Virtual marshalling train distributed cooperative tracking control method, system and electronic equipment

Abstract

The application discloses a virtual marshalling train distributed cooperative tracking control method and system and electronic equipment, and relates to the technical field of rail transit signal control. The application solves the global optimal control problem, obtains the optimal control command of each train unit, and optimizes the control command of the overall tracking operation effect of the virtual marshalling train. The rear train can be prevented from having no feasible solution by proper adjustment of the front train, and any train unit can be effectively prevented from triggering a safety constraint. In addition, the application controls each train unit in the virtual marshalling train by using a distributed control algorithm, which conforms to the architecture of independent calculation and control of the control command of the on-board equipment of each train unit in reality. Furthermore, each train unit only needs to obtain the information of adjacent train units when calculating the control command of the train unit, which is more practical and feasible compared with a centralized control algorithm, and greatly saves communication resources.

Description

Technical Field

[0001] This invention relates to the field of rail transit signal control technology, and in particular to a distributed cooperative tracking control method, system and electronic equipment for virtual train formation. Background Technology

[0002] Virtual Coupling (VC) technology, based on vehicle-to-vehicle communication and active control, combines multiple train units that do not have physical coupling relationships into a virtual train formation, while maintaining a very small tracking distance between adjacent train units. This virtual coupling method allows all train units to provide transport services similar to physically coupled trains. Based on this flexible formation method, the number of train units can be quickly and dynamically adjusted, thereby achieving real-time flexible matching between line capacity and passenger flow needs. Therefore, VC technology has attracted widespread attention from industry professionals and is a key research focus for future rail transit.

[0003] To provide the desired transportation service, a virtual train formation requires all its train units to maintain a desired close-gap tracking speed. Common virtual train formation control methods can be broadly categorized into centralized and distributed methods. The centralized method uses a single device to centrally calculate the control commands for all train units, achieving optimal overall tracking performance. However, this device needs to interact with all train units simultaneously during the calculation. A commonly used distributed method adjusts the control commands (usually acceleration) of the current train unit to maintain the desired distance and speed with the train ahead; this is called the follow-the-leader control method. In this approach, the current train unit only considers optimizing its tracking performance relative to the train ahead, without considering whether the following train units can respond well to its control commands to maintain the desired tracking performance. Besides maintaining a close-gap tracking performance with the train ahead, each train unit is also subject to safety constraints related to the state of the train ahead. Therefore, the state and control commands of each train unit within the virtual formation propagate backward along the train convoy, affecting the overall performance of the virtual train formation. Therefore, the following problems still need to be solved in realizing virtual grouping small-pitch tracking operation:

[0004] 1) The overall tracking performance of virtual train formations is difficult to optimize: Since virtual train formations provide transportation services as a single train, their overall synchronization performance is subject to high requirements. However, the forward and backward following control method can only optimize the tracking performance of individual train units according to the train arrangement order within the virtual formation, and cannot optimize the overall tracking performance of the virtual train formation. Therefore, the overall tracking performance is difficult to optimize.

[0005] 2) The preceding train applies a control command that exceeds the limit capability of the following train, causing the safety constraint to be triggered: Since the current train unit does not consider whether the train unit behind it can respond well when calculating the control command, if the current train unit applies an excessive braking rate, the train unit behind it will be unable to avoid triggering the safety constraint even if it brakes to its limit capability.

[0006] 3) If a centralized control algorithm is adopted, existing technology is insufficient to meet the requirements of on-board equipment to simultaneously exchange information with all train units. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this invention provides a distributed cooperative tracking control method, system, and electronic equipment for virtual train formations.

[0008] To achieve the above objectives, the present invention provides the following solution:

[0009] A distributed cooperative tracking control method for virtual train formations includes:

[0010] Constructing the global optimal control problem for virtual train formation tracking operation;

[0011] Based on the global optimal control problem of virtual train tracking operation, a local optimal control subproblem of virtual train tracking operation is obtained;

[0012] Based on the error state between train units, the local optimal control subproblem of virtual train formation tracking operation is solved to obtain the optimal control command sequence of the current train unit and the optimal control command sequence of the train units adjacent to the current train unit.

