Aero-engine fuel metering system high-order model reduced-order analysis method and application

Abstract

The invention discloses an aero-engine fuel metering system high-order model order reduction analysis method and application, and is used for reducing complexity of system modeling and control design on the basis of ensuring dynamic precision. According to the method, a nonlinear high-order dynamic model covering an electro-hydraulic servo valve, a fuel metering valve, a connecting cavity and other key components is constructed based on the working principle and structural characteristics of a fuel metering system, and linear processing is performed at a typical working condition steady-state point to obtain a high-order linear model in a state space form. Through frequency domain analysis, a system dominant dynamic frequency band is extracted, model order reduction is implemented accordingly, a low-order simplified model is constructed, and high fidelity of response characteristics in a target frequency band is ensured. According to the method, the complexity of fuel metering system modeling and control system design can be remarkably reduced, the control system design efficiency is improved, and the method is suitable for control system design, parameter setting, model prediction control and the like of aero-engines and has important engineering application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft engine control, relates to dynamic modeling and control of a fuel metering system, and specifically relates to a high-order model reduction analysis method and application of an aircraft engine fuel metering system. Background Art

[0002] In control engineering, almost all control systems are high-order systems, that is, systems described by high-order differential equations. Since high-order systems are difficult to solve and their performance analysis is complex, it is usually necessary to simplify the high-order systems into low-order systems (first- and second-order systems) through order reduction methods to reduce system complexity while retaining their main dynamic characteristics. The simplified first- and second-order systems are then used to approximate the high-order systems to analyze the performance of the high-order systems. Through order reduction processing, the model's comprehensibility and computational efficiency can be significantly improved, which is conducive to the rapid development of controller design, system simulation and frequency domain analysis.

[0003] Aircraft engine fuel metering systems have numerous and interconnected components. For example, an aircraft engine's main fuel system typically consists of an electro-hydraulic servo valve, a main fuel metering valve, a differential pressure valve, a constant pressure valve, a locking valve, a return valve, a boost valve, a gear fuel pump, and a LVDT. This fuel system is a typical high-order, complex system. To evaluate its performance, a linearized fuel system must be obtained through linearization. High-order fuel systems present difficulties in analyzing and solving system performance. To facilitate analysis and understanding of the fuel system's time-domain and frequency-domain characteristics, a method for analyzing fuel system performance by reducing the high-order model of the aircraft engine fuel metering system is commonly used. However, existing order reduction methods (such as those in Chinese patents CN108919638B and CN117236104B) often lack specialized treatment for the characteristics of aircraft engine fuel metering systems, making it difficult to effectively reduce the system order while ensuring accuracy. Furthermore, there is a lack of systematic methods for evaluating the consistency of the reduced-order model with the original high-order model within a specific frequency range.

[0004] In summary, as a high-order complex system, aircraft engine fuel metering system, how to effectively reduce its order and ensure that the reduced-order model can accurately reflect the dynamic characteristics of the original system within the control system bandwidth has become a technical problem that needs to be solved urgently in the field of aircraft engine fuel control. Summary of the Invention

[0005] (1) Purpose of the invention

[0006] In response to the above-mentioned defects and shortcomings of the prior art, the present invention aims to provide a high-order model reduction analysis method and application for an aircraft engine fuel metering system. By constructing a nonlinear high-order model of the fuel system and combining linearization technology with the order reduction analysis method, the complex high-order fuel system is simplified into a low-order linear model. Frequency domain analysis and comparison are then performed to ensure that the simplified low-order model can accurately reflect the dynamic characteristics of the original high-order system within the bandwidth frequency range of the aircraft engine control system, thereby significantly reducing the complexity of system analysis and control design and improving the design efficiency of the aircraft engine fuel metering system.

[0007] (2) Technical solution

[0008] In order to achieve the purpose of the invention and solve the technical problems, the present invention adopts the following technical solutions:

[0009] The first object of the present invention is to provide a high-order model reduction analysis method for an aircraft engine fuel metering system, which is used to model, linearize, and reduce the high-order dynamic model of the aircraft engine fuel metering system to reduce the complexity of the control design and preserve the dynamic characteristics of the key frequency band. When implemented, the method includes at least the following steps:

[0010] SS1. Construct a nonlinear model of the fuel metering system:

[0011] Based on the operating principle and design requirements of the fuel metering system, and in combination with the system's internal actuation response, flow regulation, and pressure coupling characteristics, a high-order nonlinear dynamic model was constructed to describe the nonlinear coupling relationship between the system's control input, state variables, and output.

