A fault-tolerant attitude tracking control method, device, storage medium and product

Abstract

The present application discloses a fault-tolerant attitude tracking control method, device, medium and product, which relate to the field of automatic control technology. The method mainly includes determining a continuous control signal output by an attitude tracking controller based on the aircraft attitude angle, aircraft attitude angular rate, aircraft expected attitude angle, RBF neural network and disturbance observer at the current moment; inputting the continuous control signal into an event trigger strategy to determine whether the trigger condition is met; when the trigger condition is not met, determining the actual control input of the actuator at the next moment as the actual control input at the current moment; when the trigger condition is met, processing the continuous control signal to obtain a discrete control signal for the next stage and inputting it to the actuator to obtain the actual control input of the actuator at the next moment, thereby avoiding the problem of waste of system communication resources and computing resources caused by the continuous transmission of the signal output by the attitude tracking controller to the actuator, thereby saving system communication resources.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to a fault-tolerant posture tracking control method, device, storage medium and product. Background Art

[0002] Aircraft are widely used in military operations, environmental monitoring, disaster relief, and other fields. The ability of these aircraft to execute their missions along predetermined trajectories in the air relies on precise tracking of their desired attitude. Traditional attitude tracking control methods continuously transmit control input signals to the actuators, which significantly wastes system communication and computing resources. Therefore, it is necessary to explore alternative control methods to reduce the system's communication burden. Summary of the Invention

[0003] The purpose of this application is to provide a fault-tolerant attitude tracking control method, device, medium and product. By introducing an event triggering strategy, the continuous control input signal is converted into a discrete control input signal and further transmitted to the actuator, thereby avoiding the communication burden caused by the continuous transmission of the control input signal in the existing technology, and greatly saving the communication resources of the aircraft attitude tracking control system.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a fault-tolerant attitude tracking control method, comprising:

[0006] Obtain the current aircraft attitude angle, the current aircraft attitude angular rate, and the current aircraft expected attitude angle;

[0007] Inputting the aircraft attitude angle at the current moment and the aircraft attitude angular rate at the current moment into an RBF neural network, and outputting a first value and a second value according to the RBF neural network and a weight update law of the neural network;

[0008] The disturbance observer is used to obtain the total estimated value of the system external disturbance and the RBF neural network approximation error;

[0009] Inputting the aircraft attitude angle at the current moment, the desired aircraft attitude angle at the current moment, the total estimated value of the external disturbance and the RBF neural network approximation error, the first value, and the second value into an attitude tracking controller to calculate a continuous control signal;

[0010] The continuous control signal is input into the event trigger strategy for judgment, and when the triggering condition of the event trigger strategy is not met, the actual control input of the actuator at the next moment is determined as the actual control input at the current moment; when the triggering condition of the event trigger strategy is met, the continuous control signal is processed to obtain a next-stage discrete control signal, and the next-stage discrete control signal is input into the actuator to obtain the actual control input of the actuator at the next moment;

[0011] The actual control input at the next moment is input into an attitude tracking control system containing system uncertainty to adjust the flight attitude of the aircraft; the attitude tracking control system containing system uncertainty is used to input the actual control input at the next moment and output the aircraft attitude angle and the aircraft attitude angular rate at the next moment.

[0012] 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 fault-tolerant attitude tracking control method described in the first aspect.

[0013] 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 fault-tolerant attitude tracking control method described in the first aspect.

[0014] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the fault-tolerant attitude tracking control method described in the first aspect.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0016] The present application provides a fault-tolerant attitude tracking control method, device, storage medium and product. The present application introduces an event triggering strategy. When the triggering condition is not met, the input of the attitude tracking control system maintains the control signal of the current stage unchanged. Only when the triggering condition is met will it be resampled to determine a new discrete control signal, and then the obtained new discrete control signal is input into the actuator to calculate the actual control input at the next moment. The attitude tracking control system adjusts the attitude of the aircraft according to the actual control input of the actuator output at the next moment, thereby solving the problem of waste of system communication resources and computing resources caused by the continuous transmission of the control input signal to the actuator in the prior art. While ensuring the expected control performance of the system, it saves system communication resources and realizes efficient control of the control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. 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 creative work.

