High-speed train data driving terminal sliding mode decoupling control method and device

Abstract

The invention discloses a high-speed train data driving terminal sliding mode decoupling control method and device, and relates to the technical field of high-speed train automatic drive.The method comprises the steps that a multi-power-unit distributed mathematical model of a high-speed train system is established and converted to obtain a dynamic linearization data model, splitting a parameter matrix in the dynamic linearization data model to obtain a decoupled dynamic linearization data model; designing a parameter matrix estimation algorithm for estimating a time-varying parameter matrix in the decoupling dynamic linearization data model, and designing an adaptive extended state observer for estimating an uncertain item; designing a nonlinear sliding mode function and a hyperbolic sliding mode reaching law; according to the data driving terminal sliding mode decoupling control method, the buffeting phenomenon of the control force of the system can be relieved, and the data driving terminal sliding mode decoupling control method has fast convergence and strong anti-interference capability.

Description

Technical Field

[0001] This application relates to the technical field of high-speed train automatic driving, and particularly to a data-driven terminal sliding mode decoupling control method and device for high-speed trains. Background Art

[0002] For high-speed trains, it is an urgent need to achieve high-performance stable tracking control of the high-speed train automatic driving system under unknown disturbances. However, the current tracking control methods of high-speed train automatic driving systems often have the problem of easy chattering, and it is difficult to meet the high-stability tracking control requirements of high-speed trains in actual operation. Summary of the Invention

[0003] The purpose of this application is to provide a data-driven terminal sliding mode decoupling control method and device for high-speed trains, which can alleviate the chattering phenomenon and has fast convergence and strong anti-disturbance ability.

[0004] To achieve the above purpose, the following solutions are provided in this application.

[0005] In the first aspect, this application provides a data-driven terminal sliding mode decoupling control method for high-speed trains, including: Based on the coupling force between the power units of the high-speed train system, establish a multi-power unit distributed mathematical model of the high-speed train system; Equivalent the multi-power unit distributed mathematical model considering input constraints into a virtual data model to achieve the dynamic linearization transformation of the nonlinear system, and obtain the dynamic linearization data model; Split the parameter matrix in the dynamic linearization data model to obtain a decoupled dynamic linearization data model; the decoupled dynamic linearization data model consists of a decoupled time-varying parameter matrix and a generalized bounded disturbance; the generalized bounded disturbance consists of an uncertain term and system coupling effects; the parameter matrix in the dynamic linearization data model includes a time-varying parameter matrix; Design a parameter matrix estimation algorithm and an adaptive extended state observer; the parameter matrix estimation algorithm is used to estimate the time-varying parameter matrix in the decoupled dynamic linearization data model, and the adaptive extended state observer is used to estimate the generalized bounded disturbance; Based on the dynamic linearization data model, the parameter matrix estimation algorithm, and the adaptive extended state observer, design a nonlinear sliding mode function and a hyperbolic sliding mode reaching law respectively; Based on the decoupled dynamic linearization data model, the parameter matrix estimation algorithm, the adaptive extended state observer, the nonlinear sliding mode function, and the hyperbolic sliding mode reaching law, and introducing a control force saturation mechanism, derive a data-driven terminal sliding mode decoupling control method to obtain the control force and speed results of each power unit of the high-speed train system.

[0006] In a second aspect, the present application provides a data-driven terminal sliding mode decoupling control device for a high-speed train, including: A multi-power unit distributed mathematical model establishment module, configured to establish a multi-power unit distributed mathematical model of the high-speed train system based on the coupling force between the power units of the high-speed train system; A dynamic linearization data model conversion module, configured to equivalently convert the multi-power unit distributed mathematical model considering input constraints into a virtual data model, implement dynamic linearization conversion of the nonlinear system, and convert to obtain a dynamic linearization data model; A parameter matrix splitting module, configured to split the parameter matrix in the dynamic linearization data model to obtain a decoupled dynamic linearization data model; the decoupled dynamic linearization data model consists of a decoupled time-varying parameter matrix and a generalized bounded disturbance; the generalized bounded disturbance includes an uncertain term and system coupling effects; the parameter matrix in the dynamic linearization data model includes a time-varying parameter matrix; A parameter estimation module, configured to design a parameter matrix estimation algorithm and an adaptive extended state observer; the parameter matrix estimation algorithm is used to estimate the time-varying parameter matrix in the decoupled dynamic linearization data model, and the adaptive extended state observer is used to estimate the generalized bounded disturbance; A sliding mode function and reaching law design module, configured to design a nonlinear sliding mode function and a hyperbolic sliding mode reaching law respectively based on the dynamic linearization data model, the parameter matrix estimation algorithm, and the adaptive extended state observer; A data-driven terminal sliding mode decoupling control module, configured to derive a data-driven terminal sliding mode decoupling control method based on the decoupled dynamic linearization data model, the parameter matrix estimation algorithm, the adaptive extended state observer, the nonlinear sliding mode function, and the hyperbolic sliding mode reaching law, and introduce a control force saturation mechanism to obtain the control force and speed results of each power unit of the high-speed train system.

