Active fault-tolerant control and optimization method for aviation fuel control pump test equipment

Abstract

The application particularly relates to an active fault-tolerant control and optimization method of an aviation fuel regulating pump test equipment, fault estimation information obtained based on a linear variable parameter adaptive sliding mode observer is used for linear variable parameter active fault-tolerant controller design by adopting adaptive sliding mode control technology and an adaptive radial basis function neural network, an adaptive gain variation law and an adaptive RBF algorithm weight adaptive updating law are designed, unknown disturbance is more accurately fitted, the control performance of the sliding mode fault-tolerant controller is improved, and the performance of the active fault-tolerant controller of the aviation fuel regulating pump test equipment is optimized and improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aviation fuel regulating pump test equipment control, and particularly relates to an aviation fuel regulating pump test equipment active fault-tolerant control and optimization method. BACKGROUND

[0002] In the related art, the aviation fuel regulating pump test equipment is a precision equipment for testing the performance of a fuel regulator and a fuel pump, is a key component of an aero-engine oil supply, and the performance of the aviation fuel regulating pump test equipment affects the overall performance of the aero-engine. The equipment realizes full-function testing of the fuel regulator and the fuel pump by integrating various advanced technologies, including servo motor dragging, test loop switching, a PLC (Programmable Logic Controller) control system, safety protection settings, and a displacement servo control system, to realize accurate control of flow parameters. However, since the aviation fuel regulating pump test equipment is a complex thermal coupling system working under high pressure and high speed, it has high safety requirements. Once a fault occurs in the equipment, it will have a significant impact on the operation stability. Therefore, to improve the safety and reliability of the equipment, effective measures must be taken to accurately diagnose and isolate the aviation fuel regulating pump test equipment to ensure normal operation of the equipment.

[0003] In the prior art, the fault-tolerant control method for the aviation fuel regulating pump test equipment can be divided into two categories: active fault-tolerant control (ATFC) and passive fault-tolerant control (PFTC). The passive fault-tolerant control method uses robust control theory to consider faults as a kind of disturbance for control design, which does not require a fault diagnosis scheme or reconfiguration of the controller. However, its fault-tolerant capability is limited, and it will cause partial performance loss of the controller. The active fault-tolerant control method reconstructs the design of the controller according to the online information of the system after the fault to maintain the stability of the system and achieve the desired dynamic and steady-state performance. This method can reconfigure the controller using fault information provided by the online fault diagnosis system, and can use available configurations in the system to change the structure to achieve the best fault-tolerant control performance, which has a larger adaptive space. The sliding mode variable structure control method is a method that is widely researched and applied in control systems.

[0004] However, the existing technical solutions also have certain defects, for example: 1) The aviation fuel regulating pump test equipment has complex working conditions, and the parameters of the system model will change with the dispatching parameters at all times under multiple working conditions. It is difficult to ensure the effect of global fault-tolerant control by designing a single fixed sliding mode surface in the existing technology; 2) The existing scheme does not consider sensor faults; 3) The aviation fuel regulating pump test equipment may be under complex interference and fault impact, and there is a certain limitation in using robust theory to suppress observer errors and system disturbances.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides an active fault-tolerant control and optimization method for aviation fuel regulation pump test equipment, a storage medium, a computer program product, and an electronic device. It solves the problem of potential sensor and actuator failures faced by aviation fuel regulation pump test equipment under multiple operating conditions in the prior art, and constructs a dynamic active fault-tolerant control system, thereby overcoming the defects existing in the prior art to a certain extent.

[0007] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0008] According to a first aspect of the present invention, an active fault-tolerant control and optimization method for an aviation fuel control pump test equipment is provided, the method comprising:

[0009] Using the operating data of the aviation fuel regulating pump test equipment, a linear variable parameter model of the aviation fuel regulating pump test equipment is constructed to describe the dynamic characteristics of the aviation fuel regulating pump test equipment under different operating conditions; wherein, the operating data includes the system speed, outlet pressure and flow rate corresponding to different fuel metering valve openings;

[0010] Based on the linear variable parameter model, an affine linear variable parameter fault model is constructed by combining the characteristics of actuator faults, sensor faults and external interference. The affine linear variable parameter fault model includes: an actuator fault subsystem for expressing actuator faults and a sensor fault subsystem for expressing sensor faults, so as to separate actuator faults and sensor faults.

[0011] Based on the adaptive observer gain term, the first sliding surface corresponding to the actuator fault observer, and the second sliding surface corresponding to the sensor fault observer, an adaptive upper limit sliding observer is configured; wherein, the adaptive upper limit sliding observer includes: the actuator fault observer and the sensor fault observer, each configured with an adaptive observer gain term;

[0012] An adaptive upper limit sliding mode observer is used to configure an adaptive gain sliding mode controller for the affine linear variable parameter fault model, in order to provide active fault-tolerant control for the aero-fuel-controlled pump test equipment.

