Modeling method for hybrid improved airborne self-adaptive model of self-adaptive cycle engine

Abstract

The invention provides a modeling method for a hybrid improved airborne adaptive model of an adaptive cycle engine. The modeling method comprises a component-level model, an adaptive linear variable parameter model and a hybrid adaptive extended Kalman filter. According to the method, the advantage of high precision of a component-level model and the advantage of high real-time performance of a self-adaptive LPV model are combined to establish a mixed structure model as an airborne model, and on the basis of a traditional extended Kalman filter, a covariance matrix is multiplied by a forgetting factor to compensate the adverse effect of an incomplete dynamic equation. In this way, the weight of old data in the filtering process is scaled, and a novel MAEKF is designed. Based on the filter, the health parameters of the hybrid structure model are corrected by using the deviation between the measurement parameters and the output of the hybrid structure model, and an airborne adaptive model is established. The airborne adaptive model has high precision and high real-time performance at the same time, and a very good model foundation is laid for adaptive cycle engine control based on the model.

Description

Technical Field

[0001] The invention relates to the technical field of modeling an adaptive cycle engine airborne adaptive model, and in particular to a hybrid improved airborne adaptive model modeling method for an adaptive cycle engine. Background Art

[0002] As an innovative aviation gas turbine technology, the Adaptive Cycle Engine (ACE) is considered a potential power solution for future military fighter jets. It also shows its potential for application in other aircraft types (such as supersonic commercial aircraft). Therefore, ACE has a very broad application prospect.

[0003] During the operation of an adaptive cycle engine, factors such as natural wear, corrosion, fouling, and thermal creep can cause the performance of gas path components to degrade. Currently, widely used airborne models include component level models (CLM), linear parameter-varying (LPV) models, and data-driven models. In fact, compared with CLM, LPV and data-driven models are more practical because they require lower computational loads. However, training data-driven models to cover the flight envelope and engine operating conditions requires a large amount of data, so the accuracy of models that do not cover the flight envelope cannot be ensured. The LPV model uses a linear model established with limited operating points and is obtained by interpolation or fitting methods. The performance degradation of adaptive cycle engine components, as well as maintenance and installation, can lead to slight differences in individual performance between engines of the same type. Therefore, the offline LPV model cannot cover all conditions, modeling errors are inevitable, and the accuracy of the LPV model is usually lower than that of the CLM. However, the estimation accuracy is highly dependent on the update frequency of the system matrix used in the filter recursive process; the higher the frequency, the higher the estimation accuracy. Since the CLM itself has a large amount of computation, the model needs to be linearized multiple times, which will greatly increase the total amount of computation and make it difficult to meet real-time requirements.

[0004] In addition, the filters that are widely used currently have their own problems. For example, the linearized Kalman filter has large estimation errors for nonlinear systems, the extended Kalman filter requires a random system with a complete process and measurement noise covariance for optimal estimation, and the unscented Kalman filter does not use the linearization prior information of different steady-state points of the nonlinear model, resulting in excessive computational complexity. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention proposes a new hybrid structure improved airborne adaptive model based on Mixed Adaptive Extended Kalman Filter (MAEKF). The model uses a component-level model to improve the model accuracy, and adopts an adaptive linear parameter-varying (ALPV) model to improve real-time performance. At the same time, MAEKF introduces a covariance scaling mechanism based on the extended Kalman filter, and uses the full covariance estimation to update the system noise covariance matrix. This can improve the estimation accuracy and real-time performance of the airborne model.

