Compensation control method and system for overhead contact line maintenance vehicles to address response delay

Abstract

This invention discloses a compensation control method and system for overhead contact line maintenance train sets to address response delays. The method includes: acquiring the desired vehicle speed trajectory and system delay parameters; constructing a delay prediction data model containing a partial derivative matrix using dynamic linearization technology; designing a model-free delay compensation predictive controller based on real-time speed deviation, and updating the predictive control gain matrix online by integrating the integral and differential characteristics of the speed deviation; furthermore, dynamically adjusting the controller parameters through an asymmetric parameter mechanism to prioritize braking safety and generate the final control command. This invention does not rely on a precise mathematical model of the train set, effectively compensates for system response delays, and achieves high-precision, high-reliability automatic positioning of the train set under complex operating conditions by introducing an asymmetric safety mechanism.

Description

Technical Field

[0001] This invention belongs to the field of rail transit automation control technology, and particularly relates to a compensation control method and system for overhead contact line maintenance train sets to address response delays. Background Technology

[0002] The inspection and maintenance of the overhead contact system of electrified railways is a crucial link in ensuring the safe operation of railways. Currently, overhead contact system maintenance is transitioning from a traditional manual mode to an intelligent and automated mode. Among the key technologies, achieving precise automatic positioning (stopping) of maintenance train sets at designated work points is one of the core technologies. The train set positioning system needs to take over vehicle control when running at low speeds, and automatically control the traction and braking system of the train set according to the target distance provided by the positioning system, so that it stops precisely at the target point.

[0003] However, the traction and braking system of the train set inherently suffers from system response delay. This delay stems from various factors, including the transmission of control commands, the polling processing of the controller, and the pressure build-up time of the pneumatic or hydraulic braking system, with a total delay that can reach hundreds of milliseconds or even seconds. Under conditions of significant delay, traditional control methods (such as PID control) are prone to causing system overshoot, oscillation, or even instability, failing to meet the requirements of high-precision positioning. Furthermore, existing solutions generally lack safety mechanisms specifically designed to enhance the braking process.

[0004] The operating environment of overhead contact line maintenance vehicles is complex and variable, belonging to typical nonlinear and time-varying systems, making it extremely difficult to establish accurate mathematical models. Model-free adaptive predictive control, as a data-driven method, is based on dynamic linearization technology, transforming the controller design problem into a parameter optimization problem within a linearized framework, offering advantages such as simple structure and convenient design. However, existing methods still fall short in predictive and compensation capabilities when dealing with the inherent system response delays of the vehicles; simultaneously, their conventional control law design limits the system's potential in suppressing steady-state errors and improving dynamic response quality. Furthermore, existing methods generally employ a symmetrical control parameter design strategy, failing to reflect the differentiated safety priority requirements of the overhead contact line vehicles during braking at the algorithmic level, making it difficult to simultaneously ensure the accuracy of the positioning process and operational reliability in complex and variable field environments.

[0005] In summary, there is an urgent need in this field for an intelligent control method that does not rely on a precise mathematical model of the controlled object, can adaptively compensate for system response delay, and can achieve high-precision and stable positioning of the train under complex working conditions. Summary of the Invention

[0006] To address the problems existing in the background art, the present invention aims to provide a compensation control method for overhead contact line maintenance vehicles to address response delays, the method comprising the following steps:

[0007] Obtain the positioning braking trajectory of the maintenance vehicle crew and the system response delay model parameters to generate the desired vehicle speed trajectory;

[0008] The real-time operating status of the maintenance vehicle group is collected, and the nonlinear maintenance vehicle group tracking and control system with response delay is transformed into a delay prediction data model using dynamic linearization technology. The delay prediction data model contains a partial derivative matrix, and the partial derivative matrix can be updated with the sampling time.

[0009] Based on the speed deviation between the real-time vehicle speed and the desired vehicle speed, a model-free delay compensation predictive controller is designed. A predictive control gain matrix update algorithm that considers the integral and derivative characteristics of the speed deviation is designed. The predictive control gain matrix is ​​updated and corrected in real time using the delay prediction data model.

[0010] An asymmetric parameter mechanism is designed to dynamically update the model-free delay compensation predictive controller, generate control commands to eliminate the speed deviation, and output the control commands to the train traction and braking system.

[0011] The model-free delay compensation predictive controller is continuously updated to obtain control commands to eliminate the speed deviation, drive the vehicle group to accurately track the desired vehicle speed trajectory, and achieve precise positioning of the vehicle group at the target point.

[0012] The method for designing a model-free delay compensation predictive controller based on the speed deviation between the real-time vehicle speed and the desired vehicle speed is as follows:

[0013] ;

[0014] in, This represents the sampling time, where N is the prediction step size, and N is a positive integer. This represents the N-step forward delay compensation predictive control command vector. , , express The velocity deviation vector at the sampling time takes into account the response delay. , for The expected vehicle speed vector considering response delay at the sampling time. , express The expected vehicle speed at the sampling time takes into account the response delay; Represents a vector consisting entirely of 1s; express Real-time vehicle speed at the time of sampling; express The first-order forward difference, , M represents the order of the model-free delay-compensated predictive controller, and M is a positive integer; For predicting the control gain matrix;

[0015] ;

[0016] in, This represents the NLth prediction control gain element predicted in step N.

