Self-adaptive super-spiral sliding mode fault-tolerant control method and device for carrier rocket under execution mechanism fault, medium and product

Abstract

The invention discloses a carrier rocket adaptive super-spiral sliding mode fault-tolerant control method and device under an execution mechanism fault, a medium and a product, and relates to the field of fault-tolerant control, and the method comprises the steps: building a carrier rocket attitude control system model containing the execution mechanism fault in combination with a built carrier rocket attitude control system model and a fault model; constructing a carrier rocket attitude tracking system model containing an execution mechanism fault by combining a carrier rocket attitude control system model based on the tracking error; selecting a sliding mode surface and an approaching law, and combining with a carrier rocket attitude tracking system model to obtain a controller stable tracking model; and adopting a controller stable tracking model to realize the tracking of the carrier rocket on the attitude instruction, and completing the adaptive super-spiral sliding mode fault-tolerant control of the carrier rocket. According to the method, the system robustness under the execution mechanism fault can be improved, so that stable operation of the carrier rocket is more effectively realized.

Description

Technical Field

[0001] The present application relates to the field of fault-tolerant control, and in particular to a method, device, medium and product for adaptive super-spiral sliding mode fault-tolerant control of a launch vehicle under an actuator failure. Background Art

[0002] In the field of aerospace science, launch vehicles, as key tools for exploring deep space, have become the focus of scientific research and technological development. Launch vehicles are responsible for important tasks such as manned spaceflight, scientific experiments, and satellite deployment. Once a mission fails, it will bring immeasurable economic losses and many other problems. Therefore, ensuring that launch vehicles can operate stably and reliably in a complex and changing space environment has become one of the core issues that need to be urgently solved in the field of aerospace engineering.

[0003] In terms of control system design, launch vehicles face many technical challenges, including but not limited to strong interference from the external environment, complexity and uncertainty of the dynamic model, and possible failure of the actuator. In order to overcome these challenges, it is particularly important to build an efficient, robust and highly fault-tolerant flight control system. The system must be able to ensure the reliable flight of the launch vehicle under various extreme conditions, thereby ensuring the successful completion of the mission.

[0004] The Sliding Mode Control (SMC) method is widely used in the fields of robot control, chemical control, and aerospace due to its advantages of simple implementation, fast response, and strong robustness. However, the SMC method still has some shortcomings, mainly including: when using a switching term based on a sign function to suppress uncertain disturbances, a larger switching gain is usually selected to cover the uncertainty boundary. However, in actual engineering applications, the actuator has system / time / space inertia, which will cause the system state to be unable to strictly move along the switching plane to the equilibrium point. Therefore, high-frequency oscillations may be caused in the closed-loop system, resulting in a jitter phenomenon. This phenomenon will have a great impact on the control accuracy of the system, and in severe cases, it may even destroy the overall stability of the system. In order to alleviate the jitter, various control technologies have been designed in the known technology, such as boundary layer control and super-helical control. The boundary layer control strategy uses a saturated function instead of a sign function to weaken the sliding film jitter phenomenon, but because the system state can only converge to the boundary layer, the robustness of the system is reduced. Summary of the invention

[0005] The purpose of this application is to provide a method, device, medium and product for adaptive super-spiral sliding mode fault-tolerant control of a launch vehicle under actuator failure, which can improve the system robustness under actuator failure, thereby more effectively achieving stable operation of the launch vehicle.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for adaptive super-spiral sliding mode fault-tolerant control of a launch vehicle under an actuator failure, comprising:

[0008] Establish a launch vehicle attitude control system model;

[0009] Consider the actuator failure and establish a fault model;

[0010] Establishing a launch vehicle attitude control system model including actuator failures by combining the launch vehicle attitude control system model and the fault model;

[0011] Determine the posture tracking instructions and construct the tracking error;

[0012] Based on the tracking error, a launch vehicle attitude tracking system model including an actuator failure is constructed in combination with a launch vehicle attitude control system model including an actuator failure;

[0013] The sliding surface and reaching law are selected, and the stable tracking model of the controller is obtained by combining the launch vehicle attitude tracking system model including actuator failure.