[0006] The technical solution of the present invention is:

[0007] The method for modeling a hybrid improved airborne adaptive model of an adaptive cycle engine is characterized in that it includes CLM, the proposed MAEKF and an ALPV model for online parameter prediction, and adopts a fast and slow dual-loop design. The basic working principle is to use the output of the airborne engine model as the steady-state reference value of the MAEKF, which is estimated in real time by extending the health parameter to the model, and the health parameter is fed back to the CLM and ALPV models for online updating to ensure its adaptive ability. The partial derivative parameters of the MAEKF are directly provided by the ALPV model to avoid the time-consuming model linearization process, thereby improving the real-time performance. This process is carried out at a high update rate to achieve accurate parameter estimation, and the thermal calculation of the CLM and the correction calculation of the ALPV model are carried out simultaneously. The adaptive CLM adopts a small deviation linearization method for linearization to correct the parameters of the ALPV model, ensure the accuracy of the ALPV model at the current working point, and meet the accuracy requirements of the system matrix in the recursive process of the MAEKF. This process is updated at a lower frequency to cope with the heavy calculation burden of the CLM, which is the slow loop of the model. Through the fast and slow dual-loop design, the accuracy and real-time performance of the model are guaranteed at the same time. The slow loop reduces the frequency of model linearization updates and ensures the accuracy of the model, while the fast loop improves the real-time performance of the onboard adaptive model.

[0008] The MAEKF is a new type of extended Kalman filter. The adaptive cycle engine has strong nonlinearity, that is, the system equations and measurement equations of the model are nonlinear. EKF first performs Taylor series expansion on the original system and measurement, approximates the system as a first-order linear system, and then performs linear Kalman filter estimation. The difference from the linear KF is that the state value and output value are calculated using a nonlinear model, which can solve the parameter estimation problem of the nonlinear system. However, EKF requires a random system with a complete process and measurement noise covariance to perform optimal estimation, which may be time-consuming in calculation and has low estimation accuracy. Therefore, the present invention is improved on the basis of EKF and proposes a MAEKF algorithm.

[0009] For the following adaptive cycle engine discrete system

[0010]

[0011] The subscript k here represents the value of the variable at the kth step.

[0012] definition:

[0013]

[0014] The design process of MAEKF is:

[0015] initialization:

[0016]

[0017] Status prediction:

[0018]

[0019] The difference here with the EKF is that the covariance matrix is ​​multiplied by a forgetting factor to compensate for the adverse effects of the incomplete dynamic equations. In this way, the weight of old data in the filtering process is scaled.

[0020] The forgetting factor in this filter

[0021]

[0022] Among them, λ0 is the reference value of the forgetting factor.

[0023] N k and T k The matrix is

[0024]

[0025] in

[0026]

[0027] By averaging the previous residual sequence of length N, the covariance estimate of the new residual can be derived as:

[0028]

[0029] in, is the innovation residual in Kalman filtering. The estimated process noise matrix is ​​as follows:

[0030]

[0031] in, In steady state, the above equation can be obtained using the variance-covariance matrix of the following equation, as follows:

[0032]

[0033] The modified step size of MAEKF can be obtained by the following formula:

[0034]

[0035] Beneficial Effects

[0036] Compared with the prior art, the hybrid improved airborne adaptive model modeling method of the adaptive cycle engine of the present invention combines the advantages of high precision of the component-level model and the advantages of high real-time performance of the adaptive LPV model to establish a hybrid structure model as an airborne model, and on the basis of the traditional extended Kalman filter, the covariance matrix is ​​multiplied by a forgetting factor to compensate for the adverse effects of the incomplete dynamic equation. In this way, the weight of the old data in the filtering process is scaled, and a new type of MAEKF is designed. Based on the deviation between the measured parameters and the output of the hybrid structure model, the filter corrects the health parameters of the hybrid structure model and establishes an airborne adaptive model. The airborne adaptive model has both high precision and high real-time performance, which lays a very good model foundation for model-based adaptive cycle engine control.