[0012] SS2. Linearization of the fuel metering system model:

[0013] Centered on the preset steady-state operating point, the constructed nonlinear high-order model is linearized using the Taylor series expansion method to generate a high-order linear model of the fuel metering system. The state matrix, input matrix, output matrix, and transfer matrix are determined by calculating the partial derivatives of the model with respect to the state variables and the control inputs, and are used to characterize the approximate linear response characteristics of the system under small disturbance conditions.

[0014] SS3. Frequency Domain Characteristics Analysis and Response Verification:

[0015] Perform frequency domain characteristic analysis on the generated high-order linear model of the system to obtain the system's Bode diagram frequency response curve. Combined with the control system's dominant dynamic frequency band (e.g., 1-30 Hz), analyze the amplitude-frequency and phase-frequency characteristics of the high-order linear model in different frequency bands to verify its dynamic accuracy within the target frequency band, providing a basis for the rationality evaluation of model reduction and control system design.

[0016] SS4. Model reduction and structural simplification:

[0017] Under the premise of ensuring that the dominant dynamic characteristics of the system remain unchanged, the linear high-order model is reduced in order based on the frequency domain analysis results to establish a simplified low-order model that can accurately reflect the dynamic behavior of the target frequency band. The simplified low-order model approximates the frequency response characteristics of the original high-order system with the minimum order, thereby reducing system complexity and improving the practicality and controllability of the model.

[0018] SS5. Validation of the simplified model:

[0019] The frequency response and dynamic behavior of the simplified model after order reduction are compared under multiple working conditions to confirm that its amplitude-frequency and phase-frequency characteristics within the dominant dynamic frequency band are highly consistent with the original high-order model, ensuring that the order reduction processing does not introduce system distortion or performance degradation, and has engineering feasibility and promotion value.

[0020] A second object of the present invention is to provide an aircraft engine fuel metering system, the design of which is based on the above-mentioned aircraft engine fuel metering system high-order model reduction analysis method of the present invention.

[0021] (3) Technical effects

[0022] Compared with the prior art, the high-order model reduction analysis method and application of the aircraft engine fuel metering system of the present invention has the following beneficial and significant technical effects:

[0023] (1) The present invention introduces a modeling mechanism based on the combination of nonlinear modeling and steady-state linearization to establish a high-order nonlinear system model covering the dynamic characteristics of key components such as electro-hydraulic servo valves and metering valves. On this basis, the first-order Taylor expansion method is used to obtain a high-order linear state space expression of the system, thereby being able to comprehensively and accurately characterize the dynamic response characteristics of the fuel metering system under small disturbance conditions, significantly improving the modeling accuracy and system observability in system frequency domain analysis and control system design.

[0024] (2) Based on the results of frequency domain characteristic analysis, the present invention further employs an order reduction method that combines dominant mode preservation with response error control to construct a low-order model with a simple structure and highly fidelity dynamic characteristics. This model is verified by various verification methods, such as frequency response curves and step response curves, to ensure its fidelity within the dominant dynamic frequency band. This method can flexibly adapt to the dynamic characteristic requirements of different types of aircraft engine fuel systems, significantly reducing the complexity and computational burden of control law design, and improving the adaptability and practicality of the model in engineering controller parameter matching, simulation prediction, and control algorithm deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute undue limitations thereon. The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0026] Figure 1 This is an overall flow chart of a high-order model reduction analysis method for an aircraft engine fuel metering system provided by the present invention;

[0027] Figure 2 Schematic diagram of the Amesim software modeling used in the embodiment of the present invention;

[0028] Figure 3 This is a comparison chart of the frequency response characteristics of different order models in the present invention at typical operating points, showing the amplitude-frequency and phase-frequency response curves of the Amesim simulation model (19th order), Amesim simplified model (6th order), mathematical derivation model (6th order) and first-order reduced-order model in the frequency domain of 0.1Hz to 500Hz. DETAILED DESCRIPTION

[0029] The present invention aims to provide a high-order model reduction analysis method for an aircraft engine fuel metering system and its application, which is used to model, linearize and reduce the high-order dynamic model of the aircraft engine fuel metering system to reduce the complexity of the control design and retain the dynamic characteristics of the key frequency band. This embodiment combines the two technical routes of Amesim simulation and mathematical derivation, and verifies the effectiveness of the reduction method by comparing the frequency domain characteristics of the 19th-order Amesim model, the 6th-order simplified model and the 1st-order mathematical model. The technical solution is described in detail below with reference to the accompanying drawings. The described embodiments are part of the embodiments of the present invention, not all of the embodiments, and the described embodiments are exemplary and are intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0030] like Figure 1 As shown, the high-order model reduction analysis method for an aircraft engine fuel metering system provided by an embodiment of the present invention mainly includes the following steps when implemented:

[0031] SS1. Construct a nonlinear model of the fuel metering system:

[0032] Based on the physical structure, working principle and design requirements of the fuel metering system, combined with the actuation response, flow regulation and pressure coupling characteristics of the system, a high-order nonlinear dynamic model is established to describe the nonlinear coupling relationship between the system control input, state variables and output, taking into account the multi-physical interaction process of actuators, electro-hydraulic servos, fuel chamber flow coupling, etc. The sum-output equation is expressed in the general form of y = g(x,u), which is used to characterize the nonlinear dynamic relationship between the system control input u (primarily electro-hydraulic control signals, such as input current, voltage, or command signals) and the state variables x (including internal state variables such as servo valve displacement, metering valve displacement, and system cavity pressure) and the system output y (representing key physical quantities that can be measured or used for control feedback, such as main fuel flow Q, fuel pressure P, valve opening or displacement feedback S, etc.). Both f(·) and g(·) are nonlinear mapping functions. The function f(·) describes the nonlinear dynamic relationship of the system's internal state over time, covering factors such as the coupled dynamic equations between the system components, actuator characteristics, and cavity pressure response. The function g(·) is the system's nonlinear output mapping function, which is used to associate the internal state with the output signal. Its structure can include static mapping terms and dynamic coupling terms.

[0033] When constructing a high-order nonlinear dynamic model, the present invention combines two technical routes: Amesim simulation and mathematical derivation: one is multi-physics field simulation based on Amesim software (such as Figure 2 As shown in the figure), a complete system simulation model including sub-modules such as electro-hydraulic servo valve, metering valve and multiple cavities is constructed to obtain a high-order nonlinear model containing 19 state variables; secondly, based on the mathematical derivation modeling path, the relevant nonlinear state equations are derived through mathematical derivation methods.

[0034] Specifically, when using mathematical derivation methods to construct a high-order nonlinear dynamic model based on the key components of the fuel metering system, the core metering system composed of the electro-hydraulic servo valve and the metering valve is considered. The system can select the servo valve input current I as the control quantity and the metering valve spool displacement as the observation quantity. The system state includes the servo valve, the metering valve spool displacement and the pressure of each cavity. The determination of the coefficients in the state space equation is based on the differentiation of the basic equations followed by the fuel system. Specifically, when establishing corresponding nonlinear differential equations for different components to characterize their dynamic behaviors, the dynamic response of the electro-hydraulic servo valve is modeled using a second-order inertial oscillation link, and its displacement response relationship is expressed as follows: Where x1 is the valve core displacement, ω m is the natural angular frequency of the servo valve, ξ m is the damping ratio of the servo valve, I is the input current for controlling the displacement of the valve core, and I m Considering the input current, the model can effectively describe the dynamic response characteristics of the electro-hydraulic servo valve under input disturbance; the motion differential equation of the metering valve is expressed as the algorithm formula Where x2 is the valve core displacement of the fuel valve, m is the equivalent mass of the valve, and ∑F is the total force acting on the valve. The differential equation for the pressure in each chamber is expressed as Where p is the fuel pressure in the chamber, β is the bulk elastic modulus of the fuel, V is the effective volume of the chamber, and ∑Q is the total flow rate through the chamber.

[0035] SS2. Linearization of the fuel metering system model:

[0036] Centered on the preset steady-state operating point, the constructed nonlinear high-order model is linearized using the Taylor series expansion method to generate a high-order linear model of the fuel metering system. The state matrix, input matrix, output matrix and transfer matrix are determined by calculating the partial derivatives of the model with respect to the state variables and the control input, which are used to characterize the approximate linear response characteristics of the system under small disturbance conditions.

[0037] In the embodiment of the present invention, the preset steady-state operating point is a typical operating state point of the engine within a specific flight envelope (for example, with the 24th steady-state operating point as the center, corresponding to the medium-to-high power cruise operating condition under the conditions of steady pressure and steady flow). At this operating point, the nonlinear high-order model is subjected to a first-order Taylor expansion, ignoring the high-order nonlinear terms, to obtain the linearized state space expression of the system. and Where: δx = x-x0 represents a small perturbation of the state variable, x0 is the steady-state state vector; δu = u-u0 represents a control input perturbation, u0 is the steady-state input vector; δy = y-y0 represents an output variable perturbation, y0 is the steady-state output vector; A, B, C, and D represent the state, input, output, and transfer matrices, respectively.