[0017] Furthermore, a predictive control gain matrix update algorithm considering the integral and derivative characteristics of the speed deviation is designed. The method for updating and correcting the predictive control gain matrix in real time using the delay prediction data model is as follows:

[0018] Construct a performance optimization objective function that considers the integral and differential characteristics of the speed deviation:

[0019] ;

[0020] in, Represent the performance optimization objective function; for The expected vehicle speed vector considering response delay at the sampling time. , express The expected vehicle speed at the sampling time takes into account the response delay; For the N-step forward vehicle speed prediction vector that takes into account response delay, , express Real-time vehicle speed at the time of sampling; express The first-order forward difference, express The velocity deviation vector at the sampling time takes into account the response delay; express The first-order forward difference; This represents the integral weighting factor for speed deviation. This represents the differential weighting factor for the speed deviation. Indicates the weighting factor of control instructions;

[0021] The objective function is solved using gradient descent to optimize and update the predictive control gain matrix. :

[0022]

[0023] in, To learn the law, for Compared to The Jacobian matrix.

[0024] Furthermore, Compared to The Jacobian matrix is ​​calculated as follows:

[0025] ;

[0026] in, , This refers to the partial derivative matrix in the delayed prediction data model; .

[0027] Furthermore, the method for transforming a nonlinear maintenance vehicle tracking control system with response delay into a delay prediction data model using dynamic linearization technology includes the following steps:

[0028] Constructing a delay prediction data model for a nonlinear maintenance vehicle tracking control system:

[0029] ;

[0030] in, Indicates the sampling time. Indicates the system response delay time. For the N-step forward vehicle speed prediction vector that takes into account response delay, N is the prediction step size; , express Real-time vehicle speed at the time of sampling; This represents the first-order forward difference of the N-step forward delay compensation predictive control command vector. express Delay-compensated predictive control commands at sampling time express First-order forward difference, ; express The partial derivative matrix at the sampling time, , express Partial derivatives at the sampling time;

[0031] Furthermore, the method for updating the partial derivative matrix is ​​as follows:

[0032] Construct the cost function:

[0033] ;

[0034] in, Represents a function; ; Indicates the limiting factor;

[0035] The cost function is solved using the function extremum method to obtain... Partial derivatives at sampling time The renewal law:

[0036] ;

[0037] in, The step size factor represents the partial derivative;

[0038] Calculation using a multi-level hierarchical forecasting method Partial derivative variable at sampling time The update law, where j = 1, 2, ..., N-1;

[0039] Define variables , This is the autoregressive coefficient vector. The nth autoregressive coefficient; based on Partial derivatives at sampling time The update law, calculation The update law is thus obtained. Partial derivatives at sampling time The renewal law:

[0040] ;

[0041] ;

[0042] Where n represents the appropriate order; ; It is a positive number. ; Represents the 2-norm;

[0043] based on Partial derivatives at sampling time The renewal law, Partial derivatives at sampling time The update law optimizes the update of the partial derivative matrix. .

[0044] Furthermore, an asymmetric parameter mechanism is designed to dynamically update the model-free delay-compensated predictive controller, generate the final control command used to eliminate the speed deviation, and output the control command to the train traction and braking system, including the following steps:

[0045] Define and calculate Braking intensity at sampling time ;

[0046] Determine by using a pre-defined monotonically non-increasing mapping function. Velocity deviation integral weighting factor used at sampling time Speed ​​deviation differential weighting factor Control instruction weighting factor The monotonically non-increasing mapping function is configured such that the greater the braking intensity, , , The smaller the value of ;

[0047] Set the speed deviation integral weight factor Speed ​​deviation differential weighting factor Control command weighting factor Substitute this into the update algorithm for the predictive control gain matrix; calculate The first element is output to the train's traction and braking system, and the control command at sampling time k is ultimately used to eliminate the speed deviation. for:

[0048] .

[0049] A catenary maintenance vehicle compensation control system for response delay, used to implement the aforementioned catenary maintenance vehicle compensation control method for response delay, the system comprising:

[0050] The trajectory generation module is used to obtain the positioning braking trajectory of the maintenance vehicle group and the system response delay model parameters to generate the desired vehicle speed trajectory.

[0051] The data modeling module is used to collect the real-time operating status of the maintenance vehicle group and use dynamic linearization technology to transform the nonlinear maintenance vehicle group tracking and control system with response delay into a delay prediction data model. The delay prediction data model contains a partial derivative matrix, and the partial derivative matrix can be updated with the sampling time.