[0037] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0039] Figure 1 It is a structural diagram of the hybrid improved airborne adaptive model modeling method of the adaptive cycle engine of the present invention;

[0040] Figure 2It is a simplified structural diagram of an adaptive cycle engine of the adaptive cycle engine hybrid improved airborne adaptive model modeling method of the present invention;

[0041] Figure 3 It is a linearization process diagram in the modeling method of the hybrid improved airborne adaptive model of the adaptive cycle engine of the present invention;

[0042] Figure 4 It is a schematic diagram of the LPV model in the hybrid improved airborne adaptive model modeling method of the adaptive cycle engine of the present invention;

[0043] Figure 5 It is a time-varying characteristic diagram of the LPV model in the hybrid improved airborne adaptive model modeling method of the adaptive cycle engine of the present invention;

[0044] Figure 6 It is a MAEKF flow chart in the hybrid improved airborne adaptive model modeling method of the adaptive cycle engine of the present invention. DETAILED DESCRIPTION

[0045] 1. Engine hybrid structure airborne adaptive model

[0046] The hybrid structure airborne adaptive model based on MAEKF is as follows Figure 1 As shown in the figure, its structure includes CLM, the proposed MAEKF and the ALPV model for online parameter prediction, and adopts a fast-slow dual-loop design. The basic working principle is to use the output of the onboard engine model as the steady-state reference value of the MAEKF, which is estimated in real time by extending the health parameters to the model, and the health parameters are fed back to the CLM and ALPV models for online updating to ensure their adaptability. The partial derivative parameters of the MAEKF are directly provided by the ALPV model to avoid the time-consuming model linearization process, thereby improving the real-time performance. This process is carried out at a high update rate to achieve accurate parameter estimation, forming Figure 1 In the fast loop shown, the thermal calculation of CLM and the correction calculation of the ALPV model are performed simultaneously. The adaptive CLM uses a small deviation linearization method for linearization to correct the parameters of the ALPV model, ensure the accuracy of the ALPV model at the current working point, and meet the accuracy requirements of the system matrix in the MAEKF recursive process. This process is updated at a lower frequency to cope with the heavy computational burden of CLM, which is the slow loop of the model. Through the fast and slow dual-loop design, the accuracy and real-time performance of the model are guaranteed at the same time. The slow loop reduces the frequency of model linearization updates and ensures the accuracy of the model, while the fast loop improves the real-time performance of the airborne adaptive model.

[0047] 2. Working principle of adaptive cycle engine

[0048] The present invention selects the ACE of the variable low-pressure compression system as the research object, and its structure and cross-sectional numbering schematic diagram are shown in FIG. Figure 2 The ACE model includes a fan with a flange, a core driven fan stage (CDFS), a high-pressure compressor (HPC), a combustion chamber (CC), a high-pressure turbine (HPT), a low-pressure turbine (LPT), an afterburner, a core nozzle (CN), a bypass nozzle (BN), a mode select valve (MSV), a front variable area bypass injector (FVABI) and a rear variable area bypass injector (RVABI). [9] The adjustable parameters include: the fuel flow rate W in the main combustion chamber fb , afterburner fuel flow W fa , FLADE guide vane angle α FLADE 、Fan guide blade angle α FAN 、CDFS guide vane angle α CDFS 、HPC guide vane angle α HPC 、HPT guide vane angle α HPT 、LPT guide vane angle α LPT , Flade duct mode selection valve, MSV, FVABI regulating valve, RVABI regulating valve, core nozzle throat area A MN , and Flade duct tail nozzle area A BN .

[0049] Flade is an innovative "fan on blade" assembly used in adaptive cycle engines that addresses the challenges of full-span supersonic flow by introducing full-span positive pre-swirl on top of the main fan. In the ACE model, Flade acts as an annular fan coaxial with the main fan. When the engine is operating in a specific mode, Flade can be shut down by closing its exhaust nozzle, turning ACE into a double bypass cycle. This design enhances the engine's versatility and performance under different operating conditions, especially the Flade fan stage set on the high-pressure rotor, i.e. the core driven fan stage, marking a major design difference compared to other engine configurations.

[0050] The adaptive cycle engine of this configuration has three outer culverts, which is two more than the traditional turbofan engine flow path. The airflow of the first culvert and the second culvert is mixed in the front mixing chamber, and then mixed with the core engine outlet airflow in the rear mixing chamber; the airflow of the third culvert is discharged separately without mixing with the airflow of other flow paths. The airflow flowing through the third culvert helps to reduce the engine body temperature and reduce the heat load, thereby extending the service life of the engine.