[0018] Figure 1 This is a control structure diagram of a fault-tolerant posture tracking control method in one embodiment of the present application;

[0019] Figure 2 A flowchart of a fault-tolerant attitude tracking control method provided in one embodiment of the present application;

[0020] Figure 3 Schematic diagram of roll angle tracking based on a fault-tolerant attitude tracking control method in one embodiment of the present application;

[0021] Figure 4 Schematic diagram of pitch angle tracking based on a fault-tolerant attitude tracking control method in one embodiment of the present application;

[0022] Figure 5 Schematic diagram of yaw angle tracking based on a fault-tolerant attitude tracking control method in one embodiment of the present application;

[0023] Figure 6 A schematic diagram of a rolling moment based on a fault-tolerant attitude tracking control method in one embodiment of the present application;

[0024] Figure 7 Schematic diagram of pitching moment based on the fault-tolerant attitude tracking control method in one embodiment of the present application;

[0025] Figure 8 A schematic diagram of a yaw moment based on a fault-tolerant attitude tracking control method in an embodiment of the present application;

[0026] Figure 9 Schematic diagram of the event triggering time interval of the first component of the control input based on the fault-tolerant posture tracking control method in one embodiment of the present application;

[0027] Figure 10 Schematic diagram of the event triggering time interval of the second component of the control input based on the fault-tolerant posture tracking control method in one embodiment of the present application;

[0028] Figure 11 Schematic diagram of the event triggering time interval of the third component of the control input based on the fault-tolerant posture tracking control method in one embodiment of the present application;

[0029] Figure 12 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] In an exemplary embodiment, Figure 1 As shown, a fault-tolerant attitude tracking control method is provided, which is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or by a terminal and a server together.

[0033] In the embodiment of the present application, multiple letters are involved, namely n d is the desired attitude angle, η d =[φ d ,θ d ,ψ d ] T ; η, η=[φ,θ,ψ] T is the attitude angle of the system; ω, ω=[p,q,r] T is the attitude angular rate of the system; is the total estimated value of the external disturbance of the system and the approximation error of the RBF neural network; is the first value containing the first systematic uncertainty, is the second value that includes unknown actuator faults and second system uncertainty; is the continuous control signal; v is the discrete control signal under the event-triggered strategy; u is the actual control input signal.

[0034] In the embodiment of the present application, the method is applied to a server as an example for description. Figure 2 As shown, the process includes the following steps 201 to 206, wherein:

[0035] Step 201: Obtain the current aircraft attitude angle, the current aircraft attitude angular rate, and the current aircraft expected attitude angle.

[0036] Step 202: Input the aircraft attitude angle and the aircraft attitude angular rate at the current moment into the RBF neural network, and output a first value and a second value according to the RBF neural network and the weight update law of the neural network.

[0037] Step 203: Obtain a total estimated value of the system external disturbance and the RBF neural network approximation error by using a disturbance observer.

[0038] Step 204: Input the current aircraft attitude angle, the current aircraft desired attitude angle, the total estimated value of the external disturbance and the RBF neural network approximation error, the first value, and the second value into an attitude tracking controller to calculate a continuous control signal.

[0039] In step 205, the continuous control signal is input into the event triggering strategy for judgment, and when the triggering condition of the event triggering strategy is not met, the actual control input of the actuator at the next moment is determined as the actual control input at the current moment. When the triggering condition of the event triggering strategy is met, the continuous control signal is processed to obtain the next stage discrete control signal, and the next stage discrete control signal is input into the actuator to obtain the actual control input of the actuator at the next moment.