[0052] The controller calculation module is used to design a model-free delay compensation predictive controller based on the speed deviation between the real-time vehicle speed and the desired vehicle speed, design a predictive control gain matrix update algorithm that considers the integral and derivative characteristics of the speed deviation, and update and correct the predictive control gain matrix in real time using the delay prediction data model.

[0053] The safety decision module is used to design an asymmetric parameter mechanism, dynamically update the model-free delay compensation predictive controller, generate control commands to eliminate the speed deviation, and output the control commands to the train traction and braking system.

[0054] The control output module is used to continuously update the model-free delay compensation predictive controller, obtain control commands to eliminate the speed deviation, drive the train to accurately track the desired speed trajectory, and achieve precise positioning of the train at the target point.

[0055] Furthermore, the present invention adopts the following technical solution:

[0056] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for compensation control of overhead contact line maintenance vehicles for response delay.

[0057] Furthermore, the present invention adopts the following technical solution:

[0058] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for compensating for response delays in overhead contact line maintenance vehicles.

[0059] The beneficial technical effects of this invention are as follows:

[0060] (1) Effectively improves the compensation capability and control accuracy of system response delay: This invention can identify and dynamically compensate for response delay caused by factors such as instruction transmission, processor polling and braking system decompression in real time without relying on the precise mathematical model of the train. This data-driven delay compensation mechanism fundamentally overcomes the control failure problem caused by model mismatch in traditional model-dependent methods, and significantly improves the final stopping accuracy of the train at the target point.

[0061] (2) Enhanced dynamic response performance and adaptability of the control system: By taking into account the integral and differential characteristics of speed deviation in the design of the predictive controller and designing a corresponding gain matrix update algorithm, the system can not only effectively suppress steady-state error, but also make a more sensitive and smoother response to rapid changes in vehicle speed; This design makes the train set more adaptable and robust when facing complex disturbances, ensuring the smoothness and stability of the positioning process.

[0062] (3) An innovative adaptive mechanism for braking safety is introduced, which significantly improves the system's operational reliability: The designed asymmetric parameter mechanism can dynamically adjust the controller's weight parameters according to the real-time braking intensity, prioritizing the system's safety and stability during braking; This safety-oriented differentiated control strategy can strictly constrain the amplitude and rate of change of control commands while pursuing high-precision positioning, effectively preventing safety hazards such as impact exceeding limits, and achieving a unity of precise positioning and operational reliability. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the catenary maintenance vehicle compensation control method for response delay provided in an embodiment of the present invention.

[0064] Figure 2 This is a schematic diagram of the connection of the overhead contact line maintenance vehicle group compensation control system module for response delay provided in an embodiment of the present invention. Detailed Implementation

[0065] This invention discloses a compensation control method and system for overhead contact line maintenance train sets to address response delays. It obtains the desired vehicle speed trajectory and system delay parameters; constructs a delay prediction data model including a partial derivative matrix using dynamic linearization technology; designs a model-free delay compensation predictive controller based on real-time speed deviations, and updates the predictive control gain matrix online by integrating the integral and differential characteristics of the speed deviations; furthermore, it dynamically adjusts the controller parameters through an asymmetric parameter mechanism to prioritize braking safety and generate the final control command. This invention does not rely on a precise mathematical model of the train set, effectively compensates for system response delays, and achieves high-precision, high-reliability automatic positioning of the train set under complex operating conditions by introducing an asymmetric safety mechanism.

[0066] The following description, in conjunction with the accompanying drawings, provides a clearer and more complete account of the catenary maintenance vehicle compensation control method and system for response delay provided by the present invention:

[0067] Example 1

[0068] Figure 1 This is a flowchart illustrating the catenary maintenance vehicle compensation control method for response delay provided in this embodiment. The catenary maintenance vehicle compensation control method for response delay provided in this embodiment includes the following steps:

[0069] Step (1): Obtain the positioning braking trajectory of the maintenance vehicle group and the system response delay model parameters, and generate the desired vehicle speed trajectory;

[0070] Step (1.1): Method for obtaining system response delay model parameters

[0071] System response delay is the time required for a vehicle to receive a control command and then actually produce a change in acceleration. The parameters of the system response delay model are typically obtained through the following experimental measurements and system identification methods:

[0072] Experimental method: In a test line or laboratory simulation environment, a step control command (e.g., suddenly switching from 0 to a fixed braking gear) is applied to the traction / braking system of the train set, while the actual acceleration response of the train set is measured with high precision.

[0073] Parameter extraction: By analyzing the time difference between the moment the control command is issued and the moment when the acceleration begins to change significantly, the system's response delay time can be obtained. The causes of this delay may include communication delay, controller processing cycle, mechanical / electrical inertia of the actuator, etc.

