Adaptive control method and device for high-precision speed and position tracking of train under satellite positioning interference scenario

Abstract

The application discloses a kind of adaptive control method and equipment of train high-precision speed and position tracking under satellite positioning interference scene.The method first constructs train basic dynamics model, establishes and uses state observer to optimize train operation reference model;Again, based on the basic model, the train dynamic model under satellite positioning interference is constructed, and the to-be-estimated parameter vector and time-varying parameter vector of adaptive iterative learning algorithm are defined;Subsequently, combined with dynamic speed and position tracking error, model reference adaptive iterative learning controller control law is designed, and controller parameter iterative update law is obtained based on composite energy function;Finally, according to the updated control parameter, closed-loop adaptive tracking control is implemented on train.The application can effectively suppress the influence of GNSS positioning deviation on tracking accuracy under complex electromagnetic interference, constrain speed and position error within safety threshold, and significantly improve the anti-interference ability, convergence speed and running stability of the system.

Description

Technical Field

[0001] This invention relates to the field of rail transit train operation control technology, and in particular to an adaptive control method and system for high-precision speed and position tracking of trains in satellite positioning interference scenarios, as well as a readable storage medium and computer equipment. Background Technology

[0002] As the rail transit industry continues to develop towards higher speeds and greater intelligence, the tracking and control of train speed and position has become a core functional module of the train operation control system. Its control performance directly determines train operation safety and railway line transportation efficiency. Global Navigation Satellite Systems (GNSS), with their advantages of wide-area coverage, high positioning accuracy, and uninterrupted all-weather operation, have become the core positioning technology support for the next generation of train operation control systems. The accuracy of train speed calculation and position estimation directly depends on the reception quality and integrity of GNSS signals.

[0003] However, the electromagnetic environment along railway lines is complex and variable, with frequent occurrences of various positioning interference events, such as unintentional non-human interference, suppression interference, and deceptive interference. This directly leads to distortion of train speed observation results, causing a significant decrease in the tracking control accuracy of traditional controllers designed based on ideal, undisturbed observation values. In severe cases, it can even cause instability in the control system, posing a direct threat to train operation safety.

[0004] To address the aforementioned technical pain points, existing solutions can be broadly categorized into two main technical paths: The first is a multi-sensor fusion positioning scheme. This scheme requires additional hardware, increasing system deployment costs and exhibiting inherent flaws such as accumulated errors in the inertial measurement unit over time and susceptibility of wheel speed sensors to wheel-rail slippage, making long-term reliability difficult to guarantee. The second is a traditional robust control or adaptive control scheme. Robust control design requires prior acquisition of disturbance boundary information, exhibiting significant limitations in adaptability to unknown time-varying disturbances. Meanwhile, single-structure adaptive control algorithms struggle to simultaneously suppress repetitive disturbances and non-periodic positioning interference during train operation, failing to meet the high-precision speed tracking control requirements of trains in complex electromagnetic interference scenarios. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0006] Therefore, one objective of this invention is to provide an adaptive control method and device for high-precision speed and position tracking of trains in satellite positioning interference scenarios. This method can strictly constrain train speed tracking error and positioning deviation within the threshold range allowed for safe train operation, effectively improving the anti-interference capability, convergence performance and operational stability of the control system.

[0007] To achieve the above objectives, the first aspect of the present invention provides an adaptive control method for high-precision speed and position tracking of a vehicle in a satellite positioning interference scenario, comprising the following steps:

[0008] S1, Based on train operation data, construct a basic dynamic model of the train covering basic resistance and additional resistance, simultaneously establish a train operation reference model and optimize the train operation reference model through a state observer;

[0009] S2, construct a dynamic dynamic model of the train under satellite positioning interference environment based on the basic dynamic model of the train, and simultaneously define the parameter vector to be estimated and the time-varying parameter vector in the adaptive iterative learning algorithm;

[0010] S3, construct the control law of the model reference adaptive iterative learning controller based on the dynamic speed tracking error and dynamic position tracking error according to the train dynamic dynamics model and the optimized train operation reference model;

[0011] S4, construct the controller parameter iterative update law based on the composite energy function according to the control law of the model reference adaptive iterative learning controller;

[0012] S5, based on the iterative update law of the controller parameters, output control parameters to perform closed-loop adaptive tracking control of the train's running speed and position.

