CN111856919A - Fault-tolerant controller for gain scheduling of failure of gas path component of aero-engine - Google Patents

Fault-tolerant controller for gain scheduling of failure of gas path component of aero-engine Download PDF

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CN111856919A
CN111856919A CN202010542821.3A CN202010542821A CN111856919A CN 111856919 A CN111856919 A CN 111856919A CN 202010542821 A CN202010542821 A CN 202010542821A CN 111856919 A CN111856919 A CN 111856919A
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fault
engine
pid controller
tolerant
parameter
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缑林峰
孙楚佳
刘志丹
杨江
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Northwestern Polytechnical University
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    • G05B11/01—Automatic controllers electric
    • G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
    • G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P.I., P.I.D.

Abstract

The invention provides a fault-tolerant controller for gain scheduling of a fault of a gas circuit component of an aero-engine, which comprises a fault-tolerant control module of a PID controller group and a fault diagnosis module of the gas circuit component; the fault-tolerant control module of the PID controller group generates a control input vector u and outputs the control input vector u to the engine body, and the sensor obtains an engine measurement parameter y; the control input vector u and the measurement parameter y are jointly input into the gas path component fault diagnosis module, the gas path component fault diagnosis module diagnoses the gas path component fault of the engine, the health parameter h of the engine is obtained through calculation, and the health parameter h is output to the PID controller group fault-tolerant control module; the fault-tolerant control module of the PID controller group is internally provided with a plurality of PID controllers, the fault-tolerant control module of the PID controller group calculates and obtains an adaptive PID controller by utilizing a plurality of internally designed PID controllers according to an input health parameter h and a scheduling parameter alpha, and the PID controller generates a control input vector u according to a difference e between a reference input r and a measurement parameter y. The invention can still well control the real engine under the condition of the failure of the air path component of the engine, ensure the safe work of the engine, give full play to the performance of the engine and improve the safety and the performance of the airplane.

Description

Fault-tolerant controller for gain scheduling of failure of gas path component of aero-engine
Technical Field
The invention relates to the technical field of aero-engine control, in particular to a fault-tolerant controller for gain scheduling of aero-engine gas path component faults.
Background
An aircraft engine is a complex nonlinear dynamical system, and when the aircraft engine works in a wide flight envelope, the working state of the engine continuously changes along with the change of external conditions and flight conditions. The use of a single linear controller does not provide good control of the engine within the full flight envelope, so a non-linear controller design approach has been proposed, which is however immature and very complex. For the control of the engine, gain scheduling control is used more frequently, a linearization model corresponding to each point is obtained by linearizing a plurality of stable design points in a nonlinear model, then controllers are respectively designed for each linearization model, and the controllers are connected by a fitting or interpolation method, so that the linear controller is used for effectively controlling the nonlinear engine.
However, the requirements of modern warplanes on the performance of aircraft engines are continuously increased, the structures of the aircraft engines are more and more complex, and the engine faults account for 1/3 of the total faults of the aircraft due to the severe and variable operating environments of the engines. Wherein, the gas circuit part failure accounts for more than 90% of the total failure of the engine, and the maintenance cost accounts for 60% of the total maintenance cost of the engine. In order to ensure the safe operation of the engine and to make the failed engine provide sufficient performance to ensure the safe flight of the aircraft or have high maneuverability, the performance of the failed engine must be recovered, and the fault-tolerant control of the engine is performed to ensure the normal and stable operation of the control system and good performance. Therefore, the research on the fault tolerance control method of the gas circuit component of the engine is of great significance.
According to the traditional fault-tolerant control method for the gas circuit component, when the gas circuit component of the aeroengine fails, the control rule is corrected, so that the thrust of the engine is always matched with the throttle lever, and the thrust of the engine is effectively guaranteed. However, these design methods do not address the issue of current controller and engine model mismatches that result in degraded or even unstable control system performance. When the engine has a gas path component fault, the linear model of the engine at the same working point is also changed greatly. Therefore, a controller designed according to an engine model in a normal state generally cannot guarantee the performance of the engine when a gas path component fails, or even cannot guarantee the closed loop stability of a control system.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a fault gain scheduling fault-tolerant controller for an aircraft engine gas path component, which can still well control a real engine under the condition of the fault of the engine gas path component, ensure the safe work of the engine, give full play to the performance of the engine and improve the safety and the performance of an aircraft.
