Multi-mass-point high-speed train full-stage track iterative learning control method

Abstract

The invention relates to the technical field of train control, in particular to a full-stage track iterative learning control method for a multi-mass-point high-speed train. Comprising the following steps: describing dynamic behaviors of a high-speed train based on a multi-particle model of the high-speed train, and establishing a kinetic model of the train; introducing an iterative learning control strategy based on the dynamic model and the control target; the tracking error of the train speed in the iteration process is defined; the dynamic behavior of the tracking error is analyzed, dynamic behavior data are obtained, accurate trajectory tracking control of the high-speed train in different operation stages is completed, and the trajectory tracking accuracy and control stability of the high-speed train in the complex operation environment are improved in the mode.

Description

Technical Field

[0001] The present invention relates to the technical field of train control, and in particular to a full-stage trajectory iterative learning control method for a multi-particle high-speed train. Background Art

[0002] In recent years, high-speed trains have become an important mode of transportation due to their environmental friendliness and high-speed performance. High-speed trains involve complex dynamic systems during their travel, and their automatic control is particularly critical to ensuring the safety and efficiency of the trains. As the core technology of high-speed trains, automatic train control systems can achieve stable trajectory tracking control during high-speed travel, but there are certain challenges in dealing with nonlinear and uncertain disturbances.

[0003] Traditional high-speed train control methods are mostly based on simplified single-mass models, which simplify the entire train system into a dynamic model of a single mass point. Although this model is relatively simple in design and analysis, its accuracy and adaptability are low, and it is difficult to capture the complex interactions between the train carriages and nonlinear factors such as resistance changes in actual operation. Therefore, in practical applications, control strategies based on multi-mass models have gradually attracted attention to better simulate the actual dynamic behavior of the train. In addition, the operating environment of high-speed trains is complex and changeable, and is significantly affected by wind resistance, track slope, curves, and coupling between carriages. In response to these uncertainties and disturbances, many control methods such as adaptive control, robust control, and model predictive control have been applied to train speed control. However, these methods have limited performance in dealing with complex and periodically changing environmental factors, which can easily lead to unstable system performance or slow regulation.

[0004] Iterative learning control is a control method that improves system performance through continuous learning and optimization in periodic operation, and has good adaptability and convergence. Iterative learning control gradually reduces the control error through multiple iterations, and is suitable for high-speed trains running back and forth on fixed tracks. However, traditional iterative learning control methods usually require resetting the initial conditions after each iteration, which is difficult to achieve in the continuous operation of trains. Therefore, introducing alignment conditions as an alternative method is not only more practical, but also helps to further improve the adaptability and control accuracy of the system.

[0005] In summary, it is very necessary to propose a multi-particle high-speed train full-stage trajectory iterative learning control method based on the alignment condition to improve the trajectory tracking accuracy and control stability of high-speed trains in complex operating environments. Summary of the invention

[0006] The purpose of the present invention is to provide a full-stage trajectory iterative learning control method for a multi-particle high-speed train, aiming to improve the trajectory tracking accuracy and control stability of the high-speed train in a complex operating environment.

[0007] To achieve the above object, the present invention adopts a multi-particle high-speed train full-stage trajectory iterative learning control method, comprising the following steps:

[0008] The dynamic behavior of the high-speed train is described based on its multi-mass point model, and the dynamic model of the train is established;

[0009] Based on the dynamic model and control objectives, an iterative learning control strategy is introduced;

[0010] Define the tracking error of the train speed during the iteration process;

[0011] Analyze the dynamic behavior of the tracking error, obtain dynamic behavior data, and complete precise trajectory tracking control of high-speed trains in different operating stages.

[0012] Among them, in the step of describing the dynamic behavior of the high-speed train based on the multi-particle model and establishing the dynamic model of the train:

[0013] The kinetic model is:

[0014]

[0015] In the formula, is the order of the high-speed train, p j (t) is the displacement of the jth carriage, in meters, v j (t) represents the speed of the jth carriage in m / s, m j >0 indicates the mass of the jth carriage, in kg. is the traction / braking force of the jth carriage, c0, c1, c2 and c3 represent unknown drag coefficients, k j (t) is the other resistance of the jth carriage.

