Robust fault-tolerant control method for aero-engine fuel control pump test equipment based on parameter scheduling sliding mode observer

Abstract

The application particularly relates to a robust fault-tolerant control method of an aviation fuel regulating pump test equipment based on a parameter scheduling sliding mode observer, a linear variable parameter model is established for the aviation fuel regulating pump test equipment, an adaptive gain scheduling sliding mode observer is designed to realize fault estimation, error feedback is utilized to design a robust fault-tolerant controller, a linear matrix inequality required for solving controller design parameters is solved, and active fault-tolerant control is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of aviation fuel regulating pump test equipment control, and particularly relates to a robust fault-tolerant control method for aviation fuel regulating pump test equipment based on a parameter scheduling sliding mode observer. BACKGROUND

[0002] In related technologies, the aviation fuel regulating pump test equipment is a precision equipment for testing the performance of fuel regulators and fuel pumps, is a key component of aviation engine fuel supply, and the performance of the aviation engine as a whole is affected by the fuel regulating pump. The equipment realizes full-function testing of fuel regulators and fuel pumps by integrating various advanced technologies, including servo motor dragging, test loop switching, PLC (programmable logic controller) control system, safety protection setting, and displacement servo control system, to realize accurate control of flow parameters. However, since the 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 running stability. Therefore, in order to improve the safety and reliability of the equipment, effective measures must be taken to accurately diagnose and isolate the fuel regulating pump test equipment to ensure normal operation of the equipment.

[0003] In existing technologies, fault-tolerant control methods for aviation fuel regulating pump test equipment can be divided into two categories: active fault-tolerant control (ATFC) and passive fault-tolerant control (PFTC). Among them, 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 fault diagnosis scheme and does not need to reconfigure the controller. However, its fault-tolerant ability is limited, which will cause partial performance loss of the controller. The active fault-tolerant control synchronously reconfigures 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 the fault information provided by the online fault diagnosis system, and can use the available configuration in the system to change the structure to achieve the best fault-tolerant control performance, which has a larger adaptive space. Among them, 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 scheduling 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.

[0005] It is to be understood that the information disclosed in the Background section is only for the purpose of enhancing the understanding of the background of the present application, and thus can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The present application provides a robust fault-tolerant control method for an aero fuel regulating pump test device based on a parameter scheduling sliding mode observer, a computer program product, a storage medium, and an electronic device. A linear variable parameter model is established for the aero fuel regulating pump test device, an adaptive gain scheduling sliding mode observer is designed to realize fault estimation, an error feedback is used for robust fault-tolerant controller design, a linear matrix inequality required for solving controller design parameters is solved, active fault-tolerant control is realized, and thus the defects in the prior art can be overcome to some extent.

[0007] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0008] According to a first aspect of the present application, a robust fault-tolerant control method for an aero fuel regulating pump test device based on a parameter scheduling sliding mode observer is provided, and the method comprises:

[0009] The operating data of the aero fuel regulating pump test device is used to construct a linear variable parameter model of the aero fuel regulating pump test device, which is used to describe the dynamic characteristics of the aero fuel regulating pump test device under different working conditions. The operating data includes the corresponding system speed, outlet pressure and flow under different fuel metering valve opening degrees.

[0010] Based on the linear variable parameter mathematical model, an affine linear variable parameter fault model is constructed in combination with the characteristics of the actuator fault, the sensor fault and the external disturbance. The affine linear variable parameter fault model includes an actuator fault subsystem for expressing the actuator fault, and a sensor fault subsystem for expressing the sensor fault, so as to separate the actuator fault and the sensor fault.

[0011] Based on the adaptive observer gain term, the first sliding mode surface corresponding to the actuator fault observer, and the second sliding mode surface corresponding to the sensor fault observer, an adaptive upper limit sliding mode observer is configured. The adaptive upper limit sliding mode observer includes an actuator fault observer and a sensor fault observer, each of which is configured with an adaptive observer gain term.

[0012] A robust controller is configured for the affine linear variable parameter fault model based on the adaptive upper limit sliding mode observer, so as to realize active fault-tolerant control of the aero fuel regulating pump test device.