[0013] The local variables and Lagrange multipliers stored locally in the current train unit are updated based on the optimal control command sequence of the current train unit until the iteration termination condition is met. Then, the first value in the optimal control command sequence of the current train unit is output as the control command of the current train unit at the current time.

[0014] Preferably, the global optimal control problem for virtual train formation tracking operation is a global optimal control problem with the control command sequence of all train units in the virtual formation within a prediction time domain as the decision variable, the objective function being the sum of the quadratic terms of the error state sequence of all train units in the prediction time domain and the quadratic terms of the control command sequence, and the constraints being safety constraints, train dynamics constraints, input saturation constraints, and terminal constraints.

[0015] Preferably, the local optimal control sub-problem for virtual train tracking operation is derived based on the global optimal control problem of virtual train tracking operation, specifically including:

[0016] The global optimal control problem for virtual train formation tracking operation is decomposed into a local optimal control subproblem whose objective function includes only the error state of one train unit and the control command. The obtained local optimal control subproblem is then used as the local optimal control subproblem for virtual train formation tracking operation.

[0017] Preferably, the optimal control command sequence of the current train unit and the optimal control command sequence of the train units adjacent to the current train unit are obtained by solving the local optimal control subproblem of virtual train formation tracking operation based on the error state between train units using the sequential quadratic programming method.

[0018] A virtual train formation distributed cooperative tracking control system is applied to the aforementioned virtual train formation distributed cooperative tracking control method; each train unit is equipped with a virtual train formation distributed cooperative tracking control system; the system includes:

[0019] The error status acquisition module is used to acquire the error status of the current train unit in each control cycle.

[0020] The global optimal control problem construction module is used to construct the global optimal control problem for virtual train tracking operation;

[0021] The local optimal control subproblem construction module is used to obtain the local optimal control subproblem for virtual train tracking operation based on the global optimal control problem of virtual train tracking operation;

[0022] The quadratic programming problem-solving module is used to solve the local optimal control subproblem of virtual train formation tracking operation based on the error state between train units, and obtain the optimal control command sequence of the current train unit and the optimal control command sequence of the train units adjacent to the current train unit.

[0023] The local variable update module is used to update the local variables and Lagrange multipliers stored locally in the current train unit based on the optimal control command sequence of the current train unit until the iteration termination condition is met. Then, the first value in the optimal control command sequence of the current train unit is output as the control command of the current train unit at the current time.

[0024] The information interaction processing module is used to perform information interaction between the current train unit and external systems and other train units based on the control commands of the current train unit at the current time and the control commands of the train units adjacent to the current train unit.

[0025] An electronic device, comprising:

[0026] Memory, used to store computer programs;

[0027] A processor, connected to the memory, is used to retrieve and execute the computer program to implement the virtual train formation distributed cooperative tracking control method provided above.

[0028] Preferably, the memory is a computer-readable storage medium.

[0029] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0030] This invention solves the global optimal control problem to calculate the optimal control command for each train unit, thereby optimizing the control command for the overall tracking and operation of the virtual train formation. Furthermore, because each train unit can fully cooperate and interact with each other when calculating control commands, the leading train will make appropriate adjustments, thus avoiding situations where the trailing train has no feasible solution and effectively preventing any train unit from triggering safety constraints. This invention uses a distributed control algorithm to control each train unit within the virtual train formation, which conforms to the actual architecture where each train unit's onboard equipment independently calculates control commands. In addition, each train unit only needs to obtain information from its adjacent train units when calculating its own control commands, making it more practically feasible than centralized control algorithms and significantly saving communication resources. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 The flowchart of the distributed cooperative tracking control method for virtual train formation provided by the present invention;

[0033] Figure 2 This invention provides an overall architecture diagram of a distributed cooperative tracking control system for virtual train formations.