[0074] Step (1.2): Method for generating the desired vehicle speed trajectory

[0075] The desired vehicle speed trajectory is generated based on a kinematic model. Given a target stopping point, the system calculates an ideal speed-distance curve backward from the current position to ensure the vehicle can smoothly and accurately decelerate to the target stopping point. Specifically: First, it utilizes the current vehicle position, the target stopping point position, the maximum permissible deceleration (determined by civil engineering conditions, road speed limits, and vehicle performance), and the impact rate considering comfort; second, it is based on the uniformly accelerated motion formula... (where the final velocity) The ideal speed should be calculated at a constant deceleration g, when the vehicle is S meters away from the stopping point. Finally, for a smoother braking, a deceleration curve is usually designed. For example, the vehicle starts braking with a small deceleration, then gradually increases to the maximum deceleration, and finally decreases the deceleration when approaching the stopping point to achieve a smooth stop. This curve is the "desired speed trajectory". The ultimate purpose of generating the desired speed trajectory is to provide a dynamic desired speed tracking target for the entire control system.

[0076] Step (2): Collect the real-time operating status of the maintenance vehicle group, and use dynamic linearization technology to transform the nonlinear maintenance vehicle group tracking and control system with response delay into a delay prediction data model, wherein the delay prediction data model contains a partial derivative matrix.

[0077] Step (3): Based on the speed deviation between the real-time vehicle speed and the desired vehicle speed, design a model-free delay compensation predictive controller, design a predictive control gain matrix update algorithm that considers the integral and derivative characteristics of the speed deviation, and use the delay prediction data model to update and correct the predictive control gain matrix in real time.

[0078] Step (4): Design an asymmetric parameter mechanism to dynamically update the model-free delay compensation predictive controller described in step (3) and generate the final control command to eliminate the speed deviation;

[0079] Step (5): Output control commands to the train traction and braking system to drive the train to accurately track the desired speed trajectory and achieve precise positioning of the train at the target point.

[0080] It should be noted that the partial derivative matrix in step (2) can be updated at any time with the sampling time. The prediction control gain matrix in step (3) is updated by updating the delayed prediction data model. At the same time, the prediction control gain matrix in step (3) is updated at any time through the asymmetric parameter mechanism designed in step (4), thereby realizing the collaborative update of the modelless delay compensation prediction controller, obtaining the updated delay compensation prediction controller, and further obtaining the control command to eliminate the speed deviation.

[0081] According to the catenary maintenance train compensation control method for response delay provided by the present invention, step (2) involves collecting the real-time operating status of the train, which includes at least real-time vehicle speed and real-time position; using dynamic linearization technology, the nonlinear train tracking control system with response delay is transformed into a delay prediction data model, which includes a partial derivative matrix and can be updated with the sampling time; the specific process includes the following steps:

[0082] Step (2.1): The nonlinear train tracking control system with response delay is described in the following form:

[0083] ;

[0084] Where k represents the sampling time, express Real-time vehicle speed at the time of sampling. express Delay-compensated predictive control commands at sampling time Indicates system delay time. , These are the system order, , It is a positive integer. Represents an unknown nonlinear function;

[0085] Step (2.2): Using the dynamic linearization method proposed in Section 6.2 of "Hou Zhongsheng, Jin Shangtai. Model-Free Adaptive Control: Theory and Application [M]. Science Press, 2013," a delay prediction data model for the nonlinear train tracking control system is constructed:

[0086] ;

[0087] in, Indicates the sampling time. This represents the system response delay time, which is extracted from the system response delay model parameters in step (1). For the N-step forward vehicle speed prediction vector that takes into account response delay, N is the prediction step size, and N is a positive integer; , express Real-time vehicle speed at the time of sampling; This represents the first-order forward difference of the N-step forward delay compensation predictive control command vector. express Delay-compensated predictive control commands at sampling time express First-order forward difference, ; express The partial derivative matrix at the sampling time, , express Partial derivatives at the sampling time;

[0088] Step (2.3): Construct the cost function:

[0089]

[0090] in, Represents a function; ; Indicates the limiting factor;

[0091] The cost function is solved using the function extremum method to obtain... Partial derivatives at sampling time The renewal law:

[0092] ;

[0093] in, The step size factor represents the partial derivative;

[0094] Step (2.4): Calculate using a multi-level hierarchical forecasting method Partial derivative variable at sampling time The update law, where j = 1, 2, ..., N-1;

[0095] Define variables , This is the autoregressive coefficient vector. The nth autoregressive coefficient; based on Partial derivatives at sampling time The update law, calculation The update law is thus obtained. Partial derivatives at sampling time The renewal law:

[0096] ;

[0097] ;

[0098] Where n represents an appropriate order, which is generally an integer between 2 and 7; ; It is a positive number. ; Represents the 2-norm;

[0099] based on Partial derivatives at sampling time The renewal law, Partial derivatives at sampling time The update law optimizes the partial derivative matrix described in update step (2.1). .

