A maglev high-speed rail guidance control method with self-adjusting control sensitivity under network false data injection

Abstract

The application discloses a magnetic levitation high-speed rail guiding control method for controlling sensitivity self-adjustment under network false data injection, and belongs to the field of magnetic suspension rail transit. The method adopts a magnetic levitation high-speed rail guiding position tracking error sensitive self-adjustment technology to perform constraint control. A dynamic model of a magnetic levitation high-speed rail guiding system is established, a state equation of the magnetic levitation high-speed rail guiding system containing network false data injection is obtained from the dynamic model of the magnetic levitation high-speed rail guiding system, a control equation of the magnetic levitation high-speed rail guiding system containing network false data injection is further obtained, a combined observer under finite time network false data injection is constructed to estimate disturbance caused by an attack, and a guiding controller is designed by using constraint control to realize sensitive self-adjustment control of a guiding position tracking error. The application adjusts the sensitivity of the guiding position tracking error of the magnetic levitation high-speed rail, simultaneously compensates disturbance quickly, and guarantees smooth operation of the magnetic levitation high-speed rail.

Description

Technical Field

[0001] This invention relates to the field of magnetic levitation rail transit, and in particular to a magnetic levitation high-speed rail guidance control method with self-adjustment of control sensitivity under the injection of fake network data. Background Technology

[0002] Maglev high-speed rail has attracted widespread interest from researchers due to its convenience, comfort, and quiet operation. As a crucial part of the modern transportation system, maglev high-speed rail reaches speeds of up to 600 km / h. This extremely high speed relies on a guidance control system to maintain stability and ensure safe operation.

[0003] To ensure the safe operation of maglev high-speed trains, the guidance and control system relies on satellite navigation to obtain information about the track's extension direction. This allows the system to calculate and promptly compensate for the lateral centrifugal force experienced by the train during operation, thus avoiding the risk of lateral contact with the track caused by this force. Therefore, effective real-time communication of satellite navigation information is essential to ensuring the passive and stable operation of maglev high-speed trains under centrifugal interference caused by the loss of navigation information during attacks involving spoofed data injection.

[0004] Due to the electromagnetic nonlinearity, strong coupling, and inherent instability of the maglev high-speed rail guidance system, achieving stable control is extremely difficult. In the research of maglev high-speed rail guidance control, traditional control methods such as state feedback and sliding mode control can maintain the guidance stability of the maglev high-speed rail. However, as a system with extremely stringent robustness requirements, traditional control methods cannot guarantee frictionless control of the maglev high-speed rail with the track when subjected to spurious data injection attacks.

[0005] Therefore, this invention provides a maglev high-speed rail guidance control method with self-adjusting control sensitivity under network spoofing data injection. This method can dynamically adjust the system's sensitivity to guidance position error based on the degree of maglev high-speed rail guidance position offset, while quickly estimating and compensating for centrifugal interference distortion caused by spoofing data injection attacks, thereby achieving safe and stable operation of maglev high-speed rail. Summary of the Invention

[0006] The main objective of this invention is to address the shortcomings and gaps in existing technologies by providing a maglev high-speed rail guidance control method with self-adjusting control sensitivity under network spoofing attacks. This method designs a maglev high-speed rail guidance position error sensitivity self-adjustment function based on the maglev high-speed rail guidance position tracking error to improve the stability of the guidance system during operation. Furthermore, to address the centrifugal interference distortion caused by network spoofing attacks during maglev high-speed rail operation, especially during turning maneuvers, a finite-time network spoofing attack observer is designed to compensate for centrifugal interference distortion during operation, ensuring the smooth operation of the maglev high-speed rail.

[0007] To achieve the above objectives, the present invention provides a maglev high-speed rail guidance control method with self-adjusting control sensitivity under network spoofing data injection, comprising the following steps:

[0008] Step 1: Establish a dynamic model of the maglev high-speed rail guidance system;

[0009] Step 2: Construct the state equations for the maglev high-speed rail guidance system under the influence of injected fake data from the internet;

[0010] Step 3: Construct the control equations for the maglev high-speed rail guidance system under the influence of injected fake data from the internet;

[0011] Step 4: Design a self-adjusting function sensitive to tracking error of the maglev high-speed rail guidance system under the injection of fake network data;

[0012] Step 5: Construct a combined observer of disturbances and states under finite-time network spurious data injection;

[0013] Step 6: Design a tracking error-sensitive self-adjusting tracking controller for the maglev high-speed rail guidance position under the influence of network spurious data injection.

