Train virtual reconnection operation control method and system based on interference compensation linear quadratic regulator

Abstract

The present invention provides a train virtual reconnection operation control method and system based on an interference-compensated linear quadratic regulator, belonging to the technical field of train virtual reconnection operation optimization control. The method and system establish a longitudinal train dynamics model of the basic resistance and additional resistance of the train operation; establish a train operation safety envelope that takes into account the factors of train speed measurement positioning error and communication delay error, and establish a dynamic target interval between adjacent trains during the train virtual reconnection operation; set the controller's cost function to minimize the weighted sum of the quadratic form of the system state variable and the quadratic form of the control quantity in the infinite time domain; introduce an auxiliary matrix to transform the optimization problem into solving the algebraic Riccati equation, solve the auxiliary matrix by numerical calculation, bring it into a state feedback controller model with interference compensation, obtain the optimized control quantity, and realize the optimization control process of safe operation in the train virtual reconnection mode. The present invention can realize the optimization control of train virtual reconnection operation.

Description

Technical Field

[0001] The present invention relates to the technical field of train virtual multi-connection operation optimization control, and in particular to a train virtual multi-connection operation control method and system based on an interference compensation linear quadratic regulator. Background Art

[0002] The rapid development of high-speed rail has boosted productivity. Its advantages, including high capacity, speed, comfort, low carbon emissions, energy efficiency, and punctuality, have led to a growing public demand for high-speed rail travel. Despite the continuous increase in high-speed rail mileage, it still falls short of meeting public demand during peak travel periods. To address the current unmet demand for high-speed rail, research on high-speed rail operation at higher speeds and frequencies has become a key focus, aiming to further improve the efficiency of existing lines.

[0003] The train control system ensures that trains maintain safe intervals. Currently, China's train control system is divided into four levels: CTCS-0 to CTCS-3. CTCS-3 is applicable to lines with the highest speeds and the highest frequency of train departures. The CTCS-3 train control system uses fixed block systems to control train intervals. To prevent collisions, trains occupy a longer track section with a fixed block section length, and other trains are strictly prohibited from entering this section. Under this fixed block system, to ensure operational safety, train tracking intervals are large, resulting in low track resource utilization, which is not conducive to high-speed and high-frequency operations. Virtual train reconnection technology uses a relative braking distance-based system to control trains to operate in formation. This effectively reduces the tracking intervals between trains, enabling efficient high-speed train operations and flexible transportation capabilities with dynamic capacity allocation.

[0004] Considerable research has been conducted both domestically and internationally on train headway control in virtual reconnection technology. In addition to traditional, low-cost control methods such as PID, many experts and scholars have also applied modern intelligent control algorithms, such as model predictive control, sliding film control, and control methods based on deep learning and reinforcement learning, to train headway control in virtual reconnection operation. The linear quadratic regulator (LQR), as an optimization control method based on state feedback, can effectively optimize control efficiency. Compared to methods such as model predictive control, LQRs also have the advantages of low computational complexity and a simpler model. However, typical LQR models do not address interference from the controlled object. Trains operating in virtual reconnection mode also face complex environments with certain interference factors. Therefore, research on virtual reconnection train control methods based on interference-compensating linear quadratic regulators has cutting-edge and practical research value. Summary of the Invention

[0005] The object of the present invention is to provide a train virtual reconnection operation control method and system based on an interference compensation linear quadratic regulator to solve at least one technical problem existing in the above background technology.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a train virtual reconnection operation control method based on an interference compensation linear quadratic regulator, comprising:

[0008] Analyze the basic resistance, slope, and curve additional resistance of the train during operation, and establish a longitudinal train dynamics model using the unit mass force analysis model;

[0009] Taking into account factors such as train positioning and speed measurement errors and communication delays, a front-end and back-end model of the train operation safety envelope is established. On this basis, a dynamic safety interval is established under the virtual reconnection operation mode, and the dynamic target interval for controlling train operation is further obtained.

[0010] The position and speed deviations between adjacent trains in a train formation are set as controller system variables. The established longitudinal train dynamics model is linearized using the Taylor expansion method with first-order terms. This results in a controller system equation with disturbance terms. To ensure that the system follows the control target, the controller cost function is set to minimize the integral of the infinite-time quadratic deviation.

