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

Abstract

The invention provides a train virtual reconnection operation control method and system based on an interference compensation linear quadratic regulator, and belongs to the technical field of train virtual reconnection operation optimization control. A longitudinal train dynamic model of train operation basic resistance and additional resistance is established; establishing a train operation safety envelope considering train speed measurement positioning errors and communication time delay error factors, and establishing a dynamic target interval between adjacent trains during virtual reconnection operation of the trains; setting a cost function of the controller to be the weighted sum of the quadratic form of the system state variable and the quadratic form of the control quantity of the minimum infinite time domain; an auxiliary matrix is introduced to convert an optimization problem into solving of an algebraic Riccati equation, the auxiliary matrix is obtained through solving in a numerical calculation mode, the auxiliary matrix is substituted into a state feedback controller model with interference compensation, an optimized control quantity is obtained, and the optimization control process of safe operation of the train in the virtual reconnection mode is achieved. The train virtual reconnection operation optimization control method can realize train virtual reconnection operation optimization control.

Description

Technical Field

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

[0002] The rapid development of high-speed railways has improved people's productivity. Its advantages of large capacity, fast and comfortable, low-carbon and energy-saving, and punctuality have led to an increasing demand for high-speed rail travel. Although the mileage of high-speed rail lines continues to increase, it still cannot meet the travel needs of the public during peak travel periods. In order to solve the problem that the current growth in high-speed rail transportation demand cannot be met, research on high-speed rail operation at high speed and high frequency has become a focus, in order to further improve the transportation efficiency of existing lines.

[0003] The train operation control system controls the train to keep running at a safe interval. At present, China's train operation control system is divided into four levels from CTCS-0 to CTCS-3. CTCS-3 is applicable to the lines with the highest speed level and the highest departure frequency. The CTCS-3 level train operation control system uses a fixed block method to control the train interval operation. In order to prevent train collisions, the train will occupy a track section with a longer fixed block section length, and other trains are strictly prohibited from entering this section. In this fixed block method, in order to ensure the safety of operation, the train tracking time and space interval is large and the utilization rate of track resources is low, which is not conducive to high-speed and high-frequency operation needs. The train virtual reconnection technology uses a method based on relative braking distance to control the train to run in formation, which can effectively reduce the tracking operation interval between trains, and realize the high-efficiency operation of high-speed trains and the flexible transportation function of dynamic configuration of transportation capacity.

[0004] At present, a lot of research has been conducted on the control of train intervals in virtual reconnection technology at home and abroad. In addition to traditional low-cost PID and other control methods, many experts and scholars have also applied modern intelligent control algorithms to the control of train operation intervals in virtual reconnection operation mode, such as model predictive control, sliding film control, deep learning, reinforcement learning and other control methods. As an optimization control method based on state feedback, the linear quadratic regulator (LQR) can effectively optimize the control efficiency. Compared with methods such as model predictive control, the quadratic regulator also has the advantages of small calculation amount and simple model. However, the general quadratic regulator model does not involve the interference problem of the controlled object, and the train running in virtual reconnection mode also faces a complex environment with certain interference factors. Therefore, the study of the virtual reconnection operation control method of the train based on the interference compensation linear quadratic regulator 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-mentioned 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, and establish the longitudinal train dynamics model using the unit mass force analysis mode;

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

[0010] The position and speed deviations between two adjacent trains in the train formation are set as controller system variables. The established longitudinal train dynamics model is linearized by using the Taylor expansion method with first-order terms, and the controller system equation with interference terms is obtained. In order to make the system follow the control target, the controller cost function is set to minimize the integral of the quadratic deviation in infinite time domain.