The technical scheme of the invention is as follows:
the fault-tolerant controller for gain scheduling of the failure of the aero-engine gas path component is characterized in that: the fault diagnosis device comprises a PID controller group fault-tolerant control module and a gas circuit component fault diagnosis module;
The fault-tolerant control module and the gas circuit component fault diagnosis module of the PID controller group, the aircraft engine body and a plurality of sensors on the aircraft engine form a gas circuit component fault scheduling control loop;
the PID controller group fault-tolerant control 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 the gas circuit component fault diagnosis module, and the gas circuit component fault diagnosis module diagnoses the fault condition of the gas circuit component of the engine to obtain a health parameter h of the aircraft engine and outputs the health parameter h to the PID controller group fault-tolerant control module;
the PID controller group fault-tolerant control module, the aircraft engine body and a plurality of sensors on the aircraft engine also form a scheduling parameter scheduling control loop; the sensor outputs a scheduling parameter alpha to a PID controller group fault-tolerant control module;
the fault-tolerant control module of the PID controller group is internally provided with a plurality of PID controllers which are respectively designed by utilizing a plurality of linearization models, and the linearization models are obtained by linearizing nonlinear models of the aircraft engine under different set working points and different gas circuit component faults of the aircraft engine;
The fault-tolerant control module of the PID controller group calculates and obtains an adaptive PID controller by utilizing a plurality of PID controllers designed in the PID controller according to the input health parameter h and the scheduling parameter alpha, and the PID controller generates a control input vector u according to a difference value e between a reference input r and a measurement parameter y.
Further, the process of designing a plurality of PID controllers in the PID controller group fault-tolerant control module is as follows: selecting q working points in the full flight envelope according to the scheduling parameter alpha to linearize the engine nonlinear model containing the health parameters to obtain q linearized models containing the health parameters, obtaining 11q linearized models at the positions of the engine without air channel component failure and the specific air channel component failure by adjusting the values of the health parameters, and designing corresponding PID controllers for the 11q linearized models respectively to form a PID controller group.
Further, the gas path component fault diagnosis module comprises a nonlinear onboard engine model and a piecewise linearization Kalman filter;
the nonlinear airborne engine model is an engine nonlinear model with health parameters:
Figure BDA0002539521620000031
y＝g(x,u,h)
wherein
Figure BDA0002539521620000032
In order to control the input vector,
Figure BDA0002539521620000033
in the form of a state vector, the state vector,
Figure BDA0002539521620000034
in order to output the vector, the vector is,
Figure BDA0002539521620000035
For the health 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 on-board engine model inputs are the control input vector u and the last cycle health parameter h, whichHealthy steady state reference value (x) of outputaug,NOBEM,yNOBEM) The method comprises the steps of taking the current period as an estimated initial value of a piecewise linearization Kalman filter;
the inputs of the piecewise linearization Kalman filter are a measurement parameter y and a healthy steady-state reference value (x) output by a nonlinear airborne engine modelaug,NOBEM,yNOBEM) According to the formula
Figure BDA0002539521620000036
Calculating to obtain a health parameter h of the engine in the current period; wherein
Figure BDA0002539521620000037
K is the gain of Kalman filtering
Figure BDA0002539521620000038
P is the Ricini equation
Figure BDA0002539521620000039
The solution of (1); coefficient AaugAnd CaugAccording to the formula
Figure BDA00025395216200000310
Caug＝(C M)
Determining, and A, C, L, M is an augmented linear state variable model reflecting engine performance degradation obtained by regarding the health parameter h as the control input of the engine and linearizing the nonlinear on-board engine model at a healthy steady-state reference point
Figure BDA00025395216200000311
Coefficient (c):
Figure BDA0002539521620000041
Figure BDA0002539521620000042
w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R.
Furthermore, the fault-tolerant control module of the PID controller group obtains the adaptive PID controller according to the interpolation of the input health parameter h and the scheduling parameter alpha.
Furthermore, the fault-tolerant control module of the PID controller group selects two adjacent set working points alpha according to the current scheduling parameter alpha of the aero-engineiAnd alphai+1And obtaining two set operating points alphaiAnd alphai+1Controller K for engine without component failureiAnd Ki+1Various typical component failures Δ hbase_jController (2)
Figure BDA0002539521620000043
Δhbase_jThe value of the jth element representing the vector Δ h is Δ hbaseThe value of the other element is 0, i.e. Δ hbase_jIndicating 10 different component failures, e.g. Δ hbase_1Indicates that the fan has failed and the amount of change in fan efficiency is Δ hbase. According to the formula
Figure BDA0002539521620000044
Figure BDA0002539521620000045
Calculating to obtain the selected working point alpha of the aeroengineiAnd alphai+1PID controller K under the current component fault degree (health parameter is h) of the engineiAnd Ki+1(wherein. DELTA.hjIs the jth element of the vector Δ h; only if the | | delta h | | | is less than or equal to | | | delta hmaxFault condition of engine gas path component, when | | | delta h | | non-woven hair>||ΔhmaxThe engine has failed); according to the formula
Figure BDA0002539521620000046
And calculating to obtain the current adaptive fault-tolerant PID controller K (alpha) of the aero-engine.
