High-speed train fault-tolerant tracking control method and system based on adaptive compensation mechanism

Abstract

The present application discloses a method and system for fault-tolerant tracking control of high-speed trains based on an adaptive compensation mechanism, and relates to the technical field of high-speed train control. The method comprises: constructing a nonlinear multi-particle dynamic model through analysis of the coupling forces within the high-speed train, and substituting the error variables into the model to obtain a linear dynamic error model, which is then converted into a state-space equation, and then the actuator fault model is combined into the model to obtain a high-speed train system equation with an actuator fault model; then a disturbance observer is designed to estimate and compensate for unknown complex disturbances in the train operation process online, and an adaptive fault-tolerant controller is designed and generated according to the high-speed train system equation with an actuator fault model, so that the high-speed train can be controlled based on the disturbance observer and the adaptive fault-tolerant controller to achieve tracking of the desired speed curve and the desired displacement curve. The above scheme of the present application can achieve tracking control of the speed and displacement of the high-speed train.

Description

Technical Field

[0001] The present application relates to the technical field of high-speed train control, and in particular to a method and system for high-speed train fault-tolerant tracking control based on an adaptive compensation mechanism. Background Art

[0002] The actuator is a key component in the braking system. If the actuator fails during the braking process, it may cause braking failure or insufficient braking, thereby increasing the risk that the train will not be able to stop quickly in an emergency. If the actuator fails during the acceleration process, it may cause the train to accelerate unstably or fail to reach the predetermined speed. This failure may cause the train to lose control during acceleration and affect normal operation. The failure of the actuator usually leads to a decrease in system performance and may even make the system unstable. In a highly coupled control system such as a high-speed train, system failures have a profound impact on the safe and reliable operation of the train. In particular, when the train is running at high speed, actuator failures may reduce the accuracy of tracking control and even cause accidents. Therefore, it is of great practical value to explore how to solve the tracking control problem of high-speed trains in the case of actuator failures.

[0003] At present, solving actuator failures and ensuring system stability have become the focus of research. For control systems with actuator failures, research can generally be divided into two categories: passive fault-tolerant control and active fault-tolerant control. Active fault-tolerant control has higher flexibility and stronger fault-tolerant capabilities than passive fault-tolerant control. This is because after a fault occurs, active fault-tolerant control can obtain some relevant fault information online based on fault estimation or detection technology, and reconstruct or adjust the controller to make the system stable and obtain satisfactory performance. Active fault-tolerant control can actively adjust the relevant parameters of the controller and even change the structure of the controller when necessary. Therefore, it is very necessary for this field to find an active fault-tolerant control operation strategy to ensure that high-speed trains accurately track the speed and displacement of the train during operation. Summary of the invention

[0004] The purpose of this application is to provide a high-speed train fault-tolerant tracking control method and system based on an adaptive compensation mechanism, which can adjust control parameters in real time to cope with dynamic changes in the system and achieve tracking control of the speed and displacement of the high-speed train.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism, comprising the following steps:

[0007] According to the analysis results of the coupling forces inside the high-speed train, a nonlinear multi-particle dynamic model of the high-speed train is constructed.

[0008] Error variables are defined and substituted into the nonlinear multi-particle dynamic model to obtain a linear dynamic error model.

[0009] The linear dynamic error model is converted into a state space equation, and the actuator fault model is combined into the state space equation to obtain a high-speed train system equation with the actuator fault model.

[0010] A disturbance observer is designed to online estimate and compensate for the unknown complex disturbances experienced by the train during operation.

[0011] An adaptive fault-tolerant controller is designed and generated according to the high-speed train system equation with the actuator fault model, and the tracking of the expected speed curve and the expected displacement curve is achieved based on the control of the high-speed train by the disturbance observer and the adaptive fault-tolerant controller.

[0012] In a second aspect, the present application provides a high-speed train fault-tolerant tracking control system based on an adaptive compensation mechanism, comprising the following modules:

[0013] The multi-particle dynamic model building module is used to build a nonlinear multi-particle dynamic model of the high-speed train based on the analysis results of the coupling forces within the high-speed train.

[0014] The dynamic error model determination module is used to define error variables and substitute the error variables into the nonlinear multi-particle dynamic model to obtain a linear dynamic error model.

