fig. 5 is a schematic diagram of a ramp and a speed limit curve (No. 1 line of jinpu subway) according to another embodiment of the present invention.
Fig. 6 is a curve of a target speed trajectory for automatic train driving (a large connection subway No. 12 line) provided by an embodiment of the present invention.
Fig. 7 is a distance curve (connected with the 12 # railway line) of an automatic train driving control mode provided by the embodiment of the invention.
Fig. 8 is an iterative convergence curve (a line connecting No. 12 subway lines) of each optimization index of different optimization algorithms provided by the embodiment of the present invention.
Fig. 9 is an iterative convergence curve (a line connecting No. 12 subway lines) of evaluation indexes of different optimization algorithms provided by the embodiment of the present invention.
Fig. 10 is a curve of an automatic driving target speed trajectory of a train (jinpu subway line 1) according to another embodiment of the present invention.
Fig. 11 is a distance curve of an automatic driving control mode of a train (jinpu subway line 1) according to another embodiment of the present invention.
Fig. 12 is an iterative convergence curve (jinpu subway line 1) of each optimization index of different optimization algorithms according to another embodiment of the present invention.
Fig. 13 is an iterative convergence curve (No. 1 golden common subway line) of evaluation indexes of different optimization algorithms according to another embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by those skilled in the art, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it should be apparent that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms first, second and the like in the description and in the claims, and in the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides a subway train operation and control multi-objective optimization method, which comprises the following steps:
s1, establishing a train operation and control multi-objective optimization model by taking train energy consumption, comfort and punctuality as optimization objectives; the train operation and manipulation multi-objective optimization model established in the step S1 is as follows:
in the formula, KEThe energy consumption measurement index is represented,
KAa measure of the comfort level is represented,
KTthe punctuality measure is represented by a scale of punctuality,
wherein, aiIndicating the acceleration, s, of the i-th conditioniDisplacement, R, representing the i-th operating modeiRepresenting the resistance of the i-th condition; min represents the minimum value of the function f (u), and if the target vector of the multi-objective optimization problem is f (x) ═ f1(x),f2(x),…,fk(x) The specific optimization model can be expressed as:
where k is the number of optimization objectives, x is the decision variable, g (x) is the equality and inequality constraints for x, and x "is the absolute optimal solution (Pareto optimal), if and only if any x 'in D, F (x") is better than F (x').
S2, introducing a particle swarm optimization algorithm of a genetic evolution mechanism, and selecting a group of optimal train operation sequences as an optimal solution; in specific implementation, the train operation multi-objective optimization is any given train parameter, line condition, constraint condition and optimization objective thereof, and a group of train operation sequences { o } which are as optimal as possible are searched by adopting a particle group improved optimization algorithm of a genetic evolution mechanism1,o2,…,okIn which o isi＝(ui,pi) Denotes the ith steering mode, uiFor its train steering control mode, piFor switching its operationPoint position, i ∈ [1, 2.,. k ]]. Using the set of train steering sequences o1,o2,…,okThe train operation is controlled, so that various optimization indexes such as energy conservation, punctuality, comfort and the like are optimized to the maximum extent. Train maneuver sequence o1,o2,…,okThe method is an optimized solution of the multi-objective optimization problem of train operation and control.
And S3, on the basis of the step S2, the multi-target included angle cosine is used as an evaluation standard for solving the advantages and disadvantages, and meanwhile, the fusion distance is used as a judgment condition for judging that the particle swarm is gathered in extreme value particles, so that the comprehensive performance index of the optimal subway train in the running process is obtained. As shown in fig. 1, the step S3 includes the following steps:
s31, initialization:
initializing the speed and position of a population, setting the maximum iteration number d, giving acceleration constants c1 and c2 and a weight coefficient omega, initializing the optimal position of an individual particle, and initializing the optimal position of a population of particles;
s32, calculating the cosine value of the multi-target included angle of each particle according to the following formula;
in the formula, γ represents the cosine of the included angle, (T, C) represents the dot product of the solution target vector T and the target demand vector C, | a | | | represents the modular length of the vector a, | represents the numerical multiplication, TiAnd ciA normalized value representing the ith optimization objective of the solution objective vector T and the objective demand vector C; the specific cosine of the angle between the target vector and the target demand vector is shown in fig. 2.