[0014] The dynamic model of the maglev high-speed rail guidance system in step 1 is as follows:

[0015]

[0016] Where m is the mass of the maglev high-speed train carried by a single pair of guide coils, x is the guiding position of the maglev high-speed train, and the subscripts i = l and r represent the left and right sides of the maglev high-speed train, F r F is the electromagnetic attraction force generated by the right-side guide coil. l F is the electromagnetic attraction force generated by the right-side guide coil. i F represents r ,F l i i x represents the current flowing through the guide coils on both sides. i F is the air gap between the left and right guide coils and the track guide surface. d The expression represents the centrifugal interference force generated by the turning of the maglev high-speed train, μ0 is the permeability of free space, N is the number of turns of the guide coil, S is the effective area of ​​the magnetic poles of the guide coil facing the track guide surface, g is the local gravitational acceleration, v is the propulsion speed of the maglev high-speed train, θ is the track cross slope angle, and Υ(θ) is the track curvature, which has a special mapping relationship with the track cross slope angle. This represents the component of gravitational acceleration.

[0017] The specific steps in step 2 of constructing the state equation of the maglev high-speed rail guidance system under the injection of fake network data include:

[0018] 1) The centrifugal interference model caused by the injection of false data into the maglev high-speed rail network is as follows:

[0019]

[0020] Among them, Υ f (θ f The false orbital curvature θ is caused by a fake data injection attack. f The false track ramp angle is caused by a fake data injection attack.

[0021] 2) respectively convert F in equation (2) d f , Substitute F in equation (1) d , The state equation of the maglev high-speed rail guidance system under the influence of injected fake data from the network is as follows:

[0022]

[0023] Where, x1 = x, x0 represents the steady-state air gap for the maglev high-speed rail, i0 represents the reference current for the maglev high-speed rail, and i represents the bias current for the maglev high-speed rail.

[0024] The control equations for the maglev high-speed rail guidance system under the injection of fake network data in step 3 are as follows:

[0025]

[0026] in, μ g =i is the control input signal for the maglev high-speed railway. Δ represents the unmodeled dynamics.

[0027] The self-adjusting function sensitive to tracking error of the maglev high-speed rail guidance system under the injection of fake network data in step 4 is as follows:

[0028]

[0029] in, This represents the maximum limit value of the self-adjusting function sensitive to the guidance position tracking error of the maglev high-speed railway. Let exp be the minimum limit of the self-adjusting function sensitive to the tracking error of the maglev high-speed rail, where exp is an exponential function and m is the minimum limit of the function. e >0 is the sensitivity adjustment parameter, λ z ≥n represents the exponential feedback gain, n>0 represents the system order, p>0 represents the sensitivity range adjustment parameter, and e1=x1-x 1d For the guide position error of the maglev high-speed train, x 1d =0 represents the target for the guidance position control of the maglev high-speed railway.

[0030] The design steps for the combined observer of interference and state under finite-time network spurious data injection in step 5 are as follows:

[0031] 1) Set the observation error of the finite-time network spoofing injection attack observer to be: The basic observer is designed as follows:

[0032]

[0033] Where i = 1, 2, These are estimated values ​​for the guide position and guide speed of the maglev high-speed train, and the lateral centrifugal interference caused by a network spoofing data injection attack, respectively. x =f(x,i), g x = g(x,i), κ1,κ2>0 are the observer gains.

[0034] 2) The variable exponential finite-time auxiliary estimation system for observers under finite-time network spurious data injection is constructed as follows:

[0035]

[0036] in, It is an adjustable parameter. 0 < τ, γ0 < 1, k η >(1-γ0) / tanh(1), λ1,λ2 are auxiliary estimation functions for injecting fake data into the network, ||ο|| is a variable exponential factor, z ζ This is an auxiliary estimation function for the variable exponential factor.