[0011] In order to ensure the asymptotic stability of the system, closed-loop feedback and disturbance compensation are adopted, and the control quantity is expressed in the form of state feedback and disturbance compensation. The optimized control quantity is further solved, and the auxiliary constant matrix is ​​introduced to transform the optimization problem into a problem of solving the algebraic Riccati equation. The auxiliary constant matrix is ​​obtained by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0012] During each control cycle of the virtual reconnection operation of the train, the tracking train obtains information such as the position and speed of the leading train through wireless communication, and uses this as the state deviation input. By executing the above-mentioned optimization solution process to obtain the optimized control quantity, the virtual reconnection operation of the train can be safely tracked.

[0013] Furthermore, a longitudinal train dynamics model is established that takes into account the basic resistance, slope, and additional resistance of the train during operation. Specifically, it includes:

[0014] During the running process, the train is mainly affected by the traction / braking force, basic resistance and additional resistance of the train in the longitudinal direction of the track. In order to eliminate the influence of different train masses, the force motion analysis of the unit mass of the train is carried out, and the number is obtained. The train dynamics model is shown in formula (1):

[0015] (1)

[0016] The basic resistance of the operation and Satisfying equations (2) and (3), It indicates the unit quality control quantity of the train's position and speed, that is, the train's running status.

[0017] (2)

[0018] (3)

[0019] Furthermore, a safety dynamic interval model for virtual train reconnection operation based on safety envelope is adopted, specifically:

[0020] Taking into account objective conditions such as errors in speed measurement and positioning during actual train operation and time delays in inter-train communication, a safety envelope for train position uncertainty during operation is established, including the safety front-end and safety back-end:

[0021] (4)

[0022] (5)

[0023] To meet the high-speed and high-density operation requirements of virtual multiplexing, the interval between trains should be as small as possible and also meet the safety requirements of operation. The dynamic target interval between trains should be set as follows:

[0024] (6)

[0025] The comprehensive position deviation between adjacent trains is obtained as follows:

[0026] (7)

[0027] Furthermore, after linearization, the controller system equation with the external disturbance term is obtained, and the controller cost function is set to minimize the quadratic deviation, specifically:

[0028] train The controller system state variables are:

[0029] (8)

[0030] After linearizing the train dynamics model, the state equation of the system is obtained:

[0031] (9)

[0032] The controller is transformed into solving the following optimization model:

[0033] (10)

[0034] Furthermore, the auxiliary matrix is ​​obtained by solving the algebraic Riccati equation and then brought into the feedback control model to obtain the optimized control quantity, which is:

[0035] Assume that the state feedback control model including disturbance compensation is:

[0036] (11)

[0037] In order to solve the optimization model of formula (10), the auxiliary matrix is ​​introduced , let the auxiliary matrix and state feedback coefficient matrix satisfy the following formula:

[0038] (12)

[0039] (13)

[0040] The auxiliary matrix is ​​obtained by numerically solving the algebraic Riccati equation :

[0041] (14)

[0042] The solution of this equation is:

[0043] (15)

[0044] This is used as the output of the controller to control the safe operation of the virtual multiplexed train.

[0045] In a second aspect, the present invention provides a train virtual reconnection operation control system based on an interference compensation linear quadratic regulator, comprising:

[0046] The first building module is used to analyze the basic resistance and slope and curve additional resistance of the train operation, and establish a longitudinal train dynamics model using the unit mass force analysis mode;

[0047] The second establishment module is used to consider the train positioning and speed measurement errors and communication delays, establish the front-end and back-end models of the train operation safety envelope, establish the dynamic safety interval under the virtual reconnection operation mode, and obtain the dynamic target interval for controlling the train operation;

[0048] The linearization module is used to set the position and speed deviations between two adjacent trains in the train formation as controller system variables, linearize the established longitudinal train dynamics model, and obtain the controller system equation with interference terms. The controller cost function is set to minimize the integral of the infinite time domain quadratic deviation;

[0049] The solution module is used to express the control quantity in the form of state feedback and interference compensation using closed-loop feedback and interference compensation, solve the optimized control quantity, introduce the auxiliary constant matrix to transform the optimization problem into a problem of solving the algebraic Riccati equation, solve the auxiliary constant matrix through numerical calculation, obtain the state feedback coefficient matrix of the system, and finally solve the optimized control quantity.