[0013] In some example embodiments, the constructing a linear variable parameter model of the aviation fuel regulating pump test equipment based on the operation data of the aviation fuel regulating pump test equipment comprises:

[0014] selecting N fuel metering valve opening values at equal intervals between a fully closed state and a fully open state of the fuel metering valve, collecting corresponding system rotation speed, outlet pressure and flow data at a preset sampling frequency to obtain a plurality of groups of operation data;

[0015] configuring the fuel metering valve opening FMV as an input variable u(t); configuring the system rotation speed n, the outlet pressure P out and the flow W as output variables y(t); and defining the system rotation speed n as a state variable x(t) and configuring it as a scheduling parameter;

[0016] identifying each group of operation data respectively, fitting model parameters to obtain N groups of linear state space models S j ={A j ,B j ,C}, represented as:

[0017]

[0018] wherein x(t) is a state variable, u(t) is an input variable, y(t) is an output variable; A j ,B j ,C are respectively a state transition matrix, a control input matrix and an observation matrix of the system, and are constant matrices;

[0019] solving the coefficient matrix for the N groups of linear state space models and the corresponding scheduling parameters, and constructing a linear variable parameter model of the aviation fuel regulating pump test equipment based on the coefficient matrix to describe the dynamic characteristics of the system under different working conditions, the model being represented as:

[0020]

[0021] wherein p(t) is a scheduling parameter; A(p(t)), B(p(t)) are respectively coefficient matrices, having an affine polynomial form.

[0022] In some example embodiments,

[0023] the constructing an affine linear variable parameter fault model based on the linear variable parameter model in combination with the actuator fault, the sensor fault and the external disturbance characteristics comprises:

[0024] defining a bounded function corresponding to the actuator fault, the sensor fault and system uncertainty, and converting the linear variable parameter model based on a pre-configured conversion matrix to obtain an affine linear variable parameter fault model:

[0025]

[0026] wherein, are the transformed system matrices, is the transformed state variable, is the transformed output variable; the transformation matrix T i = [T i,1 , T i,2 ] T , the transformation matrix S i = [S i,1 , S i,2 ] T ;

[0027] Considering the coupling effect between different types of faults, the affine linear variable parameter fault model is split to obtain an actuator fault subsystem and a sensor fault subsystem to separate the actuator fault and the sensor fault.

[0028] The actuator fault subsystem includes:

[0029]

[0030] The sensor fault subsystem includes:

[0031]

[0032] wherein, L is a transformation matrix; f a , f s respectively represent the actuator fault and the sensor fault.

[0033] In some example embodiments, the method further includes:

[0034] The actuator fault observer and the sensor fault observer are respectively configured with a sliding mode nonlinear switching term v i,1 and v i,2 , including:

[0035]

[0036] wherein, α i,1 , α i,2 are constants greater than 0, P1, P3 are Lyapunov matrices to be solved, and P1= P1T, P3= P3T are symmetric positive definite matrices, and are sliding mode gains;

[0037] An adaptive law of the sliding mode gain is configured to relax the upper bound condition constraint, including:

[0038]

[0039] wherein, to estimate the error; ε1, ε2 are constants greater than 0;

[0040] configuring a sliding mode surface corresponding to the actuator fault observer, comprising:

[0041]

[0042] configuring a sliding mode surface corresponding to the sensor fault observer, comprising:

[0043]

[0044] In some example embodiments, the method further comprises:

[0045] implementing fault characteristic estimation with an equivalent output error injection term, wherein,

[0046] actuator fault f a estimate comprising:

[0047]

[0048] sensor fault f s estimate comprising:

[0049]

[0050] wherein δ1 and δ2 are small positive scalars.

[0051] In some example embodiments, the method further comprises:

[0052] observer output estimation information

[0053] According to determining an estimate of the state variable x from z1=x2+Lx3, z2=x3

[0054] In some example embodiments, the adaptive upper limit based sliding mode observer configures a robust controller for the affine linear variable parameter fault model, comprising:

[0055] substituting actuator fault estimation information and sensor fault estimation information state variable x estimation information into the robust controller u, comprising:

[0056]

[0057] Wherein, the state variable x(t) includes system rotation speed n; and p is a scheduling parameter.

[0058] In some example embodiments, the method further comprises:

[0059] A target Lyapunov function is configured for the adaptive upper bound sliding mode observer to eliminate the influence of unknown fault upper bound on the convergence of system states.