[0037] The design steps for the self-adjusting tracking controller sensitive to tracking errors in the maglev high-speed rail guidance position under the injection of false network data in step 6 are as follows:

[0038] 1) Define the second-order tracking error as α 1,c This is the output signal of the command filter with the virtual control law α1 as the input signal. The command filter is designed as follows:

[0039]

[0040] Where c > 0, 0 < k a <1 represents the adjustable parameter of the command filter, α b These are the variables for the second-order command filter. The filter error compensator is designed as follows:

[0041]

[0042] Where ξ1 and ξ2 are the filtering error compensation signals. These are the adjustable parameters of the filter error compensator.

[0043] 2) Define the filter compensation error of each order and take its derivative by combining equations (6), (8), and (9):

[0044]

[0045] The first-order filter is designed to compensate for the reconstruction error based on the tracking error sensitive self-adjustment function, and its derivative is obtained by considering equation (11):

[0046]

[0047] in,

[0048] ρ=(s0-s l )exp(-lt)+s l This serves as the boundary constraint for the guidance position tracking error of the maglev high-speed railway. The control boundary of the tracking error-sensitive self-adjusting tracking controller for the guiding position of the maglev high-speed train is s0 > 0, which is the initial value of the guiding position constraint boundary of the maglev high-speed train, and 0 < s0 > 0. l <s0 represents the guiding position performance constraint of the maglev high-speed railway, l>0 represents the convergence velocity that determines the guiding position constraint boundary of the maglev high-speed railway, and t represents time. This is a self-adjusting function sensitive to tracking error in the guidance position of the maglev high-speed rail guidance system.

[0049] 3) Select the Lyapunov Function as

[0050]

[0051] Taking the first derivative of equation (14) and substituting equation (13) into the equation, we get:

[0052]

[0053] Based on equation (15), the virtual control rate of the self-adjusting tracking controller sensitive to the guidance position tracking error of the maglev high-speed train is designed as follows:

[0054]

[0055] Where, k 1,1 ,k 1,2 >0, 0<β<1 are adjustable parameters.

[0056] 4) Select the Lyapunov Function as:

[0057]

[0058] The first derivative of equation (17) is:

[0059]

[0060] Substituting equation (11) into equation (18), we get:

[0061]

[0062] Based on equation (19), the control input signal of the maglev high-speed rail guide position tracking error sensitive self-adjusting tracking controller is designed as follows:

[0063]

[0064] Where, k 2,1 ,k 2,2 >0 is an adjustable parameter. Equations (16) and (20) are the designed maglev high-speed rail guide position tracking error sensitive self-adjusting controller. The system composed of equations (3), (6), (8), (9), (16), and (20) is a closed-loop system. Attached Figure Description

[0065] Appendix Figure 1 This is a schematic diagram of the magnetic levitation high-speed rail guidance system of the present invention.

[0066] Appendix Figure 2 The simulation curves show the centrifugal interference experienced by the maglev high-speed railway.

[0067] Appendix Figure 3 Simulation curve of centrifugal interference caused by the injection of fake data on the network.

[0068] Appendix Figure 4 The simulation curve of the tracking error of the maglev high-speed rail guide position.

[0069] Appendix Figure 5 The simulation curve of the bias current for the maglev high-speed rail is shown.

[0070] Appendix Figure 6 Simulation curve of the self-adjusting function sensitive to the tracking error of the maglev high-speed rail.

[0071] Appendix Figure 7 Simulation curves of the observer's guidance position observation error under finite-time network spurious data injection.

[0072] Appendix Figure 8 Simulation curves of observation error caused by centrifugal interference from a network spoofing attack on the observer under finite-time network spoofing.

[0073] Among them, 1 is the track where the maglev high-speed railway is located, 2 is the maglev high-speed railway carriage, 3 is the left guide surface of the track, 4 is the right guide surface of the track, 5 is the left guide coil, and 6 is the right guide coil. Detailed Implementation

[0074] The present invention will now be described in further detail with reference to the accompanying drawings.