[0034] Figure 3 This is a schematic diagram of the system composition structure on a train unit provided by the present invention;

[0035] Figure 4 This is a schematic diagram of the running trajectory of a virtual train under the control of the DMPC-based distributed cooperative control method for virtual train formation, as provided in Example 1 of the present invention; wherein, Figure 4 (a) is a schematic diagram of the speed trajectory of a virtual train under the control of the distributed cooperative control method based on DMPC provided in Example 1. Figure 4(b) is a schematic diagram of the distance trajectory of a virtual train under the control of the distributed cooperative control method based on DMPC provided in Example 1.

[0036] Figure 5 This is a schematic diagram of the running trajectory of a virtual train under centralized model predictive control, as provided in Example 1 of the present invention; wherein, Figure 5 (a) is a schematic diagram of the speed trajectory of a virtual train under centralized model predictive control provided in Example 1. Figure 5 (b) is a schematic diagram of the distance trajectory of a virtual train under centralized model predictive control provided in Example 1;

[0037] Figure 6 This is a schematic diagram of the running trajectory of a virtual train formation controlled by the existing preceding and following MPC method, as provided in Example 1 of the present invention; wherein, Figure 6 (a) is a schematic diagram of the speed trajectory of a virtual train formation controlled by the existing front-and-back following MPC method provided in Example 1. Figure 6 (b) is a schematic diagram of the distance trajectory of a virtual train under the control of the existing front-and-back following MPC method provided in Example 1;

[0038] Figure 7 A comparison diagram of the overall tracking and operation effects of the control method provided in Example 1 of the present invention and the virtual train formation under centralized MPC control;

[0039] Figure 8 This is a schematic diagram of the running trajectory of a virtual train under centralized model predictive control, as provided in Example 2 of the present invention. Figure 8 (a) is a schematic diagram of the speed trajectory of a virtual train under centralized model predictive control provided in Example 2. Figure 8 (b) is a schematic diagram of the distance trajectory of a virtual train under centralized model predictive control provided in Example 2;

[0040] Figure 9 A schematic diagram of the running trajectory of a virtual train under the control of the existing front-and-back following MPC method provided in Example 2 of the present invention; Figure 9 (a) is a schematic diagram of the speed trajectory of a virtual train formation controlled by the existing front-and-back following MPC method provided in Example 2. Figure 9 (b) is a schematic diagram of the distance trajectory of a virtual train formation controlled by the existing front-and-back following MPC method provided in Example 2. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] The purpose of this invention is to provide a distributed collaborative tracking control method, system, and electronic equipment for virtual train formations, which can optimize the overall tracking operation effect of virtual train formations, avoid train units triggering safety constraints, and meet the needs of onboard equipment to simultaneously exchange information with all train units, thus greatly saving communication resources.

[0043] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] In this invention, a virtual train consists of multiple train units. For ease of description, the first train unit at the front is called the lead train unit, and the remaining train units are called the following train units. The train unit that is further forward between two adjacent train units is called the lead car, and the train unit that is backward is called the follow car.

[0045] This invention is based on the concept of distributed cooperative control, which performs overall control of all train units within a virtual train formation. When calculating control commands, each train unit considers not only its tracking relationship with the train ahead but also its tracking relationship with the trains behind it. Since all train units maintain a consistent control objective—optimizing the overall tracking performance of the virtual train formation—the optimal control command for each train unit as a whole can be found. Furthermore, because train units can fully cooperate and interact when calculating control commands, any train unit can avoid triggering safety constraints. In addition, considering that in a real virtual train formation, each train unit independently calculates its own control commands, this invention uses the Alternating Direction Method of Multipliers (ADMM) to achieve a distributed solution for the globally optimal control command of the virtual train formation. During the solution process, each train unit only needs to interact with its adjacent train units.

[0046] Based on this, such as Figure 1 As shown, the virtual train formation distributed cooperative tracking control method provided by the present invention includes:

[0047] Step 1: Construct the global optimal control problem, namely, construct the global optimal control problem for virtual train formation tracking operation; wherein, the constructed global optimal control problem for virtual train formation tracking operation is a global optimal control problem with the control command sequence of all train units in the virtual formation in the future prediction time domain as the decision variable, the objective function being to minimize the sum of the quadratic terms of the error state sequence of all train units in the prediction time domain and the quadratic terms of the control command sequence, and the constraints being safety constraints, train dynamics constraints, input saturation constraints and terminal constraints.