[0050] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the train virtual reconnection operation control method based on the interference compensation linear quadratic regulator as described in the first aspect is implemented.

[0051] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the train virtual reconnection operation control method based on the interference compensation linear quadratic regulator as described in the first aspect.

[0052] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the train virtual reconnection operation control method based on the interference-compensated linear quadratic regulator as described in the first aspect.

[0053] The beneficial effects of the present invention are as follows: in order to ensure the asymptotic stability of the system, a state feedback mechanism with interference compensation is added, and a quadratic optimization model of the state variable is set; in order to solve the optimized control quantity, a constant auxiliary matrix is ​​introduced, and the state feedback model of interference compensation is brought into the state feedback model, and the quadratic optimization model is converted into a form for solving the auxiliary matrix; in order to solve the algebraic Riccati equation about the auxiliary matrix, the auxiliary matrix is ​​solved by a numerical calculation method, and finally the optimized control quantity is solved, thereby realizing the optimized control process of the virtual reconnection tracking operation; realizing the interference compensation optimized control of the high-speed train formation operation, and ensuring the safe operation of the virtual reconnection mode.

[0054] Additional advantages of the present invention will be more clearly given in the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 Schematic diagram of the basic principle of the interference-compensated high-speed train virtual reconnection LQR optimization control method according to an embodiment of the present invention.

[0057] Figure 2 This is a diagram of a virtual reconnection dynamic interval model of a safe operation envelope based on train operation uncertainty and communication delay according to an embodiment of the present invention.

[0058] Figure 3 This is a block diagram of a train virtual multiplexing operation controller according to an embodiment of the present invention.

[0059] Figure 4 This is a flow chart of the operation of the interference compensation LQR optimization controller according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention.

[0061] Those skilled in the art will understand that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0062] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0063] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0064] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless otherwise contradictory.

[0065] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0066] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0067] Example 1

[0068] In this embodiment 1, a train virtual reconnection operation control system based on an interference-compensated linear quadratic regulator is first provided, comprising: a first establishment module for analyzing the basic resistance, slope, and curve additional resistance of the train operation, and establishing a longitudinal train dynamics model using a unit mass force analysis model. A second establishment module is used to consider train positioning and speed measurement errors and communication delays, establish front-end and back-end models of the safety envelope of the train operation, establish a dynamic safety interval under the virtual reconnection operation mode, and obtain a dynamic target interval for controlling the train operation. A linearization module is used to set the position and speed deviations between two adjacent trains in the train formation as controller system variables, linearize the established longitudinal train dynamics model, and obtain a controller system equation with interference terms, and set the controller cost function to minimize the integral of the infinite time domain quadratic deviation. The solution module is used to express the control quantity in the form of state feedback and interference compensation using closed-loop feedback and interference compensation, solve the optimized control quantity, introduce the auxiliary constant matrix to transform the optimization problem into a problem of solving the algebraic Riccati equation, solve the auxiliary constant matrix through numerical calculation, obtain the state feedback coefficient matrix of the system, and finally solve the optimized control quantity.

[0069] In this embodiment, the above-mentioned system is used to implement a train virtual reconnection operation control method based on an interference compensation linear quadratic regulator, including:

[0070] Analyze the basic resistance, slope, and curve additional resistance of the train during operation, and establish a longitudinal train dynamics model using the unit mass force analysis model;

[0071] Taking into account factors such as train positioning and speed measurement errors and communication delays, a front-end and back-end model of the train operation safety envelope is established. On this basis, a dynamic safety interval is established under the virtual reconnection operation mode, and the dynamic target interval for controlling train operation is further obtained.

[0072] The position and speed deviations between adjacent trains in a train formation are set as controller system variables. The established longitudinal train dynamics model is linearized using the Taylor expansion method with first-order terms. This results in a controller system equation with disturbance terms. To ensure that the system follows the control target, the controller cost function is set to minimize the integral of the infinite-time quadratic deviation.

[0073] In order to ensure the asymptotic stability of the system, closed-loop feedback and disturbance compensation are adopted, and the control quantity is expressed in the form of state feedback and disturbance compensation. The optimized control quantity is further solved, and the auxiliary constant matrix is ​​introduced to transform the optimization problem into a problem of solving the algebraic Riccati equation. The auxiliary constant matrix is ​​obtained by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0074] During each control cycle of the virtual reconnection operation of the train, the tracking train obtains information such as the position and speed of the leading train through wireless communication, and uses this as the state deviation input. By executing the above-mentioned optimization solution process to obtain the optimized control quantity, the virtual reconnection operation of the train can be safely tracked.