[0007] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned high-speed train data-driven terminal sliding mode decoupling control method.

[0008] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned high-speed train data-driven terminal sliding mode decoupling control method is implemented.

[0009] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application: The present application provides a data-driven terminal sliding mode decoupling control method and device for high-speed trains. By decoupling the dynamic linearization data model, parameter matrix estimation algorithm, adaptive extended state observer, nonlinear sliding mode function, and hyperbolic sliding mode reaching law, and introducing a control force saturation mechanism, a data-driven terminal sliding mode decoupling control method is derived. Among them, the designed nonlinear sliding mode function and hyperbolic sliding mode reaching law simultaneously meet the control requirements of fast convergence and chattering elimination, and have fast convergence and strong anti-interference ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0011] Figure 1 It is an application environment diagram of a data-driven terminal sliding mode decoupling control method for high-speed trains in an embodiment of the present application.

[0012] Figure 2 It is a schematic flowchart of a data-driven terminal sliding mode decoupling control method for high-speed trains provided in an embodiment of the present application.

[0013] Figure 3 It is a schematic diagram of the dynamic analysis of the high-speed train operation process provided in an embodiment of the present application.

[0014] Figure 4 It is a control block diagram of the data-driven terminal sliding mode decoupling control (Data-Driven Terminal Sliding Mode Decoupling Control, DDTSMDC) provided in an embodiment of the present application.

[0015] Figure 5 It is a schematic diagram of the speed tracking curve of each power unit of the high-speed train under the DDTSMDC scheme provided in an embodiment of the present application.

[0016] Figure 6 It is a schematic diagram of the speed tracking curve of each power unit of the high-speed train under the PID scheme provided in an embodiment of the present application.

[0017] Figure 7 It is a schematic diagram of the speed error curve of the first power unit of the high-speed train under the DDTSMDC scheme provided in an embodiment of the present application.

[0018] Figure 8 It is a schematic diagram of the speed error curve of the second power unit of the high-speed train under the DDTSMDC scheme provided in an embodiment of the present application.

[0019] Figure 9 Schematic diagram of the three-speed error curve of the high-speed train power unit under the DDTSMDC scheme provided by an embodiment of the present application.

[0020] Figure 10 Schematic diagram of the one-speed error curve of the high-speed train power unit under the PID scheme provided by an embodiment of the present application.

[0021] Figure 11 Schematic diagram of the two-speed error curve of the high-speed train power unit under the PID scheme provided by an embodiment of the present application.

[0022] Figure 12 Schematic diagram of the three-speed error curve of the high-speed train power unit under the PID scheme provided by an embodiment of the present application.

[0023] Figure 13 Schematic diagram of the acceleration curve of each power unit of the high-speed train under the DDTSMDC scheme provided by an embodiment of the present application.

[0024] Figure 14 Schematic diagram of the acceleration curve of each power unit of the high-speed train under the PID scheme provided by an embodiment of the present application.

[0025] Figure 15 Schematic diagram of the structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0026] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0027] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0028] The data-driven terminal sliding mode decoupling control method for high-speed trains provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set separately, integrated on the server 104, placed on the cloud or other servers. The terminal 102 can send the to-be-processed tracking control request to the server 104. After receiving the to-be-processed tracking control request, the server 104 obtains the control force and speed results of each power unit of the high-speed train system based on the data-driven terminal sliding mode decoupling control method. The server 104 can feedback the obtained control force and speed results of each power unit of the high-speed train system to the terminal 102. In addition, in some embodiments, the data-driven terminal sliding mode decoupling control method for high-speed trains can also be implemented separately by the server 104 or the terminal 102. For example, the terminal 102 can directly perform train tracking control for the to-be-processed tracking control request, or the server 104 can obtain the to-be-processed tracking control request from the data storage system and perform train tracking control for the to-be-processed tracking control request.

[0029] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0030] In an exemplary embodiment, as Figure 2 shown, a data-driven terminal sliding mode decoupling control method for high-speed trains is provided. This method is executed by a computer device, and can be specifically executed separately by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server 104 in it as an example for illustration, it includes the following steps 201 to step 206.

