A train safety car-following driving method suitable for a train control system of vehicle-to-vehicle communication

Abstract

This invention discloses a train safety following driving method suitable for train-to-train communication and control systems. It fully considers the operating characteristics of train following driving in train-to-train communication and control systems, and can achieve safe tracking between trains with shorter intervals. It can also ensure that the following train can maintain a safe distance from the preceding train and run with a smaller interval within a larger initial state range of the train. This can meet the requirements of ensuring safe and short-interval operation capability for high-speed railway transportation networks.

Description

Technical Field

[0001] This invention relates to the field of train operation control technology, and in particular to a train safe following driving method suitable for a train-to-train communication control system. Background Technology

[0002] The train control system (TCS) is a system composed of ground equipment and onboard equipment used to control train speed and ensure safe and efficient train operation. It is an important component of the railway signaling control system. The TCS has evolved alongside train technology and the development of train-to-ground information transmission systems. It integrates advanced control, communication, and computer technologies with railway signaling technology to control train direction, intervals, and speed, and is the core component for ensuring safe operation and improving transportation efficiency.

[0003] Train control systems based on vehicle-to-vehicle communication (V2V) use the onboard mobile vehicle as the core to achieve train operation control. Breaking away from the traditional route-based control mode, V2V utilizes bidirectional, high-capacity, high-speed vehicle-to-ground wireless communication, multi-sensor fusion for train speed measurement and positioning, and autonomous cooperative moving block control technology to achieve intelligent operation control through its own perception and autonomous decision-making. Compared to communication-based train control (CBTC) and fully automatic operation (FAO) systems, which have numerous trackside devices and complex interfaces, V2V transforms "vehicle-to-ground-vehicle" communication into "vehicle-to-vehicle" communication data streams. This simplifies system equipment, provides richer information exchange including location, speed, and movement authorization, considers train movement trends, employs a "soft wall collision" approach for safety protection, and shortens tracking intervals.

[0004] The onboard equipment of the train control system based on car-to-car communication undertakes the functions of traditional Radio Block Center (RBC) such as movement authorization calculation and information exchange, and also has functions such as interlocking, target-distance pattern curve calculation, automatic driving, and train integrity checking. Compared with the traditional Radio Block Center (RBC), the onboard equipment of the train control system based on car-to-car communication has significant similarities and differences. Functionally, both mainly perform bidirectional communication and train permission calculation, but from the perspective of the overall system design philosophy, the specific differences are reflected in:

[0005] Structural differences: The Track Control Center (RBC) is located in a fixed physical position, requiring switching when a train passes through the range of two RBCs. The RBC generates and sends control commands to the train via two-way vehicle-to-ground communication, receiving information from external ground equipment and exchanging information with onboard equipment. Its primary function is to provide train operation permits, ensuring safe operation of the train within the RBC's jurisdiction and completing train interval control and protection. The train control system based on vehicle-to-vehicle communication, on the other hand, uses wireless communication units and GPRS radios to achieve two-way wireless communication with onboard equipment of preceding / following trains, ground system data servers, trackside controllers, and tail-end equipment. Furthermore, the onboard main control unit has the function of calculating train operation permits based on information such as station routes and section directions, electronic maps, the train's position and speed, and the position and speed of the preceding train.

[0006] Safety Protection Differences: Traditional train control systems are generally designed based on a "hard-wall" tracking mode. Considering the possibility of the preceding train stopping immediately (such as derailment or collision), the tracking endpoint of the following train is the entrance to the block section where the preceding train is located, with an additional safety protection distance. An onboard monitoring braking distance is maintained between the two trains to ensure the following train can stop at the entrance to the block section where the preceding train is located, thus guaranteeing safety. Train control systems based on car-to-car communication adopt a "soft-wall" train tracking mode. This means that assuming the preceding train will not derail or collide and stop within a short distance, after the preceding train uses emergency braking to stop, the following train can use service braking to stop behind the preceding train. Compared to the "hard-wall" mode, the tracking interval between the two trains is reduced by the emergency braking distance of the preceding train, thus significantly increasing train density.

[0007] Therefore, considering the aforementioned differences, designing a train operation control system based on vehicle-to-vehicle communication that meets the safety protection requirements for train following is an urgent problem to be solved. Currently, there are three main related solutions.

[0008] Option 1, the CTCS-2 / CTCS-3 level + ATO train control system, is a key technical solution in China's train control system. Based on the traditional ATP system, it receives operation plans, inter-station data, and basic line information (such as permanent and temporary speed limits) from the dispatch center via wireless communication technology. This system is primarily used in China's high-speed and intercity railway networks. In the CTCS-2 / CTCS-3 level train control system, the ATO (Automatic Train Operation) integrates various sensors and train control equipment to achieve automatic train departure, interval operation, precise stopping, and automatic linkage control of stations and platform screen doors. Furthermore, the CTCS-2 / CTCS-3 + ATO train control system boasts high safety and stability, ensuring trains operate at safe distances and speeds through two-way communication between ground and onboard equipment. This system is widely used in my country's high-speed railway network, significantly improving the automation and efficiency of train operations.