[0075] A longitudinal train dynamics model is established that takes into account the basic resistance, slope, and additional resistance experienced by the train during operation. Specifically, the model includes:

[0076] During the running process, the train is mainly affected by the traction / braking force, basic resistance and additional resistance of the train in the longitudinal direction of the track. In order to eliminate the influence of different train masses, the force motion analysis of the unit mass of the train is carried out, and the number is obtained. The train dynamics model is shown in formula (1):

[0077] (1)

[0078] The basic resistance of the operation and Satisfying equations (2) and (3), It indicates the unit quality control quantity of the train's position and speed, that is, the train's running status.

[0079] (2)

[0080] (3)

[0081] A safe dynamic interval model for virtual train reconnection operation based on safety envelope is adopted, specifically:

[0082] Taking into account objective conditions such as errors in speed measurement and positioning during actual train operation and time delays in inter-train communication, a safety envelope for train position uncertainty during operation is established, including the safety front-end and safety back-end:

[0083] (4)

[0084] (5)

[0085] To meet the high-speed and high-density operation requirements of virtual multiplexing, the interval between trains should be as small as possible and also meet the safety requirements of operation. The dynamic target interval between trains should be set as follows:

[0086] (6)

[0087] The comprehensive position deviation between adjacent trains is obtained as follows:

[0088] (7)

[0089] After linearization, the controller system equation with the external disturbance term is obtained, and the controller cost function is set to minimize the quadratic deviation, specifically:

[0090] train The controller system state variables are:

[0091] (8)

[0092] After linearizing the train dynamics model, the state equation of the system is obtained:

[0093] (9)

[0094] The controller is transformed into solving the following optimization model:

[0095] (10)

[0096] The auxiliary matrix is ​​obtained by solving the algebraic Riccati equation and then brought into the feedback control model to obtain the optimized control quantity, which is:

[0097] Assume that the state feedback control model including disturbance compensation is:

[0098] (11)

[0099] In order to solve the optimization model of formula (10), the auxiliary matrix is ​​introduced , let the auxiliary matrix and state feedback coefficient matrix satisfy the following formula:

[0100] (12)

[0101] (13)

[0102] The auxiliary matrix is ​​obtained by numerically solving the algebraic Riccati equation :

[0103] (14)

[0104] The solution of this equation is:

[0105] (15)

[0106] This is used as the output of the controller to control the safe operation of the virtual multiplexed train.

[0107] Example 2

[0108] In this embodiment 2, a train virtual reconnection operation control method based on an interference compensation linear quadratic regulator is provided to achieve virtual reconnection safety interval operation of high-speed trains.

[0109] In order to achieve the above object, the present invention provides the following technical solutions:

[0110] A high-speed train virtual reconnection optimization operation control method based on a linear quadratic regulator considering disturbance compensation includes:

[0111] Establish a single-point longitudinal dynamics model of train operation and a virtual reconnection operation safety interval model;

[0112] Considering that the train is subjected to motor traction, braking force, basic running resistance, slope and curve additional resistance during operation, the train number is , establish the single-particle dynamic model of the train:

[0113]

[0114] The train running status is the control force (acceleration) on the train position, speed, and unit mass of the train, It is the basic resistance per unit mass of the train operation. The additional resistance per unit mass of the train is satisfied by the following equation:

[0115]

[0116] Due to factors such as the accuracy of the train's speed sensors and communication delays, the train's current position, speed, acceleration, and other state information are uncertain. To ensure the safety of train operation, a train operation safety envelope model is constructed. This model is divided into the front-end and back-end of the train safety operation envelope:

[0117]

[0118] The safety envelope of train operation is the safety boundary of train operation. Based on this, a virtual reconnection safety operation interval model is established based on the virtual relative braking distance. Considering that the preceding train may brake in an emergency at any time, that is, the two adjacent trains (forward train) and When (tracking trains) run in a platoon model, the interval between the two trains should meet the following requirements:

[0119]

[0120] The safety interval is the minimum interval between two cars. To ensure the smooth operation of the virtual train reconnection, the target interval between the two cars is set as:

[0121]

[0122] Based on the analysis of train dynamics and the safety interval of high-speed train virtual reconnection, the linear quadratic regulator (LQR) method is used to design the train operation controller model. The process is as follows:

[0123] For a certain train , assume that the system state variables of the train operation controller are:

[0124]

[0125] in Dynamic spacing meets:

[0126]

[0127] The train dynamics model is linearized to obtain the controller system equation with disturbance terms:

[0128]

[0129] The control target of the controller is , define the cost function of the system as:

[0130]

[0131] The optimal action control quantity is obtained by solving the following optimal solution: , control the trains to follow the target interval Follow the speed of the preceding train:

[0132]

[0133] Introducing auxiliary constant matrix This optimization model transforms the above optimization model into The algebraic equation is shown below, and the numerical calculation method is used to solve the algebraic equation.

[0134]

[0135] The optimization model can be obtained by solving the ARE equation using numerical calculation method to obtain the optimal action control quantity. , the actual control quantity It is transmitted as a control instruction to the train operation actuator to achieve the control purpose of high-speed train virtual reconnection safe interval operation.

[0136] Example 3

[0137] The virtual re-coupling operation mode of high-speed trains is characterized by high speed and high frequency departure intervals. It uses advanced sensing and communication technologies as the basis for perception interaction support to realize the virtual formation operation of trains, improve the utilization rate of track resources, and thus improve transportation efficiency. In this embodiment, a virtual re-coupling operation control method of trains based on interference compensation linear quadratic regulator is provided. The method mainly includes: constructing a longitudinal dynamic model that takes into account the basic resistance and additional resistance of train operation, analyzing the safety envelope model of train operation, and on this basis establishing a dynamic safety interval model of the train according to the train safety interval control requirements, designing a train operation controller based on LQR, linearizing the system equations of the train operation controller, defining the cost function of the train operation state, and using the ARE equation to solve the cost function to minimize the optimal control quantity, and using this as a control instruction to transfer to the actuator to control the train operation, thereby realizing train safety interval control. Figure 1 shown.

[0138] The method specifically includes the following implementation steps:

[0139] Step 1: Consider the longitudinal dynamic model of the train's basic resistance and additional resistance.

[0140] In high-speed railway operation scenarios, in order to ensure smoother and faster train operation, the track is generally built relatively straight. At the same time, the train deformation is small during operation, so the entire train can be regarded as a single mass point. During the operation, the train is mainly affected by the traction / braking force actively output by the train, basic resistance and additional resistance in the longitudinal direction of the track. Based on the above analysis, the longitudinal dynamic model of the single-mass train is established according to the basic kinematic laws as shown in Equation (16):

[0141] (16)

[0142] in Indicates the train number, used to distinguish trains. are the position and speed of the train respectively, is the actual control quantity of the train unit mass, are the basic resistance and additional resistance of the train operation, respectively. The basic resistance of the train operation is composed of the track and air resistance, while the additional resistance is determined by the track parameters. The train is also subject to additional resistance caused by the track slope and curve radius during operation. The resistance per unit mass during train operation is expressed by formula (17):

[0143] (17)

[0144] In the formula 、 、 is the basic resistance parameter of the train running unit mass, is the gravitational constant, and are the slope and curve radius parameters of the track where the train is currently located, is the additional resistance parameter of the curve on the unit mass of the train operation. In order to eliminate the factor of train mass, the parameters in formula (17) are all expressed in the form of unit mass.

[0145] Step 2: Safe dynamic interval model of train virtual reconnection operation based on safety envelope.

[0146] In order to control the safe operation of trains in virtual reconnection mode, it is necessary to analyze the dynamic safety interval of trains in the formation. Since the analysis of the safety interval is based on the perception of the train's current state, the positioning and speed measurement of the train during actual operation are not accurate, and there are measurement errors. At the same time, the train-related speed measurement and positioning equipment also has measurement delay problems due to operating frequency factors, and there is also a certain delay in information communication between trains. In order to ensure the safety of train operation, it is necessary to establish a safety envelope model for train operation that takes into account various errors before analyzing the dynamic safety interval, as shown in formula (18):

[0147] (18)

[0148] The train operation safety envelope is composed of the envelope front end and envelope backend constitute, 、 are the positioning error and speed measurement error of the train respectively, For trains The length of the vehicle body, 、 They are respectively the operating frequency of the train's speed measuring equipment and the information communication delay between trains.