[0075] Maglev high-speed rail guidance system as shown in the attached document Figure 1 As shown, 3 and 4 in 1 are directly opposite 5 and 6 in 2, respectively. A reference current i0 is applied to 3 and 4, generating equal and opposite electromagnetic attraction forces from 3 to 5 and from 4 to 6, maintaining the axial centerlines of 1 and 2 aligned. When 2 is attacked by a network spoofing attack during operation, the control input μ... g The corresponding change occurs, generating a bias current i, which causes the currents i in 5 and 6 to change. l i r The corresponding changes result in a differential force on the electromagnetic attraction of 5 and 6, suppressing the gravity shift and centrifugal interference distortion of the maglev high-speed rail caused by network fake data injection attacks, and keeping the axial centerline of 2 near the centerline of 1.

[0076] This invention discloses a maglev high-speed rail guidance control method with self-adjusting control sensitivity under network spoofing data injection. To ensure that the guidance tracking error curve of the maglev high-speed rail remains within the performance boundary when subjected to network spoofing data injection attacks, the method specifically includes the following steps:

[0077] Step 1: Establish a dynamic model of the maglev high-speed rail guidance system:

[0078] As attached Figure 1 As shown, during the movement of the maglev high-speed train, it is subjected to gravity mg in the vertical direction and electromagnetic attraction F in the lateral direction. i With centrifugal force interference F d Let the positive direction be F. + According to Newton's second law and considering the inherent track slope angle θ, the guiding mechanics equations for the maglev high-speed train during its journey are as follows:

[0079]

[0080] Where m is the mass of the maglev high-speed train carried by a single pair of guide coils, x is the guiding position of the maglev high-speed train, and the subscripts i = l and r represent the left and right sides of the maglev high-speed train, F r F is the electromagnetic attraction force generated by the right-side guide coil. l F is the electromagnetic attraction force generated by the right-side guide coil. i F represents r ,F l i i x represents the current flowing through the guide coils on both sides. i F is the air gap between the left and right guide coils and the track guide surface. dThe expression represents the centrifugal interference force generated by the turning of the maglev high-speed train, μ0 is the permeability of free space, N is the number of turns of the guide coil, S is the effective area of ​​the magnetic poles of the guide coil facing the track guide surface, g is the local gravitational acceleration, v is the propulsion speed of the maglev high-speed train, θ is the track cross slope angle, and Υ(θ) is the track curvature, which has a special mapping relationship with the track cross slope angle. This is the component of gravitational acceleration;

[0081] Step 2, construct the state equations for the maglev high-speed rail guidance system under the influence of injected fake data from the internet:

[0082] 1) The centrifugal interference model caused by the injection of false data into the maglev high-speed rail network is as follows:

[0083]

[0084] Among them, Υ f (θ f The false orbital curvature θ is caused by a fake data injection attack. f The false track ramp angle is caused by a fake data injection attack.

[0085] 2) respectively convert F in equation (22) d f , Replace F in equation (21) d , Let the guiding position and speed of the maglev high-speed train be x = x1, The state equation of the maglev high-speed rail guidance system under the influence of injected fake data from the network is as follows:

[0086]

[0087] Where F(x,i)=(i0-i) 2 / (x0-x) 2 -(i0+i) 2 / (x0+x) 2 x0 represents the steady-state air gap for the maglev high-speed rail, i0 represents the reference current for the maglev high-speed rail, and i represents the bias current for the maglev high-speed rail.

[0088] Step 3, the specific steps for constructing the control equations for the maglev high-speed rail guidance system under the influence of injected fake network data are as follows:

[0089] 1) Expand F(x,i) in equation (23) at the equilibrium position (x=0,i=0) using the first-order Taylor expansion and reduce other higher-order expansion terms to model uncertainties:

[0090] F(x,i)=f(x,i)x1+g(x,i)μ g +Δ,

[0091] in, μ g =i is the control input signal of the maglev high-speed rail guidance system, and Δ is the unmodeled dynamic.

[0092] 2) Replace F(x,i) in equation (23) and reduce Δ to In the process, the control equations for the maglev high-speed rail guidance system under the influence of injected false data from the internet are obtained as follows:

[0093]

[0094] in, Δ represents the model uncertainty of the state-space control equations of the maglev high-speed rail guidance system under a false data injection attack.