[0009] Despite the advanced technology of the CTCS-2 / CTCS-3+ATO system, it has some limitations. First, the system's decisions rely on operational clearance information provided by ground equipment, lacking proactive sensing capabilities. This means the train cannot autonomously perceive changes in its surrounding environment, such as weather changes or obstacle intrusions, and must depend on external equipment for information. Furthermore, the system still requires human intervention when handling complex emergencies, lacking autonomous decision-making capabilities. In extreme weather conditions, sudden accidents, or other unconventional situations, the increased need for human intervention limits the system's autonomous operation level and cannot fully meet the demands of future intelligent train operation control.

[0010] Option 2, the Next Generation Train Control System (NGTC) from the Union of European Railway Industries (UNIFE), is an advanced automated train operation system based on satellite navigation and wireless communication technologies, designed to improve the efficiency and flexibility of rail transport. This system employs advanced satellite navigation and positioning technology, allowing trains to obtain precise location information via the Global Positioning System (GNSS), while combining it with IP-based vehicle-to-ground wireless communication technology to achieve efficient communication between trains and the control center. Another highlight of NGTC is the introduction of moving block signaling technology, which significantly reduces the interval between trains compared to traditional fixed block signaling, increasing the capacity and efficiency of rail transport. Furthermore, the NGTC system focuses on developing Automatic Train Operation (ATO) technology, enabling fully automated train operation without human intervention, supporting operation on various levels of rail networks, including intercity railways and high-speed railways.

[0011] While the NGTC system is advanced in its design concept, its technical solution has some shortcomings. First, train positioning relies entirely on satellite navigation systems, which can lead to signal loss or decreased accuracy in certain scenarios (such as tunnels, mountainous areas, or extreme weather), affecting safe train operation. Furthermore, the NGTC system lacks other auxiliary positioning technologies such as inertial navigation and speedometers, making it unable to provide stable position information when satellite signals are interrupted. Second, although the NGTC system reduces the use of trackside equipment, its design philosophy and technical framework differ significantly from China's CTCS system, making seamless integration impossible. This results in technical barriers in transnational railway operations or international train services, hindering interoperability between systems and limiting its widespread application in global railway networks.

[0012] Option 3: Some urban rail transit systems in China have successfully applied GoA4 level fully automated driving technology, a high-level automatic train operation system capable of autonomous train operation without human intervention. Based on this technology, urban rail transit in China is gradually developing towards autonomous train operation control, particularly achieving significant progress in areas such as vehicle-to-ground cooperative perception, reliable vehicle-to-vehicle communication, and virtual formation. These advanced technologies have greatly improved the automation and intelligence levels of the system, significantly enhancing train operating efficiency and safety. Furthermore, some urban rail transit systems in China have further reduced train intervals and significantly improved scheduling flexibility by optimizing train scheduling and control strategies. These technologies greatly reduce the need for human intervention, enabling trains to more intelligently adapt to different operating environments and achieving full automation and intelligence in train operation.

[0013] While GoA4 level automated driving technology in urban rail transit performs well in urban environments, its technical solutions do not fully consider the complexities of high-speed rail and intercity rail operating scenarios and environments. For example, urban rail transit systems typically operate at lower speeds, with shorter train intervals and relatively simple track structures, while high-speed rail needs to cope with complex operating environments exceeding 300 km / h, requiring higher safety redundancy and system response speeds. Furthermore, the operating scenarios of the national railway system are more complex, with factors such as station interlocking, track conditions, and cross-regional operations placing higher demands on train control systems. Urban rail transit automated driving technology is not yet fully adapted to these aspects; existing technical solutions cannot be directly transferred to high-speed rail systems and cannot meet the safety, reliability, and complex operating environment requirements of high-speed rail. Therefore, despite significant progress in automated driving in urban rail transit, its application in high-speed rail still needs further improvement and enhancement. Summary of the Invention

[0014] The purpose of this invention is to provide a safe train following driving method suitable for a train control system based on vehicle-to-vehicle communication, in order to solve the problem of unknown interference caused by the dynamic uncertainty of the preceding vehicle in high-speed railway train automatic driving, which affects the train following performance.

[0015] The objective of this invention is achieved through the following technical solution:

[0016] A train safety following driving method suitable for a car-to-car communication train control system, wherein the following steps are performed by equipment in the following car:

[0017] It periodically receives train operation data sent by the preceding train;

[0018] Based on the received train operation data from the preceding train, and combined with the predetermined safety margin and time distance requirements, the following error with the preceding train is calculated.

[0019] Based on the car-following error, a relative dynamic model of trains is constructed to describe the relative motion relationship between trains;

[0020] Calculate the minimum safety margin and set the safety interval based on the minimum safety margin;

[0021] The decision-making process of the controller is designed by combining the train relative dynamic model, the following error, and the set safety interval.