[0060] In some example embodiments, the method further comprises:

[0061] In response to the fault signal, current operation data of the aviation fuel control pump test equipment is collected;

[0062] The current operation data is processed by the adaptive upper bound sliding mode observer to obtain actuator fault estimation information, sensor fault estimation information, and state variable corresponding estimation information;

[0063] The actuator fault estimation information, sensor fault estimation information, and state variable corresponding estimation information are input into a robust controller to output system control parameters for active fault-tolerant control.

[0064] According to a second aspect of the present application, a computer program product is provided, which has a computer program stored thereon, and the computer program, when executed by a processor, implements the above-mentioned aviation fuel control pump test equipment robust fault-tolerant control method based on a parameter scheduling sliding mode observer.

[0065] According to a third aspect of the present application, a storage medium is provided, which has a computer program stored thereon, and the computer program, when executed by a processor, implements the above-mentioned aviation fuel control pump test equipment robust fault-tolerant control method based on a parameter scheduling sliding mode observer.

[0066] According to a fourth aspect of the present application, an electronic device is provided, which comprises:

[0067] a processor; and

[0068] a memory for storing executable instructions of the processor;

[0069] Wherein, the processor is configured to implement the above-mentioned aviation fuel control pump test equipment robust fault-tolerant control method based on a parameter scheduling sliding mode observer via execution of the executable instructions.

[0070] The robust fault-tolerant control method for the aviation fuel control pump test equipment based on the parameter scheduling sliding mode observer provided by the embodiment of the application estimates the actuator and sensor faults in real time through the design of the parameter adaptive sliding mode observer, generates a dynamic compensation signal to reconstruct the control strategy. The method breaks through the limitation of the poor adaptability of the traditional fault-tolerant control to the time-varying working conditions, can adjust the observer gain and the control law in real time according to the parameter change of the system, effectively suppresses the system instability risk caused by the fault, and ensures the reliable operation of the fuel control pump test equipment. The method solves the sensor and actuator fault hidden danger problem of the aviation fuel control pump test equipment under multiple working conditions in the prior art, and constructs a dynamic fault-tolerant control system.

[0071] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the application. BRIEF DESCRIPTION OF DRAWINGS

[0072] The drawings incorporated into the specification and forming part of the specification, show embodiments consistent with the application, and together with the specification, serve to explain the principles of the application. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained from these drawings without creative labor for those skilled in the art.

[0073] Figure 1 The schematic diagram of the robust fault-tolerant control method for the aviation fuel control pump test equipment based on the parameter scheduling sliding mode observer in the exemplary embodiment of the application is schematically shown;

[0074] Figure 2 The schematic diagram of the adaptive sliding mode observer principle in the exemplary embodiment of the application is schematically shown;

[0075] Figure 3 The schematic diagram of the fault-tolerant control method principle in the exemplary embodiment of the application is schematically shown;

[0076] Figure 4 The schematic diagram of the LPV model result in the exemplary embodiment of the application is schematically shown;

[0077] Figure 5 The schematic diagram of the fault estimation result based on the adaptive sliding mode observer in the exemplary embodiment of the application is schematically shown;

[0078] Figure 6 The schematic diagram of the active fault-tolerant control result in the exemplary embodiment of the application is schematically shown;

[0079] Figure 7 The schematic diagram of the composition of an electronic device in the exemplary embodiment of the application is schematically shown. DETAILED DESCRIPTION

[0080] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any

[0081] Moreover, the drawings represent a simplified diagram of the invention and are not necessarily to scale. Like reference numerals in different drawings denote like or similar parts, and so repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities that do not necessarily have to correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0082] In view of the disadvantages and deficiencies of the prior art, a robust fault-tolerant control method based on parameter scheduling sliding mode observer for aviation fuel control pump test equipment is provided in the example implementation. Referring to Figure 1 The method can specifically include the following steps:

[0083] Step S11, a linear variable parameter model of the aviation fuel control pump test equipment is constructed using operation data of the aviation fuel control pump test equipment, for describing dynamic characteristics of the aviation fuel control pump test equipment under different working conditions; wherein, the operation data includes corresponding system rotation speed, outlet pressure and flow under different fuel metering valve opening degrees;

[0084] Step S12, an affine linear variable parameter fault model is constructed based on the linear variable parameter mathematical model, combined with the execution mechanism fault, the sensor fault and the external disturbance characteristics; wherein, the affine linear variable parameter fault model includes: an execution mechanism fault subsystem for expressing the execution mechanism fault, and a sensor fault subsystem for expressing the sensor fault, for separating the execution mechanism fault and the sensor fault;

[0085] Step S13, an adaptive upper limit sliding mode observer is configured based on an adaptive observer gain term, a first sliding mode surface corresponding to the execution mechanism fault observer, and a second sliding mode surface corresponding to the sensor fault observer; wherein, the adaptive upper limit sliding mode observer includes: the execution mechanism fault observer and the sensor fault observer, respectively configured with the adaptive observer gain term;

[0086] Step S14, a robust controller is configured for the affine linear variable parameter fault model based on the adaptive upper limit sliding mode observer, for active fault-tolerant control of the aviation fuel control pump test equipment.