[0013] In some exemplary embodiments, the method further includes:

[0014] Configure a target Lyapunov function for the adaptive upper bound sliding mode observer to eliminate the influence of the unknown fault upper bound on system state convergence.

[0015] In some exemplary embodiments, the method further includes:

[0016] An adaptive RBF network is used to provide disturbances and estimation errors for an adaptive gain sliding mode controller.

[0017] In some exemplary embodiments, the adaptive RBF network includes an input layer, a hidden layer, and an output layer arranged sequentially; wherein, the input parameter of the input layer is the system state variable x, which is a g-dimensional variable; the hidden layer includes l neurons, and the activation function of each neuron has the form of a Gaussian function; the output layer is used to output the disturbance estimate.

[0018] In some exemplary embodiments, constructing a linear variable parameter model of the aviation fuel control pump test equipment using operational data includes:

[0019] For the fuel metering valve, between the fully closed and fully open states, N fuel metering valve opening values ​​are selected at equal intervals, and the corresponding system speed, outlet pressure and flow data are collected at a preset sampling frequency to obtain multiple sets of operating data;

[0020] Configure the fuel metering valve opening FMV as the input variable u(t); configure the system speed n and outlet pressure P. out The flow rate W is defined as the output variable y(t); and the system rotational speed n is defined as the state variable x(t), configured as a scheduling parameter;

[0021] Each set of operational data was identified, and model parameters were fitted to obtain N sets of linear state-space models S. j ={A j B j ,C}, is represented as:

[0022]

[0023] Where x(t) is the state variable, u(t) is the input variable, and y(t) is the output variable; A j B j C represents the system's state transition matrix, control input matrix, and observation matrix, respectively, and is a constant matrix.

[0024] For N sets of linear state-space models and their corresponding scheduling parameters, the coefficient matrix is ​​solved, and a linear variable parameter model of the aviation fuel control pump test equipment is constructed based on the coefficient matrix to describe the dynamic characteristics of the system under different operating conditions. The model is expressed as:

[0025]

[0026] Where ρ(t) is the scheduling parameter; A(ρ(t)) and B(ρ(t)) are the coefficient matrices respectively; the coefficient matrices can be solved based on the system rotation speed and the multicell vertex scheduling matrix.

[0027] In some exemplary embodiments, the construction of an affine linear variable parameter fault model based on a linear variable parameter model, combined with the characteristics of actuator faults, sensor faults, and external interference, includes:

[0028] Define bounded functions corresponding to actuator failures, sensor failures, and system uncertainties, and transform the linear variable parameter model based on a pre-configured transformation matrix to obtain an affine linear variable parameter failure model:

[0029]

[0030] in, These are the transformed system matrices, For the transformed state variables, The output variables after transformation; transformation matrix T i =[T i,1 ,T i,2 ] T Transformation matrix S i =[S i,1 ,S i,2 ] T ;

[0031] Considering the coupling effect between different types of faults, the affine linear variable parameter fault model is decomposed to obtain the actuator fault subsystem and the sensor fault subsystem, so as to separate the actuator fault and the sensor fault.

[0032] The actuator failure subsystem includes:

[0033]

[0034] The sensor fault subsystem includes:

[0035]

[0036] Where L is the transformation matrix; f a f s These represent actuator malfunction and sensor malfunction, respectively.

[0037] In some exemplary embodiments, the method further includes:

[0038] Configure sliding mode nonlinear switching term v i,1 With v i,2 ,include:

[0039]

[0040] Where, α i,1 α i,2 Let P1 and P3 be constants greater than 0, and let P1 and P3 be Lyapunov matrices to be solved, and let P1 and P3 be symmetric positive definite matrices. and These are the sliding mode gains;

[0041] The upper bound constraints are relaxed based on the adaptive law of sliding mode gain, including:

[0042]

[0043] in, The estimation error is represented by ε1 and ε2, which are constants greater than 0.

[0044] Configure the sliding surface corresponding to the actuator fault observer, including:

[0045]

[0046] Configure the sliding surface corresponding to the sensor fault observer, including:

[0047]

[0048] Fault characteristic estimation is achieved by using an equivalent output error injection term.

[0049] In some exemplary embodiments, an adaptive upper limit-based sliding mode observer is configured with an adaptive gain sliding mode controller for the affine linear variable parameter fault model, including:

[0050] u(t)=u eq (t)+u sw (t)

[0051] Where u(t) is the adaptive gain sliding mode controller, u eq (y) represents the control input on the sliding surface, u sw (t) represents the switching control item;

[0052] The control inputs on the sliding surface include disturbances and estimation errors estimated using an adaptive RBF network.

[0053] In some exemplary embodiments, the method further includes:

[0054] In response to fault signals, the current operating data of the aviation fuel control pump test equipment is collected;

[0055] The current operating data is processed using an adaptive upper limit sliding mode observer to obtain actuator fault estimation information, sensor fault estimation information, and estimation information corresponding to state variables;

[0056] The actuator fault estimation information, sensor fault estimation information, and the estimation information corresponding to the state variables are input into the adaptive gain sliding mode controller, and the system control parameters are output for active fault-tolerant control.

