Methods, equipment, media, and products for adaptive superspiral sliding mode fault-tolerant control of launch vehicles under actuator failure.

Abstract

This application discloses an adaptive superspiral sliding mode fault-tolerant control method, device, medium, and product for launch vehicles under actuator failure, relating to the field of fault-tolerant control. The method includes: establishing a launch vehicle attitude control system model incorporating actuator failure by combining an established launch vehicle attitude control system model and a fault model; constructing a launch vehicle attitude tracking system model incorporating actuator failure based on tracking errors and the launch vehicle attitude control system model; selecting a sliding surface and a reaching law, and obtaining a controller stable tracking model based on the launch vehicle attitude tracking system model; and using the controller stable tracking model to achieve launch vehicle tracking of attitude commands, thus completing the launch vehicle's adaptive superspiral sliding mode fault-tolerant control. This application can improve the system robustness under actuator failure, thereby more effectively achieving stable operation of the launch vehicle.

Description

Technical Field

[0001] This application relates to the field of fault-tolerant control, and in particular to a method, device, medium, and product for adaptive superspiral sliding mode fault-tolerant control of launch vehicles under actuator failure. Background Technology

[0002] In the field of aerospace science, launch vehicles, as key tools for exploring deep space, have become a focus of scientific research and technological development. Launch vehicles shoulder important missions such as manned spaceflight, scientific experiments, and satellite deployment. Mission failure can lead to incalculable economic losses and numerous other problems. Therefore, ensuring the stable and reliable operation of launch vehicles in the complex and ever-changing space environment has become one of the core issues that urgently need to be addressed in the aerospace engineering field.

[0003] In terms of control system design, launch vehicles face numerous technical challenges, including but not limited to strong external environmental interference, the complexity and uncertainty of dynamic models, and potential actuator failures. To overcome these challenges, constructing an efficient, robust, and highly fault-tolerant flight control system is crucial. This system must be able to ensure reliable flight of the launch vehicle under various extreme conditions, thereby guaranteeing the successful completion of the mission.

[0004] Sliding Mode Control (SMC) is widely used in robotics, chemical engineering, and aerospace due to its advantages such as simplicity, fast response, and robustness. However, SMC still has some shortcomings, mainly including: when using a sign-function-based switching term to suppress uncertain disturbances, a large switching gain is usually selected to cover the uncertainty boundary. However, in practical engineering applications, actuators have system / temporal / spatial inertia, which means the system state cannot strictly move along the switching direction to the equilibrium point. Therefore, high-frequency oscillations may occur in the closed-loop system, resulting in chattering. This phenomenon can significantly affect the control accuracy of the system, and in severe cases, even destroy the overall stability of the system. To mitigate chattering, various control techniques have been designed, such as boundary layer control and superspiral control. Boundary layer control strategies use saturation functions instead of sign functions, which weakens the chattering phenomenon, but because the system state can only converge within the boundary layer, it reduces the system's robustness. Summary of the Invention

[0005] The purpose of this application is to provide an adaptive superspiral sliding mode fault-tolerant control method, device, medium, and product for launch vehicles under actuator failure, which can improve the system robustness under actuator failure and thus more effectively achieve stable operation of launch vehicles.

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

[0007] Firstly, this application provides an adaptive superspiral sliding mode fault-tolerant control method for launch vehicles under actuator failure, including:

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

[0009] Considering actuator failures, establish a failure model;

[0010] A launch vehicle attitude control system model incorporating actuator faults is established by combining the aforementioned launch vehicle attitude control system model and the aforementioned fault model.

[0011] Determine the attitude tracking command and construct the tracking error;

[0012] Based on the tracking error, a launch vehicle attitude tracking system model including actuator failure is constructed by combining the launch vehicle attitude control system model including actuator failure.

[0013] By selecting the sliding surface and the reaching law, and combining it with the launch vehicle attitude tracking system model that includes actuator failures, a stable tracking model for the controller is obtained.

[0014] The aforementioned controller-stabilized tracking model is used to enable the launch vehicle to track attitude commands, thereby achieving adaptive superspiral sliding mode fault-tolerant control of the launch vehicle.

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

[0016]

[0017] In the formula, F(ω) is a vector with respect to angular velocity ω, Ω is the attitude angle vector, ω is the launch vehicle's attitude angular velocity vector, J is the launch vehicle's moment of inertia matrix, u is the control torque vector, D is the unknown disturbance vector, and Z(Ω) is the coordinate transformation matrix. It is the derivative of the attitude angle vector.