[0149] Further analysis of the train braking method under the virtual reconnection mode shows that in order to achieve the tracking goal of small intervals, the train adopts a relative braking strategy, that is, the tracking train assumes that the speed of the leading train will not suddenly change to 0, and the leading train will brake to a stop at any time using emergency braking, instead of the traditional strategy based on the leading train occupying a certain block section at a speed of 0 at any time, which causes waste of track resources.

[0150] On the basis of the train operation safety envelope and the relative braking method, in order to ensure the safety of the virtual multiplex train, the front end of the safety envelope of the tracking train should maintain a sufficient safety distance from the rear end of the safety envelope of the leading train, so that when the leading train brakes suddenly until it stops, the tracking train also brakes suddenly without causing a train collision. With the moving train The envelope should meet the following dynamic interval requirements to ensure driving safety:

[0151] (19)

[0152] in This is the acceleration standard for high-speed trains in emergency braking situations. This dynamic safety interval takes into account the good braking performance of both the tracking train and the leading train. To leave a certain amount of safety redundancy, the target interval between the tracking train and the leading train is actually controlled as follows:

[0153] (20)

[0154] in is the acceleration standard for high-speed trains under common braking conditions. Under this target interval, in a dynamically formed train formation, a tracking train The front end of the safety envelope and its preceding train The back end of the safety envelope is maintained Common braking distance and The distance of the emergency braking distance, such as Figure 2 shown.

[0155] Step 3: Establish a train operation controller model based on a linear quadratic regulator.

[0156] For ease of analysis, the train's speed deviation and position deviation are set as the controller's system state variables:

[0157] (twenty one)

[0158] Comprehensive position deviation Combining formula (20) and formula (18), we can get:

[0159] (twenty two)

[0160] Since the longitudinal train dynamics model represented by Equation (16) has nonlinear characteristics, the first-order Taylor expansion method is used to perform approximate linearization operations:

[0161] (twenty three)

[0162] It's speed The equilibrium state of the controller is obtained as follows:

[0163] (twenty four)

[0164] where the matrix 、 and Satisfies the following equation:

[0165] (25)

[0166] For this system, there is a feedback controller

[0167] (26)

[0168] In the formula is the state coefficient matrix, in order to offset the measurable system interference ,set up is a constant vector , and satisfy In order to make the system stable, , let the cost function of the system be, where is the diagonal matrix of state variable weights, is the control weight (constant):

[0169] (27)

[0170] matrix 、 (Constant matrix) are expressed as follows:

[0171] (28)

[0172] To make the system tend to equilibrium, the controller's goal is to minimize the cost as much as possible, that is:

[0173] (29)

[0174] The controller framework designed is as follows Figure 3 shown.

[0175] Step 4: Solve the optimal control quantity as the control instruction for controlling the virtual reconnection operation of the train.

[0176] In order to solve the optimization model shown in formula (29), suppose there is a constant matrix Satisfies the following equation:

[0177] (30)

[0178] Substituting equation (30) into equation (29), we can get the constant matrix In the form of:

[0179] (31)

[0180] in is the initial state of the system, and further expanding equation (30) yields the following equation:

[0181] (32)

[0182] The integration of equations (24), (26) and (32) yields the following equation:

[0183] (33)

[0184] Formula (33) is an identity, which requires that for all moments To satisfy this equation, we need to satisfy:

[0185] (34)

[0186] To simplify the equation, the state feedback coefficient matrix is ​​set to be about the constant matrix In the form of:

[0187] (35)

[0188] Substituting equation (35) into equation (34) yields the following equation:

[0189] (36)

[0190] The equation represented by this formula is the algebraic Riccati equation (ARE), and the auxiliary constant matrix can be obtained by numerical calculation method , substituted into formula (35) to obtain the state feedback coefficient matrix of the system, and then the optimal control quantity is obtained through the feedback controller equation shown in formula (26): :

[0191] (37)

[0192] In each control cycle of the train operation, execute step 4 and calculate the optimal control quantity according to the solution. Control and track trains With the moving train It runs in virtual reconnection mode and checks whether the communication link between trains is normal. If it is normal, it will maintain virtual reconnection operation, otherwise it will switch to other low-level operation modes. At the same time, the automatic train protection subsystem ATP always monitors whether the train is overspeeding. If it is overspeeding, it will trigger the train emergency brake to ensure the safety of train operation. The control flow chart is as follows Figure 4 shown.