[0014] The controller stable tracking model is adopted to realize the tracking of the carrier rocket to the attitude instruction, and complete the adaptive super-helical sliding mode fault-tolerant control of the carrier rocket.

[0015] Optionally, the launch vehicle attitude control system model is expressed as:

[0016]

[0017] Where F(ω) is the vector about angular velocity ω, Ω is the attitude angle vector, ω is the launch vehicle attitude angular velocity vector, J is the launch vehicle moment of inertia matrix, u is the control torque vector, D is the unknown interference vector, Z(Ω) is the coordinate transformation matrix, is the derivative of the attitude angle vector.

[0018] Optionally, the fault model is expressed as:

[0019] δ f =ρδ+θ;

[0020] In the formula, δ f is the swing angle under the fault mode, ρ is the equivalent actuator efficiency coefficient matrix, δ is the actual swing angle, and θ is the equivalent offset of the offset type fault.

[0021] Optionally, the fault conditions represented by the fault model include:

[0022] No fault condition: ρ = 1 and θ = 0;

[0023] Bias fault condition: ρ = 1 and θ ≠ 0;

[0024] Efficiency loss fault condition: 0<ρ<1 and θ=0.

[0025] Optionally, the launch vehicle attitude control system model including actuator failure is expressed as:

[0026]

[0027] Where x1 is the attitude angle signal, is the derivative of the attitude angle signal x1, x2 is the intermediate representation, is the derivative of the intermediate representation x2, f1(x1,x2) is a function of x1 and x2, f2(x1) is a function of x1, u f is the actual control torque of the launch vehicle, and d is the disturbance.

[0028] Alternatively, the launch vehicle attitude tracking system model including actuator failure is expressed as:

[0029]

[0030] Where e1 is the tracking error, e2 is the time derivative of the tracking error, f1(x1,x2) is a function of x1 and x2, f2(x1) is a function of x1, and u f is the actual control torque of the launch vehicle, x1 is the attitude angle signal, and d is the interference amount.

[0031] Optionally, the controller stable tracking model is expressed as:

[0032]

[0033] Where δ is the input swing angle of the model, f2(x1) is the function about x1, B is the torque conversion matrix between the equivalent swing angle and the control torque, λ and c1 are the parameters to be designed, s is the sliding surface, S is the vector about the sliding surface, sign(s) is the sign function about the sliding surface s, ν is the integral term in the sliding surface, e2 is the first-order derivative of the tracking error with respect to time, is the approximate term generated by fuzzy logic control, f1(x1,x2) is the function of x1 and x2, is the estimate of the bias, is the estimate of the interference, is the second-order derivative of the desired instruction with respect to time.

[0034] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the adaptive super-spiral sliding mode fault-tolerant control method for a launch vehicle under any of the above-mentioned actuator failures.

[0035] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the adaptive super-spiral sliding mode fault-tolerant control method for a launch vehicle under any of the above-mentioned actuator failures.

[0036] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the adaptive super-spiral sliding mode fault-tolerant control method for a launch vehicle under any of the above-mentioned actuator failures.

[0037] According to the specific embodiments provided in this application, this application has the following technical effects:

[0038] The present application provides a method, device, medium and product for adaptive super-helical sliding mode fault-tolerant control of a launch vehicle under actuator failure, which directly designs the control law for the launch vehicle attitude control system model without linearizing the model, thus avoiding system chattering. Combining the launch vehicle attitude tracking system model and sliding surface with actuator failure, a controller stable tracking model is derived, and the launch vehicle is able to achieve stable and accurate tracking of attitude commands by using adaptive control technology, sliding mode control and fuzzy logic control, which can improve the system robustness under actuator failure, thereby more effectively achieving stable operation of the launch vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] 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.