[0057] According to a second aspect of the present invention, a computer program product is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-described active fault-tolerant control and optimization method for aviation fuel regulation pump test equipment is implemented.

[0058] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described active fault-tolerant control and optimization method for aviation fuel regulation pump test equipment.

[0059] According to a fourth aspect of the present invention, an electronic device is provided, comprising:

[0060] Processor; and

[0061] Memory for storing the executable instructions of the processor;

[0062] The processor is configured to implement the above-described active fault-tolerant control and optimization method for aviation fuel regulating pump test equipment by executing the executable instructions.

[0063] The active fault-tolerant control and optimization method for aviation fuel control pump test equipment provided in the embodiments of the present invention establishes a linear parameter-varying (LPV) model of the aviation fuel control pump test equipment considering sensor and actuator failures, and separates actuator failures from sensor failures. For actuator and sensor failures, considering the impact of potential interference and errors in the LPV system of the aviation fuel control pump test equipment, an adaptive gain term is introduced into the sliding mode controller. This allows the adaptive sliding mode controller to dynamically adjust its structure online in a timely manner after being subjected to faults and interferences. It can use nonlinear term switching to compensate for fault mutations and online interferences, thus its fault-tolerant control effect is superior to the robustness suppression method based on robust theory, and the output value can quickly track the expected value. Considering the constraints of the adaptive gain control method and the conservatism of fault-tolerant control, compensation is made for unknown interference deviations to optimize the active fault-tolerant effect of LPV and improve the robustness of the LPV active fault-tolerant control system.

[0064] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0065] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0066] Figure 1 The illustration shows a schematic diagram of an active fault-tolerant control and optimization method for an aviation fuel pump testing equipment, an exemplary embodiment of the present invention.

[0067] Figure 2 This diagram schematically illustrates the working principle of an adaptive sliding mode observer according to an exemplary embodiment of the present invention.

[0068] Figure 3 This schematic diagram illustrates a radial basis function neural network structure according to an exemplary embodiment of the present invention.

[0069] Figure 4 This diagram schematically illustrates the results of an LPV model according to an exemplary embodiment of the present invention.

[0070] Figure 5 This diagram illustrates a fault estimation result based on an adaptive synovial observer, as an exemplary embodiment of the present invention.

[0071] Figure 6 This diagram schematically illustrates an active fault-tolerant control result according to an exemplary embodiment of the present invention.

[0072] Figure 7 This schematic diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0073] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0074] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0075] In related technologies, the control method of aviation fuel regulation pump test equipment has certain defects, such as: (1) Aviation fuel regulation pump test equipment has complex working conditions, and the parameters of the system model under multiple working conditions will change with the scheduling parameters at all times. A single fixed sliding surface is difficult to guarantee the effect of global fault-tolerant control; (2) The method does not consider sensor failures, so it is necessary to design a suitable sliding surface to achieve accurate expectation tracking of the global system when sensor and actuator failures occur, so as to ensure the safety and stability of the equipment; (3) Aviation fuel regulation pump test equipment may be under complex interference and fault impact, and the use of robust theory to suppress observer error and system interference has certain limitations.

[0076] To address the shortcomings and deficiencies of existing technologies, this exemplary embodiment provides an active fault-tolerant control and optimization method for aviation fuel regulating pump test equipment.

[0077] In this example implementation, refer to Figure 1 As shown, the active fault-tolerant control and optimization method for aviation fuel regulating pump test equipment may specifically include the following steps:

[0078] Step S11: Using the operating data of the aviation fuel regulating pump test equipment, construct a linear variable parameter model of the aviation fuel regulating pump test equipment to describe the dynamic characteristics of the aviation fuel regulating pump test equipment under different operating conditions; wherein, the operating data includes the system speed, outlet pressure and flow rate corresponding to different fuel metering valve openings;

[0079] Step S12: Based on the linear variable parameter model, an affine linear variable parameter fault model is constructed by combining the characteristics of actuator faults, sensor faults, and external interference. The affine linear variable parameter fault model includes: an actuator fault subsystem for expressing actuator faults and a sensor fault subsystem for expressing sensor faults, so as to separate actuator faults and sensor faults.

[0080] Step S13: Based on the adaptive observer gain term, the first sliding surface corresponding to the actuator fault observer, and the second sliding surface corresponding to the sensor fault observer, configure an adaptive upper limit sliding observer; wherein, the adaptive upper limit sliding observer includes: the actuator fault observer and the sensor fault observer, each configured with an adaptive observer gain term;

[0081] Step S14: Configure an adaptive gain sliding mode controller for the affine linear variable parameter fault model based on an adaptive upper limit sliding mode observer for active fault-tolerant control of the aviation fuel control pump test equipment.