S33, obtaining an individual extreme value and a global extreme value of the particle swarm by sequencing;
s34, updating the speed and the position of each particle by adopting the updating formula of the speed and the position of the particle; the updated formula of the velocity and position of the particle is as follows:
wherein i belongs to [1,2]Denotes the number of the particle, t ∈ [1, 2.,. D]The t-th dimension of the particle is represented, d represents the number of iterations, c1 and c2 are acceleration constants, rand is a random real number of the interval (0,1), omega is a weight coefficient, and a position vector is represented as
The velocity vector is expressed as
The optimal position of each particle is recorded as
The optimal position of the population is recorded as
S35, calculating the fusion distance, wherein the calculation formula of the fusion distance is as follows:
dMix＝ω×MD(X,Y)+(1-ω)×ED(X,Y)
in the formula (d)MixDenotes the fusion distance, MD denotes the Mahalanobis distance, CYA matrix of correlation coefficients representing a sample set Y, n representing the number of samples in the Y sample set, Yi(i ═ 1, …, n) denotes samples in the sample set Y, and ρ denotes a correlation coefficient;
s36, judging whether the particle swarm is aggregated in an individual extreme value by adopting the fusion distance calculated in the step S35, and if not, turning to the step S37; if the particles are aggregated, selecting, crossing and mutating corresponding particles by adopting a genetic algorithm, and then turning to step S37;
the formula for determining whether the particle swarm is gathered at the individual extremum is as follows:
in the formula (I), the compound is shown in the specification,
the method comprises the steps of representing a sample space formed by extreme values of particle individuals, representing the distance from a sample A to the sample space B by Dis | A-B | representing a threshold value, and adopting a fusion distance for Dis.
S37, judging whether the maximum iteration number d is reached, if not, adding 1 to the iteration number, and returning to the step S33; otherwise, outputting the comprehensive performance index of the operation process of the optimal subway train.
The invention also provides a multi-objective optimization system for operation and control of a subway train, as shown in fig. 3, comprising: a lower control loop and an upper optimization loop; the lower control loop comprises a sensor, a conditioning circuit, a controller, a traction transmission link, a main circuit breaker, a train line data storage unit and an intermediate link circuit which are electrically connected; the upper-layer optimization loop comprises a network connecting line, a transmitter, a signal processor, an optimizer, a main circuit breaker, a train line data storage unit and an intermediate link simulation circuit which are electrically connected; in the specific implementation:
the optimizer is used for providing a corresponding target speed track for the controller;
the controller is used for controlling the traction transmission link to track and control the target speed track;
the train line data storage unit is used for providing necessary line and train related data, conditions and parameters for subway train operation optimization and tracking control;
the sensor, the network connecting line and the transmitter respectively collect corresponding data for the controller and the optimizer to implement tracking control and operation optimization calculation;
the conditioning circuit and the signal processor are respectively used for conditioning and processing the electric signals collected by the sensor, the network connecting wire and the network signals collected by the transmitter into signals acceptable by the core chips of the controller and the optimizer;
in the traction transmission link, a traction motor is driven to apply power to the subway train to brake or carry out traction operation, and electric energy is transmitted from a power grid by an intermediate link circuit;
when a fault occurs, the main circuit breaker is used for breaking a circuit and protecting the system, and when the corresponding fault is removed, the circuit can be connected to enable the circuit to normally operate.