[0013] In the above technical solution, preferably, in step S1, the expression of the train basic dynamics model is:

[0014] ;

[0015] in, The basic resistance that a train experiences during operation; The train's real-time operating speed is given; mechanical resistance is linearly related to the train's operating speed, and can be expressed as follows: ,in The rolling resistance coefficient, The drag coefficient is related to train body vibration and wheel-rail friction loss; air resistance is positively correlated with the square of the train's operating speed, and can be obtained through... Quantitative characterization is performed, where The aerodynamic drag coefficient; The additional resistance borne by the train specifically includes additional resistance on slopes, additional resistance on curves, and additional air resistance in tunnels. For the train's maintenance quality; For the displacement of the train; This is the first derivative of the train's speed, i.e., the train's acceleration. This refers to the control input for the train dynamics model;

[0016] The expression for the train operation reference model is:

[0017] ;

[0018] in, It is the control input of the reference system; It is the speed reference value output by the reference model; It is the speed reference value output by the reference model. The first derivative; These are the displacement reference values ​​output by the reference model; to ensure the stability of the reference system, a feedback gain is introduced. The negative feedback loop, To introduce negative feedback, the state parameters of the reference model, The control gain parameters are for the reference model;

[0019] Through the state observer The optimized expression for the train operation reference model is:

[0020] ;

[0021] For the feedback gain coefficient of the adaptive iterative learning control system, This is the reference velocity value for the k-th iteration. It is the first derivative of the reference displacement value in the k-th iteration; Let be the velocity observation for the k-th iteration.

[0022] In any of the above technical solutions, preferably, in step S2, the deviation in train speed observation caused by satellite positioning interference... Defined as the speed measurement value and the actual train speed The time-varying observation error between them, and the expression for the perturbed velocity observation are: ;

[0023] The expression for the train dynamics model is:

[0024] .

[0025] In any of the above technical solutions, preferably, in step S2, to simplify controller design, a time-varying parameter to be estimated is defined. , , :

[0026] ;

[0027] ;

[0028] ;

[0029] Based on the aforementioned time-varying parameters to be estimated, a parameter vector to be estimated for the adaptive iterative learning algorithm is defined. With time-varying parameter vector :

[0030] ;

[0031] ;

[0032] In the formula, This is the first derivative of the velocity observation error in the k-th iteration; This represents a vector of continuously unknown parameters to be estimated. A vector containing known time-varying parameters is defined; the final train dynamics model is obtained:

[0033] ;

[0034] In the formula, the parameter to be estimated For bounded variables, satisfy norm constraints ,in is a known bounded constant.

[0035] In any of the above technical solutions, preferably, in step S3, the dynamic speed tracking error is defined. and dynamic position tracking error Taking the first derivative of the tracking error above, we obtain the differential equation for the dynamic error as follows:

[0036] ;

[0037] Substituting the final dynamic model of the train, which includes observation bias, and the optimized reference model into the dynamic error differential equation, the differential expression for the speed tracking error is derived:

[0038] .

[0039] In the above technical solution, preferably, in step S3, the parameter estimation error is defined. ,in For parameters to be estimated The parameter estimates satisfy the initial conditions. , This refers to the deviation between the estimated and actual parameter values; to ensure the asymptotic convergence of the system, the velocity tracking error convergence condition is set as follows: ,in The system convergence gain coefficient;

[0040] Substituting the differential expression for the speed tracking error, we can simultaneously derive the convergent expression for the speed tracking error:

[0041] ;

[0042] Through simultaneous derivation, the convergent expression for the speed tracking error can be obtained:

[0043] ;

[0044] Fusion parameter definition composite feedback gain Finally, the control law expression for the model reference adaptive iterative learning controller is obtained:

[0045] .

[0046] In any of the above technical solutions, preferably, in step S4, a composite energy function that simultaneously includes time-domain tracking error and iterative-domain parameter estimation error is constructed. The expression is:

[0047] ;

[0048] Define the energy difference between different iteration rounds for:

[0049] ;

[0050] To ensure that the system energy does not increase with each iteration, i.e., to satisfy... The iterative update law expression for the parameters to be estimated is derived; during each iteration, train speed observations are collected in real time. Calculate the speed tracking error The estimated value of the parameter vector to be estimated is updated through the parameter iteration update law. The control quantity at the current moment is calculated by substituting it into the control law expression. Output control parameters; the specific process is as follows:

[0051] For the first part of the composite energy function :

[0052]

[0053] ;

[0054] for The following parts are derived:

[0055]

[0056] ;

[0057] Integrating the above derivation process, we can obtain:

[0058] ;

[0059] ;

[0060] To ensure that the system energy does not increase with each iteration, i.e., to satisfy... The iterative update law for the parameters to be estimated is derived. as follows:

[0061] ;

[0062] in, It is a constant estimate of the gain matrix.

[0063] The second aspect of the present invention provides an adaptive control system for high-precision speed and position tracking of vehicles in satellite positioning interference scenarios, comprising:

[0064] The train operation basic model construction module is set up to build a basic dynamic model of the train based on train operation data, covering basic resistance and additional resistance, and simultaneously establish a train operation reference model and optimize the train operation reference model through a state observer.