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

[0019] δ f =ρδ+θ;

[0020] In the formula, δ f δ is the swing angle under fault mode, ρ is the equivalent actuator efficiency coefficient matrix, δ is the actual swing angle, and θ is the equivalent bias amount for bias-type faults.

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

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

[0023] Bias-type 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 that includes actuator failures is represented as:

[0026]

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

[0028] Optionally, a launch vehicle attitude tracking system model that includes actuator failures can be represented as:

[0029]

[0030] In the formula, e1 is the tracking error, e2 is the derivative of the tracking error with respect to time, f1(x1,x2) is a function of x1 and x2, f2(x1) is a function of x1, and u f denoted as the actual control torque of the launch vehicle, x1 as the attitude angle signal, and d as the disturbance quantity.

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

[0032]

[0033] In the formula, δ is the model input swing angle, f2(x1) is a function of x1, B is the torque transformation matrix between the equivalent swing angle and the control torque, λ and c1 are parameters to be designed, s is the sliding surface, S is a 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, and e2 is the first derivative of the tracking error with respect to time. For the approximate term generated by fuzzy logic control, f1(x1,x2) is a function of x1 and x2. It is an estimator of the bias. For estimating the amount of interference, Let be the second derivative of the expected instruction with respect to time.

[0034] In a second aspect, this application provides a computer device, including: 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 superspiral sliding mode fault-tolerant control method for launch vehicles under actuator failure as described in any of the above-mentioned methods.

[0035] Thirdly, this 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 superspiral sliding mode fault-tolerant control method for launch vehicles under actuator failure as described in any of the above-mentioned methods.

[0036] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the adaptive superspiral sliding mode fault-tolerant control method for launch vehicles under actuator failure as described in any of the above-mentioned methods.

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

[0038] This application provides an adaptive superspiral sliding mode fault-tolerant control method, device, medium, and product for launch vehicles under actuator failure. It directly designs control laws for the launch vehicle's attitude control system model, eliminating the need for model linearization and avoiding system chattering. Combining the launch vehicle attitude tracking system model and sliding surface under actuator failure, a stable tracking model for the controller is derived. This model employs adaptive control, sliding mode control, and fuzzy logic control to achieve stable and accurate tracking of attitude commands by the launch vehicle, improving system robustness under actuator failure and thus more effectively achieving stable launch vehicle operation. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart illustrating an adaptive superspiral sliding mode fault-tolerant control method for a launch vehicle under actuator failure, provided as an embodiment of this application;

[0041] Figure 2 An attitude angle tracking error curve of the controller under a single engine actuator failure condition, provided in another embodiment of this application;

[0042] Figure 3An attitude angle tracking error curve of the controller under multiple engine actuator failure conditions provided in another embodiment of this application;

[0043] Figure 4 An attitude angle tracking error curve diagram under time-varying fault conditions of the engine actuator provided in another embodiment of this application;

[0044] Figure 5 This is a schematic diagram of the structure of a computer device provided in another embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] The Super-Twisting Algorithm (STA) is a well-known second-order sliding modes (SOSM) algorithm. This algorithm hides the high-frequency switching function within the higher-order derivatives of the sliding mode variables, resulting in a switching control law containing an integral term based on the switching function. Since the integral itself has a filtering effect, it can effectively suppress the "chattering" problem present in sliding mode control. Based on this, this application combines adaptive control technology, sliding mode control methods, and fuzzy logic control methods to control the flight of a launch vehicle, thereby achieving more effective and stable operation of the launch vehicle.

[0047] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] In one exemplary embodiment of this application, such as Figure 2 As shown, an adaptive superspiral sliding mode fault-tolerant control method for launch vehicles under actuator failure is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps 100 to 106. Wherein:

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

[0050] Step 101: Consider actuator failure and establish a failure 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 that includes actuator faults.

[0052] Step 103: Determine the attitude tracking command and construct the tracking error.

[0053] Step 104: Based on the tracking error, construct a launch vehicle attitude tracking system model that includes actuator failures by combining the launch vehicle attitude control system model with the model of the launch vehicle attitude control system that includes actuator failures.