[0022] By combining the set constraints, the train operation data of the preceding train, its own operation data, and the controller's decision-making process, the corresponding decision information is obtained, and the train operation is controlled accordingly.

[0023] As can be seen from the technical solution provided by the present invention, it fully considers the operating characteristics of train following and driving in the train-to-train communication and control system, can realize safe tracking between trains with shorter intervals, and can ensure that the following train can still maintain a safe distance from the preceding train and run with a smaller interval within a larger initial state range of the train, which can meet the requirements of ensuring safe and small-interval operation capability for high-speed railway transportation network. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a train safety following driving method suitable for a car-to-car communication train control system, provided by an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of the structure of the train operation control system provided in an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram illustrating the train following error identification principle provided in an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of train safety interval design provided for an embodiment of the present invention.

[0029] Figure 5 A schematic diagram illustrating the calculation of the maximum initial state set for real-time train decision-making provided in this embodiment of the invention.

[0030] Figure 6 This is a schematic diagram of the overall process of a train safety following driving method suitable for a train-to-train communication control system provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0032] First, the following explanations are provided for the terms that may be used in this article:

[0033] The terms “including,” “comprising,” “containing,” “having,” or other similar semantic descriptions should be interpreted as non-exclusive inclusion. For example, “including a technical feature element (such as raw material, component, ingredient, carrier, dosage form, material, size, part, component, mechanism, device, step, process, method, reaction conditions, processing conditions, parameter, algorithm, signal, data, product or article of manufacture, etc.)” should be interpreted as including not only the expressly listed technical feature element, but also other technical feature elements that are not expressly listed and are well-known in the art.

[0034] The term "composed of" excludes any technical features not expressly listed. When used in a claim, it closes the claim to exclude all technical features other than those expressly listed, except for associated conventional impurities. If the term appears only in a clause of a claim, it limits the claim to the elements expressly listed in that clause; elements recited in other clauses are not excluded from the overall claim.

[0035] The following is a detailed description of a train safety following driving method suitable for a car-to-car communication train control system provided by the present invention. Contents not described in detail in the embodiments of the present invention are prior art known to those skilled in the art. Where specific conditions are not specified in the embodiments of the present invention, they shall be performed according to conventional conditions in the art or conditions recommended by the manufacturer. Where the manufacturers of the instruments used in the embodiments of the present invention are not specified, they are all conventional products that can be purchased commercially.

[0036] like Figure 1 The diagram shows a flowchart of a train safety following driving method suitable for a car-to-car communication train control system, provided by an embodiment of the present invention. This method is executed by equipment in the following car and mainly includes the following steps:

[0037] Step 1: Periodically receive train operation data sent by the preceding train.

[0038] Preferably, the onboard equipment of the following vehicle periodically receives train operation data sent by the preceding vehicle, including the position, speed and acceleration of the preceding vehicle.

[0039] Step 2: Based on the received train operation data from the preceding train, and in conjunction with the predetermined safety margin and time distance requirements, calculate the following error between the train and the preceding train.

[0040] Preferably, the following train acquires its own position and speed, and, in conjunction with the train operation data of the preceding train and the predetermined safety margin and time distance requirements, calculates the following error between itself and the preceding train. The following error includes: position error and speed error; wherein: the position error is calculated as follows:

[0041] e p =p l -p f -d min -v f h f

[0042] Where, p l This represents the position of the preceding train, and its train operation data pertains to that train; p f v f The positions and speeds of the following vehicles are respectively d. min For the predetermined safety margin, h f This is for time interval requirements.

[0043] Step 3: Based on the following error, construct a relative dynamic model of the trains to describe the relative motion relationship between the trains.

[0044] Preferably, the transfer function of train acceleration and desired acceleration is obtained using actual line operation data, and then combined with the following error to construct a train relative dynamic model to describe the relative motion relationship between trains.

[0045] Step 4: Calculate the minimum safety margin and set the safety interval based on the minimum safety margin.

[0046] Preferably, the acceleration of the preceding vehicle is set as the disturbance vector, and the forward reachable set is calculated in N steps from the moment the preceding vehicle begins to implement emergency braking until it comes to a complete stop. The absolute value of the maximum following error obtained is the minimum safety margin, and the safety interval is set accordingly.

[0047] Step 5: Combine the train relative dynamic model, following error, and set safety interval to design the controller's decision-making process.

[0048] Preferably, the controller decision-making process of the rear vehicle applies a hybrid H2 / H ... ∞ Control theory is used to design two robust controllers to achieve the same control objective. These controllers combine the aforementioned steps to obtain the train's relative dynamic model, following error, and safety interval.

[0049] Step 6: Combine the set constraints, the train operation data of the preceding train, the train's own operation data, and the controller's decision-making process to obtain the corresponding decision information, and use this information to control the train's operation.

[0050] Preferably, the set constraints are the following error, acceleration difference, and the constraints that the control input must satisfy, and the set constraints form the system allowable set; the train operation data of the preceding train and the train's own operation data include: the position, speed and acceleration of the preceding train, and the position, speed and acceleration of the following train; the system allowable set, the train operation data of the preceding train and the train's own operation data are input to the controller, and the corresponding decision information is obtained through the controller's decision process.