[0048] Step 2: Constructing the Local Optimal Control Subproblem. In practical applications, a local optimal control subproblem for virtual train formation tracking is obtained based on the global optimal control problem of the virtual train formation tracking operation. For example, the global optimal control problem of the virtual train formation tracking operation is decomposed into a local optimal control subproblem whose objective function only includes the error state of one train unit and the control commands. This obtained local optimal control subproblem is then used as the local optimal control subproblem for virtual train formation tracking operation. The decision variable of this subproblem becomes the sequence of control commands for the current train unit and its preceding train in the predicted time domain. This local optimal control subproblem is constructed offline and is usually not reconstructed during train operation. The error state of each train unit is a variable to be input as actual values.

[0049] Step 3: Solve the local optimal control subproblem. In practical applications, the error state value of the current train unit is input into the local optimal control subproblem constructed in Step 2, and the sequential quadratic programming method is used to solve the quadratic programming problem to obtain the optimal control command sequence between the current train unit and its predecessor.

[0050] Step 4: Exchange information with adjacent train units and update local variables and Lagrange multipliers. In practical applications, the current train unit exchanges local variables, Lagrange multipliers, and its own locally calculated optimal control command sequence with adjacent train units. Based on the optimal control command sequence of the current train unit calculated in Step 3, the local variables and Lagrange multipliers stored locally in the current train unit are updated.

[0051] Step 5: Determine whether to terminate the iteration. Specifically, you can determine whether to return to step 3 to iterate again or terminate the current iteration and proceed to step 6 based on the iteration termination condition;

[0052] Step 6: Output control command. This involves outputting the first value in the optimal control command sequence for the current train unit as the control command for the current train unit at the current moment.

[0053] In practical applications, the implementation process of steps 4 to 6 above can be as follows:

[0054] The local variables and Lagrange multipliers stored locally in the current train unit are updated based on the optimal control command sequence of the current train unit until the iteration termination condition is met. Then, the first value in the optimal control command sequence of the current train unit is output as the control command of the current train unit at the current time.

[0055] Furthermore, after the current control cycle ends, return to step 3 in the next control cycle to continue outputting control commands.

[0056] In practical applications, steps 1 and 2 are offline design steps, while steps 3 to 6 are online closed-loop control, which iterates cyclically during train operation to perform closed-loop control on the train unit.

[0057] The following embodiment illustrates the specific implementation process of the virtual train formation distributed cooperative tracking control method provided by the present invention.

[0058] First, the dynamics of a single train unit in the virtual train formation are modeled using the following equations:

[0059]

[0060] Among them, s i ,v i These represent the position and speed of train unit i, respectively (where i = 1 is the lead train unit, and subsequent trains are i = 2, 3, ...). Then, u represents the derivatives of position and velocity, respectively. i This represents the control command for train unit i, which is also the acceleration of the train unit. This example uses a second-order dynamics model, but it is not limited to this and can be easily extended to other dynamics models.

[0061] Next, a virtual train formation tracking and control objective is designed. The pilot train unit is expected to operate along a pre-designed reference trajectory, namely:

[0062] s r -s1=0,v r -v1 = 0.

[0063] Among them, s r ,v r These represent the position and velocity on a pre-designed reference trajectory, i.e., the reference position and reference velocity.

[0064] It is expected that the following train unit will maintain the same tracking distance and reference distance as the train in front, and at the same speed, that is:

[0065] s i-1 -s i -L i -di =0,v i-1 -v i =0,

[0066] Among them, L i The train unit length is represented by n, the total number of train units in the virtual formation, and d is the train unit length. i The desired spacing is defined here as d. i =hv i +c m h is the time interval, c m For a fixed spacing, h and c m All are constant coefficients.