[0087] This method targets an aviation fuel control pump test equipment. It designs an adaptive sliding mode observer for more accurate fault estimation, utilizes a robust control algorithm to suppress disturbances and observer errors, and combines system error feedback control to achieve active fault-tolerant control. First, a linear parameter-varying (LPV) model of the aviation fuel control pump test equipment considering sensor and actuator failures is established. To address the need for an upper bound in traditional sliding mode observers, an adaptive upper bound algorithm is introduced to improve the adaptability of the fault estimation observer. Fault decoupling analysis is performed using coordinate transformation, dividing the system into two subsystems affected only by a specific type of fault. An adaptive sliding mode observer is constructed for each subsystem to achieve fault estimation. Based on error feedback, robust fault-tolerant control is designed. Combining the controller's H∞ performance index and related inequality theory, active fault-tolerant gain scheduling control of the aviation fuel control pump test equipment is achieved, and the stability and robustness of the control system are proven.

[0088] The following will describe in more detail each step of the robust fault-tolerant control method for an aviation fuel control pump test equipment based on a parameter-scheduled sliding mode observer in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0089] 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.

[0090] For example, step S11 described above may specifically include:

[0091] 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.

[0092] Step S22: 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;

[0093] 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};

[0094] Step S24, solve the coefficient matrix for the N groups of linear state space models and the corresponding scheduling parameters, and construct a linear variable parameter model of the aviation fuel regulating pump test equipment based on the coefficient matrix to describe the dynamic characteristics of the system under different working conditions.

[0095] Specifically, from the fully closed state (0% opening) of the fuel metering valve to the fully open state (100% opening), N fuel metering valve opening values FMV are selected at equal intervals (such as 2.5%) j , j = 1, 2, …, N. Different fuel metering valve opening values correspond to different fuel flow rates, and data is collected at a certain sampling frequency to obtain operating data of the aviation fuel regulating pump test equipment under different fuel flow rates, a total of N groups of data. Each group of operating data includes fuel metering valve opening FMV j , system speed n j , outlet pressure P out,j , and flow rate W j .

[0096] For the N groups of aviation fuel regulating pump test equipment operating data, the fuel metering valve opening FMV is selected as the input variable u(t), the system speed n, the outlet pressure P out , and the flow rate W are selected as the output variable y(t), and the system speed n is selected as the state variable x(t). Since the working conditions (such as basic working condition, acceleration working condition, deceleration working condition, and idle working condition) of the aviation fuel regulating pump test equipment are closely related to the system speed n, the system speed n is configured as the scheduling parameter p. For each group of data, the least squares method is used to identify the data and fit the model parameters to obtain a group of linear state space models S j = {A j , B j , C}, which is further represented as:

[0097]

[0098] Where x(t) is the state variable, u(t) is the input variable, y(t) is the output variable; A j , B j , C are the state transition matrix, control input matrix, and observation matrix of the system, respectively, which are constant matrices of a certain dimension. Finally, N groups of linear state space models are obtained, which is a linear time-invariant system and can be used to describe the local dynamic characteristics of the aviation fuel regulating pump test equipment.

[0099] Based on the N groups of known linear state space models and the corresponding known scheduling variables, an affine linear variable parameter model of the aviation fuel regulating pump test equipment is constructed by solving the coefficient matrix through linear equations, as shown in the following formula, to better describe the dynamic characteristics of the system under different working conditions. Reference Figure 4The model is represented as shown:

[0100]

[0101] wherein p(t) is a scheduling parameter, which is measurable at any time and within a bounded set of allowable parameter trajectories. The matrices A(p(t)), B(p(t)) are related to the scheduling variable p(t) and have an affine polynomial form, i.e. A(p(t)) = A0+ p(t)A1+ p(t)A2, B(p(t)) = B0+ p(t)B1+ p(t)B2. The coefficient matrices A0, A1, A2, B0, B1, B2 of the affine polynomial can be determined from N known linear state space models S = {S1, S2, …, SN} and corresponding known scheduling variables p(t) = {n1, n2, …, nN} by solving the following linear equations: N N

