A high-speed train speed tracking method based on TUBE model predictive control

Abstract

The present invention discloses a high-speed train speed tracking method based on TUBE model predictive control, comprising: S1: acquiring the current state information of the high-speed train, including the speed and position; S2: judging whether the set trigger condition is met, and if so, entering step S3, otherwise, entering step S5; S3: performing rolling optimization and solving on the speed and position within the prediction step length to obtain an updated mode curve; S4: taking the updated mode curve as the current mode curve and entering step S5; S5: solving the control level of the train according to the current mode curve, obtaining the solution result and converting it into the corresponding traction braking force; then obtaining the speed and position of the high-speed train at the next moment under the action of the force; judging whether the train arrives at the next station, and if so, ending the speed tracking, otherwise, taking the speed and position at the next moment as the current state information of the train and returning to step S2.

Description

Technical Field

[0001] The present invention relates to the technical field of train control, and in particular to a high-speed train speed tracking method based on TUBE model predictive control. Background Art

[0002] High-speed train automated driving systems are a key technology for increasing high-speed rail transport capacity, improving train technology and equipment, and promoting intelligent, modern, and green development of the railway system. However, due to the large scale of my country's railway network and the complex and ever-changing operating environment, which results in complex external resistance disturbances and exhibits strong nonlinearity and randomness, the design of a speed tracking controller remains a challenge.

[0003] Model Predictive Control (MPC) is a feedback control method based on rolling optimization, suitable for complex industrial control processes and control problems requiring optimization. The designed controller not only meets traditional stability requirements but can also meet better objectives through optimization based on actual needs. MPC can also efficiently handle complex systems with strong control constraints and multiple inputs and outputs. If the system considers uncertainty or external unknown disturbances, using the nominal MPC for deterministic systems will affect its iterative feasibility and closed-loop stability, making it difficult to achieve ideal control results. TubeMPC, on the other hand, designs a state Tube invariant set sequence, which allows the state of the actual uncertain system to be tightened within the Tube invariant set sequence centered on the nominal system state. It also effectively suppresses unknown bounded disturbances by designing an interference invariant set, thereby ensuring that the system converges asymptotically to the terminal invariant set.

[0004] Conventional MPC controllers are time-triggered, requiring periodic updates and transmission of control actions. This results in high computational and communication overhead during the optimization process, posing a challenge for resource-constrained train systems. Event-triggered control, on the other hand, determines whether control updates are necessary based on system conditions, reducing controller switching frequency and improving resource utilization. This provides greater flexibility than time-triggered control. Therefore, consideration was given to combining event-triggered control with model predictive control to reduce the frequency of recalculations.

[0005] In summary, high-speed train speed tracking control is a comprehensive problem, in which the complexity of the high-speed train operating environment and the limited on-board system resources will affect the tracking control accuracy and actual effect. Summary of the Invention

[0006] The purpose of the present invention is to provide a high-speed train speed tracking method based on TUBE model predictive control, so as to reduce the controller recalculation frequency, reduce computing resource consumption and improve passenger comfort.

[0007] The technical solution of the present invention to solve the above technical problems is as follows:

[0008] The present invention provides a high-speed train speed tracking method using a Tube model predictive control, the high-speed train speed tracking method using a Tube model predictive control comprising:

[0009] S1: Acquire current status information of the high-speed train, wherein the current status information includes speed and position;

[0010] S2: Determine whether the set trigger condition is met within the current prediction step length. If so, proceed to step S3; otherwise, proceed to step S5;

[0011] S3: Using the Tube MPC speed tracking controller to perform rolling optimization on the speed and position at the current moment within the prediction step to obtain an updated mode curve;

[0012] S4: taking the updated mode curve as the current mode curve and proceeding to step S5;

[0013] S5: Calculate the control level of the train according to the current mode curve and obtain the calculation result;

[0014] S6: converting the solution result into corresponding traction braking force;

[0015] S7: Obtaining the speed and position of the high-speed train at the next moment under the action of the force according to the traction braking force;

[0016] S8: Determine whether the high-speed train has arrived at the next station based on the position at the next moment. If so, end the current speed tracking of the high-speed train. Otherwise, use the speed and position at the next moment as the current state information of the high-speed train and return to step S2.

[0017] Optionally, step S2 includes:

[0018] S21: Obtaining a weighted speed and position error at the current moment based on the current state information and the reference state information of the high-speed train;

[0019] S22: Determine whether the speed and position weighted error at the current moment is greater than a preset threshold. If so, proceed to step S3; otherwise, proceed to step S23;

[0020] S23: Determine whether the prediction step has reached the maximum prediction step. If so, proceed to step S3; otherwise, proceed to step S5.

[0021] Optionally, in step S22, the preset threshold M is:

[0022]

[0023] in, Represents the weighted error of the speed and position of the i-th prediction step at the current moment, represents the weighted error of the reference velocity position, and Q is the weight coefficient matrix.