[0051] In this embodiment of the invention, the sequence numbers of the above steps are mainly used to identify different steps, and do not refer to the execution order of the steps. The specific execution order of each step can be determined based on the content of the steps.

[0052] The method provided in this embodiment of the invention fully considers the operating characteristics of train following and driving in the train-to-train communication and control system. It can achieve safe tracking between trains with shorter intervals and ensure that the following train can still maintain a safe distance from the preceding train and run with a smaller interval within a larger initial state range of the train. It can meet the requirements of ensuring safe and small-interval operation capability for high-speed railway transportation network.

[0053] To more clearly demonstrate the technical solution and its effects provided by the present invention, the method provided by the embodiments of the present invention will be described in detail below with reference to specific examples.

[0054] I. Information Reception.

[0055] In this embodiment of the invention, the train is equipped with an information acquisition device and a communication device. The information acquisition device includes: a hybrid speed measurement device based on wheel axle speed sensors and transponders, an acceleration measurement device based on inertial positioning, and a train positioning system based on the Global Navigation Satellite System (GNSS); the communication device is a new generation railway broadband mobile communication system (Long Term Evolution-Railway, LTE-R) information transmission platform, with the on-board control platform as the core, which deeply integrates train control with on-board networks, traction, braking and other systems.

[0056] The steps for the following vehicle's onboard communication equipment to periodically receive position, speed, and acceleration information transmitted by the preceding vehicle include:

[0057] 1) After receiving a signal from the vehicle in front, the vehicle's onboard communication equipment needs to open the receiving channel in order to correctly receive and interpret the signal;

[0058] 2) Once the position, speed, and acceleration information of the vehicle in front is received, the vehicle communication equipment of the following vehicle needs to use it to update the status of the vehicle in front;

[0059] 3) The vehicle-mounted communication equipment of the following vehicle can send a confirmation message to the vehicle in front to inform the vehicle in front that the information received is correct.

[0060] II. Calculation method for following error between front and rear vehicles.

[0061] In this embodiment of the invention, the following error of two adjacent trains is calculated based on the running data of the two trains in front and behind, combined with the set safety margin and time distance requirements, including position error and speed error.

[0062] The following vehicle obtains its position and speed from satellite communication and onboard speed sensors, and calculates its acceleration; the speeds of the two vehicles affect their relative braking distance, and their maximum braking acceleration is also a decisive factor in the relative braking distance; let p j ,v j and a j These represent the train's position, speed, and acceleration, respectively, where j = l, f, where l refers to the preceding train and f refers to the following train, as shown in the example. Figure 2 As shown, the positional error between the two vehicles is calculated by subtracting the predetermined safety margins of both vehicles and the braking response of the rear vehicle from the distance between them.

[0063] e p =p l -p f -d min -v f h f

[0064] Where, d min and h f These are the predetermined safety margin and the headway requirement (train headway), respectively.

[0065] The predetermined safety margin is an additional value based on the safe distance that two adjacent trains should maintain, taking into account factors such as train communication delay, sensor measurement error, and interference caused by acceleration fluctuations of the preceding train; the time interval requirement is an additional value based on the safe distance that two adjacent trains should maintain, taking into account the reaction time of the following train.

[0066] III. Constructing a relative dynamic model of the train.

[0067] The construction method is as follows:

[0068] 1. Identify the quantitative relationship between the following error of the trains and the positions of the two trains, the predetermined safety margin, and the headway.

[0069] This step can be found in the description in Part II above.

[0070] 2. Obtain the transfer function of the train's actual acceleration and desired acceleration from actual line operation data.

[0071] In this embodiment of the invention, the transfer function derived from the step response of the train is used, assuming an acceleration a. f Described by a first-order system, it can be represented as:

[0072]

[0073] Among them, K f With τ f This corresponds to the static gain and time constant, where s is the complex frequency variable in the Laplace transform, and a F des For the expected acceleration of the following vehicle, a f To accelerate the following vehicle.

[0074] 3. Establish the state-space equations for two adjacent trains running in tandem, where the state vectors are the position difference, speed difference, and acceleration of the following train, the control input is the desired acceleration of the following train, and the disturbance vector is the acceleration of the preceding train.

[0075] The state-space form of the train's relative dynamic model is represented as follows:

[0076]

[0077] Where (t) corresponds to time t, Let x be the train's relative dynamic model at time t, that is, the derivative of the state vector x(t) at time t, where the state vector x = [e^(t / t)]. p e v af ] T Control input Perturbation vector ω = a l T is the transpose symbol, a l The acceleration of the vehicle in front. For speed error, Indicates position error e p The derivatives; A, B1, and B2 are three matrices in the form of:

[0078]

[0079] Among them, h f This is for time interval requirements.

[0080] IV. Train following error identification strategy based on vehicle-to-vehicle communication.