[0067] Furthermore, the train error state equation is established. The error state continuity equation for the pilot train unit can be:

[0068]

[0069] Where Δs1 is the position error (the difference between the actual position and the reference position), satisfying Δs1=s r -s1, This refers to the positional error of the lead train unit. Let Δv1 be the speed error of the lead train unit, and let Δv1 be the speed error (the difference between the actual speed and the reference speed), satisfying Δv1 = v r -v1.

[0070] The error state continuity equation for the following train unit can be written as:

[0071]

[0072] in, To track the position error of the train unit, Δs i This refers to the spacing error (the difference between the distance between this vehicle and the vehicle in front and the desired distance). To track the speed error of the train unit, Δv i The speed error (the difference in speed between the vehicle and the vehicle in front) Δv i =v i-1 -v i .

[0073] Discretizing the error state continuity equations of the lead train unit and the following train unit yields the discrete error state equation for the lead train unit, which is:

[0074]

[0075] And the discrete equation of the error state of the following train unit is:

[0076]

[0077] Where the subscript k represents the discrete time, which applies to all variables mentioned below, τ is the sampling interval, and Δs 1,k+1 Let Δs be the position error of the pilot train unit after discretization at time k+1. 1,k Let Δv be the position error of the pilot train unit after discrete time k. 1,k Let Δv be the speed error of the pilot train unit after discretization at time k. 1,k+1 Let u be the speed error of the pilot train unit after discretization at time k+1. 1,k The control command for the pilot train unit after discretization at time k, Δs i,k+1 Let Δs be the position error of the following train unit after discretization at time k+1. i,k Let u be the position error of the following train unit after discrete time k. i,k The control command for the following train unit after time k is discrete, Δv i,k+1 Let Δv be the speed error of the following train unit after discretization at time k+1. i,k Let u be the speed error of the following train unit after discretization at time k. i-1,k This is the control command for another following train unit after time k is discrete.

[0078] Finally, define the error state vector x. i,k =[Δs i,k ,Δv i,k ] T Then the error state equation of the pilot train unit can be written as:

[0079] x 1,k+1 =A1x 1,k +B1u 1,k .

[0080] The error state equation for the following train unit can be written as:

[0081]

[0082] Wherein, the state transition matrix A i B i C i They are constructed as follows:

[0083]

[0084]

[0085]

[0086] Furthermore, by integrating the error states of all train units, the error states of the virtual train formation can be obtained. and the corresponding error state equation x k+1 =Ax k +Bu k .

[0087] Among them, the state transition matrices A and B are based on the error state equations of the lead train unit and the following train unit, as well as matrix A. i B i C i Build.

[0088] Next, consider the safety constraints, which are:

[0089]

[0090] in, Indicates the safe protection distance, v max b represents the highest physical speed that a train can achieve, which is a known constant value. i s represents the emergency braking rate of train unit i. m The minimum safe distance is a constant value.

[0091] In addition, train input saturation constraints are also considered, which are: u i,min ≤u i,k ≤u i,max .

[0092] Among them, u i,min with u i,max These represent the minimum and maximum accelerations of train unit i, respectively.

[0093] Proceed to step 1, and establish the global optimal control problem for virtual train tracking operation in the global optimal control problem construction module. The objective function of this problem is:

[0094] in, The control command sequence for all train units is U. The control command sequence for train unit i is U. i,k =[u i,0|k ,u i,1|k ,…,u i,N-1|k ], u j|k This represents the control command (j = 0, 1, ..., N-1) at time k+j in the prediction time domain, and the global evaluation function. for:

[0095]

[0096] x in the function j|kLet represent the error state at time k+j within the prediction time domain, N be the length of the prediction time domain, and P, Q, and R be the terminal penalty weight, error state penalty weight, and control command penalty weight, respectively. Furthermore, this problem also needs to satisfy the following constraints:

[0097] x 0|k =x k

[0098]

[0099]

[0100]

[0101]

[0102] in, Let G represent the set of integers from 0 to N-1. The error state constraint matrix G and the error state constraint vector g are constructed based on the safety constraints, and the control command constraint matrix F and the control command constraint vector f are constructed based on the input saturation constraints. The set of terminal constraints to ensure the stability of the control method.