[0024] Optionally, the trigger condition is expressed as:

[0025]

[0026] in, Indicates the moment when the state error exceeds the set threshold M, t k Represents the current moment, i represents the i-th prediction step at the current moment, Represents the weighted error of the speed and position of the i-th prediction step at the current moment, represents the weighted error of the reference velocity position, Q is the weight coefficient matrix, and M is the preset threshold.

[0027] Optionally, the Tube MPC speed tracking controller includes a Tube MPC module and a trigger condition judgment module; the Tube MPC module is used to perform rolling optimization on the current speed and the current position to obtain an updated mode curve; the trigger condition judgment module is used to judge whether the set trigger condition is met within the prediction step at the current moment.

[0028] Alternatively, the Tube MPC speed tracking controller is based on the system model accurate nominal MPC controller, introducing an external bounded disturbance w k , the Tube MPC speed tracking controller is defined as:

[0029] x k+1 =Ax k +BΔu k +w k

[0030] in, is the state variable, v k is the speed, s k is the position, Δu k is the control level increment and K is the control law, and are the state variables and control level increments of the nominal MPC, and A and B are the corresponding coefficient matrices.

[0031] Optionally, the performance indicators of the Tube MPC speed tracking controller are:

[0032]

[0033] in, Represents the weighted error of velocity and position at the current moment, Represents the weighted error of the reference velocity position, Q, R, P are different weight coefficient matrices, The term is the speed and position error of the system, which is used to characterize the tracking performance. The term is the control level switching amount of the system, which represents the smooth performance. is the terminal cost term, which is used to ensure the feasibility of iteration.

[0034] The present invention has the following beneficial effects:

[0035] 1) By designing a Tube MPC speed tracking controller, the present invention realizes speed tracking control of high-speed trains that effectively responds to external unknown disturbances under complex line conditions;

[0036] 2) The MPC speed tracking controller involves a large amount of recalculation during train operation, which is computationally intensive. By integrating the event trigger mechanism, the controller recalculation frequency can be reduced, passenger comfort can be improved, and the system's computing and communication resources can be saved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of the high-speed train speed tracking method based on Tube model predictive control of the present invention;

[0038] Figure 2 This is a triggering diagram when the system state error is close to the reference curve state error;

[0039] Figure 3 This is a trigger diagram when the system state error and the reference curve state error are large and exceed the set error threshold M. DETAILED DESCRIPTION

[0040] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0041] The present invention provides a high-speed train speed tracking method based on Tube model predictive control, referring to Figure 1 As shown, the high-speed train speed tracking method based on Tube model predictive control includes:

[0042] S1: Acquire current status information of the high-speed train, wherein the current status information includes speed and position;

[0043] The current state information of the high-speed train of the present invention is obtained through sensors on the train.

[0044] S2: Determine whether the set trigger condition is met within the current prediction step length. If so, proceed to step S3; otherwise, proceed to step S5;

[0045] Specifically, step S2 includes:

[0046] S21: Obtain the current speed position weighted error based on the current state information and the reference state information of the high-speed train

[0047] S22: Determine the speed and position weighted error at the current moment Is it greater than the preset threshold M? If so, proceed to step S3; otherwise, proceed to step S23;

[0048] The preset threshold M is:

[0049]

[0050] in, Represents the weighted error of the speed and position of the i-th prediction step at the current moment, represents the weighted error of the reference velocity position, and Q is the weight coefficient matrix.

[0051] S23: Determine whether the prediction step has reached the maximum prediction step. If so, proceed to step S3; otherwise, proceed to step S5.

[0052] Here, the maximum prediction step length is a suitable value obtained by debugging the controller calculation effect, and the present invention does not impose a specific numerical limit.

[0053] The expression of the trigger condition is:

[0054]

[0055] in, Indicates the moment when the state error exceeds the set threshold M, t k Represents the current moment, i represents the i-th prediction step at the current moment, Represents the weighted error of the speed and position of the i-th prediction step at the current moment, represents the weighted error of the reference velocity position, Q is the weight coefficient matrix, and M is the preset threshold.

[0056] S3: Using the Tube MPC speed tracking controller to perform rolling optimization on the speed and position at the current moment within the prediction step to obtain an updated mode curve;

[0057] In this way, when using the mode curve output by Tube MPC at the previous moment to obtain the corresponding control level and apply control force to the train, if the state error of the system is close to the state error of the reference curve and does not exceed the set error threshold M, such as Figure 2 As shown, it is only necessary to generate an event trigger signal when the maximum prediction step T is reached, and pass the current state to TubeMPC to update the pattern curve, avoiding the rolling optimization solution process of TubeMPC at each prediction step, thus reducing the amount of calculation.

[0058] When the control level at the previous moment causes the system state error to be larger than the reference curve state error and exceeds the set error threshold M, such as Figure 3 As shown in the figure, a trigger signal is also generated to pass the current state to the Tube MPC to update the mode curve, thereby reducing the amount of calculation while ensuring that the controller has tracking performance similar to that of the original Tube MPC.

[0059] S4: taking the updated mode curve as the current mode curve and proceeding to step S5;

[0060] If the trigger condition is not met, the updated mode curve at the last moment is used as the current mode curve to enter step S5. It should be noted that, at the initial moment, the current mode curve can be determined as the reference speed position curve of the train.