[0015] The actuator fault model combining module is used to convert the linear dynamic error model into a state space equation, and combine the actuator fault model into the state space equation to obtain a high-speed train system equation with the actuator fault model.

[0016] The disturbance observer design module is used to design a disturbance observer to online estimate and compensate for unknown complex disturbances encountered during train operation.

[0017] The adaptive fault-tolerant control module is used to design and generate an adaptive fault-tolerant controller according to the high-speed train system equation with the actuator fault model, and to track the expected speed curve and the expected displacement curve based on the control of the high-speed train by the disturbance observer and the adaptive fault-tolerant controller.

[0018] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0019] The present application provides a method and system for fault-tolerant tracking control of high-speed trains based on an adaptive compensation mechanism. In the method, a nonlinear multi-particle dynamic model of the high-speed train is constructed by analyzing the coupling forces within the high-speed train, and the error variables are substituted into it to obtain a linear dynamic error model, which is then converted into a state-space equation, and the actuator fault model is then combined into it to obtain a high-speed train system equation with an actuator fault model; then an interference observer is designed to estimate and compensate for unknown complex disturbances to which the train is subjected during operation online, and an adaptive fault-tolerant controller is designed and generated based on the high-speed train system equation with an actuator fault model, so that the high-speed train can be controlled based on the interference observer and the adaptive fault-tolerant controller to achieve tracking of the desired speed curve and the desired displacement curve. The adaptive fault-tolerant controller designed in the present application scheme can adjust the control parameters in real time to cope with the dynamic changes of the system, and can adaptively compensate for the unknown lumped disturbances to which the train is subjected, so as to achieve tracking control of the speed and displacement of the high-speed train. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 A flowchart of a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism is provided in one embodiment of the present application.

[0022] Figure 2 A schematic diagram of a high-speed train multi-particle model in a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism provided in one embodiment of the present application.

[0023] Figure 3 A block diagram of a disturbance observer design in a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism provided in one embodiment of the present application.

[0024] Figure 4 A schematic diagram of a method for performing adaptive fault-tolerant tracking control of a high-speed train based on an adaptive compensation mechanism according to an embodiment of the present application.

[0025] Figure 5 A high-speed train expected speed curve diagram in a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism is provided in one embodiment of the present application.

[0026] Figure 6A graph of expected displacement of a high-speed train in a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism provided in one embodiment of the present application.

[0027] Figure 7 A high-speed train speed error curve based on a disturbance observer and PID in a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism provided in one embodiment of the present application.

[0028] Figure 8 A high-speed train displacement error curve based on a disturbance observer and PID in a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism provided in one embodiment of the present application.

[0029] Fig. 9 A high-speed train speed error curve diagram based on an adaptive compensation mechanism in a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism provided in one embodiment of the present application.

[0030] Fig.10 A high-speed train displacement error curve diagram based on an adaptive compensation mechanism in a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism provided in one embodiment of the present application.

[0031] Fig.11 A schematic diagram of functional modules of a high-speed train fault-tolerant tracking control system based on an adaptive compensation mechanism provided in one embodiment of the present application. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0034] In an exemplary embodiment, Figure 1 As shown, a high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism is provided, comprising the following steps:

[0035] S1. Based on the analysis results of the coupling forces within the high-speed train, a nonlinear multi-particle dynamic model of the high-speed train is constructed.

[0036] Specifically, through Figure 2The force analysis of the high-speed train results in the multi-mass point dynamic model as shown in formula (1):

[0037] (1)

[0038] Among them, the basic resistance (The mechanical resistance per unit mass a + bv and air resistance Two parts), a , b and c is the Davis coefficient obtained through wind tunnel testing; , and Respectively represent i Carriage displacement, velocity and acceleration; k and d are the elastic coupling coefficient and the damping coupling coefficient respectively; and They are i the mass and traction of the carriage; and Respectively expressed as i The additional resistance (tunnel, ramp, curve additional resistance) and unknown resistance of the carriage. Figure 2 The high-speed train multi-mass model is shown in the schematic diagram , coupling force The definition is shown in the following formula (2):

[0039] (2)

[0040] Considering the coupling force inside the vehicle and assuming that the air resistance acts mainly on the leading vehicle, the nonlinear multi-mass point dynamic model is shown in the following equation (3):

[0041] (3)

[0042] in, It is i The quality of the carriage, It is i The traction of the carriage, that is, i Carriage control input, , and Respectively represent i The displacement, velocity and acceleration of the carriage, k is the elastic coupling coefficient, d is the damping coupling coefficient, a , b and cThe Davis coefficients are obtained through wind tunnel tests. n is the number of carriages in the high-speed train, Indicates i The additional resistance on the carriage is Indicates i The unknown resistance experienced by the carriage.