[0038]

[0039] Where f is the nonlinear state equation, g is the nonlinear output equation, and the second-order and higher-order terms in the Taylor series expansion are ignored during the calculation. The parameters of this steady-state point are determined through dynamic sensitivity analysis to ensure the effectiveness of the linearized model under small disturbance conditions.

[0040] SS3. Frequency Domain Characteristics Analysis and Response Verification:

[0041] The generated high-order linear model of the system is subjected to frequency domain characteristic analysis to obtain the system's Bode diagram frequency response curve. Combined with the dominant dynamic frequency band of the control system (such as 1-30 Hz), the amplitude-frequency and phase-frequency characteristics of the high-order linear model in different frequency bands are analyzed to verify its dynamic accuracy within the target frequency band, providing a basis for the rationality evaluation of model reduction and control system design.

[0042] In an embodiment of the present invention, frequency domain analysis uses the Bode plot method to analyze high-order linear models, depicting the system frequency response curve in logarithmic amplitude-frequency and phase-frequency coordinate systems to determine the dynamic characteristics of the system in the frequency range of 0.1-500Hz, with a focus on analyzing the frequency response characteristics of the system in the 1-30Hz frequency band, which covers the main operating bandwidth range and its extended range of traditional engine fuel control systems.

[0043] Specifically, this fuel case uses the parameters of the 24th steady-state operating point for linearization. Therefore, it is necessary to define the parameters in the 6th-order state matrix derived in the section in combination with the parameters of the 24th steady-state operating point. By performing frequency domain analysis on the 19th-order Amesim model, the 6th-order Amesim simplified model, the derived 6th-order mathematical model, and the 1st-order simplified mathematical model, and comparing the results, we obtain the following: Figure 3 The Bode diagram shown is from Figure 3 It can be seen that:

[0044] In the frequency range of 1 to 30 Hz, the frequency response curves of all models have a very high degree of overlap. This frequency band covers the bandwidth of the control system, indicating that the mathematical model derived during system design can accurately describe the dynamic behavior of the system, and the dynamic response of the fuel device can be simplified to a first-order inertia link.

[0045] It should also be noted that within the frequency range of 30 to 500 Hz, differences between the different models begin to emerge. Within this range, the first-order linearized system (mathematical model) still exhibits first-order system characteristics, while the other three models exhibit second-order system characteristics. This indicates that if the system characteristics within this frequency range need to be accurately reflected, simple first-order or third-order models may not accurately describe the system's dynamic behavior. Therefore, when designing control systems for high-frequency response, more complex models are required to more accurately reflect the actual system characteristics.

[0046] Frequency-domain analysis of the fuel system shows a high degree of overlap in the frequency response curves of all models within the 1-30 Hz frequency range. This frequency range covers the control system's bandwidth, indicating that the derived mathematical model accurately describes the system's dynamic behavior within this frequency range. However, within the 30-500 Hz frequency range, differences between the models begin to emerge, and simple, low-order models may not accurately describe the system's dynamic behavior. Therefore, when designing control systems for high-frequency response, more complex models are required to more accurately reflect the system's actual characteristics.

[0047] SS4. Model reduction and structural simplification:

[0048] Under the premise of ensuring that the dominant dynamic characteristics of the system remain unchanged, the linear high-order model is reduced in order based on the frequency domain analysis results to establish a simplified low-order model that can accurately reflect the dynamic behavior of the target frequency band. The simplified low-order model approximates the frequency response characteristics of the original high-order system with the minimum order, thereby reducing system complexity and improving the practicality and controllability of the model.

[0049] In this embodiment of the present invention, for the basic control bandwidth (0-2 Hz), a simplified low-order model is constructed as a first-order system. Its transfer function is expressed as G(s) = 1 / (Ts+1), where s is the Laplace operator and T is the system time constant. This function characterizes the inertial delay characteristics of the system's response speed to disturbances or control commands. The time constant, T, is determined by analyzing the amplitude-frequency and phase-frequency characteristics of the original high-order system within the dominant dynamic frequency band, ensuring that the dynamic response of the simplified first-order system within this frequency band is highly consistent with that of the original high-order system. The time constant, T, is determined using one of two methods: Method 1: Based on an analysis of the frequency response curve of the high-order system's Bode plot, the amplitude-frequency and phase-frequency characteristics within the dominant dynamic frequency band are extracted. The value of T is determined through curve fitting, ensuring that the frequency response characteristics of the first-order system match those of the original high-order system within the target frequency band to the greatest extent possible. Method 2: Using an experimental method, a known input signal is applied to an actual fuel metering system, and the output response is recorded. System identification techniques are then used to analyze the relationship between the input and output data to calculate the time constant, T, that most accurately characterizes the system's dynamic characteristics, and its reliability and accuracy are verified.