The system has the functions of off-line optimization and on-line optimization at the same time; the subway train operation and control multi-target optimization method is applied to an off-line optimization function of the system, is embedded in an off-line optimization function module area of an optimizer core chip, does not need actual train tracking control during off-line optimization, and at the moment, a lower-layer control loop and an intermediate link circuit are isolated and replaced by a simulation circuit; the off-line optimization is used for optimizing the operation sequence which is obtained by optimizing the comprehensive performance index of the train operation process to the maximum extent based on off-line data when the train stops operating; during on-line optimization, the lower control loop and the middle link circuit are started to implement actual train tracking control. The online optimization is used for adjusting the operation sequence obtained by the offline optimization in combination with the current control situation when the train operates online so as to obtain the optimal operation optimization effect of the actual tracking control.
Examples
In this embodiment, trains of a large continuous subway No. 12 and a large continuous golden subway No. 1 (also called a large continuous subway No. 13 and golden common intercity railways) and their operation lines are selected as research objects. The simulated operation line of the large-connection subway No. 12 line is from a new port in voyage to the iron mountain town, the length of the section is 2.94 kilometers, and two sections of long descending ramps and one section of long ascending ramp are arranged. The subway 12 number line is a railway traffic line which is running and extends from a estuary station in a high and new park area of the city to a new port station in a voyage economic technology development area of the city as a terminal point, and 8 stations are arranged. The simulation operation line of the Dalian Jinpu No. 1 line is from nine miles to nineteen offices, and the interval length is 2.74 km. The Jinpu line No. 1 is an intercity railway line of a Xinhui to Poland shop area of the Jinzhou area of Dalian city, is under construction, has a total length of 46.76km and is provided with 11 stations at the initial stage. The ramp and speed limit curves of subway simulation running lines of the subway 12 # line of the grand connection subway and the Jinpu No. 1 subway are shown in figures 4 and 5, and the specific basic attributes of the train are shown in tables 1-2:
TABLE 1 subway train main parameters of large-connection subway No. 12 line
TABLE 2 gold general No. 1 subway train main parameters
In the above embodiment, the following initialization parameters are given in combination with relevant scientific research literature, field experience, and simulation effect of multiple tests on the basis of taking both the convergence rate and the optimization effect into consideration. The simulation optimization result must satisfy the following conditions: the instantaneous speed of the train cannot exceed the speed limit; the whole process must be operated; the parking error is less than 0.2 meter. The parameters in the examples are specifically as follows: particle group size 30, weight coefficient 0.9, acceleration coefficient c1And c20.5, the maximum iteration number is 150, the selection probability is 40%, the cross probability is 85%, and the mutation probability is 5%. The multi-objective optimization parameters of the operation and control optimization scene of the large continuous subway train with the number 12 are as follows: when the planning running time is 180s, the basic requirement of the optimization target is KE∈[80000，130000]、KA∈[5,10]、KT∈[0,02]Intrinsic weight factor ω'1、ω′2And ω'3Take 0.5, 0.3 and 0.2 respectively, and the target demand vector is [90000,5.2,0.01 ]]. The multi-objective optimization parameters of the Jinpu No. 1 line train operation control optimization scene are as follows: when the planning running time is 177s, the basic requirement of the optimization target is KE∈[95000,150000]、KA∈[6,10]、KT∈[0,0.2]Intrinsic weight factor ω'1、ω′2And ω'3Respectively taking 0.5, 0.3 and 0.2, and the target demand vector is [98000,6.2,0.01 ]]。
Example 1
In this embodiment, a Matlab/GUI simulation system-based subway operation control optimization experimental verification platform is adopted, and the main configuration is as follows:
the simulation software is Matlab/GUI software (Matlab Gui 2016 b); the system of the display computer is configured as "cpu core i 7" and "windows 10". In order to verify the effectiveness of the improvement of the invention, based on the metro train with the number 12 and the selected simulation operation line (from the new port of the voyage to the town of iron mountains), in the embodiment, the metro train operation and operation multi-objective optimization method (marked as I-GA-PSO), the particle swarm optimization algorithm (marked as GA-PSO) introducing the genetic evolution mechanism and the particle swarm optimization algorithm (marked as PSO) are respectively adopted to carry out the operation and operation multi-objective optimization so as to obtain the corresponding metro train operation and operation optimization result. Specific train automatic driving target speed trajectory curves, control mode distance curves, iterative convergence curves of various optimization targets of different optimization algorithms and iterative convergence curves of evaluation indexes are shown in fig. 6, fig. 7, fig. 8 and fig. 9, and the final results of train automatic driving operation optimization are shown in tables 3 and 4:
table 3 final optimization results of various optimization indexes of different optimization algorithms (Dalian subway line No. 12)
Table 4 final optimization results of various evaluation indexes of different optimization algorithms (Dalian subway line No. 12)
As can be seen from the simulation results in tables 2 and 3, the optimized solution obtained by the subway train operation and control multi-target optimization method I-GA-PSO provided by the invention is superior to the optimized solution obtained by the genetic evolution mechanism-introduced particle swarm optimization algorithm GA-PSO and the particle swarm optimization algorithm PSO, and the three indexes of energy conservation, punctuality and comfort are improved to a considerable extent. In this embodiment, a hilly area in the great-connection travel intersection area of the operation route is selected, and multiple hills are typical topographic features of the great connection. When the train is operated in the terrain, the operation sequence is simple, and the large and large downhill section can be used for acceleration and the large and large uphill section can be used for deceleration as far as possible. Obviously, such an operation contributes to saving energy and avoiding jerkiness. As can be seen from fig. 6 and 7, the multi-objective optimization method I-GA-PSO for subway train operation and control provided by the present invention can obtain an extremely simple control sequence and can utilize a long uphill segment and a long downhill segment to the maximum extent. As can be seen from the iterative convergence curves in fig. 8 and 9, compared with the GA-PSO and PSO, which introduce genetic evolution mechanisms, the I-GA-PSO provided by the present invention has a faster convergence rate and a stronger global optimization capability even in the middle and later stages of iteration.
Example 2
In this embodiment, a hardware-in-loop simulation system subway operation and manipulation optimization experiment verification platform based on dSPACE is adopted, and the main configuration is as follows:
the simulation software is dSPACE software (Control Desk 7.0) and Matlab/Simulink software (Matlab/Simulink 2016 b); the system configuration of the computer is shown as "cpu core i 7" and "windows 10"; the model of the embedded chip of the optimizer and the controller is 'TMS 320F 28335'. In order to verify the effectiveness of the improvement of the invention, based on the jinpu subway train No. 1 and the selected simulation operation line (from nineteen to nineteen), in the embodiment, the subway train operation and operation multi-objective optimization method (marked as I-GA-PSO), the particle swarm optimization algorithm (marked as GA-PSO) introducing the genetic evolution mechanism and the existing particle swarm optimization algorithm (marked as PSO) are respectively adopted to carry out operation and operation multi-objective optimization so as to obtain the corresponding operation and operation optimization result of the subway train. Specific iteration convergence curves of various optimization indexes and evaluation indexes of the train automatic driving target speed track curve, the tracking control speed track curve, the control mode distance curve and different optimization algorithms are shown in fig. 10, fig. 11, fig. 12 and fig. 13, and final results of train automatic driving operation optimization and tracking control thereof are shown in tables 5, 6 and 7:
TABLE 5 Final optimization results of various optimization indexes of different optimization algorithms (Jinpu subway No. 1 line)
Table 6 final tracking control results of various optimization indexes of different optimization algorithms (jinpu subway No. 1 line)
TABLE 7 Final optimization results of various evaluation indexes of different optimization algorithms (Jinpu subway No. 1 line)
As can be seen from the simulation results in tables 5 and 6 and 7, the optimal solution obtained by the subway train operation and control multi-objective optimization method I-GA-PSO provided by the invention is superior to the optimal solution obtained by the particle swarm optimization algorithm GA-PSO and the particle swarm optimization algorithm PSO which introduce a genetic evolution mechanism, and three indexes of energy conservation, timeliness and comfort are improved to a considerable extent. In the embodiment, the undulating region of the large Jinzhou area at the operation route is selected, and the train is operated on the terrain, so that the operation sequence is simple, the undulating section can be used for acceleration and the large ascending section can be used for deceleration as far as possible, and the energy consumption is saved and the jolt is avoided. As can be seen from fig. 10 and 12, the multi-objective optimization method I-GA-PSO for subway train operation and control according to the present invention can obtain a very simple control sequence and can utilize a steep slope section to the maximum extent, thereby obtaining a better target speed trajectory. Further, as can be seen from fig. 11 (which is particularly evident in 2 enlarged areas), the target speed trajectory obtained by the subway train operation and control multi-objective optimization method I-GA-PSO provided by the present invention is subjected to tracking control, so that a better tracking control speed trajectory curve can be obtained, because the target speed trajectory obtained by the I-GA-PSO is smoother and less harsh operation and control switching conditions are present, the tracking control is easier. As can be seen from the iterative convergence curve of fig. 13, compared with the GA-PSO and PSO of the particle swarm optimization algorithm introduced with the genetic evolution mechanism, the I-GA-PSO of the multi-objective optimization method for subway train operation and control provided by the present invention has a faster convergence speed, and has a stronger global optimization capability even in the middle and later stages of iteration.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A multi-objective optimization method for operation and control of a subway train is characterized by comprising the following steps:
s1, establishing a train operation and control multi-objective optimization model by taking train energy consumption, comfort and punctuality as optimization objectives;
s2, introducing a particle swarm optimization algorithm of a genetic evolution mechanism, and selecting a group of optimal train operation sequences as an optimal solution;
and S3, on the basis of the step S2, the multi-target included angle cosine is used as an evaluation standard for solving the advantages and disadvantages, and meanwhile, the fusion distance is used as a judgment condition for judging that the particle swarm is gathered in the extreme value particles, so that the optimal comprehensive performance index of the subway train in the operation process is obtained.
2. The multi-objective optimization method for subway train operation and manipulation according to claim 1, wherein said multi-objective optimization model for train operation and manipulation established in step S1 is as follows:
in the formula, KERepresenting an energy consumption measure，
KAA measure of the comfort level is represented,
KTthe punctuality measure is represented by a scale of punctuality,
wherein, aiAcceleration representing the ith condition, si represents displacement of the ith condition, RiRepresenting the resistance of the i-th condition; min represents the minimum value of the function f (u), and if the target vector of the multi-objective optimization problem is f (x) ═ f1(x),f2(x),…,fk(x) The specific optimization model can be expressed as:
where k is the number of optimization objectives, x is the decision variable, g (x) is the equality and inequality constraints for x, and x "is the absolute optimal solution (Pareto optimal), if and only if D is*Any of x ', F (x ') is superior to F (x ').