[0054] Step 105: Select the sliding surface and the reaching law, and combine them with the launch vehicle attitude tracking system model that includes actuator failures to obtain the controller stable tracking model.

[0055] Step 106: Use a controller-stabilized tracking model to track the attitude commands of the launch vehicle and complete the adaptive super-helical sliding mode fault-tolerant control of the launch vehicle.

[0056] In another exemplary embodiment of this application, in order to directly design the control law for the nonlinear attitude dynamics model of the launch vehicle, it is not necessary to linearize the model. The control law design considers not only unknown external disturbances, but also uncertainties arising from changes in rotational inertia caused by fuel consumption and tank sway, as well as potential system model changes caused by actuator failures and unmodeled dynamics within the model. Based on this, the launch vehicle attitude control system model constructed in step 100 is represented as follows:

[0057]

[0058] In the formula, F(ω) is a vector with respect to 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 angular velocity vector of the launch vehicle's attitude. × Let J be a skew-symmetric matrix composed of vectors ω. Let J be 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 J is the moment of inertia of the roll angle channel. ψψ J is the moment of inertia of the yaw angle channel. γγ Let be the moment of inertia of the pitch channel. Let u be the control torque vector, u∈R. 3D is the unknown disturbance vector, mainly including uncertainties arising from unmodeled system dynamics, perturbations caused by changes in rotational inertia due to rocket fuel consumption and fuel tank sway, and external uncertainties. D = [D φ D ψ D γ ] T D φ D represents the interference amount of the roll angle channel. ψ D represents the interference amount in the yaw angle channel. γ Z(Ω) represents the interference amount in the pitch angle channel. Z(Ω) is the coordinate transformation matrix. It is the derivative of the attitude angle vector.

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

[0060]

[0061] In the formula, x1 is the attitude angle signal, which includes three channels: roll, yaw, and pitch. x1 is the derivative of the attitude angle signal x1, and x2 is an intermediate representation. Let f1(x1,x2) be the derivative of x2, f2(x1) be a function of x1 and x2, and u be a function of x1. f The actual control torque of the launch vehicle is denoted by d, and the disturbance quantity is denoted by d, which has the following specific form:

[0062]

[0063] In the formula, Z(x1) is the coordinate transformation matrix of the attitude angle signal x1. Let x1 be the first derivative of the coordinate transformation matrix with respect to the attitude angle signal x1.

[0064] In another exemplary embodiment of this application, considering the failure of the engine actuator in the attitude control system model, the two most common engine failures in launch vehicles are engine efficiency loss and offset failure. Based on this, the failure model constructed in step 101 can be represented as:

[0065] δ f =ρδ+θ.

[0066] In the formula, δ f R represents the engine's fault swing angle under fault conditions. ρ is the equivalent actuator efficiency coefficient matrix, ρ = diag(ρ1, ρ2, ρ3) ∈ R. 3×3 , ρ i,i=1,2,3 are the efficiency coefficients when the actuator experiences 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 condition: ρ = 1 and θ = 0.

[0069] 2) Bias-type fault condition: 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] In the formula, B is the torque transformation 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] In the formula, X R X is the distance from the engine nozzle to the top of the rocket. Z Let P be the center of mass, P be the thrust of each engine, and R and r be 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 this application, the launch vehicle attitude control system model containing actuator failure constructed in step 102 is represented as follows:

[0077]

[0078] In another exemplary embodiment of this application, in step 103, the established attitude angle tracking command is represented as x. 1d The constructed tracking error is e1 = x1 - x 1d ,Right now

[0079] In the formula, e1 is the tracking error. To track the first derivative of the error with respect to time, Let be the first derivative of the desired signal.

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

[0081]

[0082] In the formula, To track the second derivative of the error with respect to time, Let be the second derivative of the desired signal with respect to time.

[0083] In another exemplary embodiment of this application, sliding mode control has the advantages of speed and robustness. However, traditional sliding mode control contains a sign function, requiring high-frequency switching of the control signal, which not only places high demands on hardware but also causes system chattering. To solve this problem, the super-spiral sliding mode control provided in this application incorporates an integral operation to obtain the actual control quantity, without high-frequency switching, thus avoiding system chattering. Based on this, the suitable sliding surface selected in step 105 can be expressed as:

[0084] s = c·e1 + e2.