[0043] S2, define error variables and substitute the error variables into the nonlinear multi-particle dynamic model to obtain a linear dynamic error model. Specifically, the error variables include: displacement tracking error variables, velocity tracking error variables, control input error variables and additional resistance error variables.

[0044] Assume first that the high-speed train system has the desired speed when it reaches equilibrium. is the expected speed value of the high-speed train when it reaches the equilibrium state, as shown in formula (4):

[0045] (4)

[0046] When the high-speed train reaches the expected speed, the expected control force and expected basic resistance of the high-speed train are expressed as shown in (5):

[0047] (5)

[0048] Assume that the expected acceleration is as follows:

[0049] (6)

[0050] Assuming that there is no relative displacement between the carriages and the length of each carriage is negligible compared to the line along which the train travels, the expected displacement is as follows:

[0051] (7)

[0052] The displacement tracking error variable is shown as follows:

[0053] (8)

[0054] in, is the displacement tracking error variable, For the i The displacement of the carriage, For the i Expected displacement of the carriage.

[0055] The speed tracking error variable is shown as follows:

[0056] (9)

[0057] in, is the speed tracking error variable, For the i The speed of the carriage, For the i The desired speed of the carriage.

[0058] The control input error variable is shown as follows:

[0059] (10)

[0060] in, To control the input error variable, For the i Carriage control input, For the i The desired control input for the car.

[0061] The additional resistance error variable is shown as follows:

[0062] (11)

[0063] in, is the additional resistance error variable, For the i Additional resistance of the carriage, For the i Expected additional drag of the carriage.

[0064] Substituting the above error variables into the nonlinear multi-particle dynamic model (3), the obtained linear dynamic error model is shown in the following equation (12):

[0065] (12)

[0066] in, It is i The quality of the carriage, is the speed tracking error variable, The derivative is the acceleration tracking error variable, To control the input error variable, is the displacement tracking error variable, k is the elastic coupling coefficient, d is the damping coupling coefficient, a , b and c Davis coefficients are obtained through wind tunnel tests. v 0 is the expected speed of the high-speed train, n is the number of carriages in the high-speed train, is the additional resistance error variable, Indicatesi The unknown resistance experienced by the carriage.

[0067] S3. Convert the linear dynamic error model into a state space equation, and combine the actuator fault model into the state space equation to obtain the high-speed train system equation with the actuator fault model. Convert the high-speed train dynamics equation under the linear dynamic error into a more intuitive state space form, and the state space equation is shown in the following equation (13):

[0068] (13)

[0069] in, is the high-speed train system state variable, is the displacement tracking error variable, is the speed tracking error variable; is the high-speed train system control variable, To control the input error variable; is the disturbance variable of the high-speed train system, is the additional resistance of the high-speed train system. is the additional resistance error variable, The unknown resistance to the high-speed train system, Indicates i The unknown resistance of the carriage, n is the number of carriages in a high-speed train; A is the system state matrix, B is the system input matrix; the system state matrix is ​​shown in equation (14):

[0070] (14)

[0071] in, I n×n for n × n Dimensional identity matrix, intermediate matrix A 21 and A 22 They are shown in the following equations (15) to (17) respectively:

[0072] (15)

[0073] (16)

[0074] (17)

[0075] in, k is the elastic coupling coefficient, d is the damping coupling coefficient, It is i The quality of the carriage, a , b and c All are Davis coefficients obtained through wind tunnel tests. v 0 is the expected speed of the high-speed train.