[0102]

[0103] For example, the solution is A0= -1.0035, A1= 0.654, A2= -0.2863, B0= 21.5111, B1= 0.2258, B2= -3.0789;

[0104] and,

[0105] In step S12, an affine linear parameter-varying fault model is constructed based on the linear parameter-varying mathematical model, combined with the characteristics of the actuator fault, sensor fault and external disturbance; wherein the affine linear parameter-varying fault model includes: an actuator fault subsystem for expressing the actuator fault, and a sensor fault subsystem for expressing the sensor fault, for separating the actuator fault and the sensor fault.

[0106] For example, the step S12 can specifically include:

[0107] Step S31, defining the bounded functions corresponding to the actuator fault, the sensor fault and the system uncertainty, and converting the linear parameter-varying model based on the pre-configured conversion matrix to obtain the affine linear parameter-varying fault model;

[0108] Step S32, considering the coupling effect between different types of faults, the affine linear parameter-varying fault model is split to obtain the actuator fault subsystem and the sensor fault subsystem, so as to separate the actuator fault and the sensor fault.

[0109] ​​Specifically, based on the affine linear variable parameter model of the aero fuel pump test equipment established in the above steps, an affine linear variable parameter fault model with actuator failure, sensor failure and external disturbance is established, which is expressed as:

[0110]

[0111] wherein f a and f s represent the actuator failure and the sensor failure respectively, M(ρ(t))=B(ρ(t)), N=I, D=[0I] and ε=0.01sin(t) are the constant disturbance distribution matrix and the disturbance variable respectively. A(ρ(t)), B(ρ(t)) represent the state transition matrix function and the control input matrix function respectively; C represents the observation matrix.

[0112] The actuator failure, the sensor failure and the system uncertainty are all bounded functions, i.e. ||f a ||≤λ a ,||f s ||≤λ s ,||ε(t)||≤λ ε , wherein λ a , λ s , λ ε are unknown constants greater than zero.

[0113] Suppose that the system matrix (A(ρ(t)), B(ρ(t))) is controllable, when the actuator failure and the sensor failure exist simultaneously, due to the coupling effect between different types of faults in the system, it is difficult to realize accurate fault estimation. Therefore, the affine linear variable parameter fault model is split into two subsystems: the actuator failure subsystem and the sensor failure subsystem, separating the actuator failure and the sensor failure.

[0114] The conversion matrix T i =[T i,1 ,T i,2 ] T , S i =[S i,1 ,S i,2 ] T and L are designed to perform coordinate transformation on the affine linear variable parameter fault model, obtaining new state variables and output variables , which are specifically expressed as:

[0115]

[0116] Based on the above, the new system matrix is obtained, and the matrix has the following form:

[0117]

[0118]

[0119] Based on the above, the affine linear parameter-varying fault model is converted to:

[0120]

[0121] wherein, are the converted system matrices, is the converted state variable, is the converted output variable; the conversion matrix T i = [T i,1 , T i,2 ] T , the conversion matrix S i = [S i,1 , S i,2 ] T ; ε represents the disturbance vector.

[0122] The affine linear parameter-varying fault model is split into a subsystem 1 of actuator fault influence, which is only affected by actuator fault. The actuator fault subsystem includes:

[0123]

[0124] The affine linear parameter-varying fault model is split into a subsystem 2 of sensor fault, which is only affected by sensor fault. The sensor fault subsystem includes:

[0125]

[0126] wherein, L is the conversion matrix; f a , f s respectively represent the actuator fault and the sensor fault.

[0127] The new state variable is defined as follows:

[0128]

[0129] wherein,

[0130] The system can be further represented as:

[0131]

[0132] 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, and each is configured with an adaptive observer gain term.

[0133] For example, the method further includes configuring a target Lyapunov function for the adaptive upper limit sliding mode observer to eliminate the influence of unknown fault upper bound on system state convergence.

[0134] Specifically, when designing the adaptive sliding mode observer, it is considered that the conventional sliding mode observer design method has certain limitations in application because it needs to know the upper bound information of the fault, and the adaptive upper limit sliding mode observer relaxes the upper bound condition constraint through an adaptive law, and constructs a suitable Lyapunov function to eliminate the influence of unknown fault upper bound on system state convergence.