[0081] Considering the disturbance behavior of the vehicle ahead, a hybrid H2 / H2 configuration is used. ∞ The state feedback gain of controller Ψ and the state feedback / feedforward gain of controller Φ are designed using control theory to establish a linear closed-loop system model.

[0082] The output value of the controller Ψ is determined by the state feedback gain and the relative state of the two vehicles (including tracking error, relative speed, and braking acceleration of the rear vehicle);

[0083] u(t) = K Ψ x(t)

[0084] The output value of the controller Φ is determined by the state feedback gain, the relative state of the two vehicles, and the acceleration of the vehicle in front;

[0085]

[0086] Among them, K Ψ and K Φ It is the gain matrix used to generate the control input, K Ψ Let K be the feedback gain matrix of the controller Ψ. Φ K is the feedforward gain matrix of the controller Φ. Φ1 and K Φ2 For K Φ The specific amount.

[0087] The output state of a train can be described as the combined effect of the train's relative state and disturbances.

[0088] The feedback gain of the controller can be obtained by mixing H2 / H ∞ The optimization problem is solved to obtain the value where ∥F i ∥ ∞ and ∥H i ∥2 is the method function for the impact of disturbances on the desired output of the controller.

[0089]

[0090] st∥F i ∥ ∞ ≤γ1

[0091] ∥H i ∥2≤γ2

[0092] i = Ψ, B

[0093] Where α and β are weighting parameters used to balance ∥F i ∥ ∞ and ∥H i The relative importance of ∥2, where γ1 and γ2 are both thresholds in the constraint conditions, corresponding to the constraint ∥F i ∥ ∞ and ∥H i The maximum value of ∥2.

[0094] In this embodiment of the invention, the train following error identification strategy based on vehicle-to-vehicle communication is mainly aimed at revealing and analyzing the evolution law of following error, especially when considering the disturbance behavior of the preceding vehicle. In the foregoing section, a hybrid H2 / H ∞ Two controllers (controller Ψ and controller B) were designed using control theory, and the control input was calculated using state feedback gain and feedforward gain. To ensure the optimal performance of the controllers, the following section uses the linear matrix inequality (LMI) method to solve for the gain matrix, thereby achieving the control objective proposed in the theoretical design. Specifically, by solving for the LMI, an appropriate gain matrix is ​​determined to ensure that the system can meet the minimum error and performance constraints when facing disturbances.

[0095] Specifically, the strategy includes the following steps:

[0096] 1. Use a mixture of H2 / H ∞ Using control theory, design the state feedback gain of controller Ψ and the state feedback-feedforward gain matrix of controller Φ, and establish a linear closed-loop system model.

[0097] In this step, a mixture of H2 / H2 is used. ∞ Based on control theory, two controllers (controller Ψ and controller Φ) were designed to achieve the same control objective. Controller Ψ calculates the system's control input u as a state feedback control law, while controller Φ, in addition to the model state, also uses the acceleration a of the vehicle ahead. l The system's control input u is calculated as a state feedback-feedforward control law.

[0098] 2. The design problem of two controllers is expressed as follows: Figure 3The optimization problem shown involves designing a 2-norm optimization objective z2 and an infinite-norm optimization objective z2 for each controller. ∞ The goal is to achieve the following control objectives: minimize and constrain the maximum position error of the front and rear vehicles and the acceleration of the rear vehicle, and minimize and constrain the L2 norm of the position error and the control input.

[0099] The outputs z2 and z of the two controllers ∞ The design resulted in the following system:

[0100]

[0101] z ∞ =[a f e p ] T =C F ·x(t)+D i ·ω(t)

[0102] z2=[e p u] T =C Hi ·x(t)+D i ·ω(t)

[0103] i = Ψ, Φ

[0104] in,

[0105] A Ψ =A+B1K Ψ

[0106] A Φ =A+B1K Φ1

[0107] B Ψ =B2

[0108] B Φ =B2+B1K Φ2 ,

[0109]

[0110] D Ψ =0

[0111] D Φ =K Φ2

[0112] in, Let A be the derivative of the system state vector (i.e., the relative dynamic model of the train at time t), representing the rate of change of the system state vector x(t) with time. i B is the state matrix of the controller. i C is the disturbance input matrix of the controller. FD is the output matrix with infinite norm. i B1 is the gain matrix of the controller, B2 is the control input matrix used to adjust the feedback controller, and C is the input matrix used to model the uncertainties and disturbances in the system. Hi Let be the L2 norm output matrix, and i be the identifier of the controller.

[0113] 3. Set the acceleration of the vehicle in front as the disturbance vector ω, and apply the disturbance vector ω to the output z2 and z ∞ The effect is represented by a transfer function:

[0114]

[0115]

[0116] i = Ψ, Φ

[0117] The outputs of the corresponding infinite norm transfer function and the outputs of the 2-norm transfer function perform the function of an interference amplifier.

[0118] in, From ω to z ∞ The transfer function, Let F be the transfer function from ω to z2. i Representing the infinite norm z ∞ The direct transfer matrix of the output, H i Let represent the direct transfer matrix of the output of the 2-norm z2, and s be the complex frequency variable in the Laplace transform.