[0040] Figure 1 A flow chart of a method for adaptive super-spiral sliding mode fault-tolerant control of a launch vehicle under an actuator failure provided in one embodiment of the present application;

[0041] Figure 2 A graph of an attitude angle tracking error of a controller provided by another embodiment of the present application when a single engine actuator fails;

[0042] Figure 3A graph of attitude angle tracking error of a controller provided by another embodiment of the present application when multiple engine actuators fail;

[0043] Figure 4 A graph of attitude angle tracking error under time-varying fault conditions of an engine actuator provided by another embodiment of the present application;

[0044] Figure 5 A schematic diagram of the structure of a computer device provided in accordance with another embodiment of the present application. DETAILED DESCRIPTION

[0045] 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.

[0046] The Super-Twisting Algorithm (STA) is a well-known second order sliding mode algorithm (SOSM), which hides the high-frequency switching function in the high-order derivative of the sliding variable, so that the switching control law contains an integral term based on the switching function. Since the integral itself has a filtering effect, it can effectively suppress the "chattering" problem existing in the sliding mode control. Based on this, the present application combines the adaptive control technology, the sliding mode control method and the fuzzy logic control method to control the flight of the launch vehicle, so as to more effectively achieve the stable operation of the launch vehicle.

[0047] 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 in conjunction with the accompanying drawings and specific implementation methods.

[0048] In an exemplary embodiment of the present application, Figure 2 As shown, a method for adaptive super-spiral sliding mode fault-tolerant control of a launch vehicle under an actuator failure is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation, and includes the following steps 100 to 106. Among them:

[0049] Step 100: Establish a launch vehicle attitude control system model.

[0050] Step 101: Consider the actuator failure and establish a fault model.

[0051] Step 102: Combine the launch vehicle attitude control system model and the fault model to establish a launch vehicle attitude control system model including actuator faults.

[0052] Step 103: Determine the posture tracking instruction and construct the tracking error.

[0053] Step 104: Based on the tracking error, a launch vehicle attitude tracking system model including an actuator failure is constructed in combination with a launch vehicle attitude control system model including an actuator failure.

[0054] Step 105: Select a sliding surface and a reaching law, and obtain a controller stable tracking model by combining a launch vehicle attitude tracking system model including an actuator failure.

[0055] Step 106: Use the controller to stabilize the tracking model to achieve the launch vehicle's tracking of the attitude command, and complete the launch vehicle's adaptive super-helical sliding mode fault-tolerant control.

[0056] In another exemplary embodiment of the present application, in order to directly design the control law for the nonlinear attitude dynamics model of the launch vehicle, there is no need to linearize the model. In the control law design, not only unknown external interference is considered, but also uncertainties such as the perturbation part caused by the change in the moment of inertia caused by the fuel consumption of the launch vehicle and the shaking of the fuel tank, as well as the system model changes caused by the actuator failure and the unmodeled dynamics in the model are considered. Based on this, the launch vehicle attitude control system model constructed in step 100 is expressed as:

[0057]

[0058] Where F(ω) is the vector about the angular velocity ω, F(ω) = -J -1 ω × Jω. Ω is the attitude angle vector, Ω=[φ ψ γ] T ∈R 3 , φ is the roll angle, ψ is the yaw angle, and γ is the pitch angle. ω is the launch vehicle attitude angular velocity vector, ω × is a skew-symmetric matrix composed of vector ω. J is the moment of inertia matrix of the launch vehicle, J = diag(J φφ ,J ψψ ,J γγ )∈R 3×3 , diag(·) is the diagonal function, J φφ is the moment of inertia of the roll angle channel, J ψψ is the moment of inertia of the yaw angle channel, J γγ is the moment of inertia of the pitch angle channel. u is the control torque vector, u∈R 3D is the unknown disturbance vector, which mainly includes the uncertainties and external uncertainties caused by the unmodeled dynamics of the system, the perturbation caused by the change of the moment of inertia caused by the fuel consumption of the launch vehicle and the shaking of the fuel tank, etc. φ D ψ D γ ] T , D φ is the disturbance of the roll angle channel, D ψ is the disturbance of the yaw angle channel, D γ is the interference of the pitch angle channel. Z(Ω) is the coordinate transformation matrix, is the derivative of the attitude angle vector.