[0076] The system input matrix is ​​shown in equation (18):

[0077] (18)

[0078] in, 0 n×n for n × n dimensional zero matrix, intermediate matrix B 21 As shown in the following formula (19):

[0079] (19)

[0080] During the operation of high-speed trains, actuator failure will endanger the safety of high-speed train operation. Therefore, it is of practical significance to introduce the actuator failure model into the multi-mass point model of high-speed trains. The types of actuator failures include interruption failures, partial failure failures, and stuck failures. The actuator failure model can be expressed by the following formula (20):

[0081] (20)

[0082] in, ; , ; , ; , for any actuator in the high-speed train system, the fault model of the actuator is expressed by the following formula (21):

[0083] (twenty one)

[0084] in, represents the output signal of the ith actuator in the jth fault mode, is the efficiency factor of the ith actuator in the jth fault mode, represents the input signal of the ith actuator, Indicates the fault of the i-th actuator being stuck. is the fault factor of whether the i-th actuator is stuck, which can be determined according to the following formula (22):

[0085] (twenty two)

[0086] Among them, when hour, , indicating that no failure has occurred or a partial failure has occurred; when hour, or , respectively representing an interruption fault and a stuck fault. Specifically, there are the following relationships:

[0087] when , When i There is no fault in the actuator;

[0088] when , When i An actuator has a partial failure.

[0089] when , When i An actuator has an interruption failure;

[0090] when , When i An actuator has stuck.

[0091] Substituting the above fault model expression into the system equation, the high-speed train system equation with actuator fault model is obtained as shown in equation (23):

[0092] (twenty three)

[0093] For the high-speed train system equation with actuator failure model, in order to compensate for the impact of actuator failure, the following assumptions need to be met:

[0094] 1) The system state matrix A and the system input matrix B All are controllable.

[0095] 2) is a piecewise continuous bounded function, with some undetermined positive number , so that the value of the function is always within the range defined by this positive number.

[0096] 3) The actuators of the high-speed train system are in a redundant state, i.e. .

[0097] The above assumption 1) is a basic requirement; assumption 2) is common in existing results of fault-tolerant control technology; assumption 3) indicates that the actuators of the system are in a redundant state and need to fully compensate for disturbances and stuck actuator failures.

[0098] S4. Design a disturbance observer to estimate and compensate for unknown complex disturbances in the train operation process online. Figure 3 The figure shows a design block diagram of an interference observer, which is shown in equation (24):

[0099] (twenty four)

[0100] in, is the unknown lumped disturbance The estimated value of represents the internal state of the disturbance observer, is the nonlinear function to be designed, is the high-speed train system state variable, is the high-speed train system control variable, is the nonlinear disturbance observer gain that needs to be designed, which is the function of P(x) on the state variable The partial derivative of is determined as shown in formula (25):

[0101] (25)

[0102] The disturbance observer estimation error is defined as shown in equations (26) to (27):

[0103] (26)

[0104] (27)

[0105] definition Range is , solving the first-order linear differential equation described in formula (27) yields the following formula (28):

[0106] (28).

[0107] From formula (28), we can see that , when t tends to infinity, the range of the disturbance observer estimation error is shown in formula (29):

[0108] (29)

[0109] Among them, when When , the estimated nonlinear disturbance observer error converges, and the convergence radius is .if Choose reasonably and The error is a bounded function, and the nonlinear disturbance observer error is still likely to converge to an acceptable range.

[0110] S5. Design and generate an adaptive fault-tolerant controller based on the high-speed train system equation with an actuator fault model. Control the high-speed train based on the disturbance observer and the adaptive fault-tolerant controller to achieve the desired speed curve. V e ( t ) and expected displacement curve X a ( t ) Figure 4 The schematic diagram of the adaptive fault-tolerant control based on the disturbance observer is shown in the following formula (30):

[0111] (30)

[0112] in, K x ( t )and K y ( t ) are auxiliary control functions, as shown in the following equations (31)~(32):

[0113] (31)

[0114] (32)

[0115] in, and is an unknown positive constant, and They are and Estimated value, , R and P a are all symmetric positive definite matrices, x represents the state variables of the high-speed train system, is any positive uniformly continuous bounded function that satisfies the formula.

[0116] Substituting formula (30) into formula (23), we obtain the adaptive closed-loop system shown in formula (33):

[0117] (33)

[0118] Under the above assumption 1), for any and any symmetric positive definite matrix and , are all observable. From optimal control theory, we know that there is a unique corresponding symmetric positive definite matrix , so that the Riccati equation in equation (34) holds.