[0051] When the aircraft performs diversified tasks, the engine needs to switch between different working modes. By controlling the opening and closing of the bypass, the engine can achieve multiple working modes, such as a high bypass ratio mode with low fuel consumption and a low bypass ratio mode with high thrust. Its working mode can be divided into turbojet mode and turbofan mode, and the turbofan mode is further subdivided into single bypass, double bypass and three bypass modes. Different working modes are realized by the opening and closing control of MSV, FVABI and Flade. In particular, in the three bypass working mode, FVABI adjusts the mass flow of the first and second bypasses by adjusting the inlet area of ​​the first bypass; MSV adjusts the mass flow of the second and third bypasses by adjusting the inlet area of ​​the second bypass; Flade adjusts the mass flow of the third bypass by adjusting the inlet area of ​​the third bypass. These controls ultimately achieve the adjustment of the engine bypass ratio, flow and surge margin of the core drive fan stage to optimize the engine performance under the corresponding flight conditions.

[0052] 3. Modeling of Adaptive LPV Model

[0053] Since the performance of engine components may be deteriorated by factors such as dirt, corrosion, erosion, and wear, health parameters are introduced in CLM and estimated by MAEKF. The definition of health parameters is as follows:

[0054]

[0055] Among them, Wa represents the current actual flow of engine components, Wa * represents the rated flow of the components when the engine is not degraded, η represents the current actual efficiency of the engine components, η * Indicates the rated efficiency of a component when the engine is not degraded.

[0056] This gives a general adaptive nonlinear system:

[0057]

[0058] in, is the state of the system, is the input of the system, is a health parameter, It is the external input of the system, including flight condition parameters such as flight altitude, Mach number, inlet temperature, etc. is the output of the system, f represents the differential variable function, and g represents the output function of the system. It should be noted that the influence mechanism of each engine parameter on its output is different. The state parameter reflects the influence of the current working state of the engine on the output parameter; the input parameter reflects the influence of the internal input of the engine on the state parameter and output parameter; the health parameter reflects the influence of the engine structural characteristics on the state parameter and output parameter; the flight condition parameter reflects the influence of the external input on the state parameter and output parameter.

[0059] After obtaining the nonlinear model of the engine, the engine is regarded as a simple static system. Then, the linear model of the engine is obtained by expanding the nonlinear expression at the current operating point using the Taylor series and taking the first-order approximation. The state function is expanded from a point (x',u',h') near the currently selected operating point to the current point (x0,u0,h0) using the Taylor series and ignoring the higher-order terms other than the first order, and the following expression can be obtained:

[0060]

[0061] Then, the expansion of the state function can be obtained:

[0062]

[0063] Figure 3 is a schematic diagram of the linearization process. The linearization of the working point (x0, u0, h0) will produce an effective region. On this basis, a linear model is established, and by calculating the points (x', u', h') in the region, a more accurate output estimate can be obtained. Figure 3 In the figure, the upper solid curve and the lower solid curve represent the nonlinear models before and after the system is degraded, respectively. The two black dots represent the current working points before and after the system is degraded, respectively. The two arrows represent the small deviation linearization models before and after the system is degraded, respectively.

[0064] The Taylor series expansion of the output function for the working point (x0, u0, h0) in the effective region can be expressed as:

[0065]

[0066] Thus we get:

[0067] Δy=g(x',u',h')-g(x0,u0,h0)

[0068] =CΔx+DΔu+FΔh

[0069] Get the incremental state space equation of the nonlinear system at the current steady-state point

[0070]

[0071] Considering that the engine is also affected by various noises during actual operation, the state variable model describing the dynamic characteristics of the real engine should be

[0072]

[0073] Where ω and υ are system noise and measurement noise, respectively, both of which are uncorrelated normally distributed zero-mean white noise, and their covariance matrices are Q and R, respectively.

[0074] The principle of LPV model is as follows Figure 4 As shown. After obtaining the linearized model with health parameters of a single steady-state point, an LTI system near a steady-state point is obtained. According to this method, by selecting several steady-state points in a certain working area of ​​the nonlinear system and performing Jacobian linearization, a family of LTI systems near a series of steady-state points can be obtained. By interpolating and fitting these results, the incremental adaptive LPV model expression of the nonlinear system in the form of the following formula can be obtained.