[0100] According to the catenary maintenance train compensation control method for response delay provided by the present invention, step (3) involves designing a model-free delay compensation predictive controller based on the speed deviation between the real-time vehicle speed and the desired vehicle speed, designing a predictive control gain matrix update algorithm that considers the integral and derivative characteristics of the speed deviation, and using the delay prediction data model to update and correct the predictive control gain matrix in real time, including the following steps:

[0101] Step (3.1): Based on the speed deviation between the real-time vehicle speed and the desired vehicle speed, the method for designing a model-free delay compensation predictive controller is as follows:

[0102] ;

[0103] in, This represents the sampling time, where N is the prediction step size, and N is a positive integer. This represents the N-step forward delay compensation predictive control command vector. , , express The velocity deviation vector at the sampling time takes into account the response delay. , for The expected vehicle speed vector considering response delay at the sampling time. , express The expected vehicle speed at the sampling time takes into account the response delay; Represents a vector consisting entirely of 1s; express Real-time vehicle speed at the time of sampling; express The first-order forward difference, , M represents the order of the model-free delay-compensated predictive controller, and M is a positive integer; For predicting the control gain matrix;

[0104] ;

[0105] in, This represents the NLth prediction control gain element predicted in step N;

[0106] Step (3.2): Construct a performance optimization objective function that considers the integral and differential characteristics of the speed deviation:

[0107] ;

[0108] in, Represent the performance optimization objective function; For the N-step forward vehicle speed prediction vector that takes into account response delay, ; express The first-order forward difference; This represents the integral weighting factor for speed deviation. This represents the differential weighting factor for the speed deviation. Indicates the weighting factor of control instructions;

[0109] Step (3.3): Solve the objective function described in step (3.2) using the gradient descent method to optimize and update the predictive control gain matrix. :

[0110]

[0111] in, To learn the law, for Compared to The Jacobian matrix is ​​calculated as follows:

[0112] ;

[0113] in, , This refers to the partial derivative matrix in the delayed prediction data model; .

[0114] According to the catenary maintenance train compensation control method for response delay provided by the present invention, step (4) designs an asymmetric parameter mechanism to dynamically update the model-free delay compensation predictive controller described in step (3) and generate the final control command for eliminating the speed deviation, including the following steps:

[0115] Step (4.1): Define and calculate Braking intensity at sampling time ;

[0116] It should be noted that the L2 norm This calculation measures the length of a vector in a multidimensional space. This vector represents the sequence of changes in control commands planned to be applied within the prediction time domain. Here, braking intensity is defined as the total control effort or adjustment magnitude that the controller plans to make within a future prediction time domain. The magnitude of braking intensity directly reflects the current aggressiveness of the control system; a larger braking intensity indicates a more aggressive control. This indicates that the system is about to enter a transient process with drastic changes in control commands (such as emergency braking), at which point the system faces a high risk of actuator saturation and nonlinear dynamic instability.

[0117] Step (4.2): Determine using a pre-defined monotonically non-increasing mapping function. Velocity deviation integral weighting factor used at sampling time Speed ​​deviation differential weighting factor Control instruction weighting factor The monotonically non-increasing mapping function is configured such that the greater the braking intensity, , , The smaller the value of ;

[0118] In this embodiment, the monotonically non-increasing mapping relationship can be implemented using a piecewise constant function:

[0119] like ,but , , ;

[0120] like ,but , , ;

[0121] like ,but , , ;

[0122] in, , The preset braking intensity threshold, and ;

[0123] Set asymmetric parameters for braking safety: , , The preset speed deviation integral weighting factor value, and ; , , The preset speed deviation differential weighting factor value, and ; , , The preset control command weight factor value, and ;

[0124] The asymmetric parameter mechanism of this invention refers to dynamically adjusting the speed deviation integral weight factor in the control algorithm according to the magnitude of the braking intensity during the braking process. Speed ​​deviation differential weighting factor Control command weighting factor Furthermore, this adjustment is asymmetrical, meaning it applies across different braking intensity ranges. , , Different , , Different , , The values ​​vary and are selected based on the actual operating conditions of the overhead contact line self-propelled train braking. An optimal balance is struck between control performance (fast, precise) and control robustness (smooth, safe) based on the different operating conditions reflected by the braking intensity; specifically:

[0125] In the low braking intensity zone When the train is in a stable cruising or slight adjustment phase, the tracking error is small. At this time, the control objective prioritizes high tracking accuracy and dynamic response speed. Speed ​​deviation integral weighting factor. Speed ​​deviation differential weighting factor Setting the weight relatively large assigns higher weight to speed deviation and its rate of change, enabling the controller to eliminate errors more actively and quickly; control command weight factor The setting is relatively small, which relaxes the penalty for changes in control commands and allows the controller to make more flexible and powerful adjustments.

[0126] In the medium braking intensity zone During the normal deceleration and braking phase of the train, the dynamic characteristics of the system begin to emerge, requiring a balance between accuracy and stability. The control objective at this stage is to achieve a balance between accuracy and stability; the weighting factor is set to a medium level, typically between parameter values ​​in the low-intensity and high-intensity regions.