[0193] Example 4

[0194] This embodiment 4 provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the aforementioned method for controlling virtual train reconnection operation based on an interference-compensated linear quadratic regulator is implemented. The method includes:

[0195] Analyze the basic resistance, slope, and curve additional resistance of the train during operation, and establish a longitudinal train dynamics model using the unit mass force analysis model;

[0196] Taking into account the train positioning and speed measurement errors and communication delays, the front-end and back-end models of the train operation safety envelope are established, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling train operation is obtained;

[0197] The position and speed deviations between two adjacent trains in a train formation are set as controller system variables. The established longitudinal train dynamics model is linearized to obtain the controller system equation with interference terms. The controller cost function is set to minimize the integral of the quadratic deviation in the infinite time domain.

[0198] Using closed-loop feedback and disturbance compensation, the control quantity is expressed in the form of state feedback and disturbance compensation to solve the optimized control quantity. The auxiliary constant matrix is ​​introduced to transform the optimization problem into a problem of solving the algebraic Riccati equation. The auxiliary constant matrix is ​​obtained by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0199] Example 5

[0200] This embodiment 5 provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the above-mentioned method for controlling virtual train reconnection operation based on an interference compensation linear quadratic regulator, the method comprising:

[0201] Analyze the basic resistance, slope, and curve additional resistance of the train during operation, and establish a longitudinal train dynamics model using the unit mass force analysis model;

[0202] Taking into account the train positioning and speed measurement errors and communication delays, the front-end and back-end models of the train operation safety envelope are established, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling train operation is obtained;

[0203] The position and speed deviations between two adjacent trains in a train formation are set as controller system variables. The established longitudinal train dynamics model is linearized to obtain the controller system equation with interference terms. The controller cost function is set to minimize the integral of the quadratic deviation in the infinite time domain.

[0204] Using closed-loop feedback and disturbance compensation, the control quantity is expressed in the form of state feedback and disturbance compensation to solve the optimized control quantity. The auxiliary constant matrix is ​​introduced to transform the optimization problem into a problem of solving the algebraic Riccati equation. The auxiliary constant matrix is ​​obtained by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0205] Example 6

[0206] This embodiment 6 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the above-mentioned method for controlling virtual train reconnection operation based on an interference-compensated linear quadratic regulator. The method includes:

[0207] Analyze the basic resistance, slope, and curve additional resistance of the train during operation, and establish a longitudinal train dynamics model using the unit mass force analysis model;

[0208] Taking into account the train positioning and speed measurement errors and communication delays, the front-end and back-end models of the train operation safety envelope are established, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling train operation is obtained;

[0209] The position and speed deviations between two adjacent trains in a train formation are set as controller system variables. The established longitudinal train dynamics model is linearized to obtain the controller system equation with interference terms. The controller cost function is set to minimize the integral of the quadratic deviation in the infinite time domain.

[0210] Using closed-loop feedback and disturbance compensation, the control quantity is expressed in the form of state feedback and disturbance compensation to solve the optimized control quantity. The auxiliary constant matrix is ​​introduced to transform the optimization problem into a problem of solving the algebraic Riccati equation. The auxiliary constant matrix is ​​obtained by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0211] In summary, the train virtual reconnection operation control method based on the interference compensation linear quadratic regulator described in the embodiment of the present invention. Establish a train longitudinal dynamic model based on the basic resistance and additional resistance of operation, wherein the basic resistance is the sum of various friction forces between the wheel, rail, car body and environment, and the additional resistance is generated by factors such as the track slope and curve radius; consider the front and rear ends of the train operation safety envelope with uncertainty in train state caused by train speed positioning error and communication delay between trains, and on this basis, adopt the method of emergency braking of the leading train at any time and normal braking of the trailing train to establish a dynamic target operation interval model between adjacent trains in virtual reconnection; establish an LQR optimization controller model with interference terms for a certain train in the train formation to match the position of the leading train The speed deviation is set as the state variable of the system, and the Taylor first-order approximation method is used to obtain the linear system equation with interference terms. In order to ensure the asymptotic stability of the system, a state feedback mechanism with interference compensation is added, and a quadratic optimization model of the state variable is set; in order to solve the optimized control quantity, a constant auxiliary matrix is ​​introduced, and the state feedback model of interference compensation is brought into the state feedback model, and the quadratic optimization model is converted into a form for solving the auxiliary matrix. Further, in order to solve the algebraic Riccati equation about the auxiliary matrix, the auxiliary matrix is ​​solved by numerical calculation method, and finally the optimized control quantity is solved, thereby realizing the optimization control process of virtual reconnection tracking operation. The present invention can realize interference compensation optimization control of high-speed train formation operation and ensure the safe operation of virtual reconnection mode.