[0059] Furthermore, let x1 = Ω, x2 = Z(Ω)ω, then the launch vehicle attitude control system model can be expressed as follows:

[0060]

[0061] Where x1 is the attitude angle signal, including three channel signals of roll, yaw and pitch. is the derivative of the attitude angle signal x1, x2 is the intermediate representation, is the derivative of the intermediate representation x2, f1(x1,x2) is a function of x1 and x2, f2(x1) is a function of x1, u f is the actual control torque of the launch vehicle, d is the disturbance, and the specific form is:

[0062]

[0063] Where Z(x1) is the coordinate transformation matrix of the attitude angle signal x1, is the first-order derivative of the coordinate transformation matrix with respect to the attitude angle signal x1.

[0064] In another exemplary embodiment of the present application, considering the situation where the engine actuator in the attitude control system model fails, the two most common engine failures of the launch vehicle are engine efficiency loss and bias failure. Based on this, the fault model constructed in step 101 can be expressed as:

[0065] δ f =ρδ+θ.

[0066] In the formula, δ f is the fault swing angle of the engine under fault mode. ρ is the equivalent actuator efficiency coefficient matrix, ρ=diag(ρ1,ρ2,ρ3)∈R 3×3 , ρ i, i = 1, 2, 3 is the efficiency coefficient when the actuator has efficiency loss. δ is the actual input swing angle of the engine, and θ is the bias fault in the fault mode.

[0067] The fault conditions represented by the fault model include:

[0068] 1) No-fault case: ρ=1 and θ=0.

[0069] 2) Bias type fault case: ρ=1 and θ≠0.

[0070] 3) Efficiency loss fault condition: 0<ρ<1 and θ=0.

[0071] Based on the above description, the actual control torque of the launch vehicle is:

[0072] u f =Bδ f .

[0073] Where B is the torque conversion matrix between the equivalent swing angle and the control torque, B∈R 3×3 , specifically:

[0074] B=-Pdiag((4R+2r),3(X R -X Z ),3(X R -X Z )).

[0075] Where, X R is the distance from the engine nozzle to the rocket tip, X Z is the position of the center of mass, P is the thrust of each engine, R and r are the distances from the center of the booster engine and the center of the core stage engine to the x-axis of the launch vehicle body, respectively.

[0076] In another exemplary embodiment of the present application, the launch vehicle attitude control system model including actuator failure constructed in step 102 is expressed as:

[0077]

[0078] In another exemplary embodiment of the present application, in step 103, the established attitude angle tracking instruction is represented by x 1d , the constructed tracking error is e1 = x1-x 1d ,Right now

[0079] Where e1 is the tracking error, is the first-order derivative of the tracking error with respect to time, is the first-order derivative of the desired signal.

[0080] In another exemplary embodiment of the present application, the launch vehicle attitude tracking system model including actuator failure constructed in step 104 can be expressed as:

[0081]

[0082] In the formula, is the second-order derivative of the tracking error with respect to time, is the second-order derivative of the desired signal with respect to time.

[0083] In another exemplary embodiment of the present application, sliding mode control has the advantages of rapidity and robustness. However, the traditional sliding mode control contains a sign function, and the control signal needs to achieve high-frequency switching, which not only has high hardware requirements but also causes system chattering. In order to solve this problem, the super-helical sliding mode control provided by the present application adds an integral operation to obtain the actual control amount, which does not contain a high-frequency switching amount, thereby avoiding system chattering. Based on this, the appropriate sliding mode surface selected in step 105 can be expressed as:

[0084] s=c·e1+e2.