[0040] Step 206: Input the actual control input at the next moment into the attitude tracking control system containing system uncertainty to adjust the flight attitude of the aircraft; the attitude tracking control system containing system uncertainty is used to input the actual control input at the next moment and output the aircraft attitude angle and the aircraft attitude angular rate at the next moment.

[0041] By implementing the above-mentioned steps 201 to 206, the present application introduces an event triggering strategy. When the triggering condition is not met, the input of the attitude tracking control system maintains the control signal of the current stage unchanged. Only when the triggering condition is met will it be resampled to determine a new discrete control signal, and then the obtained new discrete control signal is input into the actuator to calculate the actual control input at the next moment. The attitude tracking control system adjusts the attitude of the aircraft according to the actual control input at the next moment output by the actuator, thereby solving the problem of waste of system communication resources and computing resources caused by the continuous transmission of the control input signal to the actuator in the prior art, saving system communication resources while ensuring the expected control performance of the system, and realizing efficient control of the control system.

[0042] In order to better explain the principle of the fault-tolerant attitude tracking control method described in this application, the derivation process of the formulas involved in the method is now explained:

[0043] Step 1: Establish the aircraft attitude dynamics model and aircraft attitude kinematics model containing system uncertainties and external disturbances.

[0044] Step 2: Give the failure mode of the actuator and design the event triggering strategy.

[0045] Step 3: Given the desired attitude angle, the error is calculated and the virtual control variable is designed according to the aircraft attitude dynamics model and passed through a first-order low-pass filter. At the same time, the RBF neural network is used to approximate the uncertainty of the system and unknown actuator failures.

[0046] Step 4: Use the disturbance observer to observe the external disturbance of the system and the approximation error of the RBF neural network.

[0047] Step 5: The final continuous control signal is calculated based on the aircraft attitude kinematic model and combined with the intermediate control quantity and system parameter estimation value.

[0048] The aircraft attitude dynamics model and aircraft attitude kinematics model containing system uncertainty and external disturbances described in step 1 are established as follows:

[0049] The attitude dynamics equation of the aircraft is:

[0050]

[0051] The attitude kinematic equation of the aircraft is:

[0052]

[0053] Where φ, θ, ψ are the roll angle, pitch angle, and yaw angle, respectively; p, q, r are the roll angular rate, pitch angular rate, and yaw angular rate, respectively; They are the aircraft's roll rate derivative, pitch rate derivative, and yaw rate derivative, respectively. are the aircraft's roll angle derivative, pitch angle derivative, and yaw angle derivative respectively; L, M, and N are the roll moment, pitch moment, and yaw moment respectively; I ii (i=x, y, z) are the moment of inertia and the product of moments of inertia.

[0054] Taking into account the uncertainty of the system and the interference caused by the external environment during flight, the attitude dynamics equation and attitude kinematics equation of the aircraft are written as follows:

[0055]

[0056] Where, the state variable η=[φ,θ,ψ] T is the vehicle attitude angle, ω=[p,q,r] T Is the aircraft attitude angular rate, the upper right corner is marked T represents transpose, is the derivative of the vehicle's attitude angle, is the derivative of the vehicle's attitude angular rate, u=[L,M,N] T represents the actual control input; non-singular function matrix Non-singular outlier matrix Σ2=I yy ; ΔM and ΔN represent the uncertain parts of M and N respectively, ΔM is the first system uncertainty, ΔN is the second system uncertainty, D = [d p ,d q ,d r ] T is an unknown bounded external disturbance, d p d q and d r They represent the external disturbance affected by the roll angular rate, the external disturbance affected by the pitch angular rate, and the external disturbance affected by the yaw angular rate, respectively.