[0135] Specifically, referring to FIG. 1, Figure 2

[0136] 1) Design an actuator fault observer, specifically including:

[0137]

[0138] Define a sensor fault observer, specifically including:

[0139]

[0140] Wherein the superscript ^ represents an estimated value, is a stable matrix to be designed, K i is an observer gain to be designed, v i,1 , v i,2 is a sliding mode nonlinear switching term to be designed.

[0141] 2) Define the sliding mode nonlinear switching term v i,1 and v i,2 :

[0142]

[0143] Wherein, α i,1 , α i,2 is a constant greater than 0, P1, P3 are Lyapunov matrices to be solved, and are symmetric positive definite matrices, and are the designed sliding mode gains.

[0144] 3) Design and ​The adaptive law is designed to relax the upper bound condition constraint:

[0145]

[0146] where, is the estimation error. ε1, ε2 are positive constants.

[0147] 4) The sliding mode surface is designed as:

[0148]

[0149] where, s1 is the sliding mode surface designed by the actuator fault observer, s2 is the sliding mode surface designed by the sensor fault observer.

[0150] 5) Solve the sliding mode observer to be designed matrix and K i . If there is a positive definite symmetric matrix P and a full rank matrix H with the following form:

[0151]

[0152] And, satisfy the following linear matrix inequality (LMI) constraint conditions, including:

[0153]

[0154] where, Qi,,3=KiTP3+P3Ki, then the system state is asymptotically stable, and has robust performance to disturbance.

[0155] 6) The equivalent output error injection term is used to realize the fault characteristic estimation, and the estimated values of the actuator fault f a and the sensor fault f s and can be calculated as:

[0156]

[0157] where, where, δ1 and δ2 are small positive scalars.

[0158] In addition, the observer can output the estimated information

[0159] According to z1=x2+Lx3,z2=x3, the estimated value of the state variable x can be obtained

[0160] ​In step S14, a robust controller is configured 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.

[0161] For example, the fault estimation information obtained by the adaptive sliding mode observer can be used... and State variable x estimation information The design incorporates this technology into the controller to achieve proactive fault-tolerant control in the event of a failure in the aviation fuel regulating pump test equipment.

[0162] Specifically, the robust controller u can be represented as:

[0163]

[0164] Where, x a For the desired state, For B i The pseudo-inverse matrix.

[0165] For example, the method further includes:

[0166] Step S41: In response to the fault signal, collect the current operating data of the aviation fuel control pump test equipment;

[0167] 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;

[0168] Step S43: Input the actuator fault estimation information, sensor fault estimation information, and the estimation information corresponding to the state variables into the robust controller, and output the system control parameters for active fault-tolerant control.

[0169] For details, please refer to Figure 3 As shown, 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, a robust controller is used to process the estimated data to obtain system control parameters, thus achieving active fault-tolerant control.

[0170] For example, refer to Figure 5 , Figure 6As shown, the system has a pressure difference valve sticking fault with an amplitude of 0.5 at the 10th second, and the control parameter K is-0.8261 when the rotating speed is 0.8, and the control parameter K is-1.7282 when the rotating speed is 0.9. The system output response overshoot is less than 5%, and the regulation time is less than 3s, and the control effect is good; when a constant bias hard fault occurs at the 10th second, the estimation error instantaneously becomes large and reaches an extreme value within 1 second after the fault occurs, but then converges rapidly. Within 3.5s after the constant bias fault of the actuator occurs and within 0.35s after the constant bias fault of the sensor occurs, online adjustment of fault-tolerant control is completed.

[0171] The method provided by the embodiment of the application is used for realizing active fault-tolerant control of the aviation fuel regulating pump test equipment under sensor and actuator faults, and a robust fault-tolerant control method for the aviation fuel regulating pump test equipment based on a parameter scheduling sliding mode observer is provided. For the aviation fuel regulating pump test equipment, an adaptive sliding mode observer is designed to perform more accurate fault estimation, a robust control algorithm is used to suppress interference and observer error, and feedback control is designed in combination with system error to realize active fault-tolerant control. First, a linear parameter-varying (LPV) model of the aviation fuel regulating pump test equipment considering sensor and actuator faults is established. In view of the situation that a traditional sliding mode observer needs to provide an upper limit, an adaptive upper limit algorithm is introduced to improve the adaptability condition of the fault estimation observer to a certain extent. Fault decoupling analysis is performed by using coordinate transformation, the system is divided into two sub-systems that are only affected by a type of fault, and an adaptive sliding mode observer is constructed for each sub-system to realize fault estimation. Robust fault-tolerant control is designed based on error feedback, the H performance index of the controller and related inequality theory are combined to realize gain scheduling active fault-tolerant control of the aviation fuel regulating pump test equipment, and the stability and robustness of the control system are proved.