[0065] The train dynamic model construction module is configured to construct a train dynamic dynamic model under satellite positioning interference environment based on the train basic dynamic model, and simultaneously define the parameter vector to be estimated and the time-varying parameter vector in the adaptive iterative learning algorithm.

[0066] The adaptive iterative control law construction module is configured to construct a control law for a model reference adaptive iterative learning controller based on dynamic speed tracking error and dynamic position tracking error, according to the train dynamic dynamics model and the optimized train operation reference model.

[0067] The adaptive iterative update law construction module is configured to construct an iterative update law for controller parameters based on a composite energy function according to the control law of the adaptive iterative learning controller referenced by the model.

[0068] The control parameter output module is configured to output control parameters based on the controller parameter iterative update law to perform closed-loop adaptive tracking control of the train's speed and position.

[0069] The third aspect of the present invention provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the adaptive control method for high-precision speed and position tracking of a vehicle in a satellite positioning interference scenario provided by the first aspect of the present invention.

[0070] The fourth aspect of the present invention provides a computer device, including a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the steps of the adaptive control method for high-precision speed and position tracking of a vehicle in a satellite positioning interference scenario provided by the first aspect of the present invention.

[0071] Compared with existing technologies, the adaptive control method and equipment for high-precision speed and position tracking of trains in satellite positioning interference scenarios provided by this invention have the following advantages: The technical solution described in this invention does not rely on precise modeling of all train dynamic parameters. Through a fusion control architecture combining AILC iterative learning and MRAC model reference adaptation, and incorporating a parameter adaptive update law designed using the Composite Energy Function (CEF) method, it significantly improves the system's robustness to GNSS positioning interference, avoiding speed tracking accuracy degradation, position calculation deviation, and train control instability caused by positioning interference. The technology employs an iterative axis parameter recursive update and a feedback mechanism with a state observer for optimization design. It boasts high computational efficiency and rigorously proven convergence. It can adaptively adjust the control law and control parameters online based on train operation data and multi-round iterative data from a fixed line. It can quickly suppress speed deviations when the intensity and characteristics of positioning interference change abruptly, accurately constraining speed and position tracking errors within the allowable range for safe train operation, and adapting to the complex operating conditions of the entire train operation cycle, including traction, cruise, coasting, and braking. Attached Figure Description

[0072] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0073] Figure 1 A flowchart of the adaptive control method according to an embodiment of the present invention is shown;

[0074] Figure 2 The control principle diagram of the adaptive control method involved in the embodiment of the present invention is shown;

[0075] Figure 3 A structural block diagram of the adaptive control system according to an embodiment of the present invention is shown;

[0076] Figure 4 This is a schematic diagram of the reference control input and reference running speed control in an embodiment of the present invention;

[0077] Figure 5This is a schematic diagram of a weaker interference and its derivative control in an embodiment of the present invention;

[0078] Figure 6 This is a comparison diagram of control inputs for different iteration rounds under weak interference conditions in an embodiment of the present invention;

[0079] Figure 7 This is a schematic diagram of speed tracking control under weak interference conditions in different iterations in an embodiment of the present invention;

[0080] Figure 8 This is a schematic diagram of position tracking control under weak interference conditions in different iterations in an embodiment of the present invention;

[0081] Figure 9 This is a schematic diagram illustrating the maximum tracking error control of velocity and position after 20 iterations under weak interference conditions in an embodiment of the present invention.

[0082] Figure 10 This is a schematic diagram illustrating the parameter update under weak interference conditions in an embodiment of the present invention;

[0083] Figure 11 This is a schematic diagram of a strong interference and its derivative in an embodiment of the present invention;

[0084] Figure 12 This is a comparison chart of control inputs for different iteration rounds under strong interference conditions in an embodiment of the present invention;

[0085] Figure 13 This is a schematic diagram of speed tracking in different iterations under strong interference conditions in an embodiment of the present invention;

[0086] Figure 14 This is a schematic diagram of position tracking in different iterations under strong interference conditions in an embodiment of the present invention;

[0087] Figure 15 This is a schematic diagram of the maximum tracking error of velocity and position after 20 iterations under strong interference conditions in an embodiment of the present invention;

[0088] Figure 16 This is a schematic diagram illustrating parameter updates under strong interference conditions in an embodiment of the present invention. Detailed Implementation

[0089] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0090] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0091] like Figure 1 , Figure 2 as well as Figures 4 to 16 As shown, an adaptive control method for high-precision speed and position tracking of a vehicle in a satellite positioning interference scenario, according to an embodiment of the present invention, includes the following steps:

[0092] S1, Based on train operation data, construct a basic dynamic model of the train covering basic resistance and additional resistance, simultaneously establish a train operation reference model and optimize the train operation reference model through a state observer;

[0093] S2, construct a dynamic dynamic model of the train under satellite positioning interference environment based on the basic dynamic model of the train, and simultaneously define the parameter vector to be estimated and the time-varying parameter vector in the adaptive iterative learning algorithm;

[0094] In embodiments of the present invention, train position information is obtained through integral calculation of train speed. Since the total length of the track and curve parameters are fixed and known quantities within the closed-loop section of train operation, the displacement information of the train in the one-dimensional coordinate system of the track can be obtained by integrating the train speed. In practical engineering applications, the precise coordinates of the train can be further calculated by combining the electronic map of the track and track foundation data. Based on the above logic, the impact of satellite positioning interference on position calculation essentially stems from the deviation of speed observations; therefore, it is only necessary to quantify and define the speed observation deviation caused by interference.

[0095] S3, construct the control law of the model reference adaptive iterative learning controller based on the dynamic speed tracking error and dynamic position tracking error according to the train dynamic dynamics model and the optimized train operation reference model;

[0096] S4, construct the controller parameter iterative update law based on the composite energy function according to the control law of the model reference adaptive iterative learning controller;

[0097] S5, based on the iterative update law of the controller parameters, output control parameters to perform closed-loop adaptive tracking control of the train's running speed and position.

[0098] In this embodiment, by organically integrating train dynamics modeling, interference observation, model reference tracking, iterative learning adaptation, and composite energy stability criteria, a complete closed-loop control architecture is formed. This architecture can maintain high-precision speed and position tracking even when satellite positioning is subject to complex interferences such as suppression, deception, and Doppler shift. Simultaneously, relying on iterative recursive updates and state observation feedback mechanisms, control parameters can be adjusted online in real time, effectively suppressing observation deviations and tracking errors caused by interference. This ensures that the train stably converges to the reference trajectory under all operating conditions, including traction, cruise, coasting, and braking, significantly improving the anti-interference capability, control accuracy, and operational safety of the rail transit operation control system in complex electromagnetic environments.

[0099] In the above embodiments, preferably, in step S1, the expression of the train basic dynamics model is:

[0100] ;

[0101] in, The basic resistance that a train experiences during operation; The train's real-time operating speed is given; mechanical resistance is linearly related to the train's operating speed, and can be expressed as follows: ,in The rolling resistance coefficient, The drag coefficient is related to train body vibration and wheel-rail friction loss; air resistance is positively correlated with the square of the train's operating speed, and can be obtained through... Quantitative characterization is performed, where The aerodynamic drag coefficient; The additional resistance borne by the train specifically includes additional resistance on slopes, additional resistance on curves, and additional air resistance in tunnels. For the train's maintenance quality; For the displacement of the train; This is the first derivative of the train's speed, i.e., the train's acceleration. This refers to the control input for the train dynamics model;

[0102] The expression for the train operation reference model is:

[0103] ;

[0104] in, It is the control input of the reference system; It is the speed reference value output by the reference model; It is the speed reference value output by the reference model. The first derivative; These are the displacement reference values ​​output by the reference model; to ensure the stability of the reference system, a feedback gain is introduced. The negative feedback loop, To introduce negative feedback, the state parameters of the reference model, The control gain parameters are for the reference model;

[0105] Through the state observer The optimized expression for the train operation reference model is:

[0106] ;

[0107] For the feedback gain coefficient of the adaptive iterative learning control system, This is the reference velocity value for the k-th iteration. It is the first derivative of the reference displacement value in the k-th iteration; Let be the velocity observation for the k-th iteration.

[0108] In this embodiment, by constructing a train dynamics model that fits the real working conditions and introducing a reference model with a state observer, a stable and smooth speed and position reference trajectory can be provided for the train under the conditions of measurement noise and external disturbances, thereby improving the robustness and tracking accuracy of the control system.

[0109] In any of the above embodiments, preferably, in the case of satellite positioning interference, various types of interference affect the train speed observation and calculation process through Doppler frequency shift deviation, signal loss, and injection of false positioning parameters, which ultimately leads to a time-varying observation error δ between the speed measurement value v̅ and the actual train running speed v. Affected by this observation error, the satellite positioning calculation result will synchronously generate a corresponding deviation.

[0110] Therefore, in step S2, the train speed observation deviation caused by satellite positioning interference is addressed. Defined as the speed measurement value and the actual train speed The time-varying observation error between them, and the expression for the perturbed velocity observation are: ;

[0111] The expression for the train dynamics model is:

[0112] .