[0031] Step 201, based on the coupling force between the power units of the high-speed train system, establish a multi-power unit distributed mathematical model of the high-speed train system.

[0032] Step 202, equivalent the multi-power unit distributed mathematical model considering input constraints into a virtual data model to realize the dynamic linearization transformation of the nonlinear system, and the transformed dynamic linearization data model is obtained.

[0033] Step 203: Split the parameter matrix in the dynamic linearization data model to obtain a decoupled dynamic linearization data model; the decoupled dynamic linearization data model consists of a decoupled time-varying parameter matrix and a generalized bounded disturbance; the generalized bounded disturbance includes an uncertain term and system coupling effects; the parameter matrix in the dynamic linearization data model includes a time-varying parameter matrix.

[0034] Step 204: Design a parameter matrix estimation algorithm and an Adaptive Extended State Observer (AESO); the parameter matrix estimation algorithm is used to estimate the time-varying parameter matrix in the decoupled dynamic linearization data model, and the Adaptive Extended State Observer is used to estimate the generalized bounded disturbance.

[0035] Step 205: Based on the dynamic linearization data model, the parameter matrix estimation algorithm, and the Adaptive Extended State Observer, design a nonlinear sliding mode function and a hyperbolic sliding mode reaching law respectively.

[0036] Step 206: Based on the decoupled dynamic linearization data model, the parameter matrix estimation algorithm, the Adaptive Extended State Observer, the nonlinear sliding mode function, and the hyperbolic sliding mode reaching law, and introducing a control force saturation mechanism, derive a data-driven terminal sliding mode decoupling control method to obtain the control force and speed results of each power unit of the high-speed train system.

[0037] Implementing the above Steps 201 to 206, through the decoupled dynamic linearization data model, the parameter matrix estimation algorithm, the Adaptive Extended State Observer, the nonlinear sliding mode function, and the hyperbolic sliding mode reaching law, and introducing a control force saturation mechanism, a data-driven terminal sliding mode decoupling control method is derived. Among them, the designed nonlinear sliding mode function and hyperbolic sliding mode reaching law simultaneously meet the control requirements of fast convergence and chattering elimination, have fast convergence, and strong disturbance rejection ability. To achieve high-performance stable tracking control of the high-speed train automatic driving system under unknown disturbances, a multi-input multi-output (MIMO) data-driven variable structure control scheme that can alleviate chattering is proposed, which is a data-driven method based on the decoupling of the extended state observer.

[0038] (1) In another exemplary embodiment of the present application, as Figure 3As shown, first, a dynamic analysis of the high-speed train operation process is carried out to establish a distributed mathematical model of multiple power units of the high-speed train. The power units of the high-speed train are connected together through coupler devices. During the operation of the high-speed train, there will be mutual coupling forces between the power units. Therefore, the entire high-speed train can be regarded as a strongly coupled system composed of multiple power units. The high-speed train is subjected to traction / braking forces generated by the traction drive device during operation, as well as resistance forces generated by environmental factors and route factors. The force analysis of the high-speed train multiple power unit model is as Figure 3 shown. During the operation of the high-speed train, each power unit provides a certain traction force, and the telescoping of the coupler will cause relative movement between adjacent carriages. The main forces acting on each power unit include: control force, inter-carriage force, air resistance, and additional resistance. According to the analysis, the dynamic model of the high-speed train can be established as shown in Equation (1) below, that is, the distributed mathematical model of multiple power units is expressed as follows: (1); where, represents the speed of the power unit ; is the current sampling time; is the acceleration coefficient of the power unit ; represents the resultant force acting on the power unit ; is the control force of the power unit ; is the basic resistance of the power unit ; is the inter-carriage force between the power unit and the power unit ; is the inter-carriage force between the power unit and the power unit +1; , is the total number of power units.

[0039] The specific forms of the basic resistance and the inter-carriage force are as follows: (2); In the formula: represents the displacement of the power unit ; the parameter is the basic resistance coefficient, which has a high degree of uncertainty; is the elastic coefficient between adjacent power units; is the displacement of the power unit +1; is the damping coefficient, is the power unit The speed of +1.

[0040] Applying the forward Euler formula to the distributed mathematical model of the multi-power unit of the high-speed train system, i.e., Equation (1), the following discrete-time nonlinear model can be obtained: (3); Where: is the speed of the power unit at time +1; is a non-linear time-varying function; are respectively at time, the power unit -1, the power unit , the power unit +1; are respectively at time, the power unit -1, the power unit , the power unit +1, is the control force of the power unit at time .

[0041] (2) Equivalent the distributed mathematical model of the multi-power unit considering input constraints into a virtual data model to achieve the dynamic linearization transformation of the non-linear system.

