CN111456856B - Robust controller for reducing conservative maximum thrust state of aero-engine - Google Patents
Robust controller for reducing conservative maximum thrust state of aero-engine Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02C—GAS-TURBINE PLANTS; AIR INTAKES FOR JET-PROPULSION PLANTS; CONTROLLING FUEL SUPPLY IN AIR-BREATHING JET-PROPULSION PLANTS
- F02C9/00—Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Abstract
The invention provides a conservative robust controller for reducing the maximum thrust state of an aircraft engine, which is characterized in that a gain scheduling controller group is improved by adding a degradation parameter estimation loop, a conservative robust controller for reducing the maximum thrust state under a certain degradation degree of the engine is added, and a resolving module for reducing the conservative robust controller group for reducing the maximum thrust state is obtained. The designed robust controller for reducing the conservative property of the maximum thrust state adopts a small perturbation uncertainty engine model, eliminates a degradation term in the uncertainty of the engine, reduces the perturbation range of the uncertainty model, and reduces the conservative property of the robust gain scheduling controller. The degradation parameter estimation loop realizes reliable estimation of degradation parameters, and gain scheduling control during engine performance degradation is realized by using the degradation parameters. The invention has strong robustness and low conservation, and improves the performance of the engine in the maximum thrust state to the maximum extent, so that the engine not only stably works in the maximum thrust state, but also improves the thrust in the maximum thrust state.
Description
Technical Field
The invention relates to the technical field of control of aero-engines, in particular to a robust controller for reducing conservatism of a maximum thrust state of an aero-engine.
Background
An aircraft engine is a complex nonlinear dynamical system whose control system is susceptible to operating conditions, engine degradation, changes in environmental conditions, and it is difficult to know in advance the effects of external disturbances and measurement noise. Because the working process of the aircraft engine is very complicated and an accurate mathematical model is difficult to establish, the mathematical model always has a difference from an actual system. Therefore, there is a need for a robust controller for stabilizing an aircraft engine control system with good performance in the presence of external disturbance signals, noise disturbances, unmodeled dynamics and parameter variations.
The performance of the maximum thrust state of the engine is of great importance due to the need to achieve high maneuverability of the fighter. Conventional robust controllers, while providing stable control of the engine at maximum thrust conditions, are very conservative, as they address engine degradation as an uncertainty in the engine model. In fact, the performance degradation degree of the engine can be estimated by measuring parameters, so that a degradation term in an uncertainty model is eliminated, the range of the uncertainty model is narrowed, the conservatism of a robust controller is reduced, the performance of the engine in the maximum thrust state is improved, the airplane has better maneuverability, and the airplane has more obvious advantages in battle.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a robust controller for reducing the conservative property of the maximum thrust state of an aero-engine, which has strong robustness and low conservative property, and improves the performance of the engine in the maximum thrust state to the maximum extent, so that the engine not only stably works in the maximum thrust state, but also improves the performance of the engine in the maximum thrust state, and improves the maneuverability of a fighter plane.
The technical scheme of the invention is as follows:
the conservative robust controller for the maximum thrust state reduction of the aircraft engine is characterized in that: the method comprises a maximum thrust state reduction conservative robust controller group resolving module and a degradation parameter estimation loop;
the system comprises a maximum thrust state conservative robust controller group resolving module, a degradation parameter estimation loop, an aeroengine body and a plurality of sensors on the aeroengine, wherein the degradation parameter scheduling control loop is formed by the maximum thrust state conservative robust controller group resolving module, the degradation parameter estimation loop, the aeroengine body and the plurality of sensors on the aeroengine;
the maximum thrust state conservative robust controller group resolving module generates a control input vector u and outputs the control input vector u to the aeroengine body, and the sensor obtains an aeroengine measurement parameter y; the control input vector u and the measurement parameter y are jointly input into a degradation parameter estimation loop, the degradation parameter estimation loop obtains a degradation parameter h of the aero-engine through calculation, and the degradation parameter h is output to a maximum thrust state reduction conservative robust controller group calculation module;
two maximum thrust state conservative robust controllers are designed in a resolving module of the maximum thrust state conservative robust controller group, and are obtained by adopting the following processes: respectively in the normal state h of the engine 1 And setting the degree of degradation h base The method comprises the steps that an engine nonlinear model containing degradation parameters is linearized to obtain 2 linearized models in the maximum thrust state of an aircraft engine, a perturbation block without engine performance degradation is added to the linearized models to obtain a small perturbation uncertainty engine model, and robust controllers are respectively designed for the 2 small perturbation uncertainty engine models to serve as corresponding maximum thrust state conservation robust controllers;
the maximum thrust state conservative robust controller group resolving module calculates and obtains an adaptive maximum thrust state conservative robust controller by utilizing two internally designed maximum thrust state conservative robust controllers according to an input degradation parameter h, and the maximum thrust state conservative robust controller generates a control input vector u according to a difference e between a reference input r and a measurement parameter y.