[0016] Among them, in the step of introducing iterative learning control strategy based on dynamic model and control objective:

[0017] definition:

[0018]

[0019] In the formula, p i,j (t) and v i,j (t) represent the displacement and velocity of the jth carriage in the i-th iteration, u i,j (t) represents the control input signal;

[0020] The dynamic model of high-speed train can be expressed in the iterative framework as follows:

[0021]

[0022] Wherein, in the step of defining the tracking error of the train speed during the iteration:

[0023] Get the total resistance F during train operation i (P i (t),V i (t),t) range:

[0024] 0<||F i (P i (t),V i (t),t)||2≤δ i (t);

[0025] In the formula, δ i (t) is a known local Lipschitz continuous function, which is used to limit the upper bound of the total resistance;

[0026] Define the tracking error of the train speed during the i-th iteration:

[0027]

[0028] In the formula, e i,j (t) represents the speed tracking error of the jth carriage in the i-th iteration, is the reference speed.

[0029] Among them, in the step of analyzing the dynamic behavior of the tracking error and obtaining dynamic behavior data:

[0030] In each iteration, the initial state of the high-speed train needs to meet the following alignment conditions:

[0031] The velocity at the end of each iteration should be equal to the initial velocity of the next iteration:

[0032] V i-1 (T) = V i (0);

[0033] The initial and final states of the reference speed should be consistent:

[0034] V r (T) = V r (0);

[0035] The initial and final states of the tracking error must remain consistent:

[0036] e i-1 (T) = e i (0);

[0037] By calculating the tracking error e i (t) and combined with the system dynamics model, we can get:

[0038]

[0039] Among them, in the step of analyzing the dynamic behavior of the tracking error and obtaining dynamic behavior data:

[0040] Define the iterative learning control strategy:

[0041]

[0042] In the formula, Β -1 is the inverse of the matrix Β, is a symmetric positive definite matrix, representing the control gain, is the estimated parameter in the i-th iteration, and α(t) represents a nonlinear function based on error and speed.

[0043] The invention discloses an iterative learning control method for the full-stage trajectory of a multi-particle high-speed train. The method describes the dynamic behavior of the high-speed train based on a multi-particle model and establishes a dynamic model of the train. An iterative learning control strategy is introduced based on the dynamic model and the control target. The tracking error of the train speed is defined during the iteration process. The dynamic behavior of the tracking error is analyzed and the dynamic behavior data is obtained to complete the precise trajectory tracking control of the high-speed train in different operation stages, thereby improving the trajectory tracking accuracy and control stability of the high-speed train in a complex operation environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a flowchart of the steps of the full-stage trajectory iterative learning control method of a multi-particle high-speed train of the present invention.

[0046] Figure 2 This is the first iteration train motion state control effect diagram of the present invention.

[0047] Figure 3 This is the 30th iteration train motion state control effect diagram of the present invention.

[0048] Figure 4 It is the displacement and speed tracking error curve of the iterative learning control of the high-speed train of the present invention.

[0049] Figure 5 is the maximum absolute displacement and velocity tracking error along the iteration axis of the present invention.

[0050] Figure 6 These are the 1st and 30th iteration input curves of the present invention. DETAILED DESCRIPTION

[0051] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0052] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0053] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0054] See also Figure 1 to Figure 6 The present invention provides a full-stage trajectory iterative learning control method for a multi-particle high-speed train, comprising the following steps:

[0055] S100: Describe the dynamic behavior of the high-speed train based on its multi-particle model and establish a dynamic model of the train.

[0056] In this embodiment, in order to realize the high-speed train trajectory tracking control, firstly, the dynamic behavior of the high-speed train is described based on the multi-particle model of the high-speed train, and the comprehensive influence of traction, braking force and resistance, such as wind resistance, coupler force and mechanical resistance, on the train movement is comprehensively considered to establish the dynamic model of the train; this model is the basis for realizing trajectory tracking control, and the dynamic model is:

[0057]

[0058] In the formula, is the order of the high-speed train, p j (t) is the displacement of the jth carriage, in meters, v j(t) represents the speed of the jth carriage in m / s, m j >0 indicates the mass of the jth carriage, in kg. is the traction / braking force of the jth carriage, c0, c1, c2 and c3 represent unknown drag coefficients, k j (t) is the other resistance of the jth carriage.

[0059] S200: Based on the dynamic model and control objectives, an iterative learning control strategy is introduced.