[0113] In this embodiment, by precisely introducing velocity observation bias... Furthermore, a disturbance-inducing dynamic model is derived, which can realistically reproduce the impact mechanism of GNSS interference on train observation and control, enabling the controller design to have clear physical meaning and engineering relevance.

[0114] In any of the above embodiments, preferably, in step S2, to simplify controller design, a time-varying parameter to be estimated is defined. , , :

[0115] ;

[0116] ;

[0117] ;

[0118] Based on the aforementioned time-varying parameters to be estimated, a parameter vector to be estimated for the adaptive iterative learning algorithm is defined. With time-varying parameter vector :

[0119] ;

[0120] ;

[0121] In the formula, This is the first derivative of the velocity observation error in the k-th iteration; This represents a vector of continuously unknown parameters to be estimated. A vector containing known time-varying parameters is defined; the final train dynamics model is obtained:

[0122] ;

[0123] In the formula, the parameter to be estimated For bounded variables, satisfy norm constraints ,in is a known bounded constant.

[0124] In this embodiment, by using parameter vectorization and model normalization, the complex nonlinear disturbed model is transformed into a linear parameterized form that is easy for adaptive algorithms to solve, which significantly reduces the complexity of controller design and ensures the boundedness of parameters, thus providing support for proving system stability.

[0125] In any of the above embodiments, preferably, in step S3, the dynamic speed tracking error is defined. and dynamic position tracking error Taking the first derivative of the tracking error above, we obtain the differential equation for the dynamic error as follows:

[0126] ;

[0127] Substituting the final dynamic model of the train, which includes observation bias, and the optimized reference model into the dynamic error differential equation, the differential expression for the speed tracking error is derived:

[0128] .

[0129] In this embodiment, by constructing dynamic equations for both speed and position errors, closed-loop control of both speed tracking accuracy and position tracking accuracy can be achieved simultaneously, meeting the engineering requirements of train operation control for dual constraints on speed and position.

[0130] In the above embodiment, preferably, in step S3, the parameter estimation error is defined. ,in For parameters to be estimated The parameter estimates satisfy the initial conditions. , This refers to the deviation between the estimated and actual parameter values; to ensure the asymptotic convergence of the system, the velocity tracking error convergence condition is set as follows: ,in The system convergence gain coefficient;

[0131] Substituting the differential expression for the speed tracking error, we can simultaneously derive the convergent expression for the speed tracking error:

[0132] ;

[0133] Through simultaneous derivation, the convergent expression for the speed tracking error can be obtained:

[0134] ;

[0135] Fusion parameter definition composite feedback gain Finally, the control law expression for the model reference adaptive iterative learning controller is obtained:

[0136] .

[0137] In this embodiment, by introducing parameter estimation error and asymptotic convergence condition, the tracking error can be made to converge quickly along a preset stable trajectory. At the same time, the composite feedback gain is used to achieve unified suppression of disturbances and model errors, thereby improving control response speed and steady-state accuracy.

[0138] In any of the above embodiments, preferably, in step S4, a composite energy function that simultaneously includes time-domain tracking error and iterative-domain parameter estimation error is constructed. The expression is:

[0139] ;

[0140] Define the energy difference between different iteration rounds for:

[0141] ;

[0142] To ensure that the system energy does not increase with each iteration, i.e., to satisfy... The iterative update law expression for the parameters to be estimated is derived; during each iteration, train speed observations are collected in real time. Calculate the speed tracking error The estimated value of the parameter vector to be estimated is updated through the parameter iteration update law. The control quantity at the current moment is calculated by substituting it into the control law expression. Output control parameters; the specific process is as follows:

[0143] For the first part of the composite energy function :

[0144]

[0145] ;

[0146] for The following parts are derived:

[0147]

[0148] ;

[0149] Integrating the above derivation process, we can obtain:

[0150] ;

[0151] ;

[0152] To ensure that the system energy does not increase with each iteration, i.e., to satisfy... The iterative update law for the parameters to be estimated is derived. as follows:

[0153] ;

[0154] in, It is a constant estimate of the gain matrix.

[0155] In this embodiment, the parameter iteration update law based on the composite energy function design can ensure the stable convergence of the system from both the time domain and the iteration domain, so that the tracking error and parameter estimation error continue to decrease with the number of iterations, thereby achieving adaptive suppression of GNSS interference.

[0156] Specific Implementation Examples and Simulation Verification

[0157] To verify the effectiveness of the proposed method for achieving high-precision adaptive tracking control of train speed and position under satellite positioning interference scenarios, this embodiment builds a closed-loop simulation system based on the MATLAB simulation platform, using a certain type of high-speed train as the simulation object, to simulate the train operation control process under GNSS satellite positioning interference scenarios.