[0103] Proceeding to step 2, the objective function and constraints related to the error state of each train unit i in the global optimal control problem are extracted separately as local subproblems. For train unit i, the objective function of its subproblem is:

[0104] in, The local evaluation function is used, and the decision variable is the control command sequence U of the current train unit. i,k Control command sequence U of the preceding vehicle i-1,k ,have:

[0105]

[0106] Among them, P i Q i ,R i Let P, Q, R be the terminal penalty weight, error state penalty weight, and control command penalty weight of train unit i, respectively. Let Q be the components of the penalty weights P, Q, R with respect to train unit i, and let R satisfy the following relationship: P = diag{P1, P2, ..., P} n}, Q = diag{Q1,Q2,…,Q n}, R = diag{R1,R2,…,R n}, where the function diag{} represents the diagonal matrix with respect to its input. Therefore, the evaluation functions for the local subproblem and the global optimal control problem satisfy the following relationship:

[0107]

[0108] In addition, the local subproblem also needs to satisfy the following constraints:

[0109] x i,0|k =x i,k

[0110]

[0111]

[0112]

[0113]

[0114] Wherein, the error state constraint matrix Ξ of train unit i i and the error state constraint vector ξ i,j|k Constructed based on security constraints The components of the terminal constraint set related to train unit i are used to ensure the stability of the control method.

[0115] Next, based on the local subproblem, a local optimal control subproblem is constructed according to the ADMM. The objective function of the local optimal control subproblem is:

[0116] The decision variable is the local optimal control command. Evaluation function for The Lagrange augmented function is defined as:

[0117]

[0118] Where, λ i,k For Lagrange multipliers, z is a vector of local variables related to train unit i. i,k ρ is a local variable. i As a penalty factor, ||·|| 2 This represents the square of the Euclidean norm.

[0119] Proceed to step 3, input the error state of train unit i at time k, and use the sequential quadratic programming method in the quadratic programming solution module to solve the local optimal control subproblem. The solution can be expressed as:

[0120]

[0121] Where the superscript (q) represents the number of iterations, for example, This represents the optimal control command sequence obtained in the (q+1)th iteration.

[0122] In step 4, each train unit i exchanges information with its adjacent train units i-1 and i+1. Train unit i sends... The first N variables in (written as )as well as The first N variables in (written as () to the preceding vehicle i-1, receive the data sent by the following vehicle i+1. as well as

[0123] Next, in the local variable update module, the local variables and Lagrange multipliers are updated according to ADMM. Specifically, the local variable z... i,k The update law is:

[0124]

[0125] Lagrange multiplier λ i,k The update law is:

[0126]

[0127] Proceed to step 5. After each iteration update, determine whether to terminate the iteration. The termination condition is:

[0128]

[0129] Among them, the original residual Dual residuals ||·|| represents the Euclidean norm. and These represent the original residual threshold and the dual residual threshold, respectively. If the termination condition is met, the iteration stops and proceeds to step 6; otherwise, it returns to step 3.

[0130] Proceed to step 6 and output the control command for each train unit i, as follows:

[0131] in, for The first value in the list.

[0132] Furthermore, two examples are used to illustrate the advantages of the present invention.

[0133] Example 1

[0134] Figure 4 The figure shows the running trajectory of a virtual train under the control of the DMPC-based distributed cooperative control method for virtual train formation described in this invention. Figure 5The running trajectory of a virtual train under centralized model predictive control (MPC) (which directly solves the global optimal control problem) is presented. Figure 6 The paper presents the running trajectory of a virtual train formation under the control of the existing front-and-back following MPC method. Figure 7 A comparison chart is provided showing the overall tracking and operation performance of the virtual train formation under the control of the control method provided by this invention and the control of centralized MPC.

