CN111456856A - Robust controller for reducing conservative maximum thrust state of aero-engine - Google Patents

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

Conservative robust controller for maximum thrust state drop of aero-engine

technical field

The invention relates to the technical field of aero-engine control, in particular to an aero-engine maximum thrust state-down conservative robust controller.

Background technique

Aeroengine is a complex nonlinear dynamic system, and its control system is easily affected by working conditions, engine performance degradation, changes in environmental conditions, and it is difficult to know in advance the influence of external disturbances and measurement noise. Because the working process of an aircraft engine is very complex, it is difficult to establish an accurate mathematical model, so there is always a difference between the mathematical model and the actual system. Therefore, it is necessary to design a robust controller for stabilizing the aero-engine control system with good performance in the presence of external disturbance signals, noise disturbances, unmodeled dynamic characteristics and parameter changes.

Due to the need to achieve high maneuverability in fighter aircraft, the performance of the engine's maximum thrust state is critical. Although the traditional robust controllers can achieve stable control of the engine at the maximum thrust state, however, they are very conservative because they consider the engine degradation as the uncertainty of the engine model to design the robust controller. In fact, the degree of performance degradation of the engine can be estimated by measuring parameters, thereby eliminating the degradation term in the uncertainty model, reducing the range of the uncertainty model, reducing the conservatism of the robust controller, and improving the engine's performance in the maximum thrust state. performance, so that the aircraft has better maneuverability and has a more obvious advantage in combat.

SUMMARY OF THE INVENTION

In order to solve the problems existing in the prior art, the present invention proposes an aero-engine maximum thrust state reduction conservative robust controller, which has strong robustness and low conservativeness, and maximizes the performance of the engine in the maximum thrust state. Making the engine in the maximum thrust state not only works stably, but also improves the performance of the engine at the maximum thrust state and improves the maneuverability of the fighter.

The technical scheme of the present invention is:

The aero-engine maximum thrust state reduction conservative robust controller is characterized in that: it includes a maximum thrust state reduction conservative robust controller group solution module and a degradation parameter estimation loop;

Among them, the maximum thrust state reduction conservative robust controller group solution module, the degradation parameter estimation loop, the aero-engine body and several sensors on the aero-engine form the degradation parameter scheduling control loop;

The maximum thrust state degradation conservative robust controller group solution module generates a control input vector u and outputs it to the aero-engine body, and the sensor obtains the aero-engine measurement parameter y; the control input vector u and the measurement parameter y are jointly input to the degradation parameter estimation The loop, the degradation parameter estimation loop solves the aero-engine degradation parameter h, and outputs it to the maximum thrust state degradation conservative robust controller group solution module;

The maximum thrust state drop conservative robust controller group solution module is designed with two maximum thrust state drop conservative robust controllers, and the two maximum thrust state drop conservative robust controllers are obtained by using the following process : At the normal state of the engine h ₁ and the set degradation degree h _base , the nonlinear model of the engine including the degradation parameters is linearized under the maximum thrust state of the aero-engine to obtain two linearized models, and the linearized model is added without The engine model with small perturbation uncertainty is obtained from the perturbation block whose engine performance is degraded, and robust controllers are designed for these two engine models with small perturbation uncertainty as the corresponding maximum thrust state reduction conservative robust controller. ;

The maximum thrust state reduction conservative robust controller group calculation module calculates the adapted maximum thrust state reduction conservative robustness by using two internally designed maximum thrust state reduction conservative robust controllers according to the input degradation parameter h. Rod controller, the maximum thrust state drop conservative robust controller generates a control input vector u according to the difference e between the reference input r and the measured parameter y.