[0060] In this embodiment, based on the dynamic model and control objectives, an iterative learning control strategy is introduced to extract control experience and optimize control performance through multiple iterative operations to cope with complex nonlinear disturbances and uncertainties in train operation.

[0061] definition:

[0062]

[0063] In the formula, p i,j (t) and v i,j (t) represent the displacement and velocity of the jth carriage in the i-th iteration, u i,j (t) represents the control input signal; Figure 2 , Figure 3 and Figure 6 As shown, Figure 2 This is the control effect diagram of the train motion state in the first iteration. Figure 3 This is the control effect diagram of the train motion state in the 30th iteration. Figure 6 are the input curves for the 1st and 30th iterations.

[0064] The dynamic model of high-speed train can be expressed in the iterative framework as follows:

[0065]

[0066] S300: Define the tracking error of the train speed during the iteration process.

[0067] In this embodiment, the total resistance F during train operation is obtained. i (P i (t),V i (t),t) range, in order to ensure the robustness of the control algorithm, the total resistance F during train operation i (P i (t),V i (t),t) is bounded, and its Euclidean norm satisfies the following relationship:

[0068] 0<||F i (P i(t),V i (t),t)||2≤δ i (t);

[0069] In the formula, δ i (t) is a known local Lipschitz continuous function, which is used to limit the upper bound of the total resistance.

[0070] In order to design the controller of the high-speed train, we first define the tracking error of the train speed during the i-th iteration:

[0071]

[0072] In the formula, e i,j (t) represents the speed tracking error of the jth carriage in the i-th iteration, as Figure 4 As shown, Figure 4 is the displacement and speed tracking error curve of the iterative learning control of the high-speed train, is the reference speed;

[0073] In the control objective, design a robust iterative learning controller U i (t), so that the train speed V i (t) can track the reference speed V in the interval t∈[0,T] r (t), and as the number of iterations i increases, the tracking error gradually converges.

[0074] S400: Analyze the dynamic behavior of the tracking error, obtain dynamic behavior data, and complete the precise trajectory tracking control of the high-speed train in different operation stages.

[0075] In this embodiment, in order to find a control input signal suitable for the reference trajectory, it is necessary to analyze the dynamic behavior of the tracking error, obtain dynamic behavior data, and complete the precise trajectory tracking control of the high-speed train in different operation stages;

[0076] In order to achieve smooth transition during the iterative update process, the initial state of the high-speed train must meet the following alignment conditions during each iteration:

[0077] The velocity at the end of each iteration should be equal to the initial velocity of the next iteration:

[0078] V i-1 (T) = V i (0);

[0079] The initial and final states of the reference speed should be consistent:

[0080] V r (T) = V r (0);

[0081] The initial and final states of the tracking error must remain consistent:

[0082] e i-1 (T) = e i (0);

[0083] By calculating the tracking error e i (t) and combined with the system dynamics model, we can get:

[0084]

[0085] like Figure 5 As shown, Figure 5 is the maximum absolute displacement and velocity tracking error along the iteration axis;

[0086] Define the iterative learning control strategy:

[0087] Defined as:

[0088]

[0089] In the formula, Β -1 is the inverse of the matrix Β, is a symmetric positive definite matrix, representing the control gain, is the estimated parameter in the i-th iteration, and α(t) represents a nonlinear function based on error and speed.

[0090] parameter The following iterative update rules are adopted:

[0091]

[0092] In the formula, And t∈[0,T], μ>0 is the learning gain, which determines the learning speed, and η∈(0,1) is the attenuation factor used to improve stability.

[0093] The nonlinear function α(t) is defined as:

[0094]

[0095] In the formula, is a design parameter used to adjust the nonlinear amplitude of the function, and tanh(·) is a hyperbolic tangent function used to limit the error growth and improve the robustness of the control.

[0096] Through the above steps, accurate trajectory tracking control of high-speed trains in different operation stages can be achieved.

[0097] In the present invention, by effectively dealing with nonlinear uncertainties in the train operation process, such as wind resistance, coupler force, mechanical resistance, etc., the control strategy of alignment conditions and composite energy functions is adopted to achieve rapid convergence of control errors, thereby improving control accuracy; it is suitable for precise displacement and speed control of trains in traction, cruising, taxiing and braking stages. Compared with traditional methods, the present invention reduces the risk of deviation from the track while improving the safety of train driving; reduces unnecessary acceleration and deceleration through precise track tracking, thereby improving energy saving effects; adapts to various disturbances during operation, and improves train punctuality and operational reliability. At the same time, the present invention uses alignment conditions to replace traditional reset conditions, which simplifies the control process and reduces the complexity and maintenance costs of the system.