[0042] Convert all displacement terms in Equation (3) into forms related to speed and control force, and represent external disturbances, parameter estimation errors, etc. with a total disturbance term. Then Equation (3) can be equivalently the following MIMO high-speed train discrete-time non-linear system: (4); Wherein, is the total speed of the high-speed train system at time; , are respectively the total speed and total control force of the high-speed train system; are respectively at time, the speed of the first power unit and the th power unit; are respectively at time, the control force of the first power unit and the th power unit; is the bounded unknown disturbance of the high-speed train system; are respectively at time, the bounded unknown disturbance of the first power unit and the th power unit; Let , is a constant; is a non - linear function; , , are the orders of the system output (i.e., speed), input (control force), and unknown term (also called uncertain term), respectively.

[0043] Considering that the output torque of the traction motor in the actual operation of the high - speed train is limited by the motor power, let the system control force and the control force change rate be subject to the following input constraints: (5); In the formula: and represent the maximum braking force and the maximum traction force of the power unit, respectively; is a constant representing the constraint of any control force; is the control force of the power unit at - 1 moment, is the control force change rate.

[0044] For the MIMO high - speed train discrete - time non - linear system subject to the input constraint (5), the following two assumptions are made.

[0045] Assumption 1: The partial derivative of the non - linear time - varying function with respect to the control force exists, is not zero, and has a constant sign.

[0046] Assumption 2: The system satisfies the generalized Lipschitz condition, that is: (6); In the formula: ; and represent the th element in the non - linear function , is the non - linear function the total number of elements in.

[0047] Theorem 1: For the discrete - time high - speed train system (4) that satisfies Assumptions 1 - 2 and is subject to the input constraint (5), when is satisfied, there must exist a time - varying parameter matrix such that the discrete - time high - speed train system (4) can be transformed into the following virtual but equivalent dynamic linearization data model: (7); In the formula: is the total uncertainty term of the dynamic linearization data model, ; at any sampling moment , the time-varying parameter matrix is bounded, is the element in the th row and th column of , .

[0048] (3) Split the parameter matrix in the dynamic linearization data model into a diagonal system matrix and a parameter matrix composed of an unknown term (also called an uncertain term) and a coupling effect.

[0049] To further improve the dynamic performance of the system, with the help of the disturbance decoupling idea, split the parameter matrix in the dynamic linearization data model into a diagonal system matrix and an uncertain term and a second parameter matrix composed of a coupling effect, then Equation (7) can be rewritten as the decoupled dynamic linearization data model shown in the following equation: (8); In the formula: is the change amount of the total control force of the high-speed train system at time; the superscript T represents the transpose; is the generalized bounded disturbance of the high-speed train system containing the uncertain term and the system coupling effect at time; are respectively the generalized bounded disturbances of the first power unit and the th power unit at time; ; is the decoupled time-varying parameter matrix at time, is the diagonal matrix symbol, is the element in the first row and the first column of the decoupled time-varying parameter matrix, is the element in the th row and th column of the decoupled time-varying parameter matrix.

[0050] (4) Design a parameter matrix estimation algorithm and an adaptive extended state observer to process the two matrices respectively, and solve the estimation of the data model parameters and the total uncertainty term.

[0051] Since the time-varying parameter matrix is unknown, introduce the following parameter matrix estimation algorithm to estimate the time-varying parameter matrix: (9); In the formula: is The estimated value of the time-varying parameter matrix at a certain moment is the element in the th row and th column of ; is the estimated value of the time-varying parameter matrix at -1 moment; is the step size factor, ; is the change in the total speed of the high-speed train system at a certain moment; is the change in the total control force of the high-speed train system at -1 moment; The superscript T represents transpose;

[0052] To enhance the robustness of the parameter matrix estimation algorithm, the following parameter reset algorithm is given: (10) In the formula: is the element in the th row and th column of ; is a relatively small positive number; is the initial value of is the element in the th row and [[ID=6,2]]th column of the time-varying parameter matrix at moment 1.

[0053] Since the uncertainty term is unknown, the AESO estimated uncertainty term is designed as follows: (11); In the formula: is the estimated value of the total speed of the high-speed train system at +1 moment; is the estimated value of the total speed of the high-speed train system at a certain moment ; is the estimated value of the generalized bounded disturbance of the high-speed train system at a certain moment ; is the change in the total control force of the high-speed train system at a certain moment; and are the gains of the Adaptive Extended State Observer (AESO); is the estimated value of the generalized bounded disturbance of the high-speed train system at time +1; Define the state variables and the observed values of the state variables of the AESO as and .