[0050] For high frequency active control, when the first order cannot meet the accuracy requirements, a second or third order reduced order system can be selected. The second order is The third order is K is the static gain parameter, which represents the steady-state output level under unit amplitude input. T1, T2, and T3 are all time constants used to characterize the high-frequency resonant dynamics of the servo valve.

[0051] SS5. Validation of the simplified model:

[0052] The frequency response and dynamic behavior of the simplified model after order reduction are compared under multiple working conditions to confirm that its amplitude-frequency and phase-frequency characteristics within the dominant dynamic frequency band are highly consistent with the original high-order model, ensuring that the order reduction processing does not introduce system distortion or performance degradation, and has engineering feasibility and promotion value.

[0053] SS6. Control System Design and Engineering Application:

[0054] Based on the simplified low-order model after validity verification, the control law modeling and parameter design of the fuel metering system are carried out, so that the obtained control law can meet the requirements of response speed, stability and robustness while covering the dominant dynamic frequency band, ensuring the dynamic performance and precise control capability of the fuel system under typical operating conditions.

[0055] As a preference, the designed control law is optimized for the main fuel control loop bandwidth of 0-2Hz (about 10rad / s), which includes the dominant dynamic characteristics of the engine. The control law design adopts a parameterized method based on a first-order simplified model, and verifies its actual control effect on the original high-order system through simulation to ensure that the control performance meets the steady-state accuracy and dynamic response requirements.

[0056] The above embodiments fully and effectively achieve the objectives of the present invention. Those skilled in the art will appreciate that the present invention includes, but is not limited to, the contents described in the accompanying drawings and the above specific embodiments. Although the present invention has been described with reference to the embodiments currently considered to be the most practical and preferred, it should be understood that the present invention is not limited to the disclosed embodiments, and any modifications that do not deviate from the functional and structural principles of the present invention are intended to be included within the scope of the claims.

Claims

1. A high-order model reduction analysis method for an aircraft engine fuel metering system, characterized in that: At least the following steps are included: SS1. Based on the operating principles and design requirements of the fuel metering system, and incorporating the system's internal actuation response, flow regulation, and pressure coupling characteristics, a high-order nonlinear dynamic model is constructed to describe the nonlinear coupling relationship between the system's control inputs, state variables, and outputs. SS2. Centered around the preset steady-state operating point, the constructed nonlinear high-order model is linearized using the Taylor series expansion method to generate a high-order linear model of the fuel metering system. Its state, input, output, and transfer matrices are determined by calculating the partial derivatives of the model with respect to the state variables and control inputs. SS3. Perform frequency domain analysis on the generated high-order linear model of the system to obtain the system's Bode plot frequency response curve. Combined with the control system's dominant dynamic frequency band, analyze the high-order linear model's amplitude-frequency and phase-frequency characteristics at different frequency bands to verify its dynamic accuracy within the target frequency band. SS4. While ensuring that the dominant dynamic characteristics of the system remain unchanged, reduce the linear high-order model based on the frequency domain analysis results to establish a simplified low-order model that accurately reflects the dynamic behavior of the target frequency band. This simplified low-order model approximates the original high-order system frequency response characteristics with the minimum order. SS5. Compare the frequency response and dynamic behavior of the simplified model after order reduction under multiple operating conditions to confirm that its amplitude-frequency and phase-frequency characteristics within the dominant dynamic frequency band are highly consistent with those of the original high-order model, ensuring that the order reduction process does not introduce system distortion or performance degradation.

2. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 1 is characterized in that: In step SS1, the high-order nonlinear dynamic model is constructed based on the key components of the fuel metering system, which include at least the electro-hydraulic servo valve, the main fuel metering valve and its connecting chamber. The dynamic response of the electro-hydraulic servo valve is modeled using a second-order inertial oscillation link, and its displacement response relationship is expressed as: Where x1 is the valve core displacement, ω m is the natural angular frequency of the servo valve, ξ m is the servo valve damping ratio, I is the input current, I m To consider the input current; the differential equation of motion of the metering valve is expressed as the algorithm formula Where x2 is the valve core displacement of the fuel valve, m is the equivalent mass of the valve, and ∑F is the total force acting on the valve. The differential equations for the pressure in each connected chamber are expressed as follows: Where p is the fuel pressure in the chamber, β is the bulk elastic modulus of the fuel, V is the effective volume of the chamber, and ∑Q is the total flow rate through the chamber.

3. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 1 or 2, characterized in that: In step SS1, the high-order nonlinear dynamic model is constructed in the form of state equation and output equation, where the state equation is The output equation is y = g(x,u), which is used to characterize the nonlinear dynamic relationship between the system control input u and the state variable x and the system output y. f(·) and g(·) are both nonlinear mapping functions.

4. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 3 is characterized in that: In step SS2, the preset steady-state operating point is a typical operating state point of the engine within a specific flight envelope. At this operating point, the nonlinear high-order model is subjected to a first-order Taylor expansion, ignoring the high-order nonlinear terms, to obtain the linearized state space expression of the system: and Where: δx = x-x0 represents a small perturbation of the state variable, x0 is the steady-state state vector; δu = u-u0 represents a control input perturbation, u0 is the steady-state input vector; δy = y-y0 represents an output variable perturbation, y0 is the steady-state output vector; A, B, C, and D represent the state, input, output, and transfer matrices, respectively. Where f is the nonlinear state equation, g is the nonlinear output equation, and the second-order and higher-order terms in the Taylor series expansion are ignored during the calculation to ensure the effectiveness of the linearized model under small disturbance conditions.

5. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 1 is characterized in that: In step SS3, the frequency domain characteristic analysis uses the Bode plot method to analyze the high-order linear model, depicts the system frequency response curve in the logarithmic amplitude-frequency and phase-frequency coordinate systems, determines the dynamic characteristics of the system in the frequency range of 0.1-500Hz, and focuses on analyzing the frequency response characteristics of the system in the frequency band of 1-30Hz.

6. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 1 is characterized in that: In step SS4, the simplified low-order model is constructed as a first-order system, and its transfer function is expressed as G(s)=1 / (Ts+1), where s is the Laplace operator and T is the system time constant, which is used to characterize the inertial delay characteristics of the system's response speed to disturbances or control instructions, and is determined by analyzing the amplitude-frequency and phase-frequency characteristics of the original high-order system in the dominant dynamic frequency band.

7. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 6 is characterized in that: In step SS4, the time constant T is determined by one of the following two methods: Method 1: Based on the analysis of the Bode frequency response curve of the high-order system, the amplitude-frequency and phase-frequency characteristics within the dominant dynamic frequency band are obtained. The T value is determined through curve fitting to maximize the frequency response characteristics of the first-order system and the original high-order system within the target frequency band. Method 2 uses an experimental method. By applying a known input signal to the actual fuel metering system and recording the output response, the relationship between the input and output data is analyzed using system identification technology. The time constant T value that can most accurately characterize the dynamic characteristics of the system is calculated and its reliability and accuracy are verified.

8. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 1 or 6, characterized in that: In step SS4, the order of the reduced model is flexibly selected for different types of aircraft engine fuel metering systems. When the first-order system cannot meet the accuracy requirements, the second-order or third-order system is selected. The second-order system is expressed as The third-order system is Where K is the static gain parameter of the system, which represents the steady-state output level under unit amplitude input, and T1, T2, and T3 are all time constants.

9. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 1, characterized in that: It also includes the implementation of control system design and engineering application step SS6: Based on the simplified low-order model after validity verification, the control law modeling and parameter design of the fuel metering system are carried out, so that the obtained control law can meet the requirements of response speed, stability and robustness while covering the dominant dynamic frequency band, ensuring the dynamic performance and precise control capability of the fuel system under typical operating conditions.

10. The high-order model reduction analysis method for an aircraft engine fuel metering system according to claim 9, characterized in that: In step SS6, the designed control law is optimized for the 0-2 Hz bandwidth of the main fuel control loop, which encompasses the engine's dominant dynamic characteristics. The control law is designed using a parameterized approach based on a first-order simplified model. Simulation verifies its actual control effect on the original high-order system, ensuring that the control performance meets steady-state accuracy and dynamic response requirements.

11. An aviation engine fuel metering system, characterized in that: The design is based on the high-order model reduction analysis method of the aviation engine fuel metering system as described in any one of claims 1 to 10 above.

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