[0082] The method disclosed herein establishes a linear variable-parameter LPV fault model for an aviation fuel control pump test equipment, and designs a parameter-adaptive sliding mode observer based on this model to estimate actuator and sensor fault signals in real time. It combines the inherent robustness of variable structure control with the flexible adjustment capability of adaptive control to design an adaptive sliding mode fault-tolerant control method, improving the robustness, accuracy, and response speed of fault-tolerant control. Leveraging the advantages of adaptive RBF networks, such as strong nonlinear fitting ability and fast convergence speed, unknown disturbances are estimated. Combined with a sliding mode control algorithm, dynamic compensation signals are generated to reconstruct the control strategy. This method overcomes the limitations of traditional fault-tolerant control in adapting to time-varying conditions, enabling real-time adjustment of the observer gain and control law according to changes in system parameters. This improves the accuracy of active fault-tolerant control, effectively suppresses the risk of system instability caused by faults, and ensures the reliable operation of the fuel control pump test equipment.

[0083] The following will describe in more detail each step of the active fault-tolerant control and optimization method for the aviation fuel regulating pump test equipment in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0084] In step S11, a linear variable parameter model of the aviation fuel adjustment pump test equipment is constructed using the operating data of the test equipment to describe the dynamic characteristics of the test equipment under different operating conditions. The operating data includes the system speed, outlet pressure, and flow rate corresponding to different fuel metering valve openings.

[0085] For example, step S11 described above may specifically include:

[0086] Step S21: Select N fuel metering valve opening values ​​at equal intervals between the fully closed and fully open states of the fuel metering valve, and collect the corresponding system speed, outlet pressure and flow data at a preset sampling frequency to obtain multiple sets of operating data.

[0087] Step S22: Configure the fuel metering valve opening FMV as the input variable u(t); configure the system speed n and outlet pressure P. outThe flow rate W is defined as the output variable y(t); and the system rotational speed n is defined as the state variable x(t), configured as a scheduling parameter;

[0088] Step S23: Identify each group of running data, fit the model parameters, and obtain N sets of linear state-space models S. j ={A j B j ,C};

[0089] Step S24: Solve the coefficient matrix for N sets of linear state-space models and corresponding scheduling parameters, and construct a linear variable parameter model of the aviation fuel regulation pump test equipment based on the coefficient matrix to describe the dynamic characteristics of the system under different operating conditions.

[0090] Specifically, from the fully closed state (0% opening) to the fully open state (100% opening) of the fuel metering valve, N fuel metering valve opening values ​​(FMV) are selected at equal intervals (e.g., 2.5%). j j = 1, 2, ..., N. Different fuel metering valve opening values ​​correspond to different fuel flow rates. Data is collected at a certain sampling frequency to obtain the operating data of the aviation fuel control pump test equipment under different fuel flow rates, for a total of N sets of data. Each set of operating data includes the fuel metering valve opening FMV. j System speed n j Export pressure P out,j and traffic W j .

[0091] Based on the operating data of N sets of aviation fuel control pump test equipment, the fuel metering valve opening FMV is selected as the input variable u(t), and the system speed n and outlet pressure P are used as input variables. out Let the flow rate W be the output variable y(t), and the system speed n be the state variable x(t). Since the operating conditions of the aviation fuel control pump test equipment (such as basic operating condition, acceleration operating condition, deceleration operating condition, and idling operating condition) are closely related to the system speed n, the system speed n is configured as the scheduling parameter ρ. For each set of data, the least squares method is used to identify the data and fit the model parameters, resulting in a set of linear state-space models S. j ={A j B j ,C}, further expressed as:

[0092]

[0093] Where x(t) is the state variable, u(t) is the input variable, and y(t) is the output variable; A j B jLet C be the system's state transition matrix, control input matrix, and observation matrix, respectively, and C be constant matrices of a certain dimension. Finally, N linear state-space models are obtained. This model is a linear time-invariant system and can be used to describe the local dynamic characteristics of an aviation fuel control pump test device.

[0094] Based on N known linear state-space models and corresponding known scheduling variables, an affine linear variable parameter model of the aviation fuel control pump test equipment is constructed by solving the coefficient matrix through an interpolation fitting approach and solving the linear equations. This model better describes the dynamic characteristics of the system under different operating conditions. The model is represented as follows:

[0095]

[0096] Here, ρ(t) is the scheduling parameter, which is measurable at any time and lies within a bounded set of admissible parameter trajectories. The matrices A(ρ(t)) and B(ρ(t)) are related to the scheduling variable ρ(t) and have affine polynomial form, i.e., A(ρ(t)) = A0 + ρ(t)A1 + ρ(t)A2, B(ρ(t)) = B0 + ρ(t)B1 + ρ(t)B2. The coefficient matrices A0, A1, A2, B0, B1, B2 of this affine polynomial can be derived from N known linear state-space models S = {S1, S2, ..., S...}. N} and the corresponding known scheduling variable ρ(t)={n1,n2,…,n N The following system of linear equations is obtained by solving:

[0097]

[0098] In step S12, based on the linear variable parameter model, an affine linear variable parameter fault model is constructed by combining the characteristics of actuator faults, sensor faults, and external interference. The affine linear variable parameter fault model includes an actuator fault subsystem for expressing actuator faults and a sensor fault subsystem for expressing sensor faults, so as to separate actuator faults and sensor faults.