[0054] (V) Design a non-linear sliding mode function and a hyperbolic sliding mode reaching law respectively based on the split dynamic linearization data model, the parameter matrix estimation algorithm, and the adaptive extended state observer.

[0055] Define the system speed error as , where is the desired output. To enhance the system control performance, consider introducing a new discrete-time terminal sliding mode function, that is, the non-linear sliding mode function is in the following form: (12); In the formula: is the sliding mode variable of the high-speed train system at time are respectively the sliding mode variables of the first power unit and the th power unit at time , are both adjustable weight parameters; is the system speed error of the high-speed train system at time is the system speed error of the high-speed train system at time -1; is the ratio of two odd numbers; is an adjustable parameter that affects the error convergence boundary of the closed-loop system.

[0056] Considering that the traditional discrete sliding mode control cannot meet the control requirements of both fast convergence and chattering elimination, an improved hyperbolic sliding mode reaching law is proposed: (13); (14); In the formula: is the sliding mode variable of the high-speed train system at time +1; is the sign function; is the convergence parameter, satisfying ; and are the parameters that adjust the change speed of the exponential term; is the gain parameter. and are two non - linear functions. are respectively the element in the first row and first column and the element in the first row and the th column in are respectively the element in the second row and first column and the element in the second row and the th column in. When the sliding - mode variable is large, decreases, and at the same time increases, thus accelerating the convergence process of the system to the sliding - mode surface. When the sliding - mode variable is small, increases, decreases, obtaining a smaller controller gain and alleviating the chattering phenomenon.

[0057] (6) Based on the decoupled dynamic linearization data model, parameter matrix estimation algorithm, adaptive extended - state observer, non - linear sliding - mode function, and hyperbolic sliding - mode reaching law, and introducing a control - force saturation mechanism, a data - driven terminal - sliding - mode decoupled control (DDTSMDC) method is derived to achieve fast error convergence and alleviate the system chattering. All the design processes of the proposed scheme only use the input - output data of the high - speed train system, which is easy to implement, and has strong parameter self - adaptability and anti - interference ability. To sum up, combining the decoupled dynamic linearization data model, parameter matrix estimation algorithm, AESO, non - linear sliding - mode function, and hyperbolic sliding - mode reaching law, the proposed DDTSMDC scheme can be obtained, and its control block diagram is as Figure 4 shown.

[0058] (15); In the formula, is the desired trajectory, are respectively the speeds of the first and the th power units at time

[0059] (7) Finally, the proposed scheme is tested by comparison on the CRH380A - type high - speed train simulation test bench equipped in the laboratory. The simulation results show that: the speed tracking errors of each power unit of the high - speed train under the proposed control scheme are all within [-0.138 km / h, 0.142 km / h], the control force and acceleration are respectively within [-52.1 kN, 46.5 kN] and [-0.557 m / s 2 , 0.478 m / s 2 and change smoothly, and the system chattering degree is low.

[0060] Simulation settings: During the operation, white noise is introduced to simulate the external interference encountered in the actual operation of high-speed trains, so as to verify the robustness of the proposed algorithm. The vehicle information and control strategy are input into the simulation test bench, and the train operation speed, control force, position and other information of various schemes are recorded, and compared with the MFASMC, MFAC schemes based on the dynamic linearization data model and the GPC scheme based on the system model.

[0061] (1) The DDTSMDC scheme proposed in this application: The initial conditions of the system are set as , . The controller parameters are set as , , , , , , , , , , , .

[0062] (2) MFASMC scheme: The initial conditions of the system are set as , . The controller parameters are set as , , , , , , , .

[0063] (3) MFAC scheme: The initial conditions of the system are set as , . The controller parameters are set as , , , , .

[0064] (4) GPC scheme: The initial conditions of the system are set as . The controller parameters are set as: the prediction horizon and control horizon are = 3, = 2; the forgetting factor = 0.95.

[0065] Simulation analysis: AESO's observations of the total system uncertainty, including disturbance estimates for power units 1, 2, and 3, show that AESO estimates disturbances well, with an error within ±0.005. This total uncertainty will be compensated for in the DDTSMDC scheme.