[0098] By combining a multi-particle dynamic model with a control scheme based on a repeated operation mode, the present invention can effectively capture the complex interactions between different carriages and solve the bounded uncertainty problem, thereby significantly improving the practical applicability and robustness of the train control system. It has more advantages than the traditional iterative learning control method based on the single-particle model.

[0099] The iterative learning control algorithm proposed in the present invention can achieve precise control in the four operating stages of traction, cruising, coasting and braking, ensuring strict convergence of control errors. Compared with existing controllers, the algorithm of the present invention can handle nonlinear uncertainties, avoiding the limitations of relying on Lipschitz continuous uncertainty and model linearization, thereby showing better adaptability and control accuracy under complex operating conditions.

[0100] The present invention reduces the complexity of the system and enhances the continuity and stability of the control system by adopting alignment conditions instead of traditional reset conditions. It is particularly suitable for the actual operating environment of high-speed trains, improves the smoothness and safety of train operations, and has high practical application value.

[0101] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0102] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A multi-particle high-speed train full-stage trajectory iterative learning control method, characterized in that: The steps include: The dynamic behavior of the high-speed train is described based on its multi-mass point model, and the dynamic model of the train is established; Based on the dynamic model and control objectives, an iterative learning control strategy is introduced; Define the tracking error of the train speed during the iteration process; Analyze the dynamic behavior of the tracking error, obtain dynamic behavior data, and complete precise trajectory tracking control of high-speed trains in different operating stages.

2. The multi-particle high-speed train full-stage trajectory iterative learning control method according to claim 1, characterized in that: In the steps of describing the dynamic behavior of a high-speed train based on a multi-mass model and establishing a dynamic model of the train: The kinetic model is: In the formula, is the order of the high-speed train, p j (t) is the displacement of the jth carriage, in meters, v j (t) represents the speed of the jth carriage in m / s, m j >0 indicates the mass of the jth carriage, in kg. is the traction / braking force of the jth carriage, c0, c1, c2 and c3 represent unknown drag coefficients, k j (t) is the other resistance of the jth carriage.

3. The multi-particle high-speed train full-stage trajectory iterative learning control method as claimed in claim 2, characterized in that: In the step of introducing iterative learning control strategy based on dynamic model and control objective: definition: In the formula, p i,j (t) and v i,j (t) represent the displacement and velocity of the jth carriage in the i-th iteration, u i,j (t) represents the control input signal; The dynamic model of high-speed train can be expressed in the iterative framework as follows:

4. The multi-particle high-speed train full-stage trajectory iterative learning control method as claimed in claim 3, characterized in that: In the step of defining the tracking error of the train speed during the iteration: Get the total resistance F during train operation i (P i (t),V i (t),t) range: 0<||F i (P i (t),V i (t),t)||2≤δ i (t); In the formula, δ i (t) is a known local Lipschitz continuous function, which is used to limit the upper bound of the total resistance; Define the tracking error of the train speed during the i-th iteration: In the formula, e i,j (t) represents the speed tracking error of the jth carriage in the i-th iteration, is the reference speed.

5. The multi-particle high-speed train full-stage trajectory iterative learning control method as claimed in claim 4, characterized in that: In the step of analyzing the dynamic behavior of the tracking error and obtaining dynamic behavior data: In each iteration, the initial state of the high-speed train needs to meet the following alignment conditions: The velocity at the end of each iteration should be equal to the initial velocity of the next iteration: V i-1 (T)=V i (0); The initial and final states of the reference speed should be consistent: V r (T)=V r (0); The initial and final states of the tracking error must remain consistent: And i-1 (T)=and i (0); By calculating the tracking error e i (t) and combined with the system dynamics model, we can get:

6. The multi-particle high-speed train full-stage trajectory iterative learning control method according to claim 5, characterized in that: In the step of analyzing the dynamic behavior of the tracking error and obtaining dynamic behavior data: Define the iterative learning control strategy: In the formula, Β -1 is the inverse of the matrix Β, is a symmetric positive definite matrix, representing the control gain, is the estimated parameter in the i-th iteration, and α(t) represents a nonlinear function based on error and speed.

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