[0099] For example, step S12 described above may specifically include:

[0100] Step S31: Define bounded functions corresponding to actuator failure, sensor failure, and system uncertainty, and transform the linear variable parameter model based on the pre-configured transformation matrix to obtain an affine linear variable parameter failure model.

[0101] Step S32: Considering the coupling effect between different types of faults, the affine linear variable parameter fault model is split into actuator fault subsystem and sensor fault subsystem to separate actuator faults and sensor faults.

[0102] Specifically, based on the established affine linear variable parameter model of the aviation fuel control pump test equipment, an affine linear variable parameter fault model with actuator faults, sensor faults, and external interference is established, which can be specifically represented as:

[0103]

[0104] Among them, f a and f s These represent actuator faults and sensor faults, respectively; M(ρ(t))=B(ρ(t)), N=I; D and ε(t) are the constant disturbance distribution matrix and the disturbance variable, respectively.

[0105] Actuator failure, sensor failure, and system uncertainty are all bounded functions, i.e., ||f a ||≤λ a ,||f s ||≤λ s ,||ε(t)||≤λ ε ; where λ a , λ s , λ ε It is an unknown constant that is greater than zero.

[0106] Assuming the system (A(ρ(t)), B(ρ(t))) is controllable, when both actuator and sensor faults exist simultaneously, accurate fault estimation is difficult due to the coupling effect between different types of faults in the system. Therefore, the affine linear variable parameter fault model can be decomposed into two subsystems: an actuator fault subsystem and a sensor fault subsystem, thereby separating actuator and sensor faults.

[0107] Design the transformation matrix T i =[T i,1 ,T i,2 ] T S i =[S i,1 ,S i,2 ] T Using L, a coordinate transformation is performed on the affine linear variable parameter fault model to obtain new state variables. and output variables

[0108]

[0109] A new system matrix is ​​obtained. And the matrix has the following form:

[0110]

[0111]

[0112] Based on the above, the affine linear variable parameter fault model is transformed into:

[0113]

[0114] in, These are the transformed system matrices, For the transformed state variables, The output variables after transformation; transformation matrix T i =[T i,1 ,T i,2 ] T Transformation matrix S i =[S i,1 ,S i,2 ] T ; u is the system input variable, namely the fuel metering valve opening FMV.

[0115] The affine linear variable parameter fault model is decomposed into subsystem 1, which is only affected by actuator faults.

[0116]

[0117] The affine linear variable parameter fault model is decomposed into a sensor fault subsystem 2, which is only affected by sensor faults. The sensor fault subsystem includes:

[0118]

[0119] Where L is the transformation matrix; f a f s These represent actuator malfunction and sensor malfunction, respectively.

[0120] Define the following new state variables:

[0121]

[0122] in,

[0123] The system can then be further represented as:

[0124]

[0125] In step S13, an adaptive upper limit sliding mode observer is configured based on the adaptive observer gain term, the first sliding surface corresponding to the actuator fault observer, and the second sliding surface corresponding to the sensor fault observer; wherein, the adaptive upper limit sliding mode observer includes: the actuator fault observer and the sensor fault observer, each configured with an adaptive observer gain term.

[0126] For example, the method further includes configuring a target Lyapunov function for the adaptive upper bound sliding mode observer to eliminate the influence of the unknown fault upper bound on system state convergence.

[0127] Specifically, refer to Figure 2 As shown, in designing an adaptive sliding mode observer, the traditional sliding mode observer design method has certain limitations in application because it requires knowledge of the upper bound information of the fault. The adaptive upper bound sliding mode observer relaxes the upper bound condition constraint through an adaptive law and constructs a suitable Lyapunov function to eliminate the influence of the unknown upper bound of the fault on the convergence of the system state.

[0128] Specifically,

[0129] 1) Design two sliding mode observers with the following structures, representing an actuator fault observer and a sensor fault observer, respectively:

[0130]

[0131]

[0132] Where the superscript ^ indicates an estimated value, For the stable matrix to be designed, K i v is the gain of the observer to be designed. i,1 v i,2 This refers to the nonlinear switching term of the sliding mode to be designed.

[0133] 2) Design the sliding mode nonlinear switching term v i,1 With v i,2 :

[0134]

[0135] Where, α i,1 α i,2 Let P1 and P3 be constants greater than 0, and let P1 and P3 be Lyapunov matrices to be solved, and let P1 and P3 be symmetric positive definite matrices. and These represent the designed sliding mode gains.

[0136] 3) Design and The adaptive law relaxes the upper bound constraints:

[0137]

[0138] in, This represents the estimation error. ε1 and ε2 are constants greater than 0.