[0075]

[0076] Δy=C(θ)Δx+D(θ)Δu+F(θ)Δh+υ

[0077] The LTI system family is just a collection of LTI systems after the nonlinear system is linearized at a series of equilibrium points. In essence, it is a number of systems with a certain number; while the LPV system is an independent time-varying system that can approximately describe the motion trajectory of the nonlinear system. Figure 5 In the figure, the dotted line is the motion trajectory of the nonlinear system, the circles represent several linearized LTI systems selected from the motion trajectory of the nonlinear system, and the solid line represents the time-varying characteristics of the system reflected by the LPV model.

[0078] 4. Research on system parameter estimation methods

[0079] The health parameter deviation directly reflects the performance degradation of the current engine components, while the health parameters of the components cannot be directly measured. However, when the engine working environment does not change, the change of health parameters will cause corresponding changes in the measured parameters such as pressure, temperature, and speed of each section of the engine, and the two are interconnected through the aerodynamic thermodynamic relationship. Therefore, the present invention designs two optimal estimation filters to estimate the health parameters of the engine gas path components using the measured parameters of the engine.

[0080] The adaptive cycle engine has strong nonlinearity, that is, the system equations and measurement equations of the model are nonlinear. EKF first performs Taylor series expansion on the original system and measurement, approximates the system as a first-order linear system, and then performs linear Kalman filter estimation. The difference from linear KF is that it uses a nonlinear model to calculate state values ​​and output values, which can solve the parameter estimation problem of nonlinear systems. However, EKF requires a random system with a complete process and measurement noise covariance to perform optimal estimation, which may be time-consuming in calculation and has low estimation accuracy. Therefore, the present invention improves on the basis of EKF and proposes a MAEKF algorithm.

[0081] Since the engine performance degradation occurs slowly, it can be considered that Therefore, the formula

[0082] Δy=C(θ)Δx+D(θ)Δu+F(θ)Δh+υ

[0083] can be rewritten as:

[0084]

[0085] In the formula

[0086]

[0087] C new =(CF),D new =D, In order to facilitate the design of MAEKF, Eq.

[0088]

[0089] Δy=C(θ)Δx+D(θ)Δu+F(θ)Δh+υ

[0090] Can be converted to discrete form:

[0091]

[0092] The subscript k here represents the value of the variable at the kth step.

[0093] definition:

[0094]

[0095] initialization:

[0096]

[0097] Status prediction:

[0098]

[0099] The difference here with the EKF is that the covariance matrix is ​​multiplied by a forgetting factor to compensate for the adverse effects of the incomplete dynamic equations. In this way, the weight of old data in the filtering process is scaled.

[0100] The forgetting factor in this filter

[0101]

[0102] Among them, λ0 is the reference value of the forgetting factor.

[0103] N k and T k The matrix is

[0104]

[0105] in

[0106]

[0107] By averaging the previous residual sequence of length N, the covariance estimate of the new residual can be derived as:

[0108]

[0109] in, is the innovation residual in Kalman filtering. The estimated process noise matrix is ​​as follows:

[0110]

[0111] in, In steady state, the above equation can be obtained using the variance-covariance matrix of the following equation, as follows:

[0112]

[0113] Measurement prediction:

[0114]

[0115] Figure 6 The MAEKF flow chart is shown. MAEKF is able to compensate for the effects of incomplete dynamic equations, avoiding degradation of the estimation performance of the traditional EKF observer when the dynamic equations are incomplete. These inconsistencies may lead to divergence, which may cause control instability of the controlled object. At the same time, the observer uses a covariance scaling mechanism to improve the estimation performance under dynamic changes in the state of the estimated object, without the need for a time-consuming tuning phase.