[0095] Step 4: Design a self-adjusting function sensitive to tracking error of the maglev high-speed rail guidance system under the injection of spurious network data:

[0096]

[0097] in, This represents the maximum limit value of the self-adjusting function sensitive to the guidance position error of the maglev high-speed railway. Let exp be the minimum limit of the self-adjusting function sensitive to the guidance position error of the maglev high-speed railway, and m be an exponential function. e >0 is the sensitivity adjustment parameter, λ z ≥n represents the exponential feedback gain, n>0 represents the system order, p>0 represents the sensitivity range adjustment parameter, and e1=x1-x 1d For the tracking error of the maglev high-speed rail guide position, x 1d =0 represents the target position for the maglev high-speed rail's guidance position control.

[0098] Step 5: Construct a combined observer of disturbances and states under finite-time network spurious data injection. The specific construction steps are as follows:

[0099] 1) Set the observation error of the observer under finite-time network spurious data injection as:

[0100]

[0101] The basic observer is designed as follows:

[0102]

[0103] Where i = 1, 2, These are estimated values ​​for the guide position and guide speed of the maglev high-speed train, and the lateral centrifugal interference caused by a network spoofing data injection attack, respectively. x =f(x,i), g x= g(x,i), κ1,κ2>0 are the observer gains.

[0104] 2) The variable exponential finite-time auxiliary estimation system for observers under finite-time network spurious data injection is constructed as follows:

[0105]

[0106] in, It is an adjustable parameter. 0 < τ, γ0 < 1, k η >(1-γ0) / tanh(1), λ1,λ2 are auxiliary estimation functions for injecting fake data into the network, ||ο|| is a variable exponential factor, z ζ This is an auxiliary estimation function for the variable exponential factor.

[0107] Step 6: Design a tracking error-sensitive self-adjusting tracking controller for the maglev high-speed rail guidance position under the influence of network spoofing data injection. The specific design steps are as follows:

[0108] 1) Define the second-order tracking error as α 1,c This is the output signal of the command filter with the virtual control law α1 as the input signal. The command filter is designed as follows:

[0109]

[0110] Where c > 0, 0 < k a <1 represents the adjustable parameter of the command filter, α b These are the variables for the second-order command filter. The filter error compensator is designed as follows:

[0111]

[0112] Where ξ1 and ξ2 are the filtering error compensation signals. These are the adjustable parameters of the filter error compensator.

[0113] 2) Define the filter compensation error of each order and obtain its derivative by combining equations (26), (28), and (29):

[0114]

[0115] The first-order filter is designed to compensate for the reconstruction error based on the tracking error sensitive self-adjustment function, and its derivative is obtained by considering equation (31):

[0116]

[0117] in,

[0118] ρ=(s0-s l )exp(-lt)+s l This serves as the boundary constraint for the guidance position tracking error of the maglev high-speed railway. The control boundary of the tracking error-sensitive self-adjusting tracking controller for the guiding position of the maglev high-speed train is s0 > 0, which is the initial value of the guiding position constraint boundary of the maglev high-speed train, and 0 < s0 > 0. l <s0 represents the guiding position performance constraint of the maglev high-speed railway, l>0 represents the convergence velocity that determines the guiding position constraint boundary of the maglev high-speed railway, and t represents time. This is a self-adjusting function sensitive to tracking error in the guidance position of the maglev high-speed rail guidance system.

[0119] 3) Select the Lyapunov Function as

[0120]

[0121] Taking the first derivative of equation (34) and substituting equation (33) into the equation, we get:

[0122]

[0123] Based on equation (35), the virtual control rate of the self-adjusting tracking controller sensitive to the guidance position tracking error of the maglev high-speed train is designed as follows:

[0124]

[0125] Where, k 1,1 ,k 1,2 >0, 0<β<1 are adjustable parameters.

[0126] 4) Select the Lyapunov Function as:

[0127]

[0128] The first derivative of equation (37) is:

[0129]

[0130] Substituting equation (31) into equation (38), we get:

[0131]

[0132] Based on equation (39), the control input signal of the maglev high-speed rail guide position tracking error sensitive self-adjusting tracking controller is designed as follows:

[0133]

[0134] Where, k 2,1 ,k 2,2 >0 indicates an adjustable parameter.