[0172] The effective effects realized by the application are as follows:

[0173] (1) Compared with the existing fault-tolerant control method based on a fixed sliding mode surface, since the parameter scheduling sliding mode observer is used in the application, a dynamic sliding mode surface is constructed in combination with a linear parameter-varying system model, so that the switching gain of the fault observer can be adjusted in real time according to the rotating speed working condition parameter of the fuel regulating pump.

[0174] (2) In view of the problem that the prior art ignores sensor faults, the application decouples sensor faults and actuator faults through sub-system decomposition.

[0175] (3) Since an error feedback compensation strategy is used, the application can suppress sliding mode chattering while ensuring the dynamics of the system after a fault.

[0176] It should be noted that the above-described diagrams are only schematic representations of the processes involved in the method according to exemplary embodiments of the application, and are not intended to limit the scope of the processes in any way. It is readily appreciated by a person skilled in the art that the processes depicted in the above diagrams are not necessarily carried out in the recited order. Further, it is readily appreciated that the processes can be carried out synchronously or asynchronously, e.g. in several modules.

[0177] It should be noted that, although several modules or units for a device for action execution are mentioned in the foregoing detailed description, such a division into modules or units is not mandatory. Indeed, according to an embodiment of the application, the features and functionalities of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functionalities of one module or unit described above can be further divided into several modules or units.

[0178] Figure 7 A schematic diagram of an electronic device suitable for implementing embodiments of the application is shown.

[0179] It should be noted that, Figure 7 The electronic device 1000 shown is only one example of an electronic device and should not be taken as limiting the scope of the functionality or use of embodiments of the application. The electronic device can be an electronic device for data processing assembled on an aviation fuel regulating pump test device. Alternatively, it can also be a host computer device connected with the aviation fuel regulating pump test device, for controlling and processing operation data.

[0180] As Figure 7 shown, the electronic device 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or loaded from a storage section 1008 into a random access memory (RAM) 1003. Various programs and data required for system operation are also stored in the RAM 1003. The CPU 1001, the ROM 1002, and the RAM 1003 are connected to each other through a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0181] The following components are connected to the I / O interface 1005: an input part 1006 including a keyboard, a mouse, etc.; an output part 1007 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage part 1008 including a hard disk, etc.; and a communication part 1009 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication part 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as necessary. A removable medium 1011 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 1010 as necessary, so that a computer program read out therefrom is installed in the storage part 1008 as necessary.

[0182] In particular, according to embodiments of the present application, the processes described below with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, various functions defined in the system of the present application are executed.

[0183] It should be noted that the storage medium shown in the embodiments of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the above two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the 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, device or component. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any storage medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or component. The program code contained in the storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination of the above.

[0184] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment, or a part of code containing one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two blocks that are shown in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0185] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can be located in one place, or distributed over several places. The names of the units in some cases do not limit the units themselves.

[0186] It should be noted that, as another aspect, the present application also provides a storage medium, which can be included in an electronic device, or exist separately without being assembled into the electronic device. The storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to implement the method described in the embodiments below. For example, the electronic device can implement each step of the method as shown in Figure 1

[0187] In one embodiment, the present application provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps in the method embodiments described above.

[0188] In addition, the above-described figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is readily understood that the processes shown in the above-described figures do not indicate or limit the time sequence of the processes. In addition, it is readily understood that the processes can be executed synchronously or asynchronously, for example, in multiple modules.

[0189] Other embodiments of the present application will be readily apparent to those skilled in the art upon considering the description of the application in conjunction with the accompanying drawings. The present application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such further uses of the application as come within the scope of the present application. The description and examples are to be regarded as illustrative only and the true scope and spirit of the application is indicated by the appended claims.