[0127] In the high braking intensity zone During emergency or full braking phases, control commands change drastically, actuators are prone to saturation, and the system exhibits strong nonlinearity. Stability and safety are paramount concerns. The control objective at this time prioritizes ensuring control smoothness, robustness, and system stability, preventing control command oscillations and train instability. Speed ​​deviation integral weighting factor. Speed ​​deviation differential weighting factor Setting it relatively small reduces the requirements for error variation and avoids severe oscillations in the controller due to excessive pursuit of accuracy under strong nonlinearity; control command weighting factor Setting it to a relatively large value significantly increases the penalty for changes in control commands, resulting in smoother generated control commands and effectively suppressing drastic fluctuations in control values. This is a key measure to ensure the safety and stability of the braking process.

[0128] The above parameters are manually adjusted in practical applications according to the above principles using empirical trial-and-error methods, such as classic control tuning methods (e.g., the Ziegler-Nichols method), and the system response is observed until satisfactory results are achieved. Based on the results of actual vehicle testing, minor adaptive adjustments are then made to the parameters to ensure optimal performance.

[0129] Of course, it can also be considered that the monotonically non-increasing mapping relationship can also be implemented using a continuous, bounded, monotonically decreasing function:

[0130] Speed ​​deviation integral weighting factor ;

[0131] Velocity deviation differential weighting factor ;

[0132] Control command weighting factor ;

[0133] in, , , , , , As a preset constant, As the attenuation factor, It is a positive number;

[0134] In the low braking intensity zone When the train is in a stable cruising or slight adjustment phase, it can adjust its speed by setting a higher setting. To ensure that the integral term can still accumulate effectively even under small deviation scenarios, quickly offsetting steady-state errors caused by system latency and slight load changes, a higher setting is required. To enhance the sensitivity of the differential term to changes in vehicle speed, a lower setting is used. The penalty for changes in control commands is relaxed, allowing the controller to output flexible, small-scale adjustments; at the same time, manual adjustment is also possible. , , Allow for adjustment margins to correct deviations, and observe the system response until satisfactory results are achieved;

[0135] In the medium braking intensity zone The train is in the normal deceleration and braking phase. The control target needs to achieve a balance between tracking accuracy and running stability. The six extreme parameters are set as the transition level between the low and high intensity zones.

[0136] In the high braking intensity zone When the train is in an emergency or full braking phase, by setting a lower... To weaken the moderating effect of the integral term and avoid overcompensation due to the continuous accumulation of deviations in highly nonlinear scenarios, a lower integral term is set. To reduce extreme values ​​and suppress the sensitivity of the derivative to changes in deviation, this avoids the derivative amplifying due to a rapid drop in vehicle speed, which could trigger a sudden, high-intensity braking command and cause excessive braking impact. Simultaneously, the weakened derivative effect can filter sensor noise in emergency conditions. A higher setting is also possible. Strengthening the penalty for changes in control commands forces commands to adjust with a gradual trend, protecting actuators from frequent shocks and ensuring the train stops smoothly with reduced speed, preventing instability risks; simultaneously, manual adjustments are made. , , Allow room for adjustment to correct deviations, and observe the system response until satisfactory.

[0137] Step (4.3): Integrate the velocity deviation weighting factor after setting it through the asymmetric parameter mechanism. Speed ​​deviation differential weighting factor Control command weighting factor Substitute this into the update algorithm for the predictive control gain matrix; calculate The first element is output to the train's traction and braking system, and the control command at sampling time k is ultimately used to eliminate the speed deviation. for:

[0138] .

[0139] It should be noted that the aforementioned asymmetric parameter mechanism refers to dynamically adjusting three weighting factors (speed deviation integral weighting factor, speed deviation integral weighting factor, and speed deviation integral weighting factor) in the control algorithm according to the magnitude of the braking intensity during the braking process. Speed ​​deviation differential weighting factor Control command weighting factor Furthermore, this adjustment is asymmetrical, meaning that the weighting factor takes different values ​​in different braking intensity ranges. Specifically, when the braking intensity is low, the system uses a larger error weighting factor and a smaller control command weighting factor to emphasize control smoothness and stability; when the braking intensity is high, the system uses a smaller error weighting factor and a larger control command weighting factor to allow for stronger control intervention in order to quickly eliminate speed deviations.

[0140] The benefits of the asymmetric parameter mechanism are as follows:

[0141] (1) Adapting to the control requirements of different braking stages:

[0142] During the light braking phase, the system prioritizes smoothness and avoids frequent and drastic adjustments;

[0143] During heavy braking, the system prioritizes response speed to ensure rapid deceleration and prevent speeding or deviation from the stopping position.

[0144] (2) Improve the robustness and adaptability of the system:

[0145] By dynamically adjusting control parameters, the system can better adapt to different operating conditions (such as changes in slope and load), thereby improving the adaptability and robustness of the control.

[0146] (3) Beneficial effects on braking safety:

[0147] During emergency braking, the system can respond quickly, rapidly reduce speed deviation, and prevent accidents from occurring.

[0148] During smooth braking, the system avoids over-adjustment, improving ride comfort and equipment lifespan;

[0149] In summary, by dynamically balancing control response speed and stability, the safety and reliability of the braking process are significantly improved.