[0011] In order to ensure the asymptotic stability of the system, the closed-loop feedback and disturbance compensation method is adopted, and the control quantity is expressed in the form of state feedback and disturbance term 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 ​​solved by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0012] In each control cycle of the virtual reconnection operation of the train, the tracking train obtains the position, speed and other information 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, including:

[0014] During the running process, the train is mainly subject to the traction / braking force, basic resistance and additional resistance actively output by the train along 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 dynamic model of the train numbered i is obtained as shown in formula (1):

[0015]

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

[0017]

[0018] r a,i =g[θ(s i )+ζ·R -1 (s i )] (3)

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

[0020] Taking into account the objective conditions such as the errors in speed measurement and positioning during the actual operation of the train and the delay in the communication between trains, a safety envelope of the train position uncertainty during operation is established, including the safety front end and the safety back end:

[0021]

[0022] In order to meet the high-speed and high-density operation of virtual multiplexing, the interval between trains should be as small as possible, and the safety requirements of operation should also be met. The dynamic target interval between trains should be set to:

[0023]

[0024] The comprehensive position deviation between adjacent trains is obtained as follows:

[0025]

[0026] Furthermore, after linearization, the controller system equation with external disturbance terms added is obtained, and the cost function of the controller is set to minimize the quadratic deviation, specifically:

[0027] The controller system state variables of train i are:

[0028] x i (t) = [s i-1 (t)-s i (t)-ΔD i,i-1 v i (t)-v i-1 (t)] T (8)

[0029] After linearizing the train dynamics model of claim 2, the state equation of the system is obtained:

[0030]

[0031] The controller is transformed into solving the following optimization model:

[0032]

[0033] Furthermore, the auxiliary matrix is ​​obtained by solving the algebraic Riccati equation and brought into the feedback control model to obtain the optimized control quantity, which is:

[0034] The state feedback control model including disturbance compensation is assumed to be:

[0035] u i =-Kx i -ZW (11)

[0036] In order to solve the optimization model of formula (10) in claim 4, the auxiliary matrix is ​​introduced Let the auxiliary matrix and state feedback coefficient matrix satisfy the following formula:

[0037]

[0038]

[0039] The auxiliary matrix is ​​obtained by numerically solving the algebraic Riccati equation

[0040]

[0041] The solution of this equation is:

[0042]

[0043] This is used as the output of the controller to control the safe operation of the virtual multiplexed train.

[0044] In a second aspect, the present invention provides a train virtual reconnection operation control system based on an interference compensation linear quadratic regulator, comprising:

[0045] The first building module is used to analyze the basic resistance and slope and curve additional resistance of the train operation, and establish the longitudinal train dynamics model using the unit mass force analysis mode;

[0046] The second establishment module is used to consider the train positioning speed measurement error and communication delay, establish the front-end and back-end models of the safety envelope of the train operation, establish the dynamic safety interval under the virtual reconnection operation mode, and obtain the dynamic target interval for controlling the train operation;

[0047] 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 the controller system equation with interference terms, and set the controller cost function to minimize the integral of the infinite time domain quadratic deviation;

[0048] The solution module is used to express the control quantity in the form of state feedback and interference compensation by means of closed-loop feedback and interference compensation, solve the optimized control quantity, introduce the auxiliary constant matrix to transform the optimization problem into the problem of solving the algebraic Riccati equation, solve the auxiliary constant matrix by numerical calculation, obtain the state feedback coefficient matrix of the system, and finally solve the optimized control quantity.

[0049] 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.

[0050] 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 executable 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.

[0051] 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, 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 so that the electronic device executes instructions for implementing the train virtual reconnection operation control method based on the interference compensation linear quadratic regulator as described in the first aspect.

[0052] 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 state variables 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 virtual reconnection tracking operation; realizing the interference compensation optimized control of high-speed train formation operation, and ensuring the safe operation of the virtual reconnection mode.

[0053] Additional advantages of the present invention will be more clearly given in the following description or learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0055] Figure 1 It is a schematic diagram of the basic principle of the interference compensated high-speed train virtual reconnection LQR optimization control method described in an embodiment of the present invention.

[0056] 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.

[0057] Figure 3 This is a block diagram of a virtual multiplexing operation controller for controlling trains according to an embodiment of the present invention.

[0058] Figure 4 This is a flow chart of the operation of the interference compensation LQR optimization controller described in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The embodiments of the present invention are described in detail below, and 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 with the same or similar functions. The embodiments described below by the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0060] It should be understood by those skilled in the art 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.

[0061] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with that in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0062] 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 features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements and / or groups thereof.

[0063] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", 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 one or more embodiments or examples in a suitable manner. Different embodiments or examples described in this specification and features of different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

[0064] 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, and the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0065] 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.