[0190] It should be understood that the present application is not limited to the precise construction and compositions described above and shown in the attached drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present application. The scope of the present application is limited only by the claims that follow.​

Claims

1. A robust fault-tolerant control method for an aero-fuel control pump test equipment based on a parameter-scheduled sliding mode observer, characterized in that, The method includes: 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; Based on a linear variable parameter mathematical model, an affine linear variable parameter fault model is constructed by combining the characteristics of actuator faults, sensor faults, and external interference, including: 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: in, For the transformed state variables, The output variables after transformation; transformation matrix Transformation matrix ; Let be the state transition matrix of the system. The system's control input matrix, The system's observation matrix, , These are the system matrices; For scheduling variables; and These respectively indicate actuator failure and sensor failure; As a disturbance variable; Considering the coupling effect between different types of faults, the affine linear variable parameter fault model is decomposed into an actuator fault subsystem and a sensor fault subsystem to separate actuator faults and sensor faults. 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. The actuator failure subsystem includes: The sensor fault subsystem includes: in, This is the transformation matrix; , These respectively indicate actuator failure and sensor failure; For input variables; These are the transformed system matrices; , The converted systems are respectively Elements in; The transformed system matrix Elements in; , , , The converted systems are respectively Elements in; , The converted systems are respectively Elements in; , The converted systems are respectively Elements in; The transformed system matrix Elements in; 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; An adaptive upper limit sliding mode observer is used to configure a robust controller for the affine linear variable parameter fault model for active fault-tolerant control of the aero-fuel-controlled pump test equipment.

2. The method according to claim 1, characterized in that, The process of constructing a linear variable parameter model for the aviation fuel control pump test equipment using operational data includes: For the fuel metering valve, select equal intervals between the fully closed and fully open states. Each fuel metering valve opening value is used to collect corresponding system speed, outlet pressure, and flow data at a preset sampling frequency to obtain multiple sets of operating data; Configure the fuel metering valve opening (FMV) as an input variable. Configure system speed Export pressure and traffic For output variables And define the system speed. State variables Configured as a scheduling parameter; Each set of operational data was identified, and model parameters were fitted to obtain N sets of linear state-space models. , represented as: in, For state variables, For input variables, For output variables; , , These are the system's state transition matrix, control input matrix, and observation matrix, respectively, and are constant matrices. 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: in, For scheduling parameters; , These are the coefficient matrices, which have affine polynomial form.

3. The method according to claim 1, characterized in that, The method further includes: Sliding mode nonlinear switching terms are configured for both the actuator fault observer and the sensor fault observer. and ,include: in, , A constant greater than 0 , Let be the Lyapunov matrix to be solved, and be a symmetric positive definite matrix. and These are the sliding mode gains; Configure an adaptive law for the sliding mode gain to relax the upper bound constraints, including: in, To estimate the error; , A constant greater than 0; Configure the sliding surface corresponding to the actuator fault observer, including: Configure the sliding surface corresponding to the sensor fault observer, including: 。 4. The method according to claim 3, characterized in that, The method further includes: Fault characteristic estimation is achieved using an equivalent output error injection term, where, Actuator failure The estimated value The calculations include: Sensor failure The estimated value The calculations include: in, and It is a tiny positive scalar.

5. The method according to claim 4, characterized in that, The method further includes: Observer output estimation information ; according to Determine the state variables The estimated value .

6. The method according to claim 1, characterized in that, The adaptive upper bound-based sliding mode observer configures a robust controller for the affine linear variable parameter fault model, including: The actuator fault estimation information obtained by the adaptive sliding mode observer and sensor fault estimation information State variables Estimated information Substitute into a robust controller ,include: Among them, state variables Including system speed ; For scheduling parameters; For the system matrix The pseudo-inverse matrix; This is the desired state; , These are the system matrices.

7. The method according to claim 1, characterized in that, The method further includes: 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.

8. The method according to claim 1, characterized in that, The method further includes: In response to fault signals, the current operating data of the aviation fuel control pump test equipment is collected; 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; The actuator fault estimation information, sensor fault estimation information, and the estimation information corresponding to the state variables are input into the robust controller, and the system control parameters are output for active fault-tolerant control.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the robust fault-tolerant control method for an aviation fuel control pump test equipment based on a parameter-scheduled sliding mode observer, as described in any one of claims 1 to 8.

10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to implement the robust fault-tolerant control method for an aviation fuel control pump test equipment based on a parameter-scheduled sliding mode observer, as described in any one of claims 1 to 8, when executing the executable instructions.

Images