[0119] According to the transfer function and It is possible to analyze the effect of disturbance ω on the system optimization objective z ∞ The influence of z2 is also considered. These transfer functions provide a theoretical basis for subsequent optimization of the control gain using linear matrix inequalities (LMI). Specifically, the LMI method incorporates these transfer function models into the constraints, thereby solving for a suitable gain matrix during the optimization process to ensure that the steady-state performance and dynamic response of the system meet the design requirements.

[0120] 4. Using Lyapunov functions, solve for the gain matrices of the two controllers through linear matrix inequalities.

[0121] The linear matrix inequality for solving the feedback gain of controller Ψ is shown below:

[0122]

[0123] The linear matrix inequalities for solving the feedback gain and feedforward gain of the controller Φ are shown below:

[0124]

[0125] Where γ1 is the slack variable used to adjust the boundary conditions of the linear matrix inequality, Z represents the performance constraint, trace(Z) represents the sum of the diagonal elements of the matrix, X is the covariance matrix describing the system state, Y is the control gain matrix, I is an all-one matrix, and D... ∞,1 D is the transfer function matrix representing the effect of controller gain on disturbance response. ∞,2 Let C1 be the transfer function matrix of the system in the face of external disturbances, C2 be the output matrix with L2 norm, and D be the output matrix with L2 norm. 2,1 This is the transfer function matrix that describes the performance of the 2-norm.

[0126] By employing the Linear Matrix Inequality (LMI) method and combining it with the disturbance response of the transfer function, the feedback gain matrix K of the controller was optimized. Ψ and feedforward gain matrix K Φ This optimization process ensures that the system can maintain the expected output performance under disturbances. It also guarantees performance indicators such as the maximum positional error between the front and rear vehicles, acceleration constraints, and the L2 norm of the control input.

[0127] V. Construct a safe interval.

[0128] In this embodiment of the invention, a safe distance between the preceding and following vehicles is constructed based on their speed information, combined with their emergency braking performance and safety margin information. This mainly includes the following steps:

[0129] 1. Consider the scenario of emergency braking of the vehicle in front, where the fluctuation of acceleration has the most severe impact on the closed-loop system.

[0130] In this step, consider the following autonomous linear system:

[0131]

[0132] Using sampling time T s Discretizing it yields:

[0133]

[0134] The one-step forward reachable set of the above closed-loop system can be computed as follows:

[0135]

[0136] in, and The coefficient matrix represents the state equations of two different discrete post-controllers Ψ. and The coefficient matrix represents the state equations of two different discrete-time controllers Φ, where k is the sampling time. Given uncertain initial conditions, The set of additional perturbations, Reach is the forward reachable set, and ° represents element-wise multiplication between matrices or vectors. For Minkowski and operators.

[0137] The study investigates when the system is subjected to additional disturbances. When starting from uncertain initial conditions Starting from this point, can the growth of all state trajectories of the system remain within the allowable set? Within this range, we obtain the N-step forward reachable set, which proves the system's ability to tolerate disturbances.

[0138] 2. In order to obtain the minimum safety margin, it should be reflected in the fact that it can guarantee that the following error after a complete stop is 0. Therefore, the initial state set is set near the equilibrium point.

[0139] In this step, we consider the initial state to be near the equilibrium point of the closed-loop system described above, i.e. and The disturbance set ω is defined as the acceleration of the previous train, i.e.

[0140] 3. From the moment the vehicle in front begins emergency braking until it comes to a complete stop, such as... Figure 4 The absolute value of the maximum following error obtained by performing N-step iterative calculation of the forward reachable set is the minimum safety margin to be sought.

[0141] In this step, the absolute value of the maximum position error calculated is the headway h. f The expected value of d when it is 0 min This shows that under the influence of How much margin is needed to ensure system safety in the event of interference?

[0142] VI. Train driving strategy generation process.

[0143] In this embodiment of the invention, corresponding decision information is obtained by combining set constraints, the train operation data of the preceding vehicle, the vehicle's own operation data, and the controller's decision-making process. The set constraints include following error, acceleration difference, and constraints that the control input u must satisfy, forming a system allowable set. The train operation data of the preceding vehicle and the vehicle's own operation data include the position, speed, and acceleration of the preceding vehicle, and the position, speed, and acceleration of the following vehicle. The system allowable set, the train operation data of the preceding vehicle, and the vehicle's own operation data are input to the controller. Through the controller's decision-making process (including solving for the maximum initial state set and calculating the train's driving control strategy, etc.), the corresponding decision information is obtained.

[0144] In this section, a polyhedron represented by multiple constraints is defined as the system's allowable set, such as... Figure 5As shown, by iterating through the backward reachable set until the reachable set calculated in this step is the same as that in the previous step, the resulting invariant set is the maximum initial state set we are looking for. Specifically:

[0145] Carrying error includes: position error e p With speed error e v The constraints are expressed as follows: Among them, e p,min and e p,max These are the minimum and maximum allowable deviations from the position of the vehicle in front, respectively, e v,min and e v,max These are the minimum and maximum permissible deviations from the speed of the vehicle in front, respectively; for example: e p,min The distance between the trains must be greater than d. min +v f h f e p,max It can be selected based on performance standards.