Further, the degradation parameter estimation loop includes a nonlinear airborne engine model and a Kalman filter at the maximum thrust state;

The nonlinear airborne engine model is an engine nonlinear model with degradation parameters:

y=g(x,u,h)

为控制输入向量，

为状态向量，

为输出向量，

为退化参数向量，f(·)为表示系统动态的n维可微非线性向量函数，g(·)为产生系统输出的m维可微非线性向量函数；非线性机载发动机模型输入为控制输入向量u以及上一周期的退化参数h，其输出的健康稳态参考值(x_aug,NOBEM,y_NOBEM)作为最大推力状态处卡尔曼滤波器当前周期的估计初始值；in

For the control input vector,

is the state vector,

is the output vector,

is the degradation parameter vector, f(·) is the n-dimensional differentiable nonlinear vector function representing the system dynamics, g(·) is the m-dimensional differentiable nonlinear vector function that generates the system output; the input of the nonlinear airborne engine model is the control The input vector u and the degradation parameter h of the previous cycle, and the output healthy steady-state reference value (x _{aug, NOBEM} , y _NOBEM ) is used as the estimated initial value of the current cycle of the Kalman filter at the maximum thrust state;

The input of the Kalman filter at the maximum thrust state is the measured parameter y and the healthy steady-state reference value (x _{aug, NOBEM} , y _NOBEM ) output by the nonlinear airborne engine model, according to the formula

K为卡尔曼滤波的增益，满足

P为Ricati方程

的解；系数A_aug和C_aug根据公式Calculate the degradation parameter h of the engine in the current cycle; where

K is the gain of the Kalman filter, satisfying

P is the Ricati equation

The solution of ; the coefficients A _aug and C _aug according to the formula

Determined, while A, C, L, and M are the augmented linearity reflecting the degradation of engine performance obtained by taking the degradation parameter h as the control input of the engine, and linearizing the nonlinear airborne engine model at the healthy steady-state reference point state variable model

的系数：

The coefficient of :

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 degrades conservative robust controller group calculation module according to the normal state h ₁ of the aero-engine and the maximum thrust state degrade conservative robust controllers K, K _{h_base} at the set degradation degree h _base , by formula

The conservative robust controller K _h is obtained by calculating the maximum thrust state adapted to the current degradation state of the aero-engine.

Further, the measurement parameters include the temperature and pressure at the outlet of the intake duct, the outlet of the fan, the outlet of the compressor, after the high-pressure turbine and after the low-pressure turbine, the rotational speed of the fan and the rotational speed of the compressor.

beneficial effect

Compared with the prior art, the conservative robust controller of the aero-engine maximum thrust state reduction of the present invention utilizes the design method of the traditional robust controller, adds a degradation parameter estimation loop, and improves the gain scheduling controller group. , a conservative robust controller for the maximum thrust state drop under a certain degree of engine degradation is added, and the maximum thrust state drop conservative robust controller group solution module is obtained. The designed maximum thrust state-drop conservative robust controller adopts the engine model with small perturbation uncertainty, which eliminates the degradation term in the engine uncertainty, reduces the perturbation range of the uncertain model, and reduces the robust gain scheduling control. Conservativeness of the device. The degradation parameter estimation loop realizes the reliable estimation of the degradation parameters, and uses the degradation parameters to realize the gain scheduling control when the engine performance is degraded. The invention realizes the conservative and robust control of the maximum thrust state of the engine, has strong robustness and low conservatism, and maximizes the performance of the engine in the maximum thrust state, so that the engine not only works stably in the maximum thrust state, but also improves the performance of the engine. The thrust of the engine at the maximum thrust state improves the maneuverability of the fighter.

Additional aspects and advantages of the present invention will be set forth, in part, from the following description, and in part will be apparent from the following description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of embodiments taken in conjunction with the accompanying drawings, wherein:

Fig. 1 is a schematic diagram of the structure of the aero-engine maximum thrust state drop conservative robust controller of the present invention;

FIG. 2 is a schematic structural diagram of a quantification parameter estimation loop in the degradation parameter scheduling control loop of the present embodiment;

3 is a schematic structural diagram of a Kalman filter in the degradation parameter estimation loop of the present embodiment;

Fig. 4 is the perturbation structure diagram of the engine model;

Fig. 5 is the engine model perturbation structure diagram with the separation of the degradation term;

Fig. 6 is the perturbation structure diagram of the new engine model after degradation;

Figure 7 is a schematic diagram of the structure of the uncertain model.