[0085] Where s is the sliding surface, s = [s1 s2 s3] T ∈R 3 ,s i (i=1,2,3) is the sliding surface. λ and c1 are parameters to be designed, c=diag(c1,c2,c3)∈R 3×3 ,c1=diag(c 11 ,c 12 ,c 13 )∈R 3×3 , satisfying c i >0,i=1,2,3,c 1i for.

[0086] In order to extend the super-helical sliding mode nonlinear control law applied to single-input single-output to a multi-input multi-output system, the sliding mode reaching law selected in step 106 can be expressed as:

[0087]

[0088] In the formula, λ = diag (λ1, λ2, λ3)∈R 3×3 ,λ i is the i-th parameter in the vector λ. α is the parameter to be designed, α=diag(α1,α2,α3)∈R 3×3 , α i is the i-th parameter in α, and λ i ,α i > 0. S is the vector about the sliding surface, ν is the integral term in the sliding surface, ν=[ν1ν2ν3] T ∈R 3 , ν i is the i-th integral term in the integral term vector. sign(s) is the sign function vector about the sliding surface, sign(s) = [sign(s1)sign(s2)sign(s3)] T ∈R 3 ,sign(s i ) is the sign function of sign(s) with respect to the i-th sliding surface.

[0089] In another exemplary embodiment of the present application, the controller stable tracking model constructed in step 106 is expressed as:

[0090]

[0091] In the formula, The estimation term introduced for fuzzy logic control,

[0092] In the formula, and They are all adaptive laws. S i (Λ) is the fuzzy basis function vector in fuzzy logic control, and Λ is the input item in fuzzy logic control. is the estimated value of the fault bias. is an estimate of the amount of interference.

[0093] First, select the following Lyapunov candidate function Taking the derivative we get:

[0094]

[0095] In the formula, is the derivative of the sliding surface quantity, and All are intermediate representations.

[0096] Among them, because λ i >0,α i >0, so,

[0097] That is, in the control law design, It can be guaranteed that the attitude tracking error can converge to zero in a finite time along the super-helical sliding surface.

[0098] Selecting Lyapunov candidate functions The derivative is:

[0099]

[0100] Where V is the designed Lyapunov function, is the derivative of the Lyapunov function, is the estimation error of the equivalent offset of the offset fault, θ is the equivalent offset of the offset fault, is the estimated value of the equivalent bias of the bias fault, is the estimation error of the composite interference, d is the composite interference, is the estimated value of the composite interference, is the error of the intermediate representation variable.

[0101] Taking the derivative of the sliding surface, we have:

[0102]

[0103] Adding the fuzzy logic control part, we get:

[0104]

[0105] Where ρ is the equivalent actuator efficiency coefficient matrix, and I is the unit matrix. F(Λ) is the fuzzy logic control, F i (Λ i )(i=1,2,3) is the fuzzy logic control part of channel i, Λ i It is the input quantity of the fuzzy logic control part of channel i.

[0106] Furthermore, the fuzzy logic control part can be:

[0107]

[0108] Where W i is the expected weight, S i (Λ i ) is the fuzzy basis function, σ i (Λ i )(i=1,2,3) represents the approximation error.

[0109] Substitute the derivative of the Lyapunov function into the equation, and we get:

[0110]

[0111] Substituting the control law into the output, we get:

[0112]

[0113] Because there are:

[0114]

[0115] In the formula, the unknown variable P is defined as i =||W i || 2 , ε i is the error term of fuzzy logic control.

[0116]

[0117] in, is a positive real number, denoted by ζ.

[0118] Then we can get:

[0119]

[0120] Among them, because λ i >0,α i >0, so, And there must be real numbers Make

[0121] That is, in the control law design, The system can be guaranteed to be semi-globally stable in practical finite time. Therefore, the sliding surface is reachable in practical finite time. According to the characteristics of the super-helical sliding surface, the attitude tracking error converges to zero in a finite time.

[0122] The tracking error can converge to a small neighborhood near the origin in a finite time, and the convergence time t s The upper limit value of is:

[0123]

[0124] Among them, 0<κ0≤1.