[0116] The nonlinear term h(x k )The error and noise introduced during linearization v k compared to, The noise nearby can be ignored. k Performing a first-order Taylor expansion yields

[0117]

[0118] Then we can get the batch regression formula of MAEKF

[0119]

[0120] in, The estimation problem in the above formula can be rewritten as

[0121]

[0122]

[0123] The MAEKF update state calculated in the correction step It can also be obtained by performing weighted least squares, linear regression

[0124]

[0125] Can be converted into

[0126]

[0127] The modified step size of MAEKF can be obtained by the following formula:

[0128]

[0129] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and purpose of the present invention.

Claims

1. A hybrid improved airborne adaptive model modeling method for an adaptive cycle engine, characterized in that: Including component-level models, adaptive linear variable parameter models, and hybrid adaptive extended Kalman filters; The component-level model and the adaptive linear variable parameter model constitute a hybrid structural airborne model; The hybrid structure airborne model generates estimated output parameters under the control input vector u The difference between the measured parameter y of the sensor of the real adaptive cycle engine is transmitted to the hybrid adaptive extended Kalman filter; the component-level model continuously corrects the parameters of the adaptive LPV model to ensure the accuracy of the adaptive LPV model; The adaptive LPV model continuously modifies the parameters F and H of the hybrid adaptive extended Kalman filter to ensure the estimation accuracy of the hybrid adaptive extended Kalman filter; the hybrid adaptive extended Kalman filter estimates the performance degradation of the real engine based on the difference 2. According to claim 1, a hybrid improved airborne adaptive model modeling method for an adaptive cycle engine is characterized by: The airborne adaptive model includes a hybrid structure airborne model and MAEKF; the hybrid structure airborne model includes an ALPV model for online parameter prediction, which adopts a fast and slow dual-loop design; The basic working principle is to use the output of the onboard engine model as the steady-state reference value of the MAEKF, which is estimated in real time by extending the health parameters to the model, and the health parameters are fed back to the component-level model and the ALPV model for online update to ensure its adaptive capability; The partial derivative parameters of MAEKF are directly provided by the ALPV model to avoid the time-consuming model linearization process, thereby improving the real-time performance; this process is carried out at a high update rate to achieve accurate parameter estimation, and the thermal calculation of the component-level model and the correction calculation of the ALPV model are carried out simultaneously; the adaptive component-level model is linearized using a small deviation linearization method to correct the parameters of the ALPV model, ensure the accuracy of the ALPV model at the current working point, and meet the accuracy requirements of the system matrix in the MAEKF recursive process; this process is updated at a lower frequency to cope with the heavy computational burden of the component-level model, which is the slow loop of the model; through the fast and slow dual-loop design, the accuracy and real-time performance of the model are guaranteed at the same time, the slow loop reduces the model linearization update frequency and ensures the accuracy of the model, while the fast loop improves the real-time performance of the airborne adaptive model.

3. The method for modeling a hybrid improved airborne adaptive model of an adaptive cycle engine according to claim 1, characterized in that: The hybrid adaptive extended Kalman filter is a new type of extended Kalman filter; for the following form of adaptive cycle engine discrete system The subscript k in the formula represents the value of the variable at the kth step; definition: The design process of MAEKF is: initialization: Status prediction: The difference here with EKF is that the covariance matrix is ​​multiplied by a forgetting factor to compensate for the adverse effects of the incomplete dynamic equation; in this way, the weight of the old data in the filtering process is scaled; The forgetting factor in this filter Among them, λ0 is the reference value of the forgetting factor; N k and T k The matrix is in By averaging the previous residual sequence of length N, the covariance estimate of the new residual can be derived as: in, is the innovation residual in Kalman filtering; the estimated process noise matrix is ​​as follows: in, In steady state, the above equation can be obtained using the variance-covariance matrix of the following equation, as follows: The modified step size of MAEKF can be obtained by the following formula:

4. The method for modeling a hybrid improved airborne adaptive model of an adaptive cycle engine according to claim 1, characterized in that: The measurement parameters include the temperature and pressure at the inlet outlet, fan outlet, compressor outlet, CDFS outlet, after the high-pressure turbine, after the low-pressure turbine, fan speed and compressor speed.

Images