Further, the degradation parameter estimation loop comprises a nonlinear airborne engine model and a Kalman filter in the maximum thrust state;
the nonlinear airborne engine model is an engine nonlinear model with degradation parameters:
y＝g(x,u,h)
wherein
For controlling the input vector>
Is a status vector>
Is output vector, is asserted>
For the degenerate parameter vector, f (-) is an n-dimensional differentiable nonlinear vector function representing the system dynamics, and g (-) is an m-dimensional differentiable nonlinear vector function producing the system output; the nonlinear onboard engine model is input into a control input vector u and a degradation parameter h of a previous period, and the output healthy steady-state reference value (x) of the nonlinear onboard engine model aug,NOBEM ,y NOBEM ) The estimated initial value of the current period of the Kalman filter in the maximum thrust state is used;
the input of the Kalman filter at the maximum thrust state is a measurement parameter y and a healthy steady-state reference value (x) output by the nonlinear airborne engine model aug,NOBEM ,y NOBEM ) According to the formula
Calculating to obtain a degradation parameter h of the engine in the current period; wherein
K is the gain of Kalman filtering and satisfies->
P is the Riccati equation>
The solution of (1); coefficient A aug And C aug According to the formula
Determining, wherein A, C, L and M are augmented linear state variable models which reflect the performance degradation of the engine and are obtained by regarding the degradation parameter h as the control input of the engine and linearizing the nonlinear onboard engine model at a healthy steady-state reference point
w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R.
Further, the maximum thrust state conservative robust controller group resolving module is used for resolving a normal state h of the aero-engine 1 And setting the degree of degradation h base The maximum thrust state of the system is reduced by conservative robust controllers K, K h_base By the formula
The conservative robust controller K of the maximum thrust state drop for adapting to the current degradation state of the aero-engine is obtained through calculation h 。
Further, the measurement parameters include the temperature and pressure at the outlet of the air inlet, the outlet of the fan, the outlet of the air compressor, the rear of the high-pressure turbine and the rear of the low-pressure turbine, the rotating speed of the fan and the rotating speed of the air compressor.
Advantageous effects
Compared with the prior art, the robust controller for the maximum thrust state degradation conservation of the aircraft engine utilizes a design method of the traditional robust controller, improves the gain scheduling controller group by adding a degradation parameter estimation loop, adds the robust controller for the maximum thrust state degradation conservation of the engine under a certain degradation degree, and obtains the resolving module of the robust controller group for the maximum thrust state degradation conservation. The designed robust controller for reducing the conservative property of the maximum thrust state adopts a small perturbation uncertainty engine model, so that a degradation term in the uncertainty of the engine is eliminated, the perturbation range of the uncertain model is reduced, and the conservative property of the robust gain scheduling controller is reduced. The degradation parameter estimation loop realizes reliable estimation of degradation parameters, and gain scheduling control during engine performance degradation is realized by using the degradation parameters. The method realizes conservative robust control of the maximum thrust state of the engine, has strong robustness and low conservative property, improves the performance of the engine in the maximum thrust state to the maximum extent, ensures that the engine not only stably works in the maximum thrust state, but also improves the thrust in the maximum thrust state of the engine and improves the maneuvering performance of the fighter.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic diagram of a robust controller for reducing conservation of maximum thrust conditions of an aircraft engine according to the present invention;
FIG. 2 is a schematic structural diagram of a quantized parameter estimation loop in a degraded parameter scheduling control loop according to this embodiment;
FIG. 3 is a schematic diagram of the structure of the Kalman filter in the degradation parameter estimation loop of the present embodiment;
FIG. 4 is a block diagram of an engine model perturbation configuration;
FIG. 5 is a plot of a perturbation of an engine model with the degeneration term isolated;
FIG. 6 is a perturbation map of a new engine model after degradation;
FIG. 7 is a schematic diagram of an uncertain model structure.