Detailed ways

Due to the need to achieve high maneuverability in fighter aircraft, the performance of the engine's maximum thrust state is critical. Although the traditional robust controllers can achieve stable control of the engine at the maximum thrust state, however, they are very conservative because they regard engine degradation as the uncertainty of the engine model for robust controller design, which seriously reduces the engine performance. In view of this problem, the analysis and research process of the present invention is given below.

1. Estimation of engine performance degradation

Engine performance degradation refers to the normal aging phenomenon caused by natural wear, fatigue, fouling, etc. of the engine after many cycles of operation. At this time, the performance of some engines will slowly deviate from the rated state. Take the turbine component, for example, as it works with the engine for multiple cycles, its efficiency slowly decreases. The ability to convert high temperature and high pressure gas into mechanical energy will be reduced and the linearized model of the engine at one operating point will change.

The final characteristic of engine performance degradation is the change in operating efficiency and flow rate of different rotor components. Changes in the efficiency coefficient or flow coefficient of fan, compressor, main combustion, high pressure turbine and low pressure turbine components can characterize engine performance degradation. The efficiency or flow coefficients of the engine, main combustion chamber, high pressure turbine and low pressure turbine components are referred to as degradation parameters or health parameters.

Building a nonlinear engine model with degradation parameters based on the component method

y=g(x,u,h)

为控制输入向量，

为状态向量，

为输出向量，

为退化参数向量，f(·)为表示系统动态的n维可微非线性向量函数，g(·)为产生系统输出的m维可微非线性向量函数。in

For the control input vector,

is the state vector,

is the output vector,

is the degradation parameter vector, f(·) is the n-dimensional differentiable nonlinear vector function representing the system dynamics, and g(·) is the m-dimensional differentiable nonlinear vector function that produces the system output.

The degradation parameter h is regarded as the control input of the engine, and the nonlinear model of the engine is linearized at the healthy steady-state reference point by the small disturbance method or the fitting method.

in

A'=A, B'=(B L), C'=C,

D′=(DM),Δu′=(ΔuΔh) ^T

w is the system noise, v is the measurement noise, h is the degradation parameter, Δh=hh ₀ ; the above-mentioned w and v are both uncorrelated Gaussian white noise, their mean values are 0, and the covariance matrices are diagonal matrices Q and R, That is, the following conditions are met:

E(w)=0E[ww ^T ]=Q

E(v)=0E[vv ^T ]=R

Δ represents the variation of this parameter, and h ₀ represents the initial state degradation parameter of the engine.

Further, an augmented linear state variable model reflecting engine performance degradation is obtained

The coefficient matrix can be obtained by the following formula:

These coefficients have different values in different operating states of the engine.

In fact, the degradation parameters are difficult to measure, or even impossible to measure, while the pressure, temperature, speed and other parameters of each part of the engine are relatively easy to obtain by measurement, which are usually called "measured parameters", mainly including the outlet of the intake duct, the outlet of the fan, Temperature and pressure at compressor outlet, after high pressure turbine, after low pressure turbine, fan speed and compressor speed. When the working environment of the engine does not change, the change of the degradation parameter will cause the corresponding change of the measured parameter, and there is an aero-thermodynamic relationship between the two. Therefore, an optimal estimation filter can be designed to achieve the optimal estimation of the degradation parameters by measuring the parameters.

将退化参数进一步转化为状态变量，可以得到Since the engine's performance degradation process is relatively slow, a reasonable assumption can be made that the rate of change of Δh

By further transforming the degradation parameters into state variables, we can get

in

The established degradation parameter estimation loop is mainly composed of two parts, one part is the nonlinear airborne engine model based on performance degradation, and the other part is the maximum thrust state composed of the model at the maximum thrust state and the Kalman filter corresponding to the steady state point. Kalman filter. The basic working principle is to take the output of the nonlinear airborne engine model as the steady-state reference value of the Kalman filter at the maximum thrust state, expand the degradation parameters, conduct online real-time estimation through the Kalman filter at the maximum thrust state, and finally feed back to Online real-time updating of nonlinear airborne engine models. Real-time tracking of the actual engine is realized, and an airborne adaptive model of the engine is established.