[0135] pass Figure 4 As can be seen from (a) and (b), the control method provided by this invention can achieve stable, same-speed, small-interval operation of virtual train formations, while ensuring that each train unit meets safety constraints. Through comparison... Figure 4 (a) and (b) and Figure 5 As can be seen from (a) and (b), the control effect of the control method provided by this invention is close to that of centralized MPC; through Figures 4 to 6 The comparison shows that collaboration enables virtual train formations to achieve better tracking performance. This is mainly because the lead train unit adjusts itself based on the status of the following train units. For example, if it detects that the following train is unable to maintain an ideal tracking relationship due to its low speed, the lead train unit will slightly reduce its speed and wait. Figure 7 As can be seen, the control method provided by this invention can achieve a global evaluation function value close to that of the centralized MPC method. Furthermore, the control method only requires train units to acquire information from their neighboring train units, whereas the centralized MPC method requires simultaneous acquisition of information from all train units. Therefore, the control method provided by this invention is easier to implement and significantly saves communication resources. In addition, the global evaluation function value under the control method provided by this invention is much smaller than that under the preceding and following MPC method. Therefore, compared to the preceding and following MPC method, the control method provided by this invention can further optimize performance indicators, thereby achieving better overall tracking performance of virtual train formations.

[0136] Example 2

[0137] pass Figure 8 and Figure 9 A comparison of the two methods reveals that the lead train unit under the preceding and following MPC method applies a greater braking rate during braking, causing the following train unit 2 to be in a state that violates safety constraints from the 4th to the 18th second, a state that is unacceptable in actual operation. However, the virtual train unit controlled by the method provided in this invention always meets the safety constraints. That is, the virtual train unit controlled by the method described in this invention can meet the safety constraints, while the virtual train unit controlled by the preceding and following MPC method cannot.

[0138] Furthermore, the overall architecture of the distributed cooperative tracking control system for virtual trains within a virtual train formation is as follows: Figure 2 As shown, the distributed cooperative tracking control system for virtual train formations on each train unit consists of an error state acquisition module, an information interaction and processing module, a global optimal control problem construction module, a local optimal control sub-problem construction module, a quadratic programming problem solving module, and a local variable update module. Figure 3 The document presents the system structure of a single train unit. Systems in adjacent train units exchange information via an information interaction processing module. The functions of each module in each train unit are as follows:

[0139] The error status acquisition module acquires the error status of the current train unit within each control cycle. The error status of the lead train unit includes the differences between its actual position and speed and the position and speed on the reference track, respectively; the error status of the following train unit includes the difference between the distance between itself and the preceding train and the desired distance, as well as the difference between the speeds of the two trains. The error status can be obtained by subtracting the positions and speeds of the two trains after interaction, or by directly measuring the error status using sensors installed on the train unit.

[0140] The information interaction processing module is responsible for handling information interaction between this train and external systems and other train units. Each train unit communicates with its neighboring train units to transmit its error status, local variables, and Lagrange multipliers; in addition, the lead train unit in the virtual train formation also receives the reference trajectory from the lead train unit of the virtual train formation from outside the system.

[0141] The global optimal control problem construction module, based on the control objective, train dynamics equations, and the error states of all train units, establishes the overall tracking and operation control problem of the virtual train as an optimal control problem with a quadratic programming form. The control objective is to minimize the sum of the quadratic terms of the error state sequence and the control command sequence of all train units in the prediction time domain, while the decision variables are the control command sequences of all train units in the prediction time domain.

[0142] The local optimal control subproblem construction module is responsible for decomposing the global optimal control problem into local optimal control subproblems with a quadratic programming form, which are equal in number to the number of train units in the virtual train formation. The control objective of each local optimal control subproblem is to minimize the sum of the quadratic terms of the error state sequence and the control command sequence in the prediction time domain of the current train unit. The decision variables are the control command sequences of the current train unit and its preceding train in the prediction time domain.

[0143] The quadratic programming problem-solving module is responsible for solving optimal control problems with quadratic programming form, and obtaining the optimal control command sequence over a future period of time.

[0144] The local variable update module receives the optimal control command sequence calculated by the quadratic programming problem solving module of the current train unit and its successor train. Then, it updates the local variables and Lagrange multipliers and transmits the updated Lagrange multipliers to its predecessor train.