[0119] (34)

[0120] Will Substituting the above Riccati equation into equation (35) yields:

[0121] (35)

[0122] Under assumption 3), Lemma 1) is given: There exists an unknown positive constant , so that formula (36) holds true:

[0123] (36)

[0124] From Assumption 3), we know that there is an unknown positive constant , in formula (33), ,let ,visible It is also unknown.

[0125] against and , design an adaptive update strategy as shown in (37) and (38):

[0126] (37)

[0127] (38)

[0128] in, and is any positive number, and the estimation error is defined as , , it can be obtained that the estimation error satisfies the equations shown in (39) and (40):

[0129] (39)

[0130] (40)

[0131] The adaptive fault-tolerant controller mentioned above makes the high-speed train system under the adaptive compensation mechanism stable, and tracks and controls the speed and displacement.

[0132] For convenience, the solutions of the adaptive closed-loop system and the error system can be expressed as .

[0133] Consider the adaptive closed-loop system described by formula (33) and the error system described by formulas (39)~(40). If assumptions 1)-3) are all valid, then the solutions of the adaptive closed-loop system and the error system are is uniformly bounded and satisfies equation (41):

[0134] (41)

[0135] Therefore, it can be shown that the state variable will eventually approach 0 as time changes, and the state variable is the error of speed and displacement, so it can be tracked and controlled to achieve speed and displacement. The above inference is proved by the following process:

[0136] First, select a Lyapunov function as shown in formula (42):

[0137] (42)

[0138] Derivative the above Lyapunov function yields equation (43):

[0139] (43)

[0140] in, , .

[0141] If the above inequality is Integrating above, we can get equation (44) and equation (45):

[0142] (44)

[0143] (45)

[0144] in, is finite. After in-depth analysis, it can be concluded that the solutions of the adaptive closed-loop system described by formula (33) and the error system characterized by formulas (39) to (40) all show the characteristic of uniform boundedness. This means that the solutions of these systems always remain within a certain range during operation and will not increase or decrease infinitely, thus ensuring the stability and controllability of the system. In addition, from , the above integral formula (44) and formula (45) give the following formula (46):

[0145] (46)

[0146] in, is uniformly bounded, we can get is uniformly continuous. From Barbalat's lemma we can deduce: It is shown that the designed adaptive controller can stabilize the whole system and realize the tracking control of the speed and displacement of the high-speed train.

[0147] The following is a simulation example to verify the performance of the disturbance observer and adaptive compensation mechanism proposed in this application. A series of simulation experiments are carried out on 8 carriages of a high-speed train subject to actuator failure and multiple disturbances. The parameters of the high-speed train are shown in Table 1:

[0148] Table 1 High-speed train parameters

[0149]

[0150] like Figure 5 The expected speed curve of the high-speed train shown and Figure 6 The expected displacement curve of the high-speed train is shown in Figure 4. The expected curve of the running speed of 8 carriages is selected in the simulation experiment of this example as shown in formula (47):

[0151] (47)

[0152] The simulation experiment sets the additional resistance and other resistance of the high-speed train at different times as shown in formula (48):

[0153] (48)

[0154] In the formula, tunnel resistance, curve resistance, other resistance and slope resistance are , , , In simulation, , , , , , , , .

[0155] Assumptions , which means that except for the 3rd and 7th actuators, all are partially faulty.

[0156] Four methods, namely proportional integral differential control (PIDC), disturbance observation-based proportional integral differential control (DOB-PIDC), adaptive control (APC) and the disturbance observation-based adaptive fault-tolerant control (DOB-APC) proposed in this application, are used for simulation verification. The speed tracking error and displacement tracking error are compared and analyzed to verify the advantages of the control method in this paper. The comparison results are shown in Figure 2. Figure 7~Figure 10 As shown, Figure 7 is the speed error curve of high-speed train based on disturbance observer and PID, Figure 8 is the displacement error curve of high-speed train based on disturbance observer and PID; Fig. 9 is the speed error curve of high-speed train based on adaptive compensation mechanism, Fig.10 It is the displacement error curve of high-speed train based on adaptive compensation mechanism.