[0125] In summary, this application is mainly based on super-helical sliding mode control, and uses a fuzzy logic system to process unknown functions in the model.

[0126] In another embodiment provided by the present application, the validity of the present application is verified by simulation. The simulation parameters are as follows:

[0127] Launch vehicle parameters: r = 3.47m, R = 6.025m, T = 1200000N, I xx =2900000(kg·m 2 ), I yy ,I zz =59000000(kg·m 2 ),X R =56.74m, XZ =66.6678m. T is the thrust of each engine, I xx ,I yy ,I zz is the moment of inertia of each channel.

[0128] The initial value of the attitude angle of the launch vehicle attitude control system state quantity is set to Ω = [φ ψ γ] T =[0° 0° 90°] T , the initial value of the attitude angular velocity is set to ω φ =ω ψ =ω γ =0(°) / s. The attitude tracking command signal is set to x 1d =Ω 1d =[-10° 10° 70°] T , and use the filter

[0129] Smoothing is performed, and the initial value of the attitude tracking command signal is the same as the initial value of the attitude angle of the launch vehicle attitude control system state quantity.

[0130] Set the expression for external disturbances such as pneumatics to: D = 0.05 [sint cos (2t) sin (3t)] T , where t represents time. The controller parameters are set to: c1 = diag(1,1,1)∈R 3×3 ,λ=diag(88,88,88)∈R 3×3 , α=diag(5,5,5)∈R 3×3 .

[0131] In order to verify the effectiveness of the adaptive super-helical sliding mode fault-tolerant controller (i.e., controller stable tracking model) designed in this application for actuator failure conditions and fault types, three different fault conditions are considered.

[0132] For the more common single engine actuator failure, the following fault types are considered: offset failure, that is, when t = 20s, the booster engine has an offset failure with an offset of -2°. Figure 2 is the attitude angle tracking error curve. According to the engine layout analysis, the failure of the booster engine mainly affects the roll and pitch channels. The simulation results show that the roll and pitch channels produce tracking errors of approximately 0.0025° and 0.0005° respectively when the failure occurs.

[0133] For the possible multiple engine actuator failures, the following failure forms are considered: different types of failures occur at different times, that is, when t=20s, the booster engine and the core stage engine have offset failures, with offsets of -3° and -1° respectively; when t=40s, the booster engine has an efficiency loss failure, with a loss of 50%. Figure 3 is the attitude angle tracking error curve. According to the engine layout analysis, the engine actuator failure at t = 20s and t = 40s will have different degrees of impact on the three channels of the launch vehicle. The simulation results show that the three channels all have tracking errors with an amplitude of less than 0.004° when the failure occurs at t = 20s and t = 40s.

[0134] When the engine actuator fails, the possible time-varying faults caused by external disturbances, mechanism aging, etc. are considered as follows: when t=20s, the booster engine has a time-varying offset fault with an offset of 3+0.5sin(0.5t)°. Figure 4 is the attitude angle tracking error curve. According to the engine layout analysis, the engine actuator failure at t = 20s mainly affects the roll and yaw channels. The simulation results show that the roll and yaw channels both produce tracking errors with an amplitude of less than 0.003° when the failure occurs.

[0135] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. 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. 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 launch vehicle adaptive super spiral sliding mode fault-tolerant control data. The input / output interface of the computer device is used to exchange information between the processor and an external device. 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, a launch vehicle adaptive super spiral sliding mode fault-tolerant control method under an actuator failure is implemented.

[0136] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0137] 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.

[0138] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0139] 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 used 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 must comply with relevant regulations.

[0140] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. 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), magnetic 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 may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0141] The database involved in each embodiment provided in this application 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., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0142] 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.