[0066] Example 1

[0067] In this embodiment 1, a train virtual reconnection operation control system based on an interference compensation linear quadratic regulator is first provided, including: a first establishment module, which 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 a unit mass force analysis mode. A 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 safety envelope of the train operation, establish the dynamic safety interval under the virtual reconnection operation mode, and obtain the 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 the controller system equation with interference terms, and set the controller's 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 by means of closed-loop feedback and interference compensation, solve the optimized control quantity, introduce the auxiliary constant matrix to transform the optimization problem into the problem of solving the algebraic Riccati equation, solve the auxiliary constant matrix by numerical calculation, obtain the state feedback coefficient matrix of the system, and finally solve the optimized control quantity.

[0068] In this embodiment, the above system is used to implement a train virtual reconnection operation control method based on an interference compensation linear quadratic regulator, including:

[0069] Analyze the basic resistance, slope and curve additional resistance of the train, and establish the longitudinal train dynamics model using the unit mass force analysis mode;

[0070] Considering factors such as train positioning and speed measurement errors and communication delay, the front-end and back-end models of the safety envelope of train operation are established. On this basis, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling train operation is further obtained.

[0071] The position and speed deviations between two adjacent trains in the train formation are set as controller system variables. The established longitudinal train dynamics model is linearized by using the Taylor expansion method with first-order terms, and the controller system equation with interference terms is obtained. In order to make the system follow the control target, the controller cost function is set to minimize the integral of the quadratic deviation in infinite time domain.

[0072] In order to ensure the asymptotic stability of the system, the closed-loop feedback and disturbance compensation method is adopted, and the control quantity is expressed in the form of state feedback and disturbance term 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 ​​solved by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0073] In each control cycle of the virtual reconnection operation of the train, the tracking train obtains the position, speed and other information 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.

[0074] A longitudinal train dynamics model is established that takes into account the basic resistance, slope, and additional resistance of the train during operation, including:

[0075] During the running process, the train is mainly subject to the traction / braking force, basic resistance and additional resistance actively output by the train along 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 dynamic model of the train numbered i is obtained as shown in formula (1):

[0076]

[0077] The basic resistance of the operation is r b,i and r a,i Satisfying equations (2) and (3), s i ,v i ,u i It indicates the unit quality control quantity of the train's running position and speed, that is, the train's running status.

[0078]

[0079]

[0080] The safety dynamic interval model of virtual train reconnection operation based on safety envelope is adopted, which is as follows:

[0081] Taking into account the objective conditions such as the errors in speed measurement and positioning during the actual operation of the train and the delay in the communication between trains, a safety envelope of the train position uncertainty during operation is established, including the safety front end and the safety back end:

[0082]

[0083] In order to meet the high-speed and high-density operation of virtual multiplexing, the interval between trains should be as small as possible, and the safety requirements of operation should also be met. The dynamic target interval between trains should be set to:

[0084]

[0085] The comprehensive position deviation between adjacent trains is obtained as follows:

[0086]

[0087] After linearization, the controller system equation with external disturbance terms added is obtained, and the controller cost function is set to minimize the quadratic deviation, specifically:

[0088] The controller system state variables of train i are:

[0089] x i (t) = [s i-1 (t)-s i (t)-ΔD i,i-1 v i (t)-v i-1 (t)] T (8)

[0090] After linearizing the train dynamics model of claim 2, the state equation of the system is obtained:

[0091]

[0092] The controller is transformed into solving the following optimization model:

[0093]

[0094] The auxiliary matrix is ​​obtained by solving the algebraic Riccati equation and brought into the feedback control model to obtain the optimized control quantity, which is:

[0095] The state feedback control model including disturbance compensation is assumed to be:

[0096] u i =-Kx i -ZW (11)

[0097] In order to solve the optimization model of formula (10) in claim 4, the auxiliary matrix is ​​introduced Let the auxiliary matrix and state feedback coefficient matrix satisfy the following formula:

[0098]

[0099] The auxiliary matrix is ​​obtained by numerically solving the algebraic Riccati equation

[0100]

[0101] The solution of this equation is:

[0102]

[0103] This is used as the output of the controller to control the safe operation of the virtual multiplexed train.

[0104] Example 2

[0105] 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.