[0066] Figures 5 - 6 The speed tracking curves of each power unit of the high-speed train from Jinan West Station to Xuzhou East Station under the DDTSMDC and existing PID schemes are shown. It can be seen that the tracking results of the four schemes will not exceed the system speed limit curve, indicating that both schemes meet the most basic operation safety requirements. Figure 7 、 Figure 8 and Figure 9 They are the speed error curves of power unit 1, power unit 2 and power unit 3 from Jinan West Station to Xuzhou East Station under the DDTSMDC scheme of this application, Figure 10 、 Figure 11 and Figure 12 The speed error curves for power units 1, 2, and 3 from Jinan West Station to Xuzhou East Station under the PID scheme are shown. The tracking error range for each power unit in the DDTSMDC scheme remains stable between [-0.138 km / h and 0.147 km / h] throughout the entire process. However, due to the improved control law and AESO, the DDTSMDC scheme significantly outperforms the PID scheme in terms of rapid convergence and disturbance rejection.

[0067] This application also obtains the control force curves of each power unit of the high-speed train under the DDTSMDC, MFASMC, MFAC, and GPC schemes, showing the changes in the control force of the high-speed train under different control schemes. During the starting, braking, and inertia phases of the DDTSMDC scheme, the control force of each power unit remains stable, with the traction / braking force range being [-52.1 kN, 46.5 kN]. In addition, during the operating condition transition period, the change in control force also shows a smooth transition with a moderate rate of change. The traction / braking force range of the MFASMC scheme is within [-57.4 kN, 49.8 kN]. Due to the introduction of the terminal sliding surface and hyperbolic function, the DDTSMDC scheme significantly alleviates the system chattering phenomenon compared to the MFASMC scheme. The MFAC and GPC schemes experience large variations in control force during starting and braking, with the control force ranges being [-56.3 kN, 49.1 kN] and [-61.4 kN, 47.2 kN], respectively. Furthermore, the control force variations for both schemes are frequent and have large amplitudes. In actual control, most actuators cannot withstand the frequent switching between positive and negative outputs, posing a certain degree of safety hazard to train operation.

[0068] Figure 13 and Figure 14For the acceleration curves of each power unit of the high-speed train from Jinan West Station to Xuzhou East Station under the DDTSMDC and PID schemes, it is not difficult to see that the acceleration change of the PID scheme is too fast, with the ranges being [-0.6 m / s 2 , 0.537 m / s 2 respectively, and the change amplitude is relatively large. While for the high-speed train adopting the DDTSMDC scheme, the acceleration transition changes more smoothly. Except for the starting stage, the range is between [-0.558 m / s 2 , 0.478 m / s 2 . While the amplitude is smaller than that of the PID scheme, it also meets the comfort requirements of passengers.

[0069] Furthermore, in order to more intuitively analyze the control performance of each controller, the following several performance indicators are considered to evaluate the controller. The DDTSMDC method of this application is compared with the MFASMC, MFAC and GPC schemes, and the situations of each performance indicator are shown in Table 1. It is not difficult to see that both the MSE value and the IAE value of the DDTSMDC method are smaller than those of the other three methods; the maximum acceleration / deceleration reflects the stability of the system input. The maximum acceleration / deceleration of the train using the DDTSMDC method is 0.558 m / s 2 , with a relatively small change; the maximum acceleration / deceleration of the MFASMC and GPC methods are relatively large, being 0.586 m / s 2 and 0.613 m / s 2 respectively. In addition, the energy consumption index can reflect the energy loss situation of the train. It can be calculated that compared with the MFASMC, MFAC and GPC schemes, the DDTSMDC scheme saves 7.7%, 4.5% and 18.5% of energy respectively.

[0070] Table 1 Performance indicators of the DDTSMDC method and the MFASMC, MFAC and GPC schemes

[0071] The present application also provides an application scenario, which applies the above-mentioned high-speed train data-driven terminal sliding mode decoupling control method. Specifically: The high-speed train data-driven terminal sliding mode decoupling control method provided in this embodiment can be applied to the automatic driving scenario of high-speed trains. The automatic driving scenario of high-speed trains includes a request issuing link and a data-driven terminal sliding mode decoupling control link; the control request to be processed enters the data-driven terminal sliding mode decoupling control link from the request issuing link, and the input and output results of the corresponding high-speed train system are obtained. The high-speed train data-driven terminal sliding mode decoupling control method provided in this embodiment belongs to the data-driven terminal sliding mode decoupling control link. Specifically, in the process of the data-driven terminal sliding mode decoupling control link for the control request to be processed, the control force and speed results of each power unit of the high-speed train system can be obtained based on the data-driven terminal sliding mode decoupling control method.

[0072] Based on the same inventive concept, the embodiment of the present application also provides a high-speed train data-driven terminal sliding mode decoupling control device for implementing the above-mentioned high-speed train data-driven terminal sliding mode decoupling control method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the high-speed train data-driven terminal sliding mode decoupling control device provided below can refer to the limitations on the high-speed train data-driven terminal sliding mode decoupling control method in the above text, and will not be repeated here.