[0146] The constraint condition for the acceleration difference is the acceleration constraint for the following vehicle, expressed as: Among them, a min and a max These are the minimum and maximum allowable accelerations, respectively.

[0147] An actuator is a device that executes the control input u, and the constraints are as follows:

[0148] u min ≤K Ψ x(k)≤u max ,

[0149]

[0150] Among them, u min and u max These are the minimum and maximum accelerations that the actuator can provide, respectively.

[0151] The above constraints are represented as a polyhedral set of system allowables:

[0152]

[0153] in, It is a linear coefficient matrix constrained by a polyhedron. It is the right-hand side term of the inequality for each constraint.

[0154] For the closed-loop system described above, the one-step backward reachable set can be written as:

[0155]

[0156] One-step backward reachable sets can also be computed using Pontryagin differences and affine mappings of polyhedra:

[0157]

[0158] in, For Pontryagin differences, ° represents element-wise multiplication between matrices or vectors, and Pre is the backward reachable set. It is the symbol for the set of real numbers.

[0159] For any disturbance We need to find the largest set of initial states. To ensure that the state trajectory of the closed-loop system always lies within the allowable set Internal. The maximum initial state set of a constrained autonomous system. It can be represented as:

[0160]

[0161] k = 0, 1, 2...

[0162] Where f(.) is the autonomous system function.

[0163] Maximum initial state set The maximum initial state set can be obtained by iterating through the backward reachable set until the reachable set calculated in this step is the same as that in the previous step. The invariant set obtained is the maximum initial state set. Then, the decision information can be obtained by combining it with the train driving control strategy.

[0164] Therefore, let U = [u min ,u max Given the complete set of control variables, the train driving control strategy is as follows:

[0165]

[0166] in Represents a set Inside Represents a set The boundary of the decision information is the control input (the desired acceleration of the following vehicle) obtained through the above formula.

[0167] like Figure 6 The diagram illustrates the overall process of a safe following-driving method for trains. It is a closed-loop process: the following train receives train operation data from the preceding train, calculates the following-driving error, constructs a relative dynamic model of the train, establishes a safe distance, iterates the maximum initial state set using the system's allowable set, and combines this with a hybrid H2 / H... ∞The controller, designed using control theory, generates decision information, which is then used by the following train to control the train's operation. Simultaneously, it continues to receive train operation data from the preceding train and begins a new cycle.

[0168] Through the above description of the embodiments, those skilled in the art can clearly understand that the above embodiments can be implemented by software, or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of the above embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.), including several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0169] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims. The information disclosed in the background section is intended only to enhance the understanding of the overall background technology of the present invention and should not be construed as an admission or implication in any way that such information constitutes prior art known to those skilled in the art.