[0158] Train operation is a process of dynamically switching operating states based on track conditions, dispatching instructions, and operational goals. The four core operating states—traction, cruising, coasting, and braking—together constitute the complete operational cycle from start to stop. Therefore, in this simulation experiment, the train's reference control input and reference speed trajectory are set according to... Figure 4 The design is shown, and the control input curve fully covers the control requirements of the four operating states of the train. The corresponding speed change process presents a complete operating law of "traction acceleration → constant speed cruise → coasting → braking and stopping".

[0159] The train parameters used in this simulation are as follows: the train's total mass with passenger capacity is 810 tons, the maximum operating speed is 97.2 m / s, and the additional drag coefficient is... The rolling resistance coefficient is 0.1. The coefficient of vibration friction is 1.61. The aerodynamic drag coefficient is 0.0035. It is 0.000157.

[0160] This simulation includes two sets of control experiments, corresponding to two typical scenarios: weak interference and strong interference in satellite positioning. The basic simulation parameters for both sets of experiments are consistent: total simulation time of 500 seconds, number of iterations set to 20, and negative feedback gain. The composite feedback gain is 0.003. The feedback gain of the AILC system is 3.10005375. It is 0.00005.

[0161] Weak interference scenarios:

[0162] The weak interference signal and its derivative used in this simulation are as follows: Figure 5 As shown. To simulate the impact of complex GNSS interference on speed calculation during actual train operation, the interference signal... Composed of periodic sinusoidal components, exponentially decaying modulated sinusoidal components, and a superposition mechanism involving the asymptotic activation of the sigmoid function, this method can accurately characterize the asymptotic, fluctuating, and random characteristics of the impact of disturbances on velocity observations. In weak disturbance scenarios, the amplitudes of each component are set to relatively small values, and the mathematical expression for the disturbance is as follows:

[0163] ;

[0164] In this scenario, the controller's parameter estimation gain is set as follows: .

[0165] Figure 6 The changes in control input under different iteration rounds are shown. It can be seen that the controller can dynamically adjust the control input according to the influence of disturbances. As the number of iterations increases, the control input gradually converges to the optimal reference value. Figure 7 The speed tracking effect under different iterations was demonstrated. The tracking error between the actual train speed and the reference speed gradually decreased with the increase of the number of iterations and eventually converged to a stable value. Figure 8 The diagram and its subgraphs illustrate the train position tracking performance under different iteration rounds. As the number of iterations increases, the distance between the actual train position and the reference position gradually decreases, almost coinciding in the final iteration. After 20 iterations of optimization, the system's maximum speed tracking error decreased by 91.667%, and the maximum position tracking error decreased by 94.423%. The specific trends are shown below. Figure 9 As shown. In this disturbance scenario, the iterative update process of the four parameters to be estimated in the adaptive update law is as follows: Figure 10 As shown, it can be seen that with the increase of the number of iterations, the adaptive update law can dynamically complete the online estimation and real-time update of the controller parameters, and continuously optimize the tracking control performance of the system.

[0166] Strong interference scenarios:

[0167] The strong interference signal and its derivative used in this simulation are as follows: Figure 11 As shown. In strong interference scenarios, the amplitude of each component is set to a large value, and an additional random disturbance component is added. The mathematical expression for the interference is as follows:

[0168] .

[0169] In this scenario, the controller's parameter estimation gain is set as follows: .

[0170] Figure 12 This demonstrates the control input output of the controller at different iteration rounds under strong interference scenarios. With reference control input The comparison results show that under the influence of strong interference, the controller dynamically adjusts the control input by a larger margin. However, as the number of iterations increases, the control input can still gradually converge to the optimal reference value. The actual control input can accurately follow the reference control input. When facing high-intensity positioning interference, the controller can still adaptively adjust the control input according to the interference characteristics and maintain good control stability. Figure 13The speed tracking performance under strong interference scenarios was demonstrated. The actual train speed can still converge stably to the reference speed curve, with only a slight slowdown in convergence speed compared to weak interference scenarios. This effectively suppresses the adverse effects of strong interference. Figure 14 This demonstrates that even under strong interference conditions, the train's position curve can still stably converge to the reference position curve. The convergence speed is slower under weaker interference, but the influence of interference is largely eliminated. After 20 rounds of iterative optimization, the system's maximum speed tracking error decreased by 88.679%, and the maximum position tracking error decreased by 92.969%, gradually converging to stable constant values. Specific trends are shown below. Figure 15 As shown. In this disturbance scenario, the iterative update process of the four parameters to be estimated in the adaptive update law is as follows: Figure 16 As shown, even under strong disturbance conditions, the adaptive update law can still stably complete the dynamic estimation and real-time update of controller parameters, continuously optimizing the tracking control performance of the system.