Further, the scheduling parameter α includes a fan rotation speed or a compressor rotation speed of the aircraft engine.
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 aeroengine gas circuit component fault gain scheduling fault-tolerant controller utilizes the inherent modules in the traditional gain scheduling controller, improves the fault-tolerant control module of the PID controller group by additionally arranging the gas circuit component fault diagnosis module, and additionally arranging a plurality of groups of PID controllers under different gas circuit component faults of the engine. The gas circuit component fault diagnosis module realizes accurate judgment of the gas circuit component fault through reliable estimation of the health parameters, further combines the traditional scheduling parameters, realizes gain scheduling control when the engine gas circuit component is in fault, ensures that the engine still works safely when the gas circuit component is in fault, improves the control precision of the gain scheduling when the engine gas circuit component is in fault to the maximum extent, shortens the transition time of a control system, and reduces the dynamic deviation and the static deviation of the control system. The nonlinear controlled system is controlled by the controller, so that the system can obtain ideal dynamic and static control quality in the whole working range.
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.
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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 structural diagram of a fault gain scheduling fault-tolerant controller of an aircraft engine gas path component according to the invention;
fig. 2 is a schematic structural diagram of a fault diagnosis module of the gas circuit component in the gas circuit component fault scheduling control circuit according to the embodiment;
fig. 3 is a schematic structural diagram of a kalman filter in the fault diagnosis module of the gas path component according to the embodiment;
FIG. 4 is a schematic representation of a non-linear engine model of the present invention.
Detailed Description
The performance of gas circuit components can be degraded due to factors such as natural wear, corrosion, scale deposit, thermal creep and the like in the operation process of the aero-engine, and faults can be caused when the performance is degraded to a certain degree; in addition, the gas path member may also be damaged by foreign matter inhalation, mechanical fatigue fracture, or the like. The former failure occurs slowly, while the latter failure occurs rapidly. When the air path component of the engine fails and does not fail, part of the performance of the engine at the moment can seriously deviate from the rated state. Taking a turbine part as an example, when the turbine part fails, the working efficiency of the turbine part will be reduced, that is, the capability of converting the fuel gas with high temperature and high pressure into mechanical energy will be reduced, and corresponding power can be provided for a fan or a compressor part to enable the turbine part to work in a new balance state. At this time, the engine also deviates greatly from the original state. The failure of the gas circuit component can cause that a nonlinear model established during the design of the engine is seriously mismatched with a real engine during the failure of the gas circuit component, so that a gain scheduling controller designed according to the nonlinear model can not well control the engine with the failed gas circuit component, the performance of the engine is seriously reduced, the stability of a control system can not be even ensured, and the safe operation of the engine can not be ensured. The analytical study procedure of the present invention is given below in view of this problem.
1. Engine gas path component fault diagnosis
The failure of the gas path component can cause the corresponding characteristic parameter of the component to change. The engine gas circuit component faults are finally characterized on the changes of the working efficiency and the flow rate of different rotor components, namely the engine fault position and the fault degree can be revealed from the changes of the efficiency coefficients or the flow rate coefficients of the wind fan, the compressor, the main combustion, the high-pressure turbine and the low-pressure turbine components, and the efficiency coefficients or the flow rate coefficients of the fan, the compressor, the main combustion chamber, the high-pressure turbine and the low-pressure turbine components are called as health parameters.
Establishing engine nonlinear model with health parameters based on component method
Figure BDA0002539521620000061
y＝g(x,u,h)
Wherein
Figure BDA0002539521620000062
In order to control the input vector,
Figure BDA0002539521620000063
in the form of a state vector, the state vector,
Figure BDA0002539521620000064
in order to output the vector, the vector is,
Figure BDA0002539521620000065
for the health 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.
And (3) regarding the health 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.
Figure BDA0002539521620000066
Wherein
A′＝A,B′＝(B L),C′＝C,
D′＝(D M),Δu′＝(Δu Δh)T
w is system noise, v is measurement noise, h is a health parameter, Δ h ═ h-h0(ii) a The above-mentioned w and v are uncorrelated white Gaussian noises, the mean value is 0, and the covariance The difference matrix is diagonal matrix Q and R, namely the following conditions are satisfied:
E(w)＝0 E[wwT]＝Q
E(v)＝0 E[vvT]＝R
Δ represents the amount of change of the parameter, h0Representing an engine initial state health parameter.
Further obtains an augmented linear state variable model reflecting the performance degradation of the engine
Figure BDA0002539521620000071
Wherein the coefficient matrix is obtained by:
Figure BDA0002539521620000072
Figure BDA0002539521620000073
these coefficients have different values at different operating states of the engine.