[0212] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0213] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0214] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0215] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide the functions for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0216] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solutions disclosed in the present invention without the need for creative work should be included in the scope of protection of the present invention.

Claims

1. A train virtual reconnection operation control method based on an interference compensation linear quadratic regulator, characterized in that: include: Analyze the basic resistance, slope, and curve additional resistance of the train during operation, and establish a longitudinal train dynamics model using the unit mass force analysis model; Taking into account the train positioning and speed measurement errors and communication delays, the front-end and back-end models of the train operation safety envelope are established, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling train operation is obtained; The position and speed deviations between two adjacent trains in a train formation are set as controller system variables. The established longitudinal train dynamics model is linearized to obtain the controller system equation with interference terms. The controller cost function is set to minimize the integral of the quadratic deviation in the infinite time domain. Using closed-loop feedback and disturbance compensation, the control quantity is expressed in the form of state feedback and disturbance compensation to solve the optimized control quantity. The auxiliary constant matrix is ​​introduced to transform the optimization problem into a problem of solving the algebraic Riccati equation. The auxiliary constant matrix is ​​obtained by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

2. The train virtual multi-connection operation control method based on the interference compensation linear quadratic regulator according to claim 1 is characterized in that: During each control cycle of the virtual reconnection operation of the train, the tracking train obtains information such as the position and speed of the leading train through wireless communication, and uses this as the state deviation input. By executing the above-mentioned optimization solution process to obtain the optimized control quantity, the virtual reconnection operation of the train can be safely tracked.

3. The train virtual multi-connection operation control method based on the interference compensation linear quadratic regulator according to claim 1 is characterized in that: A longitudinal train dynamics model is established that takes into account the basic resistance, slope, and additional resistance experienced by the train during operation. Specifically, the model includes: During the running process, the train is mainly affected by the traction / braking force, basic resistance and additional resistance of the train in the longitudinal direction of the track. In order to eliminate the influence of different train masses, the force motion analysis of the unit mass of the train is carried out, and the number is obtained. The train dynamics model is: in, and Indicates the basic resistance of operation It indicates the unit quality control quantity of the train's position and speed, that is, the train's running status.

4. A train virtual reconnection control system based on an interference compensation linear quadratic regulator, characterized in that: include: The first building module is used to analyze the basic resistance and slope and curve additional resistance of the train operation, and establish a longitudinal train dynamics model using the unit mass force analysis mode; The second establishment module is used to consider the train positioning and speed measurement errors and communication delays, establish the front-end and back-end models of the train operation safety envelope, establish the dynamic safety interval under the virtual reconnection operation mode, and obtain the dynamic target interval for controlling the train operation; The linearization module is used to set the position and speed deviations between two adjacent trains in the train formation as controller system variables, linearize the established longitudinal train dynamics model, and obtain the controller system equation with interference terms. The controller cost function is set to minimize the integral of the infinite time domain quadratic deviation; The solution module is used to express the control quantity in the form of state feedback and interference compensation using closed-loop feedback and interference compensation, solve the optimized control quantity, introduce the auxiliary constant matrix to transform the optimization problem into a problem of solving the algebraic Riccati equation, solve the auxiliary constant matrix through numerical calculation, obtain the state feedback coefficient matrix of the system, and finally solve the optimized control quantity.

5. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the train virtual reconnection operation control method based on the interference compensation linear quadratic regulator as described in any one of claims 1 to 3 is implemented.

6. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the train virtual reconnection operation control method based on the interference compensation linear quadratic regulator as described in any one of claims 1 to 3.

7. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the train virtual reconnection operation control method based on the interference compensation linear quadratic regulator as described in any one of claims 1 to 3.

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