[0106] In order to achieve the above object, the present invention provides the following technical solutions:

[0107] A high-speed train virtual reconnection optimization operation control method based on a linear quadratic regulator considering disturbance compensation includes:

[0108] Establish a single-point train longitudinal dynamics model and a virtual reconnection operation safety interval model;

[0109] Considering that the train is subject to motor traction, braking force, basic running resistance, ramp and curve additional resistance during operation, let the train number be i, and establish the single-particle dynamic model of the train:

[0110]

[0111] The train running state t i =(s i v i u i ) T is the train position, speed, and control force (acceleration) per unit mass of the train, r b,i is the basic resistance per unit mass of the train, r a,i The additional resistance per unit mass of the train operation satisfies the following equation:

[0112]

[0113] Affected by the accuracy of the train speed positioning sensor and communication delay, the train's current position, speed, acceleration and other state information are uncertain. In order to ensure the safety of train operation, a train operation safety envelope model is constructed, which is divided into the front end and back end of the train safety operation envelope:

[0114]

[0115] 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 leading train may brake in an emergency braking manner at any time, that is, when the two adjacent trains i-1 (leading train) and i (tracing train) run in a formation model, the interval between the two trains should meet the following requirements:

[0116]

[0117] The safety interval is the minimum interval between two cars. For the smoothness of the virtual reconnection operation of the train, the target interval between the two cars is set as:

[0118]

[0119] Based on the analysis of train dynamics and the safety interval of virtual reconnection of high-speed trains, the process of designing the train operation controller model using the linear quadratic regulator LQR method is as follows:

[0120] For a certain train i, the system state variables of its train operation controller are assumed to be:

[0121] x i (t) = [s i-1 (t)-s i (t)-ΔD i,i-1 v i (t)-v i-1 (t)] T

[0122] Where ΔD i,i-1 Dynamic spacing meets:

[0123]

[0124] The train dynamics model is linearized to obtain the controller system equation with disturbance terms:

[0125]

[0126] The control target of the controller is x i (t) = 0, the cost function of the system is defined as:

[0127]

[0128] The optimal action control quantity u is obtained by solving the following optimal solution: l,i , control the train to have a target interval ΔD i,i-1 Follow the speed of the preceding train:

[0129]

[0130] Introducing the auxiliary constant matrix R transforms the above optimization model into The algebraic equation is shown in the following formula, and the numerical calculation method is used to solve the algebraic equation.

[0131]

[0132] The optimization model can be obtained by solving the ARE equation using numerical calculation method to obtain the optimal action control quantity u i, the actual control quantity u i 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.

[0133] Example 3

[0134] The virtual multiplexing operation mode of high-speed trains is characterized by high speed and high frequency of departure intervals. It uses advanced sensing and communication technology 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 multiplexing operation control method of trains based on interference compensation linear quadratic regulator is provided. The method mainly includes: constructing a longitudinal dynamic model that considers 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 optimal control quantity that minimizes the cost function, 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.

[0135] The method specifically includes the following implementation steps:

[0136] Step 1: Longitudinal dynamic model considering basic resistance and additional resistance of train operation.

[0137] In the high-speed railway operation scenario, in order to make the train run more smoothly and quickly, the track is generally built relatively straight. At the same time, the train has a small deformation during operation, so the entire vehicle can be regarded as a single mass point. During the operation of the train, the longitudinal direction of the track is mainly affected by the traction / braking force actively output by the train, basic resistance and additional resistance. 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 formula (16):

[0138]

[0139] Where i represents the train number, which is used to distinguish trains, and s i ,v i are the position and speed of the train respectively, u i is the actual control quantity per unit mass of the train, r b,i ,r a,i They are the basic resistance and additional resistance of train operation. The basic resistance of train operation is composed of track resistance, air resistance, etc. The additional resistance is determined by track parameters. The train will also be subject to additional resistance caused by track slope and curve radius during operation. The resistance per unit mass during train operation is expressed by formula (17):

[0140]

[0141] Where C0, C1, and C2 are the basic resistance parameters of the train unit mass, g is the gravity constant, θ and R are the slope and curve radius parameters of the current track where the train is located, and ξ is the additional resistance parameter of the curve per unit mass of the train. In order to exclude the factor of train mass, the parameters in formula (17) are all expressed in the form of unit mass.