[0135] The present invention will be further described below using a preferred embodiment.

[0136] The parameters of the maglev high-speed rail guidance system are as follows: the equivalent resistance of the guide coil is R = 2.77Ω, and the effective area of ​​the guide coil magnetic poles is S = 0.0552m². 2 The guide coil has N = 200 turns. The guide coils on both sides of the maglev high-speed train have the same parameters and specifications. The total mass of the guide body is m = 1995 kg, and the power supply voltage of the drive circuit is U. dc =200V, free permeability μ0=4π×10 -7 The steady-state air gap between the guide coil and the track guide surface is x0 = 10mm, and the steady-state current is i0 = 10A.

[0137] The simulation time was set to 20 seconds, with the maglev high-speed train traveling at 600 km / h. At the 3rd second, it entered a severe curve with a radius R = 1800 m and a track cross slope angle θ = 0°, without any gentle curves at the entrance or exit. It exited the curve at the 17th second. The mathematical model of the centrifugal interference force experienced by the maglev high-speed train is as follows, and the centrifugal interference waveform is attached. Figure 2 As shown.

[0138]

[0139] As attached Figure 3 As shown, the centrifugal interference model caused by the injection of false data into the maglev high-speed rail network is as follows:

[0140]

[0141] N + ∈[2,8] are positive integers.

[0142] The system was simulated under the above conditions to verify the ability of the finite-time constraint control system for guidance position tracking error sensitivity to spoofing data injection into the maglev high-speed rail network to keep the guidance tracking error curve within the performance boundary under such attack. (See attached...) Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 As shown.

[0143] Combined with appendix Figure 4 Appendix Figure 5 Appendix Figure 6 Appendix Figure 7 Appendix Figure 8It can be seen that the proposed tracking error-sensitive self-adjusting tracking control system for the maglev high-speed rail guidance position under network spoofing injection can rapidly reduce the control sensitivity of the tracking error when the tracking error of the maglev high-speed rail guidance position exceeds the constraint boundary due to network spoofing injection attacks. This causes the control boundary of the tracking error-sensitive self-adjusting tracking controller for the maglev high-speed rail guidance position under network spoofing injection to expand rapidly. At the same time, due to the interference under network spoofing injection and the rapid estimation and compensation of the centrifugal interference caused by network spoofing injection within a finite time by the state combination observer, the bias current amplitude of the guide coil is rapidly increased while avoiding the occurrence of singularity problems, thus maintaining the maglev high-speed rail in a steady state.

[0144] The above results indicate that the maglev high-speed rail guidance control method of the present invention, which features self-adjusting control sensitivity under network false data injection, can ensure that when the guidance position tracking error exceeds the constraint boundary due to network false data injection during system operation, the control boundary can expand rapidly while offsetting the centrifugal interference caused by network false data injection, and pull the maglev high-speed rail guidance position tracking error back to the constraint boundary within a finite time. This ensures that the maglev high-speed rail can operate reliably under network false data injection, and the system has a good anti-interference capability.

Claims

1. A magnetic levitation high-speed rail guidance control method for self-adjusting sensitivity under network false data injection, characterized in that, The method comprises: S1, establishing a dynamic model of a maglev high-speed rail guiding system is: wherein m is the levitation mass of the maglev train, x is the guiding position of the maglev train, subscript represents the left and right sides of the maglev train, F i is the electromagnetic attraction force generated by the left and right side guiding coils, i i is the current passing through the left and right side guiding coils, is the air gap between the left and right side guiding coils and the track guiding surface, represents the centrifugal interference force generated by the maglev train turning, is the vacuum permeability, N is the number of turns of the guiding coil, S is the effective area of the magnetic pole of the guiding coil facing the track guiding surface, g is the local gravitational acceleration, v is the propulsion speed of the maglev train, is the track cross slope angle, is the track curvature, which has a special mapping relationship with the track cross slope angle; S2, constructing a state equation of the maglev high-speed rail guiding system containing network false data injection, the specific steps comprising: S21: the centrifugal interference model caused by the maglev high-speed rail network false data injection is: wherein, is the false track curvature due to network false data injection, is the false track cross slope angle due to network false data injection; S22: replace the in formula (1) with , the in formula (1) with , and obtain the state equation of the maglev high-speed rail guide system under the network false data injection as wherein, , , , is a magnetic levitation high-speed rail guiding steady-state air gap, is a magnetic levitation high-speed rail guiding reference current, is a magnetic levitation high-speed rail guiding bias current, , ; S3, constructing a control equation of the maglev high-speed rail guiding system containing network false data injection is: wherein , , is a control input signal for the maglev high-speed rail, , is an unmodeled dynamics; S4, designing a tracking error sensitive self-adjusting function of the guiding position of the maglev high-speed rail guiding system containing network false data injection; S5, constructing a disturbance and state combination observer under finite time network false data injection; S6, designing a tracking error sensitive self-adjusting tracking controller of the guiding position of the maglev high-speed rail guiding system containing network false data injection.