[0143] 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. An adaptive super-helical sliding mode fault-tolerant control method for a launch vehicle under actuator failure, characterized in that: The launch vehicle adaptive super-spiral sliding mode fault-tolerant control method under the actuator failure comprises: Establish a launch vehicle attitude control system model; Consider the actuator failure and establish a fault model; Establishing a launch vehicle attitude control system model including actuator failures by combining the launch vehicle attitude control system model and the fault model; Determine the posture tracking instructions and construct the tracking error; Based on the tracking error, a launch vehicle attitude tracking system model including an actuator failure is constructed in combination with a launch vehicle attitude control system model including an actuator failure; The sliding surface and reaching law are selected, and the stable tracking model of the controller is obtained by combining the launch vehicle attitude tracking system model including actuator failure. The controller stable tracking model is adopted to realize the tracking of the carrier rocket to the attitude instruction, and complete the adaptive super-helical sliding mode fault-tolerant control of the carrier rocket.

2. The method for adaptive super-helical sliding mode fault-tolerant control of a launch vehicle under actuator failure according to claim 1, characterized in that: The launch vehicle attitude control system model is expressed as: Where F(ω) is the vector about angular velocity ω, Ω is the attitude angle vector, ω is the launch vehicle attitude angular velocity vector, J is the launch vehicle moment of inertia matrix, u is the control torque vector, D is the unknown interference vector, Z(Ω) is the coordinate transformation matrix, is the derivative of the attitude angle vector.

3. The method for adaptive super-helical sliding mode fault-tolerant control of a launch vehicle under actuator failure according to claim 1, characterized in that: The fault model is expressed as: d f =ρδ+θ; In the formula, δ f is the swing angle under the fault mode, ρ is the equivalent actuator efficiency coefficient matrix, δ is the actual swing angle, and θ is the equivalent offset of the offset type fault.

4. The method for adaptive super-helical sliding mode fault-tolerant control of a launch vehicle under actuator failure according to claim 3 is characterized in that: The fault conditions represented by the fault model include: No fault condition: ρ = 1 and θ = 0; Bias fault condition: ρ = 1 and θ ≠ 0; Efficiency loss fault condition: 0<ρ<1 and θ=0.

5. The method for adaptive super-helical sliding mode fault-tolerant control of a launch vehicle under actuator failure according to claim 1, characterized in that: The launch vehicle attitude control system model including actuator failure is expressed as: Where x1 is the attitude angle signal, is the derivative of the attitude angle signal x1, x2 is the intermediate representation, is the derivative of the intermediate representation x2, f1(x1,x2) is a function of x1 and x2, f2(x1) is a function of x1, u f is the actual control torque of the launch vehicle, and d is the disturbance.

6. The method for adaptive super-helical sliding mode fault-tolerant control of a launch vehicle under actuator failure according to claim 1, characterized in that: The launch vehicle attitude tracking system model including actuator failure is expressed as: Where e1 is the tracking error, e2 is the time derivative of the tracking error, f1(x1,x2) is a function of x1 and x2, f2(x1) is a function of x1, and u f is the actual control torque of the launch vehicle, x1 is the attitude angle signal, and d is the interference amount.

7. The method for adaptive super-helical sliding mode fault-tolerant control of a launch vehicle under actuator failure according to claim 1, characterized in that: The controller stable tracking model is expressed as: Where δ is the input swing angle of the model, f2(x1) is the function about x1, B is the torque conversion matrix between the equivalent swing angle and the control torque, λ and c1 are the parameters to be designed, s is the sliding surface, S is the vector about the sliding surface, sign(s) is the sign function about the sliding surface s, ν is the integral term in the sliding surface, e2 is the first-order derivative of the tracking error with respect to time, is the approximate term generated by fuzzy logic control, f1(x1,x2) is the function of x1 and x2, is the estimate of the bias, is the estimate of the interference, is the second-order derivative of the desired instruction with respect to time.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the adaptive super-spiral sliding mode fault-tolerant control method for a launch vehicle under an actuator failure as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for adaptive super-spiral sliding mode fault-tolerant control of a launch vehicle under an actuator failure described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for adaptive super-spiral sliding mode fault-tolerant control of a launch vehicle under an actuator failure described in any one of claims 1 to 7 is implemented.

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