[0171] Simulation results fully demonstrate that the adaptive control method proposed in this invention can effectively suppress the adverse effects of GNSS satellite positioning interference on train speed and position tracking control. Compared with traditional control methods, it has faster convergence speed, higher tracking accuracy, and stronger anti-interference capability. Under complex interference scenarios, it can strictly constrain the train speed and position tracking error within the range allowed for safe operation, effectively ensuring the stability of the train operation control system and driving safety.

[0172] like Figure 3 As shown, an adaptive control system 100 for high-precision speed and position tracking of vehicles in a satellite positioning interference scenario, according to another embodiment of the present invention, includes:

[0173] The train operation basic model construction module 10 is set to build a train basic dynamic model covering basic resistance and additional resistance based on train operation data, simultaneously establish a train operation reference model and optimize the train operation reference model through a state observer;

[0174] The train dynamic model construction module 20 is configured to construct a train dynamic dynamic model under satellite positioning interference environment based on the train basic dynamic model, and simultaneously define the parameter vector to be estimated and the time-varying parameter vector in the adaptive iterative learning algorithm.

[0175] The adaptive iterative control law construction module 30 is configured to construct a control law for a model reference adaptive iterative learning controller based on dynamic speed tracking error and dynamic position tracking error, according to the train dynamic dynamics model and the optimized train operation reference model.

[0176] The adaptive iterative update law construction module 40 is configured to construct a controller parameter iterative update law based on a composite energy function according to the control law of the adaptive iterative learning controller based on the model reference.

[0177] The control parameter output module 50 is configured to output control parameters based on the controller parameter iterative update law to perform closed-loop adaptive tracking control of the train's running speed and position.

[0178] In this embodiment, a modular architecture is used to automate the entire process from model building and control law design to parameter iteration and control output, which facilitates hardware implementation and engineering deployment in vehicle control systems and has good portability and practicality.

[0179] Based on the above, Figure 1 and Figure 2 Correspondingly, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the adaptive control method for high-precision speed and position tracking of a vehicle in a satellite positioning interference scenario as described in any of the above embodiments.

[0180] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0181] Based on the above, Figure 1 and Figure 2 The method shown, and Figure 3 To achieve the above objectives, the virtual device embodiment shown in this application also provides a computer device, including a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the steps of the adaptive control method for high-precision speed and position tracking of a vehicle in a satellite positioning interference scenario as described in any of the above embodiments.

[0182] Optionally, the computer device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Bluetooth interfaces, Wi-Fi interfaces), etc.

[0183] Those skilled in the art will understand that the computer device structure provided in this embodiment does not constitute a limitation on the computer device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0184] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages and stores the hardware and software resources of a computer device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software within the physical device.

[0185] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive control method for high-precision speed and position tracking of a vehicle under satellite positioning interference, characterized in that, Includes the following steps: S1, Based on train operation data, construct a basic dynamic model of the train covering basic resistance and additional resistance, simultaneously establish a train operation reference model and optimize the train operation reference model through a state observer; S2, construct a dynamic dynamic model of the train under satellite positioning interference environment based on the basic dynamic model of the train, and simultaneously define the parameter vector to be estimated and the time-varying parameter vector in the adaptive iterative learning algorithm; S3, construct the control law of the model reference adaptive iterative learning controller based on the dynamic speed tracking error and dynamic position tracking error according to the train dynamic dynamics model and the optimized train operation reference model; S4, construct the controller parameter iterative update law based on the composite energy function according to the control law of the model reference adaptive iterative learning controller; S5, based on the iterative update law of the controller parameters, output control parameters to perform closed-loop adaptive tracking control of the train's running speed and position.

2. The adaptive control method according to claim 1, characterized in that, In step S1, the expression for the basic dynamic model of the train is: ； in, The basic resistance that a train experiences during operation; The train's real-time operating speed is given; mechanical resistance is linearly related to the train's operating speed, and can be expressed as follows: ,in The rolling resistance coefficient, The drag coefficient is related to train body vibration and wheel-rail friction loss; air resistance is positively correlated with the square of the train's operating speed, and can be obtained through... Quantitative characterization is performed, where The aerodynamic drag coefficient; The additional resistance borne by the train specifically includes additional resistance on slopes, additional resistance on curves, and additional air resistance in tunnels. For the train's maintenance quality; For the displacement of the train; This is the first derivative of the train's speed, i.e., the train's acceleration. This refers to the control input for the train dynamics model; The expression for the train operation reference model is: ； in, It is the control input of the reference system; It is the speed reference value output by the reference model; It is the speed reference value output by the reference model. The first derivative; These are the displacement reference values ​​output by the reference model; to ensure the stability of the reference system, a feedback gain is introduced. The negative feedback loop, To introduce negative feedback, the state parameters of the reference model, The control gain parameters are for the reference model; Through the state observer The optimized expression for the train operation reference model is: ； For the feedback gain coefficient of the adaptive iterative learning control system, This is the reference velocity value for the k-th iteration. It is the first derivative of the reference displacement value in the k-th iteration; Let be the velocity observation for the k-th iteration.