Claims

1. A train safety following driving method suitable for a car-to-car communication train control system, characterized in that, The following steps are performed by the equipment in the rear vehicle: It periodically receives train operation data sent by the preceding train; Based on the received train operation data from the preceding train, and combined with the predetermined safety margin and time distance requirements, the following error with the preceding train is calculated. Based on the car-following error, a relative dynamic model of trains is constructed to describe the relative motion relationship between trains; Set up a train following error identification strategy, which includes: using a hybrid approach. / Control theory, controller design State feedback gain and controller The state feedback-feedforward gain matrix is ​​used to establish a linear closed-loop system model; the design problem of the two controllers is expressed as an optimization problem, and the L2 norm optimization objective of each controller is designed. And the optimization objective of the infinite norm The control objectives are as follows: minimize and constrain the maximum position error between the front and rear vehicles and the acceleration of the rear vehicle, and minimize and constrain the L2 norm of the position error and the control input; the acceleration of the front vehicle is set as the disturbance vector. , the perturbation vector For output and The effect is represented by a transfer function, with the corresponding infinity norm and L2 norm affecting the desired output, respectively, to perform the function of an interference amplifier; combined with Lyapunov functions, the gain matrices of the two controllers are solved using linear matrix inequalities; where, for the outputs of the two controllers... and The design resulted in the following system: ； ； ； ； ； ； ； ； ； ； in, The derivative of the system state vector, i.e., the train's relative dynamic model at time t, represents the system state vector. rate of change over time The state matrix of the controller, The interference input matrix for the controller, Output matrix with infinite norm. The gain matrix of the controller. It is the control input matrix used to adjust the feedback controller. It is the input matrix used to model uncertainties and disturbances in a system. For controller The feedback gain matrix, For controller The feedforward gain matrix, and for The specific amount, The output matrix is ​​a 2-norm matrix. Here, is the identifier for the controller, and u is the control input to the system. To accelerate the following vehicle, For positional error; matrix , For time interval requirements, It is a time constant; Calculate the minimum safety margin and set the safety interval based on the minimum safety margin, including: setting the acceleration of the preceding vehicle as the disturbance vector, calculating the forward reachable set in N steps from the moment the preceding vehicle begins to implement emergency braking until it comes to a complete stop, and the absolute value of the maximum following error obtained is the minimum safety margin. The decision-making process of the controller is designed by combining the train relative dynamic model, the following error, and the set safety interval. By combining the set constraints, the train's operating data from the preceding train, the train's own operating data, and the controller's decision-making process, corresponding decision information is obtained, and the train's operation is controlled accordingly. The process of obtaining the corresponding decision information by combining the set constraints, the preceding train's operating data, the train's own operating data, and the controller's decision-making process includes: the set constraints being the following error, acceleration difference, and constraints that the control input must satisfy, which form a system allowable set; the control input being the desired acceleration of the following train; the preceding train's operating data and the train's own operating data including the preceding train's position, speed, and acceleration, and the following train's position, speed, and acceleration; the system allowable set, the preceding train's operating data, and the train's own operating data are input to the controller, and the corresponding decision information is obtained through the controller's decision-making process. Obtaining the corresponding decision information includes: calculating the one-step backward reachable set: ； in, For Pontryagin differences, This represents element-wise multiplication of matrices or vectors. For backward reachable set, and Represents the discrete-time controller The coefficient matrix of the state equation, For the perturbation set, Given uncertain initial conditions, For system-allowed sets; Define the maximum initial state set for: ； in, Represents the sampling time, state vector perturbation vector T is the transpose symbol. The acceleration of the vehicle in front. and This belongs to the category of car-following error. For positional error, For speed error, For autonomous system functions; Maximum initial state set By iterating through the backward reachable set until the reachable set calculated in this step is the same as that in the previous step, the invariant set obtained is the maximum initial state set to be sought. Then, combined with the train driving control strategy, decision information is obtained. make To control the entire set of variables, the train driving control strategy is as follows: ； in, express Inside express The boundary, and the control input obtained through the above formula, is the decision information.

2. The train safety following driving method suitable for a car-to-car communication train control system according to claim 1, characterized in that, The periodic reception of train operation data sent by the preceding vehicle includes: The onboard equipment of the following train periodically receives train operation data sent by the preceding train, including the position, speed and acceleration of the preceding train.

3. A train safety following driving method suitable for a car-to-car communication train control system according to claim 1 or 2, characterized in that, The calculation of the following error with the preceding train based on the received train operation data, combined with the predetermined safety margin and time distance requirements, includes: The following train acquires its own position and speed, and, combined with the train's operating data and predetermined safety margins and time distance requirements, calculates the following error between itself and the preceding train. This following error includes position error and speed error; wherein the position error is calculated as follows: ； in, This represents the position of the preceding train, and its train operation data pertains to that train. These are the position and speed of the following vehicle, respectively. This is a predetermined safety margin.

4. A train safety following driving method suitable for a car-to-car communication train control system according to claim 1 or 2, characterized in that, The process of constructing a relative dynamic model of trains based on the following error to describe the relative motion relationship between trains includes: By using actual line operation data to obtain the transfer function of train acceleration and desired acceleration, and then combining it with the car-following error, a train relative dynamic model is constructed to describe the relative motion relationship of the train. The transfer function between the actual acceleration and the desired acceleration of the train is expressed as: ； in, To provide the desired acceleration for the following vehicle, For static gain, For the complex frequency variable in the Laplace transform, To accelerate the following vehicle; The relative dynamic model of the train is in state-space form, represented as follows: ； in, This is the relative dynamic model of the train at time t, i.e., the state vector at time t. The derivative of the state vector Control input perturbation vector T is the transpose symbol. The acceleration of the vehicle in front. and This belongs to the category of car-following error. For positional error, For speed error, Indicates position error The derivative; There are three matrices, in the form of: 。 5. A train safety following driving method suitable for a car-to-car communication train control system according to claim 1, characterized in that, The set constraints are the following error, acceleration difference, and control input constraints that must be satisfied. These set constraints form the system tolerance set, which includes: Carrying error includes: position error With speed error The constraints are expressed as follows: ; ;in, and These are the minimum and maximum permissible deviations from the position of the vehicle in front, respectively. and These are the minimum and maximum allowable deviations from the speed of the vehicle in front, respectively. The constraint condition for the acceleration difference is the acceleration constraint for the following vehicle, expressed as: ;in, and These are the minimum and maximum allowable accelerations, respectively. The constraints for controlling the input are: ； in, Indicates the sampling time. For controller The feedback gain matrix, For controller The feedforward gain matrix, and yes The amount, and These are the minimum and maximum accelerations, and the state vector. T is the transpose symbol. The acceleration of the vehicle in front. and This belongs to the category of car-following error. For positional error, For speed error; The above constraints are represented as a polyhedral set of system allowables: ； in, It is a linear coefficient matrix constrained by a polyhedron. It is the right-hand side term of the inequality for each constraint.

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