The Kalman estimator equation is:

P为Ricati方程

的解；利用非线性机载模型输出的健康稳态参考值(x_aug,NOBEM,y_NOBEM)作为式K is the gain of the Kalman filter, satisfying

P is the Ricati equation

The solution of ; use the healthy steady-state reference values (x _{aug, NOBEM} , y _NOBEM ) output by the nonlinear airborne model as the formula

The initial value of , the calculation formula can be obtained:

According to this calculation formula, the degradation parameter h of the engine can be obtained.

2. Robust controller design for uncertain models with degenerate parameters

Uncertainty is inevitable in any real system, and it can be divided into two categories: disturbance signal and model uncertainty. The disturbance signal includes interference, noise, and the like. Model uncertainty represents the difference between the mathematical model and the actual object.

There may be several reasons for model uncertainty: there are always some parameters in the linear model that are in error; parameters in the linear model may vary due to nonlinearity or changes in operating conditions; artificial simplification during modeling; due to factors such as wear and tear Degradation of engine performance.

Uncertainty can adversely affect the stability and performance of the control system.

The error between the actual engine and the nominal model (the nominal model is a conventional nonlinear model of the engine without degradation parameters) can be expressed as a perturbation block Δ. Please refer to Figure 4, adding a perturbation block to the nominal model to establish an engine uncertainty model

It can also be expressed as

G(s)=[I+Δ(s)]G _nom (s)

where G(s) is the uncertainty model of the engine, _Gnom (s) is the nominal model, and Δ(s) is the perturbation block.

The perturbation block Δ(s) contains performance degradation, see Figure 5, which can be predicted by measuring parameters. The perturbation block Δ(s) is divided into the perturbation block _Δh (s) without engine performance degradation and the degradation parameters. Referring to Figure 6, the perturbation block _Δh (s) and the degradation parameters without engine performance degradation are added to the nominal model, and the engine uncertainty model is expressed as

It can also be expressed as G(s)=[I+ _Δh (s)]G _{h_nom} (s)

where Δ _h (s) is the perturbation block without engine performance degradation, G _{h_nom} (s) is the new nominal model under the engine performance degradation state h, satisfying

G(s)=[I+Δ(s)]G _nom (s)

=[I+ _Δh (s)+h(s)] _Gnom (s)

=[I+ _Δh (s)]G _{h_nom} (s)

we can get,

Referring to Figure 7, the upper and lower small circle areas represent the linear uncertainty model of the engine without degradation and performance degradation h, respectively, and the large circle area represents the linear uncertainty model of the engine in the general robust controller design. In the design of general robust controllers, the degradation of the engine is directly regarded as the uncertainty in the model, and the nominal model of the engine is not changed. 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. In this patent, a new nominal model is established under the condition of engine performance degradation h, and an uncertain engine model is established with the new nominal model as the center of the circle. For a new nominal model in a degraded state, when selecting the perturbation block _Δh (s) without engine performance degradation, the smallest perturbation radius perturbation that can cover all the uncertainties of the engine except for the degradation should be selected. piece. Referring to Fig. 7, by estimating the degradation of engine performance, the perturbation radius of the perturbation block in the engine uncertainty || _Δh ||=||Δ||-||h||<||Δ||, The perturbation range of the uncertainty model is reduced

Finally, according to the small perturbation uncertainty model, the traditional robust controller design method is used to design a robust controller, and the robust controller designed here is less conservative.

3. Interpolation of the controller

This part explains the scheduling calculation principle of the maximum thrust state drop conservative robust controller group solution module in Fig. 1 to obtain the corresponding maximum thrust state drop conservative robust controller through linear interpolation of degenerate parameter scheduling.

A conservative robust controller is designed in the normal state and the performance degraded h _base state under the maximum thrust state of the engine, respectively. This will produce the maximum thrust state drop in Fig. 1 The controllers K _h , K _{h_base} in the conservative robust controller group solver module

According to the formula

The conservative robust controller K _h is obtained by calculating the maximum thrust state adapted to the current degradation state of the aero-engine, and the engine is effectively controlled.

Based on the above process, an aero-engine maximum thrust state drop conservative robust controller proposed in this embodiment is given below. As shown in Figure 1, it mainly includes a maximum thrust state drop conservative robust controller group solution module and degradation parameter estimation loops.