[0057] Among them, the actuator failure in step 2 and the designed event triggering strategy are as follows:

[0058] During the flight of an aircraft, its actuators may experience various faults. Considering damage and drift faults of aircraft actuators, the specific fault forms are as follows:

[0059] u=A(t)v+B(t)(4);

[0060] Where u = [L, M, N] T , u is the actual control input, v=[v1,v2,v3] T is the discrete control signal under the event-triggered strategy, A(t) = diag{a1(t), a2(t), a3(t)} indicates the actuator is damaged, a i (t)(i=1,2,3) is the actuator effectiveness factor and satisfies 0<a i (t)≤1, B(t)=[b1(t),b2(t),b3(t)] T Indicates actuator drift fault, and satisfies b i (t) Smooth and bounded.

[0061] Design an event triggering strategy as follows:

[0062]

[0063] in is a function of time t Represents the designed continuous control signal The i-th component of ; Indicates continuous control signal The i-th component of The value at this moment; represents the kth triggering moment of the i-th component, Indicates the k+1th triggering moment of the i-th component, i.e., the triggering moment The next trigger moment after is the final continuous intermediate control signal to be designed, m i and n i It is a constant that can be adjusted and determined according to the simulation effect of the event triggering time interval simulation diagram. The simulation effect adjustment according to the event triggering time interval simulation diagram refers to adjusting m according to the density and length of the tree diagram. i and n i The density of the tree diagram corresponds to the number of event triggering, the length of the tree diagram corresponds to the event triggering interval, and 0<m i <1, n i >0.

[0064] Indicates v i (t) is correct The specific sampling process is as follows:

[0065] Before the next event triggering moment arrives, the discrete control input signal v i (t) is always equal to the time when the current event is triggered This moment The value of remains unchanged, and then when the next event is triggered, v i (t) is always equal to This event triggers the moment The value of v i The value of (t) only undergoes a step change at the moment the event is triggered.

[0066] v i The sign of (t) is unknown, so there are two possible cases and Unified writing: And m(t),n(t)∈[-1,1], so:

[0067]

[0068] Writing the above formula in a compact form:

[0069]

[0070] in,

[0071] At the same time, J(t) and K(t) are unknown and bounded.

[0072] The calculation method of the virtual control amount α in step 3 is as follows:

[0073] 1) Given the desired attitude angle η d =[φ d ,θ d ,ψ d ] T , where φ d ,θ d , ψ d They are the desired roll angle, the desired pitch angle, and the desired yaw angle, and the tracking error e1 is calculated. Specifically:

[0074] e1=η-η d (8).

[0075] 2) Use RBF neural networks to approximate system uncertainties and unknown actuator failures:

[0076] Let F1=ΔMω,F2=ΔN+G(AI 3×3 )v+GB, Among them, F1 and are all unknown nonlinear functions, F1 contains the first system uncertainty, Including unknown actuator faults and second system uncertainty, two RBF neural networks are used to approximate the unknown nonlinear functions F1 and Thus:

[0077]

[0078] in, It's W i * The estimated value of T represents transpose, satisfy express It is the optimal weight that minimizes the approximation error of the neural network among all available weights. ij Any m×1 weight vector, F ij It is an unknown nonlinear function approximated by a neural network. In the process of RBF neural network approximating an unknown nonlinear function, the neural network weights Continuously update and iterate, using the weight update law of the neural network to update, H i =[H i1 ,H i2 ,H i3 ] T , H ij =[h ij1 ,h ij2 ,...,h ijm ] T , ||H ij || represents the column vector H ij The 2-norm of is a positive constant, h ijk is the output of the kth neuron in the hidden layer, c k =[c k1 ,c k2 ,...,c km ] T is the center point vector value of the kth hidden layer neuron in the RBF neural network, b k >0 is the width of the Gaussian function of the hidden layer neurons of the RBF neural network, m is the number of neurons in the RBF neural network, x is the input of the neural network, In the corresponding neural network, x=[φ,θ,ψ,p,q,r] T (x=[η,ω] T ),exist In the corresponding neural network, ε i =[ε i1 ,ε i2 ,ε i3 ] T is the approximation error of the RBF neural network, ε ij There exists a positive constant upper bound δ ij .