2. The method according to claim 1, wherein the method is characterized in that, The tracking error sensitive self-adjusting function of the guiding position of the maglev high-speed rail guiding system containing network false data injection in S4 is: wherein, is a maximum limit value of the tracking error sensitive self-adjusting function, is a minimum limit value of the tracking error sensitive self-adjusting function, exp is an exponential function, is a sensitivity adjustment parameter, is an exponential feedback gain, is a system order, is a sensitive range adjustment parameter, is a tracking error of a maglev high-speed rail guide position, is a maglev high-speed rail guide position control target.

3. The method of claim 2, wherein the method further comprises: determining a sensitivity of the magnetic levitation high-speed rail to the false data injection attack; and adjusting the sensitivity of the magnetic levitation high-speed rail to the false data injection attack. The design steps of the disturbance and state combination observer under finite time network false data injection in S5 include: S51: Set the observation error of the observer under the limited-time network false data injection as and the basic observer is designed as: wherein, , are the estimated values of the lateral centrifugal disturbance caused by the maglev high-speed rail guiding position, guiding speed and network false data injection attack, respectively, , , is the observer gain; S52: constructing a variable exponential finite time auxiliary estimation system of the observer under finite time network false data injection is: wherein is a tunable parameter, , , , is a network false data injection auxiliary estimation function, is a variable exponent factor, is a variable exponent factor auxiliary estimation function.

4. The method of claim 3, wherein the method further comprises: The design steps of the tracking error sensitive self-adjusting tracking controller of the guiding position of the maglev high-speed rail guiding system containing network false data injection in S6 include: S61: define a second order tracking error as , is the virtual control rate is the output signal of a command filter designed as: wherein is a tunable parameter of the command filter, is a second order command filter variable, the filter error compensator is designed as: wherein is a filtered error compensation signal, is an adjustable parameter of the filtered error compensator; S62: define the filter compensation error of each order and combine equations (6), (8) and (9) to obtain its derivative as: Combine the tracking error sensitive self-adjusting function to design the first-order filter compensation reconstruction error, and consider equation (11) to obtain its derivative as: Wherein, ， , a constraint boundary for tracking error of a maglev high-speed rail guide position, a control boundary for a tracking error sensitive self-adjusting tracking controller of a maglev high-speed rail guide position, an initial value of a constraint boundary for a maglev high-speed rail guide position, a performance constraint for a maglev high-speed rail guide position, a convergence speed for determining a constraint boundary for a maglev high-speed rail guide position, t time; S63: select Lyapunov Function as Take the first-order derivative of equation (14) and bring equation (13) into it as: Based on equation (15), the virtual control rate of the maglev high-speed rail guiding position tracking error sensitive self-adjusting tracking controller is designed as: wherein is an adjustable parameter; S64: select Lyapunov Function as: Take the first-order derivative of equation (17) as: Bring equation (11) into equation (18) as: Based on equation (19), the control input signal of the maglev high-speed rail guiding position tracking error sensitive self-adjusting tracking controller is designed as: wherein, is a tunable parameter, and the equations (16) and (20) are the designed magnetic levitation high-speed rail guiding position tracking error sensitive self-adjusting tracking controllers, and the system composed of the equations (3), (6), (8), (9), (16), and (20) is a closed-loop system.

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