[0157] According to the comparative analysis, the scheme proposed in this application first considers the coupling force between train cars, assumes that the air resistance acts on the leading car, and constructs a nonlinear multi-particle dynamic model. Error variables such as speed, displacement and control input are defined, and the error variables are substituted into the dynamic model to obtain a linearized error model, which is then converted into a more intuitive state-space expression. On this basis, the actuator failure factor is considered to establish a system equation with actuator failure. Furthermore, an interference observer is designed to estimate and compensate for unknown disturbances to improve the robustness of the train system. Finally, an adaptive controller is designed to form a fault-tolerant tracking control technology based on an interference observer and an adaptive compensation mechanism. Through this active fault-tolerant technology, the control parameters can be adjusted in real time to cope with dynamic changes in the system, and the unknown lumped disturbances to the train can be adaptively compensated to achieve tracking control of the speed and displacement of the high-speed train.

[0158] Based on the same inventive concept, the embodiment of the present application also provides a high-speed train fault-tolerant tracking control system based on an adaptive compensation mechanism for implementing the above-mentioned high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism. In an exemplary embodiment, Fig.11 As shown, the high-speed train fault-tolerant tracking control system based on the adaptive compensation mechanism includes the following functional modules:

[0159] The multi-particle dynamic model building module is used to build a nonlinear multi-particle dynamic model of the high-speed train based on the analysis results of the coupling forces within the high-speed train.

[0160] The dynamic error model determination module is used to define error variables and substitute the error variables into the nonlinear multi-particle dynamic model to obtain a linear dynamic error model.

[0161] The actuator fault model combining module is used to convert the linear dynamic error model into a state space equation, and combine the actuator fault model into the state space equation to obtain a high-speed train system equation with the actuator fault model.

[0162] The disturbance observer design module is used to design a disturbance observer to online estimate and compensate for unknown complex disturbances encountered during train operation.

[0163] The adaptive fault-tolerant control module is used to design and generate an adaptive fault-tolerant controller according to the high-speed train system equation with the actuator fault model, and to track the expected speed curve and the expected displacement curve based on the control of the high-speed train by the disturbance observer and the adaptive fault-tolerant controller.

[0164] certainly, Fig.11 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Fig.11 One or at least two components of the system shown.

[0165] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0166] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A high-speed train fault-tolerant tracking control method based on an adaptive compensation mechanism, characterized in that: include: According to the analysis results of the coupling forces inside the high-speed train, a nonlinear multi-particle dynamic model of the high-speed train is constructed; Defining error variables and substituting the error variables into the nonlinear multi-particle dynamic model to obtain a linear dynamic error model; Converting the linear dynamic error model into a state space equation, and incorporating the actuator fault model into the state space equation to obtain a high-speed train system equation with the actuator fault model; The disturbance observer is designed to estimate and compensate the unknown complex disturbances in the train operation process online. Designing and generating an adaptive fault-tolerant controller according to the high-speed train system equation with the actuator fault model, and controlling the high-speed train based on the disturbance observer and the adaptive fault-tolerant controller to track the desired speed curve and the desired displacement curve; The state space equation is shown as follows: ； in, is the high-speed train system state variable, is the displacement tracking error variable, is the speed tracking error variable; is the high-speed train system control variable, To control the input error variable; is the disturbance variable of the high-speed train system, is the additional resistance of the high-speed train system. is the additional resistance error variable, The unknown resistance to the high-speed train system, Indicates i The unknown resistance of the carriage, n is the number of carriages in a high-speed train; A is the system state matrix, B is the system input matrix; the system state matrix is ​​shown as follows: ； in, I n×n for n × n Dimensional identity matrix, intermediate matrix A 21 and A 22 They are shown as follows: ； ； ； in, k is the elastic coupling coefficient, d is the damping coupling coefficient, It is i The quality of the carriage, a , b and c All are Davis coefficients obtained through wind tunnel tests. v 0 is the expected speed of the high-speed train; The system input matrix is ​​shown as follows: ； in, 0 n×n for n × n dimensional zero matrix, intermediate matrix B 21 As shown below: ； The actuator fault model can be expressed by the following formula: ； in, ; , ; , ; , for any actuator in the high-speed train system, the fault model of the actuator is expressed by the following formula: ； in, represents the output signal of the ith actuator in the jth fault mode, is the efficiency factor of the ith actuator in the jth fault mode, represents the input signal of the ith actuator, Indicates the fault of the i-th actuator being stuck. is the fault factor of whether the i-th actuator is stuck; ； Among them, when hour, , indicating that no failure has occurred or a partial failure has occurred; when hour, or , representing an interruption fault and a stuck fault respectively; The high-speed train system equation with actuator failure model is shown below: ； For the high-speed train system equation with actuator failure model, in order to compensate for the impact of actuator failure, the following assumptions need to be met: 1) The system state matrix A and the system input matrix B All are controllable; 2) is a piecewise continuous bounded function, with some undetermined positive number , so that the value of the function is always within the range defined by this positive number; 3) The actuators of the high-speed train system are in a redundant state; The disturbance observer is shown as follows: ； in, is the unknown lumped disturbance The estimated value of represents the internal state of the disturbance observer, is the nonlinear function to be designed, is the nonlinear disturbance observer gain that needs to be designed, is the high-speed train system state variable, Control variables for high-speed train systems; The adaptive fault-tolerant controller is shown as follows: ； in, K x ( t )and K y ( t ) are auxiliary control functions, as shown below: ； ； in, and is an unknown positive constant, and They are and Estimated value, , R and P a are all symmetric positive definite matrices, x represents the state variables of the high-speed train system, is any positive uniformly continuous bounded function that satisfies the formula.