Detailed Description
The performance of the maximum thrust state of the engine is of great importance due to the need to achieve high maneuverability of the fighter. Conventional robust controllers, while providing stable control of the engine at maximum thrust conditions, are very conservative, however, because they consider engine degradation as an uncertainty in the engine model, which severely degrades engine performance. In response to this problem, the analytical study procedure of the present invention is given below.
1. Estimation of engine performance degradation
The performance degradation of the engine refers to the normal aging phenomenon of the engine caused by natural wear, fatigue, fouling and the like after the engine runs for many times in a circulating way. At this time, the performance of some engines may slowly deviate from the rated state. Taking the turbine component as an example, its operating efficiency slowly decreases as it operates with the engine for multiple cycles. The ability to convert high temperature and high pressure gases into mechanical energy will be reduced and the engine's linearized model at one operating point will change.
The final characteristic of the degradation of the engine performance is the variation of the working efficiency and the flow of the different rotor components, the variation of the efficiency or flow coefficients of the fan, compressor, main combustion, high-pressure turbine and low-pressure turbine components, which are called degradation or health parameters, can characterize the degradation of the engine performance.
Establishing a nonlinear model of an engine with degradation parameters based on a component method
y＝g(x,u,h)
Wherein
For controlling the input vector>
Is a state vector >>
Is output vector, is asserted>
For the degenerate parameter vector, f (-) is an n-dimensional differentiable nonlinear vector function representing the system dynamics, and g (-) is an m-dimensional differentiable nonlinear vector function that produces the system output.
And (4) taking the degradation parameter h as the control input of the engine, and linearizing the nonlinear model of the engine at a healthy steady-state reference point by adopting a small perturbation method or a fitting method.
Wherein
A′＝A,B′＝(B L),C′＝C,
D′＝(D M),Δu′＝(ΔuΔh) T
w is the system noise, v is the measurement noise, h is the degradation parameter,. DELTA.h = h-h 0 (ii) a W and v are uncorrelated white gaussian noise, the mean value is 0, and the covariance matrix is diagonal matrices Q and R, which satisfies the following conditions:
E(w)＝0E[ww T ]＝Q
E(v)＝0E[vv T ]＝R
Δ represents the variation of the parameter, h 0 Representing an engine initial state degradation parameter.
Further obtains an augmented linear state variable model reflecting the performance degradation of the engine
Wherein the coefficient matrix is obtained by:
these coefficients have different values at different operating states of the engine.
In fact, the degradation parameters are difficult or even impossible to measure, and the pressure, temperature, speed, etc. of each part of the engine are relatively easy to obtain by measurement, and are generally called "measurement parameters", and mainly include the temperature and pressure at the outlet of the air inlet, at the outlet of the fan, at the outlet of the compressor, after the high-pressure turbine and after the low-pressure turbine, the speed of the fan and the speed of the compressor. When the working environment of the engine does not change, the change of the degradation parameter can cause the corresponding change of the measured parameter, and an aerodynamic-thermodynamic relation exists between the degradation parameter and the measured parameter. Thus, an optimal estimation filter can be designed to achieve optimal estimation of the degradation parameters by measuring the parameters.
Since the process of engine performance degradation is relatively slow, a reasonable assumption can be made that the rate of change of Δ h is
Further converting the degradation parameter into a state variable to obtain
Wherein
The established degradation parameter estimation loop mainly comprises two parts, wherein one part is a nonlinear airborne engine model based on performance degradation, and the other part is a Kalman filter at the maximum thrust state, which consists of a model at the maximum thrust state and a Kalman filter corresponding to a steady-state point. The basic working principle is that the output of the nonlinear airborne engine model is used as a steady-state reference value of a Kalman filter in the maximum thrust state, the degradation parameters are expanded, online real-time estimation is carried out through the Kalman filter in the maximum thrust state, and finally the online real-time update is fed back to the nonlinear airborne engine model. The real-time tracking of the actual engine is realized, and an airborne self-adaptive model of the engine is established.