[0079] 3) Design virtual control quantity α:

[0080]

[0081] in represents the Hadamard product, k1 is a known parameter of the design, k1=[k 11 ,k 12 ,k 13 ] T , and all its components are positive numbers, is the estimated value of F1, It is W1 * The estimated value of is the derivative of the desired attitude angle.

[0082] 4) Let α pass through a first-order low-pass filter with a time constant of τ

[0083] Among them, the observer used to observe the external interference of the system and the approximation error of the RBF neural network in step 4 is designed as follows:

[0084] 1) Let Ω = ε2 + MD, use the disturbance observer to observe Ω, and define the auxiliary design variable Θ = Ω - ΛMω, where Λ is a known positive definite symmetric matrix completed by the design.

[0085] 2) The derivative of the estimated value of Θ (i.e. The update law of is designed as:

[0086]

[0087] in, yes The estimated value of yes The estimated value of

[0088]

[0089] 3) The estimated value of Ω is:

[0090]

[0091] Specifically, since Θ=Ω-ΛMω is defined, Ω=Θ+ΛMω, and taking the estimated values ​​on both sides can be obtained

[0092] Among them, the final continuous control signal in step five The calculation method is as follows:

[0093] 1) Design the intermediate control quantity β:

[0094]

[0095] Among them, e2=Mω-υ is the intermediate quantity, υ is the filtering variable, is the derivative of the filter variable, k2 is a known parameter of the design, k2=[k 21 ,k 22 ,k 23 ] T , and all its components are positive.

[0096] 2) Estimated values ​​of given parameters:

[0097] Let infλ min (J) = ζ, ξ=μ·sup||Ξ||, ||Ξ|| is the 2-norm of Ξ, Ξ=MGK, and are the estimated values ​​of μ and ξ, respectively.

[0098] 3) Design the final continuous control signal

[0099]

[0100] in is an intermediate quantity and ι is a positive constant.

[0101] 4) Design the weight update law of the neural network, The update law and The update law of:

[0102]

[0103] Among them, the positive definite symmetric matrix is the design parameter, σ ij (i=1,2,j=1,2,3), κ1, κ2, ρ1 and ρ2 are designed positive constants, and ||e2|| is the 2-norm of e2.

[0104] In another exemplary embodiment of the present application, in step 202, the aircraft attitude angle and the aircraft attitude angular rate at the current moment are input into the RBF neural network, and a first value and a second value are output according to the RBF neural network and the weight update law of the neural network, specifically including:

[0105] The first value is described Contains first system uncertainty;

[0106] The second value is described Including unknown actuator faults and second system uncertainties;

[0107] According to the formula Get the

[0108] According to the formula Get the

[0109] In another exemplary embodiment of the present application, in step 202, It's W i * The estimated value of The weight update law of the neural network is used for updating. The weight update rate of the neural network is calculated according to the above formula (15), i=1,2,j=1,2,3.

[0110] In another exemplary embodiment of the present application, in step 203, the total estimated value of the system external disturbance and the RBF neural network approximation error is obtained according to formula (12).

[0111] In another exemplary embodiment of the present application, in step 204, the aircraft attitude angle at the current moment, the desired aircraft attitude angle at the current moment, the total estimated value of the external disturbance and the RBF neural network approximation error, the first value, and the second value are input into the attitude tracking controller, and a continuous control signal is calculated according to formula (14).

[0112] In another exemplary embodiment of the present application, in step 204, The update law and The update law of is calculated according to formula (16).

[0113] In another exemplary embodiment of the present application, in step 205, the event triggering strategy is in the form of formula (6).

[0114] In another exemplary embodiment of the present application, in step 205, the next stage discrete control signal is input to the actuator, and the actual control input of the actuator at the next moment is obtained according to formula (4).