[0142] Step 2: Safe dynamic interval model of train virtual reconnection operation based on safety envelope.

[0143] 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 current state of the train, the positioning and speed measurement of the train in the actual operation process are not accurate, and there are measurement errors. At the same time, the train-related speed measurement and positioning equipment also has working frequency factors, which lead to measurement delay problems, 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):

[0144]

[0145] The train operation safety envelope consists of the envelope front end and envelope back end B i Composition, Δp i , Δv i are the positioning error and speed measurement error of the train respectively, L train,i is the length of train i, f u ,t d They are respectively the operating frequency of the train's speed measuring equipment and the information communication delay between trains.

[0146] 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 believes that the speed of the leading train will not suddenly change to 0, and the leading train will stop at any time by emergency braking, instead of the traditional strategy that is based on the leading train occupying a certain block section at a speed of 0 at any time, resulting in a waste of track resources.

[0147] On the basis of the train operation safety envelope and the relative braking method, in order to ensure the safety of virtual reconnected train operation, the front end of the safety envelope of the tracking train should maintain a sufficient safety interval with the rear end of the safety envelope of the leading train, so that when the leading train is emergency braked until it stops, the tracking train also takes emergency brakes without causing a train collision. Based on the above analysis process, the envelope of the tracking train i and the leading train i-1 should meet the following dynamic interval requirements to ensure driving safety:

[0148]

[0149] where a EB This is the acceleration standard for high-speed trains in emergency braking situations. This dynamic safety interval takes into account that both the tracking train and the leading train have good braking performance. In order to leave a certain amount of safety redundancy, the target interval between the actual control tracking train and the leading train is:

[0150]

[0151] where a SB is the acceleration standard for high-speed trains under normal braking conditions. Under this target interval, in a dynamically formed train formation, the front end of the safety envelope of a tracking train i and the rear end of the safety envelope of its preceding train i-1 maintain the distance between i's normal braking distance and i-1's emergency braking distance, as shown in Figure 2 shown.

[0152] Step 3: Establish a train operation controller model based on a linear quadratic regulator.

[0153] For the convenience of analysis, the speed deviation and position deviation of the train are set as the system state variables of the controller:

[0154] x i (t) = [s i-1 (t)-s i (t)-ΔD i,i-1 v i (t)-v i-1 (t)] T (twenty one)

[0155] Comprehensive position deviation ΔD i,i-1 Combining equation (20) and equation (18), we can get:

[0156]

[0157] 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:

[0158]

[0159] v e is the speed v i The equilibrium state of the controller is obtained as follows:

[0160]

[0161] The matrices A, B, and W satisfy the following equations:

[0162]

[0163] For this system, there is a feedback controller

[0164] u i =-Kx i -ZW (26)

[0165] Where K is the state coefficient matrix. In order to offset the measurable system disturbance W, Z is set to a constant vector [0 1] and satisfies -BZW+W=0. In order to make the system stable, that is, x i →0, let the cost function of the system be, where M is the state variable weight diagonal matrix and N is the control variable weight (constant):

[0166]

[0167] The matrices K and M (constant matrices) are expressed as follows:

[0168] K=[k1 k2] M=diag(m1,m2) (28)

[0169] To make the system tend to equilibrium, the goal of the controller is to minimize the cost as much as possible, that is:

[0170]

[0171] The controller framework designed is as follows Figure 3 shown.

[0172] Step 4: Solve the optimal control quantity as the control instruction for controlling the virtual reconnection operation of the train.

[0173] In order to solve the optimization model shown in equation (29), suppose there is a constant matrix Satisfies the following equation:

[0174]

[0175] Substituting equation (30) into equation (29), we can obtain the constant matrix In the form of:

[0176]

[0177] where x i (0) is the initial state of the system. Further expansion of equation (30) yields the following equation:

[0178]

[0179] The integration of equations (24), (26) and (32) yields the following equation:

[0180]

[0181] Formula (33) is an identity, which requires that for all x at all times i To satisfy this equation, we need to satisfy:

[0182]

[0183] To simplify the equation, the state feedback coefficient matrix is ​​set to be about the constant matrix In the form of:

[0184]

[0185] Substituting equation (35) into equation (34) yields the following equation:

[0186]