[0139] 4) The sliding surface is designed as follows:

[0140]

[0141] Wherein, s1 is the sliding surface designed for the actuator fault observer, and s2 is the sliding surface designed for the sensor fault observer.

[0142] 5) Solve for the matrix to be designed for the sliding mode observer. and K i If there exist positive definite symmetric matrices P and full-rank matrices H with the following forms:

[0143]

[0144]

[0145] Furthermore, the following linear matrix inequality (LMI) constraints are satisfied, including:

[0146]

[0147] in, The system state is asymptotically stable and has robust performance against disturbances.

[0148] 6) Fault characteristic estimation is achieved using the equivalent output error injection term, and the actuator fault f a and sensor failure f s The estimated value and It can be calculated as:

[0149]

[0150] Among them, δ1 and δ2 are small positive scalars.

[0151] In addition, the observer is able to output estimation information.

[0152] according to The estimated value of the state variable x can be obtained.

[0153] For example, the method further includes: using an adaptive RBF network to provide disturbances and estimation errors for the adaptive gain sliding mode controller.

[0154] Specifically, fault estimation information obtained based on the LPV adaptive sliding mode observer and State variable x estimation information An active fault-tolerant controller for LPV is designed using adaptive sliding mode control technology and an adaptive RBF network. The adaptive gain variation law of the sliding mode controller and the adaptive update law of the weight matrix of the RBF network are designed to realize active fault-tolerant control and optimization under the fault condition of the aviation fuel control pump test equipment.

[0155] 1) For affine linear variable parameter fault models:

[0156]

[0157] Design an adaptive gain sliding mode controller u(t):

[0158] u(t)=u eq (t)+u sw (t)

[0159] Among them, u eq (t) Control inputs (equivalent control inputs) on the sliding surface, u sw (t) represents the switching control item.

[0160] They each have the following forms:

[0161]

[0162] Where E = xx d For state error, x d For the desired state, For B i The pseudo-inverse matrix. Let be the estimated variables for the disturbance and estimation error d (estimated by the adaptive RBF network); Li,2=[Bi+MiBi+D], k1,j is a positive definite symmetric matrix.

[0163]

[0164] Where P5 and K are symmetric positive definite matrices, and S = KE is the designed sliding surface. And φ>0, in The adaptive law for the design satisfies: In the formula, σ is a positive scalar.

[0165] 2) Solving the sliding mode controller's undesigned matrices P5, K and k 1,j By solving the following LMI:

[0166]

[0167] in,

[0168]

[0169] Make the parameters η, φ, If the conditions are met, the sliding mode LPV fault-tolerant control system is stable and can reach the sliding surface and stabilize on the sliding surface within a certain time.

[0170] Based on the above, u(t) can be expressed as:

[0171]

[0172] For example, refer to Figure 3 As shown, the adaptive RBF network includes an input layer, a hidden layer, and an output layer arranged sequentially. The input parameter of the input layer is the system state variable x, which is a g-dimensional variable. The hidden layer includes l neurons, and the activation function of each neuron has the form of a Gaussian function. The output layer is used to output the disturbance estimate.

[0173] Specifically, input x RBF =[x RBF,1 ,x RBF,2 ,…,x RBF,i …,x RBF,g [i = 1, 2, ..., g are g-dimensional variables. The activation function of each neuron has the Gaussian function form g = [g1, g2, ..., g] i …,h l ].in,

[0174]

[0175] Among them, c i Let b be the center vector of the i-th neuron. i x is the width of the basis functions. RBF Let x be the input variable of the adaptive RBF network, and let x be the state variable of the system.

[0176] The output variable is the disturbance estimate.

[0177]

[0178] Where W is the weight matrix, and its update rate is designed as follows:

[0179]

[0180] in, D is the disturbance distribution matrix; γ is a positive scalar; H represents the Gaussian function.

[0181] refer to Figure 4As shown, an active fault-tolerant controller for LPV is designed using an adaptive RBF network. This method leverages the advantages of the RBF network, such as its strong nonlinear fitting ability and fast convergence speed, to estimate unknown disturbances. Combined with a sliding mode control algorithm, a dynamic compensation signal is generated to reconstruct the control strategy. This method overcomes the limitation of traditional fault-tolerant control in its insufficient adaptability to time-varying conditions. It can adjust the observer gain and control law in real time according to changes in system parameters, thereby improving the accuracy of active fault-tolerant control and effectively suppressing the risk of system instability caused by faults.

[0182] For example, the method further includes:

[0183] Step S41: In response to the fault signal, collect the current operating data of the aviation fuel control pump test equipment;

[0184] Step S42: Use the adaptive upper limit sliding mode observer to process the current running data to obtain actuator fault estimation information, sensor fault estimation information, and estimation information corresponding to the state variables;

[0185] Step S43: Input the actuator fault estimation information, sensor fault estimation information, and the estimation information corresponding to the state variables into the adaptive gain sliding mode controller, and output the system control parameters for active fault-tolerant control.