3. The subway train operation and manipulation multi-objective optimization method as claimed in claim 1, wherein said step S3 includes the steps of:
s31, initialization:
initializing the speed and position of a population, setting the maximum iteration number d, giving acceleration constants c1 and c2 and a weight coefficient omega, initializing the optimal position of an individual particle, and initializing the optimal position of a population of particles;
s32, calculating the cosine value of the multi-target included angle of each particle according to the following formula;
in the formula, γ represents the cosine of the included angle, (T, C) represents the dot product of the solution target vector T and the target demand vector C, | a | | | represents the modular length of the vector a, | represents the numerical multiplication, TiAnd ciA normalized value representing the ith optimization objective of the solution objective vector T and the objective demand vector C;
s33, obtaining individual extreme values and global extreme values of the particle swarm by utilizing the multi-target included angle cosine value sequencing;
s34, updating the speed and the position of each particle by adopting the updating formula of the speed and the position of the particle; the updated formula of the velocity and position of the particle is as follows:
wherein i belongs to [1,2]Denotes the number of the particle, t ∈ [1, 2.,. D]The t-th dimension of the particle is represented, d represents the number of iterations, c1 and c2 are acceleration constants, rand is a random real number of the interval (0,1), omega is a weight coefficient, and a position vector is represented as
The velocity vector is expressed as
The optimal position of each particle is recorded as
The optimal position of the population is recorded as
S35, calculating the fusion distance, wherein the calculation formula of the fusion distance is as follows:
dMix＝ω×MD(X,Y)+(1-ω)×ED(X,Y)
in the formula (d)MixDenotes the fusion distance, MD denotes the Mahalanobis distance, CYA matrix of correlation coefficients representing a sample set Y, n representing the number of samples in the Y sample set, Yi(i ═ 1, …, n) denotes samples in the sample set Y, and ρ denotes a correlation coefficient;
s36, judging whether the particle swarm is gathered in the individual extreme value by adopting the fusion distance calculated in the step S35, and if not, turning to the step S37; if the particles are aggregated, selecting, crossing and mutating corresponding particles by adopting a genetic algorithm, and then turning to step S37;
s37, judging whether the maximum iteration number d is reached, if not, adding 1 to the iteration number, and returning to execute the step S33; otherwise, outputting the comprehensive performance index of the operation process of the optimal subway train.
4. The multi-objective optimization method for subway train operation and manipulation according to claim 3, wherein said determining formula for determining whether the particle swarm is gathered at the individual extremum in step S36 is as follows:
in the formula (I), the compound is shown in the specification,
the method comprises the steps of representing a sample space formed by extreme values of particle individuals, representing the distance from a sample A to the sample space B by Dis | A-B | representing a threshold value, and adopting a fusion distance for Dis.
5. A subway train operation and manipulation multi-objective optimization system is characterized by comprising: a lower control loop and an upper optimization loop;
the lower-layer control loop comprises a sensor, a conditioning circuit, a controller, a traction transmission link, a main circuit breaker, a train line data storage unit and an intermediate link circuit which are electrically connected;
the upper-layer optimization loop comprises a network connecting line, a transmitter, a signal processor, an optimizer, a main circuit breaker, a train line data storage unit and an intermediate link simulation circuit which are electrically connected;
the system has the functions of off-line optimization and on-line optimization at the same time; the subway train operation and manipulation multi-target optimization method is applied to an off-line optimization function of the system, is embedded in an off-line optimization function module area of an optimizer core chip, does not need actual train tracking control during off-line optimization, and at the moment, a lower-layer control loop and a middle link circuit are isolated and replaced by a simulation circuit; during on-line optimization, the lower control loop and the middle link circuit are started to implement actual train tracking control.
6. The multi-objective operation and manipulation optimization system for subway trains as claimed in claim 5, wherein said off-line optimization is used for optimizing the obtained operation sequence of the maximum degree of the comprehensive performance index of the train operation process based on off-line data when the train stops operating; and the online optimization is used for adjusting the operation sequence obtained by offline optimization in combination with the current control condition when the train operates online so as to obtain the optimal operation optimization effect of actual tracking control.
7. The subway train operation and manipulation multi-objective optimization system as claimed in claim 5,
the optimizer is used for providing a corresponding target speed track for the controller;
the controller is used for controlling the traction transmission link to track and control the target speed track;
the train line data storage unit is used for providing necessary line and train related data, conditions and parameters for subway train operation optimization and tracking control;
the sensor, the network connecting line and the transmitter respectively collect corresponding data for the controller and the optimizer to implement tracking control and operation optimization calculation;
the conditioning circuit and the signal processor are respectively used for conditioning and processing the electric signals collected by the sensor, the network signals collected by the network connecting line and the transmitter into signals acceptable by the core chips of the controller and the optimizer;
the traction transmission link is driven by a traction motor to apply power to the subway train so as to brake or carry out traction operation, and electric energy of the traction transmission link is transmitted from a power grid by an intermediate link circuit;
the main circuit breaker is used for switching on and switching off a circuit and protecting a system.
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