[0150] Example 2

[0151] Figure 2 This embodiment provides a schematic diagram of the connection of the contact network self-wheeled train data drive control system module for braking safety. This embodiment also provides a contact network maintenance train compensation control system for response delay, used to implement the contact network maintenance train compensation control method for response delay in Embodiment 1, including:

[0152] The trajectory generation module is used to obtain the positioning braking trajectory of the maintenance vehicle group and the system response delay model parameters to generate the desired vehicle speed trajectory.

[0153] The data modeling module is used to collect the real-time operating status of the maintenance vehicle group and use dynamic linearization technology to transform the nonlinear maintenance vehicle group tracking and control system with response delay into a delay prediction data model. The delay prediction data model contains a partial derivative matrix, and the partial derivative matrix can be updated with the sampling time.

[0154] The controller calculation module is used to design a model-free delay compensation predictive controller based on the speed deviation between the real-time vehicle speed and the desired vehicle speed, design a predictive control gain matrix update algorithm that considers the integral and derivative characteristics of the speed deviation, and update and correct the predictive control gain matrix in real time using the delay prediction data model.

[0155] The safety decision module is used to design an asymmetric parameter mechanism, dynamically update the model-free delay compensation predictive controller, generate control commands to eliminate the speed deviation, and output the control commands to the train traction and braking system.

[0156] The control output module is used to continuously update the model-free delay compensation predictive controller, obtain control commands to eliminate the speed deviation, drive the train to accurately track the desired speed trajectory, and achieve precise positioning of the train at the target point.

[0157] Furthermore, the present invention adopts the following technical solution:

[0158] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for compensation control of overhead contact line maintenance vehicles for response delay.

[0159] Furthermore, the present invention adopts the following technical solution:

[0160] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described method for compensating for response delays in overhead contact line maintenance vehicles.

[0161] From the above description of the embodiments, those skilled in the art will clearly understand that the facilities of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Embodiments of the present invention can be implemented using existing processors, or by dedicated processors used for this or other purposes for suitable systems, or by hardwired systems. Embodiments of the present invention also include non-transitory computer-readable storage media, comprising machine-readable media for carrying or having machine-executable instructions or data structures stored thereon; such machine-readable media can be any available medium accessible by a general-purpose or special-purpose computer or other machine with a processor. For example, such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store the required program code in the form of machine-executable instructions or data structures and is accessible by a general-purpose or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communication connection (hardwired, wireless, or a combination of hardwired and wireless), that connection is also considered a machine-readable medium.

[0162] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A compensation control method for overhead contact line maintenance vehicles to address response delays, characterized in that, The method includes the following steps: Obtain the positioning braking trajectory of the maintenance vehicle crew and the system response delay model parameters to generate the desired vehicle speed trajectory; The real-time operating status of the maintenance vehicle group is collected, and the nonlinear maintenance vehicle group tracking and control system with response delay is transformed into a delay prediction data model using dynamic linearization technology. The delay prediction data model contains a partial derivative matrix, and the partial derivative matrix can be updated with the sampling time. Based on the speed deviation between the real-time vehicle speed and the desired vehicle speed, a model-free delay compensation predictive controller is designed. A predictive control gain matrix update algorithm that considers the integral and derivative characteristics of the speed deviation is designed. The predictive control gain matrix is ​​updated and corrected in real time using the delay prediction data model. An asymmetric parameter mechanism is designed to dynamically update the model-free delay compensation predictive controller, generate control commands to eliminate the speed deviation, and output the control commands to the train traction and braking system. The model-free delay compensation predictive controller is continuously updated to obtain control commands to eliminate the speed deviation, drive the vehicle group to accurately track the desired vehicle speed trajectory, and achieve precise positioning of the vehicle group at the target point. The method for designing a model-free delay compensation predictive controller based on the speed deviation between the real-time vehicle speed and the desired vehicle speed is as follows: ； in, Indicates the sampling time. This represents the system response delay time, where N is the prediction step size, and N is a positive integer. This represents the N-step forward delay compensation predictive control command vector. , , express The velocity deviation vector at the sampling time takes into account the response delay. , for The expected vehicle speed vector considering response delay at the sampling time. , express The expected vehicle speed at the sampling time takes into account the response delay; Represents a vector consisting entirely of 1s; express Real-time vehicle speed at the time of sampling; express The first-order forward difference, , M represents the order of the model-free delay-compensated predictive controller, and M is a positive integer; For predicting the control gain matrix; ； in, This represents the NLth prediction control gain element predicted in step N.