[0186] Specifically, the aviation fuel control pump test equipment can collect operational data in real time during operation. When a system fault is detected, active fault-tolerant control calculations can be performed based on the operational data at that moment. The current fuel metering valve opening, system speed, outlet pressure, and flow rate can be input into an adaptive upper limit sliding mode observer. The input parameters are then processed using actuator fault observers and sensor fault observers to obtain actuator fault estimation information, sensor fault estimation information, and estimation information corresponding to state variables. Finally, an adaptive gain sliding mode controller is used to process the estimated data to obtain system control parameters, thus achieving active fault-tolerant control.

[0187] For example, if a differential pressure valve jamming fault with an amplitude of 0.5 occurs in the 10th second, the control parameter K = -0.8265 when the speed is 0.8; and K = -0.1533, σ = 0.008, and φ = 0.002 when the speed is 0.9. γ1=20. Among them, σ, φ, These are predefined constants.

[0188] At this point, the system output response overshoot is less than 1.8%, and the settling time is less than 1.5 seconds. The adaptive sliding mode controller based on the adaptive RBF network retains the fast response of sliding mode control during the dynamic response phase. (Refer to the figure) Figure 5 , Figure 6As shown, due to the superior interference fitting capability of the adaptive RBF network, the output of the aero-engine system can be restored to the desired control value within 1.8s after the actuator constant deviation hard fault and within 0.08s after the sensor constant deviation hard fault. Therefore, the designed LPV active controller has good performance.

[0189] The method provided in this invention obtains fault estimation information by designing a linearly variable parameter adaptive sliding mode observer, and designs an LPV active fault-tolerant controller using adaptive sliding mode control technology and an adaptive RBF neural network. First, a linearly variable parameter (LPV) model of the aviation fuel-adjusting pump test equipment is established considering sensor and actuator failures. For actuator and sensor failures, considering the potential interference and error in the LPV system, an adaptive gain term is introduced into the sliding mode controller, and an adaptive gain algorithm is designed. This allows the adaptive sliding mode controller to dynamically adjust its structure online in a timely manner after being subjected to faults and interferences, using nonlinear term switching to compensate for fault mutations and online interference. Therefore, its fault-tolerant control effect is superior to the robustness-based suppression method, and the output value can quickly track the expected value. Considering the constraints of the adaptive gain control method and the conservatism of fault-tolerant control, an adaptive RBF network is introduced to compensate for unknown interference deviations. An adaptive update law of the adaptive RBF is designed to optimize the LPV active fault-tolerant effect and improve the robustness of the LPV active fault-tolerant control system.

[0190] The beneficial effects of this invention include:

[0191] (1) Compared with the existing fault-tolerant control method based on fixed sliding surface, this invention uses a parameter-scheduled sliding surface observer and combines a linear variable parameter system model to construct a dynamic sliding surface, so that the switching gain of the fault observer can be adjusted in real time according to the speed and operating parameters of the fuel pump.

[0192] (2) In view of the problem that the existing technology ignores sensor failure, the present invention decouples sensor failure and actuator failure by subsystem decomposition.

[0193] (3) Due to the adoption of the error feedback compensation strategy, the present invention can suppress sliding mode chattering while ensuring the dynamics of the system after a fault.

[0194] (4) Since the introduction of adaptive radial basis neural network compensation makes up for the error returned to the controller by the observer estimation, the sliding mode controller based on adaptive radial basis neural network proposed in this invention improves the fault-tolerant controller's ability to suppress faults.

[0195] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0196] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0197] Figure 7 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0198] It should be noted that, Figure 7 The illustrated electronic device 1000 is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. This electronic device may be a data processing device mounted on an aviation fuel control pump test device. Alternatively, it may be a host computer device connected to the aviation fuel control pump test device for controlling and processing operational data.

[0199] like Figure 7 As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from Storage Unit 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004.