[0073] In an exemplary embodiment, a high-speed train data-driven terminal sliding mode decoupling control device is provided, which includes the following modules.

[0074] A multi-power unit distributed mathematical model establishment module, configured to establish a multi-power unit distributed mathematical model of the high-speed train system based on the coupling force between the power units of the high-speed train system.

[0075] A dynamic linearization data model conversion module, configured to equivalently convert the multi-power unit distributed mathematical model considering input constraints into a virtual data model, implement the dynamic linearization conversion of the nonlinear system, and convert to obtain a dynamic linearization data model.

[0076] A parameter matrix splitting module, configured to split the parameter matrix in the dynamic linearization data model to obtain a decoupled dynamic linearization data model; the decoupled dynamic linearization data model consists of a decoupled time-varying parameter matrix and a generalized bounded disturbance; the generalized bounded disturbance consists of an uncertain term and a system coupling effect; the parameter matrix in the dynamic linearization data model includes a time-varying parameter matrix.

[0077] A parameter estimation module is used to design a parameter matrix estimation algorithm and an adaptive extended state observer. The parameter matrix estimation algorithm is used to estimate the time-varying parameter matrix in the decoupled dynamic linearized data model, and the adaptive extended state observer is used to estimate the generalized bounded disturbance.

[0078] A sliding mode function and reaching law design module is used to design a nonlinear sliding mode function and a hyperbolic sliding mode reaching law respectively based on the dynamic linearized data model, the parameter matrix estimation algorithm and the adaptive extended state observer.

[0079] A data-driven terminal sliding mode decoupling control module is used to derive a data-driven terminal sliding mode decoupling control method based on the decoupled dynamic linearized data model, the parameter matrix estimation algorithm, the adaptive extended state observer, the nonlinear sliding mode function and the hyperbolic sliding mode reaching law, and introduce a control force saturation mechanism to obtain the control force and speed results of each power unit of the high-speed train system.

[0080] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 15 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store high-speed train control data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a data-driven terminal sliding mode decoupling control method for high-speed trains.

[0081] Those skilled in the art can understand that Figure 15 the structure shown in

[0082] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0083] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0084] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0085] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0086] In each of the embodiments provided in this application, the databases involved may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., without limitation. In each of the embodiments provided in this application, the processor may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without limitation.

[0087] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0088] Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A data-driven terminal sliding mode decoupling control method for high-speed trains, characterized in that The data-driven terminal sliding mode decoupling control method for high-speed trains includes: Based on the coupling force between the power units of the high-speed train system, establish a multi-power unit distributed mathematical model of the high-speed train system; Equivalent the multi-power unit distributed mathematical model considering input constraints into a virtual data model to achieve the dynamic linearization transformation of the nonlinear system, and obtain the dynamic linearized data model; Split the parameter matrix in the dynamic linearized data model to obtain a decoupled dynamic linearized data model; the decoupled dynamic linearized data model consists of a decoupled time-varying parameter matrix and a generalized bounded disturbance; the generalized bounded disturbance includes uncertain terms and system coupling effects; the parameter matrix in the dynamic linearized data model includes a time-varying parameter matrix; Design a parameter matrix estimation algorithm and an adaptive extended state observer; the parameter matrix estimation algorithm is used to estimate the time-varying parameter matrix in the decoupled dynamic linearized data model, and the adaptive extended state observer is used to estimate the generalized bounded disturbance; Based on the dynamic linearized data model, the parameter matrix estimation algorithm, and the adaptive extended state observer, design a nonlinear sliding mode function and a hyperbolic sliding mode reaching law respectively; Based on the decoupled dynamic linearized data model, the parameter matrix estimation algorithm, the adaptive extended state observer, the nonlinear sliding mode function, and the hyperbolic sliding mode reaching law, and introducing a control force saturation mechanism, derive a data-driven terminal sliding mode decoupling control method to obtain the control force and speed results of each power unit of the high-speed train system.

2. The data-driven terminal sliding mode decoupling control method for high-speed trains according to claim 1, characterized in that, The multi-power unit distributed mathematical model is expressed as follows: ； Among them, represents the speed of the power unit ; is the current sampling time; is the acceleration coefficient of the power unit ; represents the resultant force received by the power unit ; is the control force of the power unit ; is the basic resistance received by the power unit ; is the inter-car force between the power unit and the power unit +1; , is the total number of power units.