The maximum thrust state reduction conservative robust controller group solution module, the degradation parameter estimation loop, the aero-engine body and several sensors on the aero-engine form the degradation parameter scheduling control loop 10 .

The maximum thrust state degradation conservative robust controller group solution module generates a control input vector u and outputs it to the aero-engine body, and the sensor obtains the aero-engine measurement parameter y; the control input vector u and the measurement parameter y are jointly input to the degradation parameter estimation The loop, the degradation parameter estimation loop solves to obtain the degradation parameter h of the aero-engine, and outputs it to the maximum thrust state degradation conservative robust controller group solution module.

The maximum thrust state drop conservative robust controller group solution module is designed with two maximum thrust state drop conservative robust controllers, and the two maximum thrust state drop conservative robust controllers are obtained by using the following process : At the normal state of the engine h ₁ and the set degradation degree h _base , the nonlinear model of the engine including the degradation parameters is linearized under the maximum thrust state of the aero-engine to obtain two linearized models, and the linearized model is added without The engine model with small perturbation uncertainty is obtained from the perturbation block whose engine performance is degraded, and robust controllers are designed for these two engine models with small perturbation uncertainty as the corresponding maximum thrust state reduction conservative robust controller. . The small perturbation uncertainty engine model eliminates the degradation term in the engine uncertainty model and reduces the perturbation range of the uncertainty model.

The maximum thrust state reduction conservative robust controller group calculation module calculates the adapted maximum thrust state reduction conservative robustness by using two internally designed maximum thrust state reduction conservative robust controllers according to the input degradation parameter h. Rod controller, the maximum thrust state drop conservative robust controller generates a control input vector u according to the difference e between the reference input r and the measured parameter y.

A preferred specific implementation manner, the adaptive maximum thrust state drop conservative robust controller can be obtained by interpolation according to the input degradation parameter h:

According to the normal state h ₁ of the aero-engine and the maximum thrust state at the set degradation degree h _base , the conservative robust controllers K, K _{h_base} are reduced by the formula

The conservative robust controller K _h is obtained by calculating the maximum thrust state adapted to the current degradation state of the aero-engine.

The degradation parameter estimation loop includes a nonlinear airborne engine model and a Kalman filter at the maximum thrust state;

The nonlinear airborne engine model is an engine nonlinear model with degradation parameters:

y=g(x,u,h)

为控制输入向量，

为状态向量，

为输出向量，

为退化参数向量，f(·)为表示系统动态的n维可微非线性向量函数，g(·)为产生系统输出的m维可微非线性向量函数；非线性机载发动机模型输入为控制输入向量u以及上一周期的退化参数h，其输出的健康稳态参考值(x_aug,NOBEM,y_NOBEM)作为最大推力状态处卡尔曼滤波器当前周期的估计初始值。in

For the control input vector,

is the state vector,

is the output vector,

is the degradation parameter vector, f(·) is the n-dimensional differentiable nonlinear vector function representing the system dynamics, g(·) is the m-dimensional differentiable nonlinear vector function that generates the system output; the input of the nonlinear airborne engine model is the control The input vector u and the degradation parameter h of the previous cycle, and the output healthy steady-state reference value (x _{aug, NOBEM} , y _NOBEM ) are used as the estimated initial value of the current cycle of the Kalman filter at the maximum thrust state.

The input of the Kalman filter at the maximum thrust state is the measured parameter y and the healthy steady-state reference value (x _{aug, NOBEM} , y _NOBEM ) output by the nonlinear airborne engine model, according to the formula

K为卡尔曼滤波的增益，满足

P为Ricati方程

的解；系数A_aug和C_aug根据公式Calculate the degradation parameter h of the engine in the current cycle; where

K is the gain of the Kalman filter, satisfying

P is the Ricati equation

The solution of ; the coefficients A _aug and C _aug according to the formula

Determined, while A, C, L, and M are the augmented linearity reflecting the degradation of engine performance obtained by taking the degradation parameter h as the control input of the engine, and linearizing the nonlinear airborne engine model at the healthy steady-state reference point state variable model

的系数：

The coefficient of :

w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R.

Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and those of ordinary skill in the art will not depart from the principles and spirit of the present invention Variations, modifications, substitutions, and alterations to the above-described embodiments are possible within the scope of the present invention without departing from the scope of the present invention.

Claims (4)

1. an aero-engine maximum thrust state drops conservative robust controller, it is characterized in that: comprise maximum thrust state drop conservative robust controller group solution module and degradation parameter estimation loop; Among them, the maximum thrust state reduction conservative robust controller group solution module, the degradation parameter estimation loop, the aero-engine body and several sensors on the aero-engine form the degradation parameter scheduling control loop; The maximum thrust state degradation conservative robust controller group solution module generates a control input vector u and outputs it to the aero-engine body, and the sensor obtains the aero-engine measurement parameter y; the control input vector u and the measurement parameter y are jointly input to the degradation parameter estimation The loop, the degradation parameter estimation loop solves the aero-engine degradation parameter h, and outputs it to the maximum thrust state degradation conservative robust controller group solution module; The maximum thrust state drop conservative robust controller group solution module is designed with two maximum thrust state drop conservative robust controllers, and the two maximum thrust state drop conservative robust controllers are obtained by using the following process : At the normal state of the engine h ₁ and the set degradation degree h _base , the nonlinear model of the engine including the degradation parameters is linearized under the maximum thrust state of the aero-engine to obtain two linearized models, and the linearized model is added without The engine model with small perturbation uncertainty is obtained from the perturbation block whose engine performance is degraded, and robust controllers are designed for these two engine models with small perturbation uncertainty as the corresponding maximum thrust state reduction conservative robust controller. ; The maximum thrust state reduction conservative robust controller group calculation module calculates the adapted maximum thrust state reduction conservative robustness by using two internally designed maximum thrust state reduction conservative robust controllers according to the input degradation parameter h. Rod controller, the maximum thrust state drop conservative robust controller generates a control input vector u according to the difference e between the reference input r and the measured parameter y. 2. A kind of aero-engine maximum thrust state drop conservative robust controller according to claim 1, it is characterized in that: described degradation parameter estimation loop comprises nonlinear airborne engine model and Kalman filter at maximum thrust state ; The nonlinear airborne engine model is an engine nonlinear model with degradation parameters:

y=g(x,u,h) in

For the control input vector,

is the state vector,

is the output vector,

is the degradation parameter vector, f(·) is the n-dimensional differentiable nonlinear vector function representing the system dynamics, g(·) is the m-dimensional differentiable nonlinear vector function that generates the system output; the input of the nonlinear airborne engine model is the control The input vector u and the degradation parameter h of the previous cycle, and the output healthy steady-state reference value (x _{aug, NOBEM} , y _NOBEM ) is used as the estimated initial value of the current cycle of the Kalman filter at the maximum thrust state; The input of the Kalman filter at the maximum thrust state is the measured parameter y and the healthy steady-state reference value (x _{aug, NOBEM} , y _NOBEM ) output by the nonlinear airborne engine model, according to the formula

Calculate the degradation parameter h of the engine in the current cycle; where

K is the gain of the Kalman filter, satisfying

P is the Ricati equation

The solution of ; the coefficients A _aug and C _aug according to the formula

C _aug = (CM) Determined, while A, C, L, and M are the augmented linearity reflecting the degradation of engine performance obtained by taking the degradation parameter h as the control input of the engine, and linearizing the nonlinear airborne engine model at the healthy steady-state reference point state variable model

The coefficient of :

w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R. 3. a kind of aero-engine maximum thrust state drops conservative robust controller according to claim 1, it is characterized in that: described maximum thrust state drops conservative robust controller group solution module according to aero-engine normal state h ₁ and the maximum thrust state drop conservative robust controllers K, K _{h_base} at the set degradation degree h _base , through the formula

The conservative robust controller K _h is obtained by calculating the maximum thrust state adapted to the current degradation state of the aero-engine. 4. a kind of aero-engine maximum thrust state drop conservative robust controller according to claim 1, is characterized in that: described measurement parameter comprises inlet port outlet, fan outlet, compressor outlet, after high pressure turbine, low pressure turbine After the temperature and pressure, fan speed and compressor speed.

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