[0085] In the formula, s is the sliding surface, and s = [s1 s2 s3] T ∈R 3 s i (i = 1, 2, 3) represents the sliding surface. λ and c1 are parameters to be designed, and 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 systems to multi-input multi-output systems, 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 Let λ be the i-th parameter in vector λ. α is the parameter to be designed, α = diag(α1, α2, α3) ∈ R. 3×3 α i Let λ be the i-th parameter in α, and λ be the i-th parameter in α. i ,α i >0. S is a vector about the sliding surface. ν is the integral term in the sliding surface, ν = [ν1ν2ν3] T ∈R 3 ,ν i Let be the i-th integral term in the integral term vector. Let sign(s) be the sign function vector with respect to 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 this application, the controller stable tracking model constructed in step 106 is represented as follows:

[0090]

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

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

[0093] First, select the following Lyapunov candidate functions. Differentiation yields:

[0094]

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

[0096] Where, because λ i >0, α i >0, therefore,

[0097] That is, in the design of control laws, 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 Differentiating, we get:

[0099]

[0100] In the formula, V is the designed Lyapunov function. The derivative of the Lyapunov function. This represents the estimation error of the equivalent bias amount for bias-type faults. θ is the equivalent bias for a bias-type fault. This is an estimate of the equivalent bias for a bias-type fault. The estimation error is due to the composite interference. d represents composite interference. This is an estimate of the composite interference. This represents the error of the intermediate variable.

[0101] Differentiating with respect to the sliding surface, we have:

[0102]

[0103] With the addition of fuzzy logic control, we get:

[0104]

[0105] In the formula, ρ is the efficiency coefficient matrix of the equivalent actuator, and I is the identity matrix. F(Λ) represents fuzzy logic control. F i (Λ i (i = 1, 2, 3) is the fuzzy logic control part of channel i. Λ i This is the input quantity for the fuzzy logic control section of channel i.

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

[0107]

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

[0109] Substituting the derivative of the Lyapunov function, we get:

[0110]

[0111] Substituting the control law, we get:

[0112]

[0113] Because there are:

[0114]

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

[0116]

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

[0118] Then we can obtain:

[0119]

[0120] Where, because λ i >0, α i >0, therefore, And there must exist real numbers. Make

[0121] That is, in the design of control laws, This ensures the system's semi-global stability within a finite time. Therefore, the sliding surface can be reached within a finite time. Based on the properties of the superspiral sliding surface, the attitude tracking error converges to zero within 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 is:

[0123]

[0124] Where 0 < κ0 ≤ 1.

[0125] In summary, this application focuses on superspiral sliding mode control and utilizes a fuzzy logic system to handle the unknown functions in the model.

[0126] In another embodiment provided in this application, the validity of this 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 = 2,900,000 (kg·m 2 ), I yy ,I zz = 59,000,000 (kg·m 2 ), X R =56.74m, XZ = 66.6678m. T is the thrust of each engine, I xx ,I yy ,I zz Let be the moment of inertia of each channel.

[0128] The initial value of the attitude angle, a state variable of the launch vehicle's attitude control system, 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 a filter

[0129] The signal is smoothed, and the initial value of the attitude tracking command signal is the same as the initial value of the attitude angle state variable of the launch vehicle attitude control system.

[0130] The expression for external disturbances such as aerodynamics is set as: D = 0.05[sint cos(2t)sin(3t)] T Where t represents time. The controller parameters are set as follows: 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] 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 different fault conditions and fault types of the actuator, three different fault conditions are considered.

[0132] For common single-engine actuator failures, consider the following failure types: offset failure, i.e., when t=20s, the booster engine experiences an offset failure with an offset of -2°. Figure 2 The figure shows the attitude angle tracking error curve. Based on engine layout analysis, booster engine failure primarily affects the roll and pitch channels. Simulation results show that the roll and pitch channels generate tracking errors of approximately 0.0025° and 0.0005° respectively when the failure occurs.