3. The data-driven terminal sliding mode decoupling control method for high-speed trains according to claim 1, characterized in that The decoupled dynamic linearized data model is expressed as follows: ； wherein, is the total speed of the high - speed train system at time is the total speed of the high - speed train system at time are respectively the speeds of the first power unit and the th power unit at time is the total control force of the high - speed train system at time is the change amount of the total control force of the high - speed train system at time are respectively the control forces of the first power unit and the th power unit at time ; the superscript T represents transpose; is the generalized bounded disturbance of the high - speed train system at time are respectively the generalized bounded disturbances of the first power unit and the th power unit at time is the decoupled time - varying parameter matrix at time is the symbol of the diagonal matrix is the element in the first row and the first column of the decoupled time - varying parameter matrix is the element in the th row and the th column of the decoupled time - varying parameter matrix , is the total number of power units 4. The data-driven terminal sliding mode decoupling control method for high-speed trains according to claim 1, characterized in that The calculation formula for the parameter matrix estimation algorithm to estimate the time-varying parameter matrix is expressed as follows: ； Wherein: is the estimated value of the time-varying parameter matrix at time is the estimated value of the time-varying parameter matrix at time is the step size factor; is the change in the total speed of the high-speed train system at time is the change in the total control force of the high-speed train system at time is the weight factor. The superscript T represents the transpose; 5. The data-driven terminal sliding mode decoupling control method for high-speed trains according to claim 1, characterized in that The calculation formula for the adaptive extended state observer to estimate the uncertain term is expressed as follows: ； Wherein: is the estimated value of the total speed of the high-speed train system at the +1 moment; is the estimated value of the total speed of the high-speed train system at the moment; is the estimated value of the generalized bounded disturbance of the high-speed train system at the moment; is the estimated value of the time-varying parameter matrix after decoupling at the moment; is the change in the total control force of the high-speed train system at the and are the adaptive extended state observer gains; is the estimated value of the generalized bounded disturbance of the high-speed train system at the +1 moment.

6. The data-driven terminal sliding mode decoupling control method for high-speed trains according to claim 1, characterized in that The nonlinear sliding mode function is expressed as follows: ； Wherein: is the sliding mode variable of the high-speed train system at time are respectively the sliding mode variables of the first power unit and the th power unit at time , are both adjustable weight parameters; is the system speed error of the high-speed train system at time is the system speed error of the high-speed train system at time [[ - 1]]; is the ratio of two odd numbers; is an adjustable parameter.

7. The data-driven terminal sliding mode decoupling control method for high-speed trains according to claim 1, characterized in that The hyperbolic sliding mode reaching law is expressed as follows: ； Among them, is the sliding mode variable of the high-speed train system at time +1; and are two non-linear functions, is the sliding mode variable of the high-speed train system at time and is the sign function.

8. A data-driven terminal sliding mode decoupling control device for high-speed trains, characterized in that, The data-driven terminal sliding mode decoupling control device for high-speed trains includes: A multi-power unit distributed mathematical model establishment module, which is used to establish a multi-power unit distributed mathematical model of the high-speed train system based on the coupling force between the power units of the high-speed train system; A dynamic linearized data model conversion module, which is used to equivalent the multi-power unit distributed mathematical model considering input constraints into a virtual data model to achieve the dynamic linearization transformation of the nonlinear system and obtain the dynamic linearized data model; A parameter matrix splitting module, which is used to split the parameter matrix in the dynamic linearized data model to obtain a decoupled dynamic linearized data model; the decoupled dynamic linearized data model consists of a decoupled time-varying parameter matrix and a generalized bounded disturbance; the generalized bounded disturbance includes uncertain terms and system coupling effects; the parameter matrix in the dynamic linearized data model includes a time-varying parameter matrix; A parameter estimation module, which is used to design a parameter matrix estimation algorithm and an adaptive extended state observer; the parameter matrix estimation algorithm is used to estimate the time-varying parameter matrix in the decoupled dynamic linearized data model, and the adaptive extended state observer is used to estimate the generalized bounded disturbance; The sliding mode function and reaching law design module is used to design a non - linear sliding mode function and a hyperbolic sliding mode reaching law respectively based on the dynamic linearization data model, the parameter matrix estimation algorithm and the adaptive extended state observer; The data - driven terminal sliding mode decoupling control module is used to derive a data - driven terminal sliding mode decoupling control method based on the decoupled dynamic linearization data model, the parameter matrix estimation algorithm, the adaptive extended state observer, the non - linear sliding mode function and the hyperbolic sliding mode reaching law, and introduce a control force saturation mechanism to obtain the control force and speed results of each power unit of the high - speed train system.

9. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the high - speed train data - driven terminal sliding mode decoupling control method according to any one of claims 1 - 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the high - speed train data - driven terminal sliding mode decoupling control method according to any one of claims 1 - 7.

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