The kalman estimation equation is:
k is the gain of Kalman filtering
P is the Ricini equation
The solution of (1); healthy steady-state reference value (x) output by using nonlinear airborne model aug,NOBEM ,y NOBEM ) As formula (II)
The initial value of (a) can be obtained by the following calculation formula:
the degradation parameter h of the engine can be obtained according to the calculation formula.
2. Robust controller design with uncertain model of degradation parameters
Uncertainty inevitably exists in any practical system, and can be divided into two categories, disturbance signal and model uncertainty. The disturbing signal includes interference, noise, and the like. The uncertainty of the model represents the difference between the mathematical model and the actual object.
Model uncertainty may have several reasons, some parameters in the linear model are always in error; parameters in the linear model may change due to non-linearity or changes in operating conditions; artificial simplification during modeling; degradation of engine performance due to wear and the like.
The uncertainty may adversely affect the stability and performance of the control system.
The error between the actual engine and the nominal model (which is a conventional non-linear model of the engine without degradation parameters) can be expressed as a camera block Δ. Referring to FIG. 4, an uncertain model of the engine is built by adding a camera block to the nominal model
It can also be represented as
G(s)＝[I+Δ(s)]G nom (s)
Where G(s) is an uncertain model of the engine, G nom (s) is the nominal model and Δ(s) is the perturbation block.
The uptake block Δ(s) contains performance degradation, which can be predicted by measuring the parameters, see fig. 5. Dividing the perturbation blocks Delta(s) into perturbation blocks Delta(s) without engine performance degradation h (s) and degradation parameters. Referring to FIG. 6, perturbation blocks Δ without engine performance degradation are added to the nominal model h (s) and a degradation parameter, representing the engine uncertainty model as
It can also be expressed as G(s) = [ I + Δ ] h (s)]G h_nom (s)
In the formula,. DELTA. h (s) is a pickup block free from engine performance degradation, G h_nom (s) is a new nominal model in the engine performance degradation state h, and satisfies
G(s)＝[I+Δ(s)]G nom (s)
＝[I+Δ h (s)+h(s)]G nom (s)
＝[I+Δ h (s)]G h_nom (s)
We can obtain that the content of the Chinese patent application,
referring to fig. 7, the upper and lower small circular areas represent the linear uncertainty model of the engine without degradation and performance degradation h, respectively, and the large circular area represents the linear uncertainty model of the engine in the general robust controller design. In the design of a general robust controller, the degradation of the engine is directly considered as uncertainty in the model, without changing the nominal model of the engine. Therefore, the uncertainty radius of the uncertainty term must be large enough to accommodate the uncertainty model of the degraded engine, making the perturbation radius of the uncertainty model too large. Aiming at the condition of engine performance degradation h, a new nominal model is established in the state, and an uncertain engine model is established by taking the new nominal model as the center of a circle. Selecting perturbation blocks delta without engine performance degradation for a new nominal model under a certain degradation state h (s) the smallest perturbation radius camera block is selected that can cover all uncertainties of the engine except for degradation. Referring to FIG. 7, through the estimation of the degradation of the engine performance, the perturbation radius | | | | Δ of the camera block in the uncertainty of the engine h If | = | | delta | - | h | | < | | | | | delta | |, the perturbation range of the uncertainty model is reduced
And finally, designing a robust controller by using a traditional robust controller design method according to a small perturbation uncertain model, wherein the designed robust controller is lower in conservation.
3. Interpolation of controller
This section illustrates the scheduling calculation principle of the maximum thrust state conservative robust controller set calculation module in fig. 1 that obtains the corresponding maximum thrust state conservative robust controller through the linear interpolation of the degeneration parameter scheduling.
Normal state and performance degradation h at maximum thrust state of engine respectively base And designing a conservative robust controller under the state. This will result in the controller K in the maximum thrust state reduction conservative robust controller set solution module in FIG. 1 h 、K h_base
According to the formula
The conservative robust controller K of the maximum thrust state drop for adapting to the current degradation state of the aero-engine is obtained through calculation h And the engine is effectively controlled.