[0133] To address the potential failures of multiple engine actuators, consider the following failure modes: different types of failures occur at different times. Specifically, when t = 20s, the booster engine and core stage engine experience offset-type failures with offsets of -3° and -1°, respectively. When t = 40s, the booster engine experiences an efficiency-loss-type failure with a loss of 50%. Figure 3 The image shows the attitude angle tracking error curve. Based on engine layout analysis, this fault in the engine actuator at t=20s and t=40s will affect all three channels of the launch vehicle to varying degrees. Simulation results show that all three channels exhibit tracking errors with an amplitude of less than 0.004° when the faults occur 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. The following time-varying fault form is considered: when t = 20s, the booster engine has a time-varying bias type fault, and the bias is 3 + 0.5sin(0.5t)°. Figure 4 The figure shows the attitude angle tracking error curve. Based on engine layout analysis, the failure of the booster engine at t=20s primarily affects the roll and yaw channels. Simulation results show that both the roll and yaw channels generated tracking errors with amplitudes less than 0.003° when the failure occurred.

[0135] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores adaptive superspiral sliding mode fault-tolerant control data for the launch vehicle. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an adaptive superspiral sliding mode fault-tolerant control method for a launch vehicle under actuator failure.

[0136] Those skilled in the art will understand that Figure 5The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0137] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0138] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[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, data stored, data displayed, 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 the relevant data must comply with relevant regulations.

[0140] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this 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 can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0141] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0142] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A launch vehicle adaptive super-twisting sliding mode fault-tolerant control method under actuator failure, characterized in that, The application relates to a launch vehicle adaptive hyper-spiral sliding mode fault-tolerant control method under actuator faults. A launch vehicle attitude control system model is established; A fault model is established by considering actuator faults; A launch vehicle attitude control system model containing actuator faults is established by combining the launch vehicle attitude control system model and the fault model; An attitude tracking instruction is determined, and a tracking error is constructed; A launch vehicle attitude tracking system model containing actuator faults is constructed by combining the launch vehicle attitude control system model containing actuator faults and the tracking error; A sliding mode surface and an approaching law are selected, and a controller stable tracking model is obtained by combining the launch vehicle attitude tracking system model containing actuator faults; the controller stable tracking model is expressed as: where δ is the model input swing angle, x1 is the attitude angle signal, x2 is the intermediate representation, f2(x1) is a function with respect to x1, B is a moment conversion matrix between the equivalent swing angle and the control moment, λ and c1 are both design parameters, s is a sliding surface, S is a vector with respect to the sliding surface, sign(s) is a sign function with respect to the sliding surface s, v is an integral term in the sliding surface, e2 is a first order derivative of the tracking error with respect to time, is an approximate term generated by fuzzy logic control, f1(x1, x2) is a function with respect to x1 and x2, is an estimation of the bias, is an estimation of the disturbance, is a second order derivative of the desired command with respect to time; The launch vehicle attitude tracking system model containing actuator faults is adopted to realize the tracking of the attitude instruction of the launch vehicle, and the launch vehicle adaptive hyper-spiral sliding mode fault-tolerant control is completed.

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

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

4. The actuator failure adaptive super-twisting sliding mode fault-tolerant control method for launch vehicle according to claim 3, characterized in that, The fault condition expressed by the fault model includes: A fault-free condition: rho = 1 and theta = 0; A bias type fault condition: rho = 1 and theta not equal to 0; An efficiency loss fault condition: 0 < rho < 1 and theta = 0.

5. The actuator failure adaptive super-twisting sliding mode fault-tolerant control method for launch vehicle according to claim 1, wherein, The launch vehicle attitude control system model containing actuator faults is expressed as: wherein is the derivative of the attitude angle signal x1, is the derivative of the intermediate representation x2, u f is the actual control torque of the launch vehicle, d is the disturbance.

6. The actuator failure adaptive super-twisting sliding mode fault-tolerant control method for launch vehicle according to claim 1, wherein, The launch vehicle attitude tracking system model containing actuator faults is expressed as: where e1 is a tracking error, e2 is a derivative of the tracking error with respect to time, f1(x1, x2) is a function with respect to x1 and x2, f2(x1) is a function with respect to x1, u f is an actual control moment of the launch vehicle, x1 is an attitude angle signal, and d is a disturbance.

7. A computer device comprising: A memory, a processor and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes the computer program to realize the launch vehicle adaptive hyper-spiral sliding mode fault-tolerant control method under actuator faults according to any one of claims 1-6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the launch vehicle adaptive hyper-spiral sliding mode fault-tolerant control method under actuator faults according to any one of claims 1-6.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to realize the launch vehicle adaptive hyper-spiral sliding mode fault-tolerant control method under actuator faults according to any one of claims 1-6.

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