[0200] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0201] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0202] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0203] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0204] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0205] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0206] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0207] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0208] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0209] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. An active fault-tolerant control and optimization method for aviation fuel control pump test equipment, characterized in that, The method comprises: Utilize the operation data of the aviation fuel regulating pump test equipment to construct a linear variable parameter model of the aviation fuel regulating pump test equipment, which is used to describe the dynamic characteristics of the aviation fuel regulating pump test equipment under different working conditions; wherein, the operation data comprises corresponding system rotating speed, outlet pressure and flow under different fuel metering valve opening degrees; Based on the linear variable parameter model, an affine linear variable parameter fault model is constructed in combination with the execution mechanism fault, sensor fault and external disturbance characteristics, comprising: Define the bounded functions corresponding to the execution mechanism fault, sensor fault and system uncertainty, and convert the linear variable parameter model based on the pre-configured conversion matrix to obtain the affine linear variable parameter fault model: wherein is the transformed state variable, is the transformed output variable; transformation matrix , transformation matrix ; denotes the input variable; is the state transition matrix of the system, is the control input matrix of the system, is the observation matrix of the system; is the scheduling parameter; and denote an actuator fault and a sensor fault, respectively; , is the identity matrix; is the system matrix; is the transformation matrix; Considering the coupling effect between different types of faults, the affine linear variable parameter fault model is split to obtain an execution mechanism fault subsystem and a sensor fault subsystem to separate the execution mechanism fault and the sensor fault; The execution mechanism fault subsystem comprises: The sensor fault subsystem comprises: wherein, is a transformation matrix; , respectively represent actuator faults and sensor faults; respectively are transformed system matrices; , are state variables; The affine linear variable parameter fault model comprises: an execution mechanism fault subsystem for expressing the execution mechanism fault, and a sensor fault subsystem for expressing the sensor fault, to separate the execution mechanism fault and the sensor fault; Based on the adaptive observer gain term, the first sliding mode surface corresponding to the execution mechanism fault observer, and the second sliding mode surface corresponding to the sensor fault observer, an adaptive upper limit sliding mode observer is configured; wherein, the adaptive upper limit sliding mode observer comprises: an execution mechanism fault observer and a sensor fault observer, which are respectively configured with an adaptive observer gain term; Based on the adaptive upper limit sliding mode observer, an adaptive gain sliding mode controller is configured for the affine linear variable parameter fault model to perform active fault-tolerant control on the aviation fuel regulating pump test equipment, comprising: wherein is an adaptive gain sliding mode controller, is a control input term on the sliding surface, is a switching control term; the control input term on the sliding surface includes disturbance and estimation error estimated by an adaptive RBF network.

2. The method of claim 1, wherein, The method further comprises: A target Lyapunov function is configured for the adaptive upper limit sliding mode observer to eliminate the influence of unknown fault upper bound on the convergence of system state.

3. The method of claim 1, wherein, The method further comprises: An adaptive RBF network is utilized to provide disturbance and estimation error for the adaptive gain sliding mode controller.

4. The method of claim 3, wherein, The adaptive RBF network comprises an input layer, a hidden layer and an output layer arranged in sequence; wherein the input parameter of the input layer is a system state variable x, and the input parameter of the hidden layer is a variable y ; the hidden layer comprises a plurality of neurons, and the activation function of each neuron has a Gaussian basis function form; and the output layer is used for outputting an interference estimation value.

5. The method of claim 1, wherein, The method of utilizing the operation data of the aviation fuel regulating pump test equipment to construct a linear variable parameter model of the aviation fuel regulating pump test equipment comprises: According to equal intervals, a plurality of fuel metering valve opening values are selected between a fully closed state and a fully open state of the fuel metering valve Corresponding system rotating speed, outlet pressure and flow data are collected at a preset sampling frequency to obtain a plurality of groups of operation data. Configuring fuel metering valve opening FMV as an input variable ; configuring system speed , outlet pressure , and flow as output variables ; and defining system speed as a state variable , configured as a scheduling parameter; The running data of each group is identified respectively, model parameters are fitted, and N groups of linear state space models are obtained , which is expressed as: wherein, is a state variable, is an input variable, is an output variable; , , are a state transition matrix, a control input matrix and an observation matrix of the system, respectively, and are constant matrices. The coefficient matrix is solved for N groups of linear state space models and corresponding scheduling parameters, and a linear variable parameter model of the aviation fuel regulating pump test equipment is constructed based on the coefficient matrix to describe the dynamic characteristics of the system under different working conditions, and the model is represented as: wherein is a scheduling parameter; , are coefficient matrices, respectively; the solution of the coefficient matrices can be based on the system speed and the polytope vertex scheduling matrix.

6. The method of claim 1, wherein, The method further comprises: Configuring a sliding mode nonlinear switch term with comprising: wherein , is a constant greater than 0, , is a Lyapunov matrix to be solved for, and is a symmetric positive definite matrix, and are the sliding mode gains, respectively. According to the adaptive law of the sliding mode gain to relax the upper bound condition constraint, comprising: wherein is an estimate of the error; , is a constant greater than 0; The sliding mode surface corresponding to the execution mechanism fault observer is configured, comprising: The sliding mode surface corresponding to the sensor fault observer is configured, comprising: The equivalent output error injection term is utilized to realize fault characteristic estimation.

7. The method of claim 1, wherein, The method further comprises: In response to the fault signal, the current operation data of the aviation fuel regulating pump test equipment is collected; The adaptive upper limit sliding mode observer is utilized to process the current operation data to obtain execution mechanism fault estimation information, sensor fault estimation information, and state variable corresponding estimation information; The actuator fault estimation information, the sensor fault estimation information, and the estimation information corresponding to the state variables are input into an adaptive gain sliding mode controller, and system control parameters are output to perform active fault-tolerant control.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by a processor to implement the active fault-tolerant control and optimization method of the aviation fuel regulating pump test equipment in any one of claims 1 to 7.

9. An electronic device, comprising: Comprise: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to implement the active fault-tolerant control and optimization method of the aviation fuel regulating pump test equipment in any one of claims 1 to 7 via execution of the executable instructions.

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