[0061] S5: Calculate the control level of the train according to the current mode curve and obtain the calculation result;

[0062] S6: converting the solution result into corresponding traction braking force;

[0063] S7: Obtaining the speed and position of the high-speed train at the next moment under the action of the force according to the traction braking force;

[0064] S8: Determine whether the high-speed train has arrived at the next station based on the position at the next moment. If so, end the current speed tracking of the high-speed train. Otherwise, use the speed and position at the next moment as the current state information of the high-speed train and return to step S2.

[0065] The Tube MPC speed tracking controller provided by the present invention includes a Tube MPC module and a trigger condition judgment module; the Tube MPC module is used to perform rolling optimization on the current speed and the current position to obtain an updated mode curve; the trigger condition judgment module is used to judge whether the set trigger condition is met within the prediction step size at the current moment.

[0066] Alternatively, the Tube MPC speed tracking controller is based on the system model accurate nominal MPC controller, introducing an external bounded disturbance w k , the Tube MPC speed tracking controller is defined as:

[0067] x k+1 =Ax k +BΔu k +w k

[0068] in, is the state variable, v k is the speed, s k is the position, Δu k is the control level increment and K is the control law, and are the state variables and control level increments of the nominal MPC, and A and B are the corresponding coefficient matrices.

[0069] Here, the present invention designs a state feedback control strategy: The Tube disturbance invariant set S is further obtained, so that the disturbed system converges to the invariant set S centered on the state variable trajectory of the nominal model, thereby reducing the fluctuation range of the high-speed train speed and achieving the robustness of the speed controller.

[0070] Optionally, the performance indicators of the Tube MPC speed tracking controller are:

[0071]

[0072] in, Represents the weighted error of velocity and position at the current moment, Represents the weighted error of the reference velocity position, Q, R, P are different weight coefficient matrices, The term is the velocity and position error of the system, which is used to characterize the tracking performance. The term is the control level switching amount of the system, which represents the smooth performance. is the terminal cost term, which is used to ensure the feasibility of iteration.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-speed train speed tracking method based on Tube model predictive control, characterized in that: The high-speed train speed tracking method using Tube model predictive control includes: S1: Acquire current status information of the high-speed train, wherein the current status information includes speed and position; S2: Determine whether the set trigger condition is met within the current prediction step length. If so, proceed to step S3; otherwise, proceed to step S5; The expression of the trigger condition is: in, Indicates that the status error exceeds the preset threshold moment, t k Indicates the current moment, Indicates the current moment prediction step length, Indicates the current moment The weighted error of the velocity position of the prediction step, represents the weighted error of the reference velocity position, Q is the weight coefficient matrix; S3: Using the Tube MPC speed tracking controller to perform rolling optimization on the speed and position at the current moment within the prediction step to obtain an updated mode curve; The Tube MPC speed tracking controller is based on the system model accurate nominal MPC controller, introducing external bounded disturbance , the Tube MPC speed tracking controller is defined as: in, is the state variable, For speed, For location, is the control level increment and , is the control law, and are the nominal MPC state variables and control level increments, and is the corresponding coefficient matrix; The performance indicators of the Tube MPC speed tracking controller are: in, , are different weight coefficient matrices, The term is the velocity and position error of the system, which is used to characterize the tracking performance. The term is the control level switching amount of the system, which represents the smooth performance. is the terminal cost term, used to ensure the feasibility of iteration; S4: taking the updated mode curve as the current mode curve and proceeding to step S5; S5: Calculate the control level of the train according to the current mode curve and obtain the calculation result; S6: converting the solution result into corresponding traction braking force; S7: Obtaining the speed and position of the high-speed train at the next moment under the action of the force according to the traction braking force; S8: Determine whether the high-speed train has arrived at the next station based on the position at the next moment. If so, end the current speed tracking of the high-speed train. Otherwise, use the speed and position at the next moment as the current state information of the high-speed train and return to step S2.

2. The high-speed train speed tracking method based on Tube model predictive control according to claim 1, characterized in that: The step S2 comprises: S21: Obtaining a weighted speed and position error at the current moment based on the current state information and the reference state information of the high-speed train; S22: Determine whether the speed and position weighted error at the current moment is greater than a preset threshold. If so, proceed to step S3; otherwise, proceed to step S23; S23: Determine whether the prediction step has reached the maximum prediction step. If so, proceed to step S3; otherwise, proceed to step S5.

3. The high-speed train speed tracking method based on TUBE model predictive control according to claim 2, characterized in that: In step S22, the preset threshold for: 。 4. The high-speed train speed tracking method based on TUBE model predictive control according to claim 1, characterized in that: The Tube MPC speed tracking controller includes a Tube MPC module and a trigger condition judgment module. The Tube MPC module is used to perform rolling optimization on the current speed and the current position to obtain an updated mode curve; The trigger condition judgment module is used to judge whether the set trigger condition is met within the prediction step at the current moment.

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