[0145] Based on the above description, the advantages of the present invention are:

[0146] 1) Optimize the overall tracking and operation effect of the virtual train formation: This invention solves the global optimal control problem in a distributed manner through ADMM, thereby calculating the control commands that can optimize the overall tracking and operation effect of the virtual train formation for each train unit;

[0147] 2) Avoid train units triggering safety constraints: Since each train unit can fully cooperate and interact with each other when calculating control commands, in this invention, the leading train will make appropriate adjustments to avoid the following train encountering a situation where there is no feasible solution, thus effectively preventing any train unit from triggering safety constraints.

[0148] 3) This invention uses a distributed control algorithm to control each train unit in the virtual train formation, which conforms to the actual architecture where each train unit's onboard equipment independently calculates control commands. In addition, each train unit only needs to obtain information from its neighboring train units when calculating its own control commands, which is more practical and feasible than a centralized control algorithm, and greatly saves communication resources.

[0149] An electronic device, comprising:

[0150] Memory, used to store computer programs;

[0151] A processor, connected to the memory, is used to retrieve and execute the computer program to implement the virtual train formation distributed cooperative tracking control method provided above.

[0152] Furthermore, when the computer program in the aforementioned memory is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0153] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0154] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A distributed cooperative tracking control system for virtual train formations, characterized in that, Each train unit is equipped with a virtual train formation distributed collaborative tracking control system; the system includes: The error status acquisition module is used to acquire the error status of the current train unit in each control cycle. The global optimal control problem construction module is used to construct the global optimal control problem for virtual train tracking operation; The local optimal control subproblem construction module is used to obtain the local optimal control subproblem for virtual train tracking operation based on the global optimal control problem of virtual train tracking operation; The quadratic programming problem-solving module is used to solve the local optimal control subproblem of virtual train formation tracking operation based on the error state between train units, and obtain the optimal control command sequence of the current train unit and the optimal control command sequence of the train units adjacent to the current train unit. The local variable update module is used to update the local variables and Lagrange multipliers stored locally in the current train unit based on the optimal control command sequence of the current train unit until the iteration termination condition is met. Then, the first value in the optimal control command sequence of the current train unit is output as the control command of the current train unit at the current time. The information interaction processing module is used to perform information interaction between the current train unit and external systems and other train units based on the control commands of the current train unit at the current time and the control commands of the train units adjacent to the current train unit.

2. The virtual train formation distributed cooperative tracking control system according to claim 1, characterized in that, The global optimal control problem for virtual train formation tracking operation is a global optimal control problem with the control command sequence of all train units in the virtual formation within a prediction time domain as the decision variable, the objective function being the sum of the quadratic terms of the error state sequence of all train units in the prediction time domain and the quadratic terms of the control command sequence, and the constraints being safety constraints, train dynamics constraints, input saturation constraints, and terminal constraints.

3. The virtual train formation distributed cooperative tracking control system according to claim 1, characterized in that, In the local optimal control sub-problem construction module, a local optimal control sub-problem for virtual train tracking operation is obtained based on the global optimal control problem of virtual train tracking operation, specifically including: The global optimal control problem for virtual train formation tracking operation is decomposed into a local optimal control subproblem whose objective function includes only the error state of one train unit and the control command. The obtained local optimal control subproblem is then used as the local optimal control subproblem for virtual train formation tracking operation.

4. The virtual train formation distributed cooperative tracking control system according to claim 1, characterized in that, The optimal control sequence for the current train unit and the optimal control sequence for the adjacent train units are obtained by solving the local optimal control subproblem of virtual train formation tracking operation based on the error state between train units using the sequential quadratic programming method.

5. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, connected to the memory, is used to retrieve and execute the computer program to implement the virtual train distributed cooperative tracking control method in the virtual train distributed cooperative tracking control system as described in any one of claims 1-4.

6. The electronic device according to claim 5, characterized in that, The memory is a computer-readable storage medium.

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