[0187] The equation represented by this formula is the algebraic Riccati equation (ARE), and the auxiliary constant matrix can be obtained by numerical calculation method: Substituting into equation (35) we get the state feedback coefficient matrix of the system, and then we get the optimal control quantity through the feedback controller equation shown in equation (26):

[0188]

[0189] In each control cycle of the train operation, execute step 4, according to the optimal control quantity u i * The control tracking train i and the preceding train i-1 operate in virtual reconnection mode, and check whether the communication link between the trains is normal. If it is normal, the virtual reconnection operation is maintained, otherwise it is switched to other low-level operation modes. At the same time, the train automatic protection subsystem ATP always monitors whether the train is overspeeding. If it is overspeeding, the train emergency brake is triggered to ensure the safety of train operation. The control flow chart is shown in Figure 4 shown.

[0190] Example 4

[0191] This embodiment 4 provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium 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 above is implemented. The method includes:

[0192] Analyze the basic resistance, slope and curve additional resistance of the train, and establish the longitudinal train dynamics model using the unit mass force analysis mode;

[0193] Considering the train positioning and speed measurement errors and communication delay, the front-end and back-end models of the safety envelope of train operation are established, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling the train operation is obtained;

[0194] The position and speed deviations between two adjacent trains in the train formation are set as controller system variables, the established longitudinal train dynamics model is linearized, and the controller system equation with interference terms is obtained. The cost function of the controller is set to minimize the integral of the quadratic deviation in infinite time domain.

[0195] Using closed-loop feedback and disturbance compensation, the control quantity is expressed in the form of state feedback and disturbance compensation, and the optimized control quantity is solved. 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 ​​solved by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0196] Example 5

[0197] This embodiment 5 provides a computer device, including 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 above-mentioned train virtual reconnection operation control method based on the interference compensation linear quadratic regulator, the method comprising:

[0198] Analyze the basic resistance, slope and curve additional resistance of the train, and establish the longitudinal train dynamics model using the unit mass force analysis mode;

[0199] Considering the train positioning and speed measurement errors and communication delay, the front-end and back-end models of the safety envelope of train operation are established, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling the train operation is obtained;

[0200] The position and speed deviations between two adjacent trains in the train formation are set as controller system variables, the established longitudinal train dynamics model is linearized, and the controller system equation with interference terms is obtained. The cost function of the controller is set to minimize the integral of the quadratic deviation in infinite time domain.

[0201] Using closed-loop feedback and disturbance compensation, the control quantity is expressed in the form of state feedback and disturbance compensation, and the optimized control quantity is solved. 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 ​​solved by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0202] Example 6

[0203] This embodiment 6 provides an electronic device, including: 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, so that the electronic device executes instructions for implementing the above-mentioned train virtual reconnection operation control method based on the interference compensation linear quadratic regulator, the method comprising:

[0204] Analyze the basic resistance, slope and curve additional resistance of the train, and establish the longitudinal train dynamics model using the unit mass force analysis mode;

[0205] Considering the train positioning and speed measurement errors and communication delay, the front-end and back-end models of the safety envelope of train operation are established, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling the train operation is obtained;

[0206] The position and speed deviations between two adjacent trains in the train formation are set as controller system variables, the established longitudinal train dynamics model is linearized, and the controller system equation with interference terms is obtained. The cost function of the controller is set to minimize the integral of the quadratic deviation in infinite time domain.

[0207] Using closed-loop feedback and disturbance compensation, the control quantity is expressed in the form of state feedback and disturbance compensation, and the optimized control quantity is solved. 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 ​​solved by numerical calculation, and the state feedback coefficient matrix of the system is obtained. Finally, the optimized control quantity is solved.

[0208] 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 the operation, wherein the basic resistance is the sum of various friction forces between the wheel-rail, the car body and the environment, and the additional resistance is generated by factors such as the slope of the track line and the radius of the curve; consider the front and rear ends of the train operation safety envelope caused by the uncertainty of the train state due to the train speed measurement and positioning error and the 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 linear system equation with interference terms is obtained by using the Taylor first-order approximation method. 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 in, and the quadratic optimization model is converted into a form for solving the auxiliary matrix. In order to further 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 optimization control process of virtual reconnection tracking operation. The present invention can realize the interference compensation optimization control of high-speed train formation operation and ensure the safe operation of the virtual reconnection mode.