2. The high-speed train fault-tolerant tracking control method based on the adaptive compensation mechanism according to claim 1 is characterized in that: The nonlinear multi-particle dynamic model is shown as follows: ； in, It is i The quality of the carriage, It is i The traction of the carriage, that is, i Carriage control input, , and Respectively represent i The displacement, velocity and acceleration of the carriage, k is the elastic coupling coefficient, d is the damping coupling coefficient, a , b and c Davis coefficients are obtained through wind tunnel tests. n is the number of carriages in the high-speed train, Indicates i The additional resistance on the carriage is Indicates i The unknown resistance experienced by the carriage.

3. The high-speed train fault-tolerant tracking control method based on the adaptive compensation mechanism according to claim 1 is characterized in that: The error variables include: displacement tracking error variable, speed tracking error variable, control input error variable and additional resistance error variable; The displacement tracking error variable is shown as follows: ； in, is the displacement tracking error variable, For the i The displacement of the carriage, For the i Expected displacement of the carriage; The speed tracking error variable is shown as follows: ； in, is the speed tracking error variable, For the i The speed of the carriage, For the i The desired speed of the carriage; The control input error variable is shown as follows: ； in, To control the input error variable, For the i Carriage control input, For the i Desired control inputs for the carriages; The additional resistance error variable is shown as follows: ； in, is the additional resistance error variable, For the i Additional resistance of the carriage, For the i Expected additional drag of the carriage.

4. The high-speed train fault-tolerant tracking control method based on the adaptive compensation mechanism according to claim 1 is characterized in that: The linear dynamic error model is shown as follows: ； in, It is i The quality of the carriage, is the speed tracking error variable, The derivative is the acceleration tracking error variable, To control the input error variable, is the displacement tracking error variable, k is the elastic coupling coefficient, d is the damping coupling coefficient, a , b and c Davis coefficients are obtained through wind tunnel tests. v 0 is the expected speed of the high-speed train, n is the number of carriages in the high-speed train, is the additional resistance error variable, Indicates i The unknown resistance experienced by the carriage.

5. A high-speed train fault-tolerant tracking control system based on an adaptive compensation mechanism, characterized in that: The method for implementing the high-speed train fault-tolerant tracking control method based on the adaptive compensation mechanism according to any one of claims 1 to 4, wherein the high-speed train fault-tolerant tracking control system based on the adaptive compensation mechanism comprises: A multi-mass point dynamic model building module is used to build a nonlinear multi-mass point dynamic model of a high-speed train based on the analysis results of the coupling forces within the high-speed train; A dynamic error model determination module, used for defining error variables and substituting the error variables into the nonlinear multi-particle dynamic model to obtain a linear dynamic error model; An actuator fault model combining module, used for converting the linear dynamic error model into a state space equation, and combining the actuator fault model into the state space equation to obtain a high-speed train system equation with the actuator fault model; The disturbance observer design module is used to design the disturbance observer, online estimate and compensate for the unknown complex disturbances in the train operation process; The adaptive fault-tolerant control module is used to design and generate an adaptive fault-tolerant controller according to the high-speed train system equation with the actuator fault model, and to track the expected speed curve and the expected displacement curve based on the control of the high-speed train by the disturbance observer and the adaptive fault-tolerant controller.

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