2. The catenary maintenance vehicle compensation control method for response delay according to claim 1, characterized in that, The design incorporates the integral and derivative characteristics of the velocity deviation into a predictive control gain matrix update algorithm. The method for real-time updating and correcting the predictive control gain matrix using the delayed prediction data model is as follows: Construct a performance optimization objective function that considers the integral and differential characteristics of the speed deviation: ； in, Represent the performance optimization objective function; for The expected vehicle speed vector considering response delay at the sampling time. , express The expected vehicle speed at the sampling time takes into account the response delay; For the N-step forward vehicle speed prediction vector that takes into account response delay, , express Real-time vehicle speed at the time of sampling; express The first-order forward difference, express The velocity deviation vector at the sampling time takes into account the response delay; express The first-order forward difference; This represents the integral weighting factor for speed deviation. This represents the differential weighting factor for the speed deviation. Indicates the weighting factor of control instructions; The objective function is solved using gradient descent to optimize and update the predictive control gain matrix. : ； in, To learn the law, for Compared to The Jacobian matrix.

3. The catenary maintenance vehicle compensation control method for response delay according to claim 2, characterized in that, Compared to The Jacobian matrix is ​​calculated as follows: ； in, , This refers to the partial derivative matrix in the delayed prediction data model; .

4. The catenary maintenance vehicle compensation control method for response delay according to claim 1, characterized in that, The method for transforming a nonlinear maintenance vehicle tracking control system with response delay into a delay prediction data model using dynamic linearization technology includes the following steps: Constructing a delay prediction data model for a nonlinear maintenance vehicle tracking control system: ； in, Indicates the sampling time. Indicates the system response delay time. For the N-step forward vehicle speed prediction vector that takes into account response delay, N is the prediction step size; , express Real-time vehicle speed at the time of sampling; This represents the first-order forward difference of the N-step forward delay compensation predictive control command vector. express Delay-compensated predictive control commands at sampling time express First-order forward difference, ; express The partial derivative matrix at the sampling time, , express The partial derivative at the sampling time.

5. The catenary maintenance vehicle compensation control method for response delay according to claim 4, characterized in that, The method for updating the partial derivative matrix is ​​as follows: Construct the cost function: ； in, Represents a function; ; Indicates the limiting factor; The cost function is solved using the function extremum method to obtain... Partial derivatives at sampling time The renewal law: ； in, The step size factor represents the partial derivative; Calculation using a multi-level hierarchical forecasting method Partial derivative variable at sampling time The update law, where j = 1, 2, ..., N-1; Define variables , This is the autoregressive coefficient vector. The nth autoregressive coefficient; based on Partial derivatives at sampling time The update law, calculation The update law is thus obtained. Partial derivatives at sampling time The renewal law: ； ； Where n represents the appropriate order; ; It is a positive number. ; Represents the 2-norm; based on Partial derivatives at sampling time The renewal law, Partial derivatives at sampling time The update law optimizes the update of the partial derivative matrix. .

6. The catenary maintenance vehicle compensation control method for response delay according to claim 2, characterized in that, The design of an asymmetric parameter mechanism to dynamically update the model-free delay-compensated predictive controller, generating the final control command to eliminate the speed deviation, and outputting the control command to the train traction and braking system includes the following steps: Define and calculate Braking intensity at sampling time ; Determine by using a pre-defined monotonically non-increasing mapping function. Velocity deviation integral weighting factor used at sampling time Speed ​​deviation differential weighting factor Control instruction weighting factor The monotonically non-increasing mapping function is configured such that the greater the braking intensity, , , The smaller the value of ; Set the speed deviation integral weight factor Speed ​​deviation differential weighting factor Control command weighting factor Substitute this into the update algorithm for the predictive control gain matrix; calculate The first element is output to the train's traction and braking system, and the control command at sampling time k is ultimately used to eliminate the speed deviation. for: 。 7. A catenary maintenance vehicle compensation control system for response delay, used to implement the catenary maintenance vehicle compensation control method for response delay as described in any one of claims 1-6, characterized in that, The system includes: The trajectory generation module is used to obtain the positioning braking trajectory of the maintenance vehicle group and the system response delay model parameters to generate the desired vehicle speed trajectory. The data modeling module is used to collect the real-time operating status of the maintenance vehicle group and use dynamic linearization technology to transform the nonlinear maintenance vehicle group tracking and control system with response delay into a delay prediction data model. The delay prediction data model contains a partial derivative matrix, and the partial derivative matrix can be updated with the sampling time. The controller calculation module is used to design a model-free delay compensation predictive controller based on the speed deviation between the real-time vehicle speed and the desired vehicle speed, design a predictive control gain matrix update algorithm that considers the integral and derivative characteristics of the speed deviation, and update and correct the predictive control gain matrix in real time using the delay prediction data model. The safety decision module is used to design an asymmetric parameter mechanism, dynamically update the model-free delay compensation predictive controller, generate control commands to eliminate the speed deviation, and output the control commands to the train traction and braking system. The control output module is used to continuously update the model-free delay compensation predictive controller, obtain control commands to eliminate the speed deviation, drive the train to accurately track the desired speed trajectory, and achieve precise positioning of the train at the target point.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the overhead contact line maintenance vehicle compensation control method for response delay as described in any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the overhead contact line maintenance vehicle compensation control method for response delay as described in any one of claims 1 to 6.

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