[0115] In an exemplary embodiment, Figure 3-Figure 11 This is a simulation diagram of a fault-tolerant attitude tracking control method in one embodiment of the present application; the aircraft attitude tracking control system is simulated, and some simulation data are selected. The initial value of the system is: η(0) = [0, 1, 0] T Degrees; expected attitude angle: η d =[2,3,2] T Degrees; External disturbance: D = [0.05sin(0.5t), 0.06sin(t), 0.04cos(2t)] T degrees / second; Actuator damage failure: A(t)=diag{0.7 / (e -0.3t +1),0.7 / (e -0.3t +1),0.6 / (e -0.4t +1)}(t=10s); actuator drift fault: B(t)=[0.06cos(0.1t),0.07,0.08sin(0.3t)] T (t=10s), the simulation results are as follows Figures 3 to 11 shown.

[0116] In the embodiments of the present application, multiple letters are involved, φ represents the actual roll angle, θ represents the actual pitch angle, ψ represents the actual yaw angle, and φ represents the actual roll angle. d represents the desired roll angle, θ d represents the desired pitch angle, ψ dIndicates the desired yaw angle; u1 is actually the actual control input u, where L represents the aircraft's roll moment; u2 is actually the actual control input u, where M represents the aircraft's pitch moment; u2 is actually the actual control input u, where N represents the aircraft's yaw moment; Indicates the event triggering time interval, i=1,2,3.

[0117] Figures 3 to 5 The actual and desired attitude angles of the system are displayed. It can be seen that all three attitude angles can track the corresponding desired attitude angles very well. Figures 6 to 8 The actual control input signal of the system is displayed. It can be seen that the control input is updated only after a certain period of time. Figures 9 to 11 The event triggering interval is shown, and we can see that the minimum event triggering interval is 0.01s, which is greater than the sampling interval of 0.005s. This event triggering condition can effectively reduce the number of transmissions, thereby alleviating the system transmission load.

[0118] 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 12 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and 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 computer program in the non-volatile storage medium. The database of the computer device is used to store aircraft attitude angles, aircraft attitude angular rates and aircraft expected attitude angle data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, a fault-tolerant attitude tracking control method is implemented.

[0119] Those skilled in the art will understand that Figure 12 The 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0120] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0121] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiment when executed by a processor.

[0122] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0123] 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.

[0124] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented 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 memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may 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 may 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).

[0125] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0126] The technical features of the above embodiments can 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.

[0127] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A fault-tolerant attitude tracking control method, characterized in that: The fault-tolerant posture tracking control method comprises: Obtain the current aircraft attitude angle, the current aircraft attitude angular rate, and the current aircraft expected attitude angle; Inputting the aircraft attitude angle at the current moment and the aircraft attitude angular rate at the current moment into an RBF neural network, and outputting a first value and a second value according to the RBF neural network and a weight update law of the neural network; The disturbance observer is used to obtain the total estimated value of the system external disturbance and the RBF neural network approximation error; Inputting the aircraft attitude angle at the current moment, the desired aircraft attitude angle at the current moment, the total estimated value of the external disturbance and the RBF neural network approximation error, the first value, and the second value into an attitude tracking controller to calculate a continuous control signal; The continuous control signal is input into the event trigger strategy for judgment, and when the triggering condition of the event trigger strategy is not met, the actual control input of the actuator at the next moment is determined as the actual control input at the current moment; when the triggering condition of the event trigger strategy is met, the continuous control signal is processed to obtain a next-stage discrete control signal, and the next-stage discrete control signal is input into the actuator to obtain the actual control input of the actuator at the next moment; Inputting the actual control input at the next moment into an attitude tracking control system containing system uncertainty to adjust the flight attitude of the aircraft; the attitude tracking control system containing system uncertainty is used to input the actual control input at the next moment and output the aircraft attitude angle and aircraft attitude angular rate at the next moment; The method of inputting the current aircraft attitude angle, the current aircraft desired attitude angle, the total estimated value of the external disturbance and the RBF neural network approximation error, the first value, and the second value into the attitude tracking controller to calculate a continuous control signal specifically includes: The continuous control signal is calculated according to the following formula: ； in, , yes The estimated value of , , , yes The estimated value of , yes The 2-norm of , , , the continuous control signal is an adaptive control process, using the parameters Update rate and parameters Update at the update rate, and It is a constant that can be adjusted based on the simulation effect of the event trigger time interval simulation diagram and satisfies , , ; is a positive constant; , , , , , is the tracking error between the aircraft attitude angle at the current moment and the aircraft desired attitude angle at the current moment, , Indicates the current roll angle of the aircraft. Indicates the pitch angle of the aircraft at the current moment. Indicates the current yaw angle of the aircraft. , Indicates the current rolling angle rate of the aircraft. Indicates the pitch rate of the aircraft at the current moment. Indicates the current yaw rate of the aircraft. , Indicates the desired roll angle of the aircraft at the current moment. Indicates the desired pitch angle of the aircraft at the current moment. Indicates the expected yaw angle of the aircraft at the current moment. for The derivative of " " represents the Hadamard product, and are known parameters after the design is completed, , , is the time constant, is the filter variable, is the derivative of the filtered variable, 、 、 and is the intermediate quantity; , , and is a non-singular function matrix, , is a nonsingular outlier matrix, , , , , , , , , , , , are the moment of inertia and the product of moments of inertia; It is the total estimated value of the external disturbance of the system and the approximation error of the RBF neural network.