3. The adaptive control method according to claim 1 or 2, characterized in that, In step S2, the train speed observation deviation caused by satellite positioning interference Defined as the speed measurement value and the actual train speed The time-varying observation error between them, and the expression for the perturbed velocity observation are: ; The expression for the train dynamics model is: 。 4. The adaptive control method according to claim 1 or 2, characterized in that, In step S2, to simplify controller design, time-varying parameters to be estimated are defined. , , : ； ； ； Based on the aforementioned time-varying parameters to be estimated, a parameter vector to be estimated for the adaptive iterative learning algorithm is defined. With time-varying parameter vector : ； ； In the formula, This is the first derivative of the velocity observation error in the k-th iteration; This represents a vector of continuously unknown parameters to be estimated. A vector containing known time-varying parameters is defined; the final train dynamics model is obtained: ； In the formula, the parameter to be estimated For bounded variables, satisfy norm constraints ,in is a known bounded constant.

5. The adaptive control method according to claim 1 or 2, characterized in that, In step S3, the dynamic speed tracking error is defined. and dynamic position tracking error Taking the first derivative of the tracking error above, we obtain the differential equation for the dynamic error as follows: ； Substituting the final dynamic model of the train, which includes observation bias, and the optimized reference model into the dynamic error differential equation, the differential expression for the speed tracking error is derived: 。 6. The adaptive control method according to claim 5, characterized in that, In step S3, the parameter estimation error is defined. ,in For parameters to be estimated The parameter estimates satisfy the initial conditions. , This refers to the deviation between the estimated and actual parameter values; to ensure the asymptotic convergence of the system, the velocity tracking error convergence condition is set as follows: ,in The system convergence gain coefficient; Substituting the differential expression for the speed tracking error, we can simultaneously derive the convergent expression for the speed tracking error: ； Through simultaneous derivation, the convergent expression for the speed tracking error can be obtained: ； Fusion parameter definition composite feedback gain Finally, the control law expression for the model reference adaptive iterative learning controller is obtained: 。 7. The adaptive control method according to claim 1 or 2, characterized in that, In step S4, a composite energy function is constructed that simultaneously incorporates the time-domain tracking error and the iterative-domain parameter estimation error. The expression is: ； Define the energy difference between different iteration rounds for: ； To ensure that the system energy does not increase with each iteration, i.e., to satisfy... The iterative update law expression for the parameters to be estimated is derived; during each iteration, train speed observations are collected in real time. Calculate the speed tracking error The estimated value of the parameter vector to be estimated is updated through the parameter iteration update law. The control quantity at the current moment is calculated by substituting it into the control law expression. Output control parameters; the specific process is as follows: For the first part of the composite energy function : ； for The following parts are derived: ； Integrating the above derivation process, we can obtain: ； ； To ensure that the system energy does not increase with each iteration, i.e., to satisfy... The iterative update law for the parameters to be estimated is derived. as follows: ； in, It is a constant estimate of the gain matrix.

8. An adaptive control system for high-precision speed and position tracking of vehicles in satellite positioning interference scenarios, characterized in that, include: The train operation basic model construction module is set up to build a basic dynamic model of the train based on train operation data, covering basic resistance and additional resistance, and simultaneously establish a train operation reference model and optimize the train operation reference model through a state observer. The train dynamic model construction module is configured to construct a train dynamic dynamic model under satellite positioning interference environment based on the train basic dynamic model, and simultaneously define the parameter vector to be estimated and the time-varying parameter vector in the adaptive iterative learning algorithm. The adaptive iterative control law construction module is configured to construct a control law for a model reference adaptive iterative learning controller based on dynamic speed tracking error and dynamic position tracking error, according to the train dynamic dynamics model and the optimized train operation reference model. The adaptive iterative update law construction module is configured to construct an iterative update law for controller parameters based on a composite energy function according to the control law of the adaptive iterative learning controller referenced by the model. The control parameter output module is configured to output control parameters based on the controller parameter iterative update law to perform closed-loop adaptive tracking control of the train's speed and position.

9. A readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the adaptive control method for high-precision speed and position tracking of a vehicle in a satellite positioning interference scenario as described in any one of claims 1 to 7.

10. A computer device, characterized in that, It includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the steps of the adaptive control method for high-precision speed and position tracking of a vehicle in a satellite positioning interference scenario as described in any one of claims 1 to 7.

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