Based on the above process, the robust controller for reducing the maximum thrust state conservatism of the aircraft engine provided in this embodiment is given below, and as shown in fig. 1, the robust controller mainly includes a maximum thrust state reduction conservative robust controller group calculation module and a degradation parameter estimation loop.
The maximum thrust state conservative robust controller group resolving module, the degradation parameter estimation loop, the aircraft engine body and a plurality of sensors on the aircraft engine form a degradation parameter scheduling control loop 10.
The maximum thrust state conservative robust controller group resolving module generates a control input vector u and outputs the control input vector u to the aeroengine body, and the sensor obtains an aeroengine measurement parameter y; and the control input vector u and the measurement parameter y are jointly input into a degradation parameter estimation loop, the degradation parameter estimation loop obtains a degradation parameter h of the aero-engine through calculation, and the degradation parameter h is output to a maximum thrust state reduction conservative robust controller group calculation module.
Two maximum thrust state conservative robust controllers are designed in a resolving module of the maximum thrust state conservative robust controller group, and are obtained by adopting the following processes: respectively in the normal state h of the engine 1 And setting the degree of degradation h base The method comprises the steps of linearizing an engine nonlinear model containing degradation parameters in the maximum thrust state of the aircraft engine to obtain 2 linearized models, adding a perturbation block without engine performance degradation to the linearized models to obtain a small perturbation uncertainty engine model, and designing robust controllers for the 2 small perturbation uncertainty engine models respectively to serve as corresponding maximum thrust state conservation robust controllers. The small perturbation uncertainty engine model eliminates a degradation term in the engine uncertainty model, and reduces the perturbation range of the uncertainty model.
The maximum thrust state conservative robust controller group resolving module calculates and obtains an adaptive maximum thrust state conservative robust controller by utilizing two internally designed maximum thrust state conservative robust controllers according to an input degradation parameter h, and the maximum thrust state conservative robust controller generates a control input vector u according to a difference e between a reference input r and a measurement parameter y.
In a preferred embodiment, the adaptive maximum thrust state conservative robust controller can be obtained by interpolation according to the input degradation parameter h:
according to the normal state h of the aircraft engine 1 And setting the degree of degradation h base The maximum thrust state of the system is reduced by conservative robust controllers K, K h_base By the formula
Calculating to obtain the current degradation state of the aero-engineAdaptive maximum thrust state conservative robust controller K h 。
The degradation parameter estimation loop comprises a nonlinear airborne engine model and a Kalman filter in the maximum thrust state;
the nonlinear airborne engine model is an engine nonlinear model with degradation parameters:
y＝g(x,u,h)
wherein
For controlling the input vector>
Is a status vector>
Is output vector, is asserted>
For the degenerate parameter vector, f (-) is an n-dimensional differentiable nonlinear vector function representing the system dynamics, and g (-) is an m-dimensional differentiable nonlinear vector function producing the system output; the nonlinear onboard engine model is input into a control input vector u and a degradation parameter h of a previous period, and the output healthy steady-state reference value (x) of the nonlinear onboard engine model aug,NOBEM ,y NOBEM ) And the estimated initial value of the current period of the Kalman filter at the maximum thrust state is used.