2. The fault-tolerant posture tracking control method according to claim 1, characterized in that: Outputting the first value and the second value according to the RBF neural network and the weight update law of the neural network specifically includes: The first value is , Contains first system uncertainty; The second value is , Including unknown actuator faults and second system uncertainties; According to the formula Get the ; According to the formula Get the ; in, yes The estimated value of represents transpose, , ,satisfy , Indicates any dimensional weight vector, It is an unknown nonlinear function approximated by a neural network. In the process of RBF neural network approximating an unknown nonlinear function, the neural network weights Continuously update and iterate, using the weight update law of the neural network to update, , , , Represents a column vector The 2-norm of is a positive constant, , , is the hidden layer The output of a neuron, It is the RBF neural network The center point vector value of hidden layer neurons, is the width of the Gaussian function of the hidden layer neurons of the RBF neural network, is the number of neurons in the RBF neural network, is the input of the neural network.

3. The fault-tolerant posture tracking control method according to claim 1, characterized in that: The event triggering strategy is as follows: ； in , is a continuous intermediate control signal, Indicates the The first A trigger moment, Indicates the The first A trigger moment.

4. The fault-tolerant posture tracking control method according to claim 1, characterized in that: The next stage discrete control signal is input to the actuator to obtain the actual control input of the actuator at the next moment, specifically including: The actual control input at the next moment is calculated according to the following formula: ； in, , is the actual control input at the next moment, is the rolling moment of the aircraft at the next moment, Indicates the pitch moment of the aircraft at the next moment, Indicates the yaw moment of the aircraft at the next moment; Indicates that the actuator is damaged. is the actuator effectiveness factor and satisfies ; Indicates actuator drift fault, and satisfies Smooth and bounded.

5. The fault-tolerant posture tracking control method according to claim 2, characterized in that: The weight update rate of the neural network is calculated according to the following formula: ； Among them, the positive definite symmetric matrix , is a design parameter, is a design constant, , .

6. The fault-tolerant attitude tracking control method according to claim 1, characterized in that: described The update law and the The update law is calculated according to the following formula: ； in, 、 、 as well as is a design constant, yes The 2-norm of .

7. 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 fault-tolerant attitude tracking control method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the fault-tolerant attitude tracking control method according to any one of claims 1 to 6 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the fault-tolerant attitude tracking control method according to any one of claims 1 to 6 is implemented.

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