[0209] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0210] 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. 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.

[0211] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0212] 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 for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0213] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Technical personnel in the relevant field should understand that various modifications or variations that can be made by those skilled in the art without creative work on the basis of the technical solution disclosed in the present invention 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, and establish the longitudinal train dynamics model using the unit mass force analysis mode; Considering the train positioning and speed measurement errors and communication delay, the front-end and back-end models of the safety envelope of train operation are established, the dynamic safety interval under the virtual reconnection operation mode is established, and the dynamic target interval for controlling the train operation is obtained; The position and speed deviations between two adjacent trains in the train formation are set as controller system variables, the established longitudinal train dynamics model is linearized, and the controller system equation with interference terms is obtained. The cost function of the controller is set to minimize the integral of the quadratic deviation in infinite time domain. Using closed-loop feedback and disturbance compensation, the control quantity is expressed in the form of state feedback and disturbance compensation, and the optimized control quantity is solved. 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 ​​solved 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 reconnection operation control method based on the interference compensation linear quadratic regulator according to claim 1 is characterized in that: In each control cycle of the virtual reconnection operation of the train, the tracking train obtains the position, speed and other information 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 multiplexing 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 of the train during operation, including: During the running process, the train is mainly subject to the traction / braking force, basic resistance and additional resistance actively output by the train along 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 dynamic model of the train numbered i is obtained as follows: Among them, r b,i and r a,i Indicates the basic resistance of operation i ,v i ,u i It indicates the unit quality control quantity of the train's running position and speed, that is, the train's running status.

4. The train virtual reconnection operation control method based on the interference compensation linear quadratic regulator according to claim 1 is characterized in that: The safety dynamic interval model of virtual train reconnection operation based on safety envelope is adopted, which is as follows: Taking into account the objective conditions such as the errors in speed measurement and positioning during the actual operation of the train and the delay in the communication between trains, a safety envelope of the train position uncertainty during operation is established, including the safety front end and the safety back end: In order to meet the high-speed and high-density operation of virtual multiplexing, the interval between trains should be as small as possible, and the safety requirements of operation should also be met. The dynamic target interval between trains should be set to: The comprehensive position deviation between adjacent trains is obtained as follows:

5. The train virtual multiplexing operation control method based on the interference compensation linear quadratic regulator according to claim 1 is characterized in that: After linearization, the controller system equation with external disturbance terms added is obtained, and the controller cost function is set to minimize the quadratic deviation, specifically: The controller system state variables of train i are: x i (t)=[s i-1 (t)-s i (t)-ΔD i,i-1 v i (t)-v i-1 (t)] T After linearizing the train dynamics model of claim 2, the state equation of the system is obtained: The controller is transformed into solving the following optimization model:

6. The train virtual multiplexing operation control method based on the interference compensation linear quadratic regulator according to claim 1 is characterized in that: The auxiliary matrix is ​​obtained by solving the algebraic Riccati equation and brought into the feedback control model to obtain the optimized control quantity, which is: The state feedback control model including disturbance compensation is assumed to be: you i =-Kx i -ZW In order to solve the optimization model of formula (10) in claim 4, the auxiliary matrix is ​​introduced Let the auxiliary matrix and state feedback coefficient matrix satisfy the following formula: The auxiliary matrix is ​​obtained by numerically solving the algebraic Riccati equation The solution to this equation is: This is used as the output of the controller to control the safe operation of the virtual multiplexed train.

7. A train virtual multiplexing 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 the longitudinal train dynamics model using the unit mass force analysis mode; The second establishment module is used to consider the train positioning speed measurement error and communication delay, establish the front-end and back-end models of the safety envelope of the train operation, establish the dynamic safety interval under the virtual reconnection operation mode, and obtain the 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 the 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 by means of closed-loop feedback and interference compensation, solve the optimized control quantity, introduce the auxiliary constant matrix to transform the optimization problem into the problem of solving the algebraic Riccati equation, solve the auxiliary constant matrix by numerical calculation, obtain the state feedback coefficient matrix of the system, and finally solve the optimized control quantity.

8. 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 1-6 is implemented.

9. 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 1-6.

10. 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 so that the electronic device executes 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 1-6.

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