The input of the Kalman filter in the maximum thrust state is a measurement parameter y and a healthy steady-state reference value (x) output by a nonlinear airborne engine model aug,NOBEM ,y NOBEM ) According to the formula
Calculating to obtain a degradation parameter h of the engine in the current period; wherein
K is the gain of Kalman filtering and satisfies->
P is the Ricini equation>
The solution of (1); coefficient A aug And C aug According to the formula
Determining, wherein A, C, L and M are augmented linear state variable models which reflect the performance degradation of the engine and are obtained by regarding the degradation parameter h as the control input of the engine and linearizing the nonlinear onboard engine model at a healthy steady-state reference point
w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (4)
1. The utility model provides an aeroengine maximum thrust state falls conservative robust controller which characterized in that: the method comprises a maximum thrust state reduction conservative robust controller group resolving module and a degradation parameter estimation loop;
the system comprises a maximum thrust state conservative robust controller group resolving module, a degradation parameter estimation loop, an aeroengine body and a plurality of sensors on the aeroengine, wherein the degradation parameter scheduling control loop is formed by the maximum thrust state conservative robust controller group resolving module, the degradation parameter estimation loop, the aeroengine body and the plurality of sensors on the aeroengine;
the maximum thrust state conservative robust controller group resolving module generates a control input vector u and outputs the control input vector u to the aeroengine body, and the sensor obtains an aeroengine measurement parameter y; the control input vector u and the measurement parameter y are jointly input into a degradation parameter estimation loop, the degradation parameter estimation loop obtains a degradation parameter h of the aero-engine through calculation, and the degradation parameter h is output to a maximum thrust state reduction conservative robust controller group calculation module;
two maximum thrust state conservative robust controllers are designed in a resolving module of the maximum thrust state conservative robust controller group, and are obtained by adopting the following processes: respectively in the normal state h of the engine 1 And setting the degree of degradation h base The method comprises the steps that an engine nonlinear model containing degradation parameters is linearized to obtain 2 linearized models in the maximum thrust state of an aircraft engine, a perturbation block without engine performance degradation is added to the linearized models to obtain a small perturbation uncertainty engine model, and robust controllers are respectively designed for the 2 small perturbation uncertainty engine models to serve as corresponding maximum thrust state conservation robust controllers;
the maximum thrust state conservative robust controller group resolving module calculates and obtains an adaptive maximum thrust state conservative robust controller by utilizing two internally designed maximum thrust state conservative robust controllers according to an input degradation parameter h, and the maximum thrust state conservative robust controller generates a control input vector u according to a difference e between a reference input r and a measurement parameter y.
2. The robust controller for reducing conservatism of the maximum thrust state of an aircraft engine as claimed in claim 1, wherein: the degradation parameter estimation loop comprises a nonlinear airborne engine model and a Kalman filter in the maximum thrust state;
the nonlinear airborne engine model is an engine nonlinear model with degradation parameters:
y＝g(x,u,h)
wherein
In order to control the input vector,
in the form of a state vector, the state vector,
in order to output the vector, the vector is,
for the degenerate parameter vector, f (-) is an n-dimensional differentiable nonlinear vector function representing the system dynamics, and g (-) is an m-dimensional differentiable nonlinear vector function producing the system output; the nonlinear onboard engine model is input into a control input vector u and a degradation parameter h of a previous period, and the output healthy steady-state reference value (x) of the nonlinear onboard engine model aug,NOBEM ,y NOBEM ) The estimated initial value of the current period of the Kalman filter in the maximum thrust state is used;
the input of the Kalman filter in the maximum thrust state is a measurement parameter y and a healthy steady-state reference value (x) output by a nonlinear airborne engine model aug,NOBEM ,y NOBEM ) According to the formula
Calculating to obtain a degradation parameter h of the engine in the current period; wherein
T is the gain of Kalman filtering
P is the Ricini equation
The solution of (2); coefficient A aug And C aug According to the formula
Determining, wherein A, C, L and M are augmented linear state variable models which reflect the performance degradation of the engine and are obtained by regarding the degradation parameter h as the control input of the engine and linearizing the nonlinear onboard engine model at a healthy steady-state reference point
Coefficient (c):
w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R.
3. The robust controller for reducing conservatism of the maximum thrust state of an aircraft engine as claimed in claim 1, wherein: the maximum thrust state conservative robust controller group resolving module is used for resolving a normal state h of the aero-engine 1 And setting the degree of degradation h base Robust controller K, K with conservative maximum thrust state reduction h_base By the formula
The conservative robust controller K of the maximum thrust state drop for adapting to the current degradation state of the aero-engine is obtained through calculation h 。
4. The robust controller for reducing conservatism of the maximum thrust state of an aircraft engine as claimed in claim 1, wherein: the measurement parameters comprise the temperature and pressure of an air inlet outlet, a fan outlet, a gas compressor outlet, a high-pressure turbine rear part and a low-pressure turbine rear part, the fan rotating speed and the gas compressor rotating speed.
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