In fact, the health parameters are difficult or even impossible to measure, and the pressure, temperature, speed, etc. of each part of the engine are easy to obtain by measurement, and are generally called "measurement parameters", mainly including the temperature and pressure at the outlet of the air inlet, the outlet of the fan, the outlet of the compressor, the temperature and pressure after the high-pressure turbine and 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 health parameter can cause the corresponding change of the measured parameter, and an aerodynamic thermodynamic relation exists between the health parameter and the measured parameter. Thus, an optimal estimation filter can be designed to achieve optimal estimation of the health parameter by measuring the parameter.
For a graded component failure, the corresponding failed component health parameter changes slowly, so over the time period in which a single failure diagnosis is performed, it can be considered that the requirements are met
Figure BDA0002539521620000074
For mutationsThe component failure is more concerned about the severity of the component failure when the engine works stably again after the failure occurs, and the health parameter change of the failed component is still met after the engine works stably again
Figure BDA0002539521620000075
Further converting the health parameters into state variables to obtain
Figure BDA0002539521620000076
Wherein
Figure BDA0002539521620000081
Caug＝(C M),Daug＝D,
Figure BDA0002539521620000082
The established gas path component fault diagnosis module mainly comprises two parts, wherein one part is a nonlinear airborne engine model based on health parameters, and the other part is a piecewise linear Kalman filter. The basic working principle is that the output of the nonlinear airborne engine model is used as a steady-state reference value of the piecewise linear Kalman filter, health parameters are expanded, online real-time estimation is carried out through the piecewise linear Kalman filter, and finally the online real-time update is fed back to the nonlinear airborne engine model, so that the real-time tracking of an actual engine is realized.
The kalman estimation equation is:
Figure BDA0002539521620000083
k is the gain of Kalman filtering
Figure BDA0002539521620000084
P is the Ricini equation
Figure BDA0002539521620000085
The solution of (1); healthy steady-state reference value (x) output by using nonlinear airborne modelaug,NOBEM,yNOBEM) As formula
Figure BDA0002539521620000086
The initial value of (a) can be obtained by the following calculation formula:
Figure BDA0002539521620000087
the health parameter h of the engine can be obtained according to the calculation formula, and the fault diagnosis of the gas circuit component of the engine is realized.
2. Gain scheduling fault tolerant control design
The essence of gain scheduling control is to design a set of linearized controllers, which are then regularly combined to be able to control a non-linear system. The basic principle of the gain scheduling fault-tolerant control is to select a series of working points, obtain engine linearization models under different set working points and different gas circuit component faults and respectively design corresponding PID controllers to obtain the PID controller group in FIG. 1.
Referring to FIG. 4, a set of scheduling parameter values α is selectedi1, 2.. q, representing the dynamic range of the system, and dividing the flight envelope into several subintervals and using these points as operating points. At the operating point, there are these equations
Figure BDA0002539521620000091
Figure BDA0002539521620000092
Wherein
Figure BDA0002539521620000093
For the selected i-th operating point, udiTo be at the moment of time
Figure BDA0002539521620000094
Steady state control input required to maintain equilibrium, hdiIs a time of day
Figure BDA0002539521620000095
The health parameter of (1).
By using a small disturbance method, a linear model of the health parameters of each working condition point can be obtained, and a linear model of the engine in a normal state and a performance degradation h state is obtained.
Referring to fig. 4, the upper and lower solid lines represent non-linear models of no-air path component failure and air path component failure h, respectively, of the engine. A series of small black dots represent different working points of the engine, and linearization is carried out at each working point to obtain a linear model. Aiming at linear models of an engine in a normal state and different gas circuit component fault states, a series of PID controllers are respectively designed to obtain the PID controller group in the figure 1. The controller gain is then linearly interpolated between the selected operating points so that the closed loop system is stable and has good performance for all fixed parameter values. The parameter α is a scheduling parameter, which may be defined herein as a fan speed or a compressor speed of the aircraft engine, and may be measured in real time. Another scheduling variable of the control system is a health parameter h reflecting the degree of failure of engine gas path components. The working principle is that the fault-tolerant control module of the PID controller group in the figure 1 carries out linear interpolation according to the scheduling parameter and the health parameter to obtain a corresponding PID controller to control the system.
3. Interpolation of controller
This section illustrates the scheduling calculation principle of the PID controller group fault-tolerant control module in fig. 1 that obtains the corresponding PID controller by scheduling linear interpolation of the scheduling parameter and the health parameter.
Respectively in the normal state of the engine and various typical component faults delta hbase_jDesigning a series of linear PID controllers under the state, and selecting each working point alphaiAnd (5) controlling. This will result in the controller in the fault tolerant control module of the PID controller set of FIG. 1
Figure BDA0002539521620000096
And then interpolating the controller according to the scheduling parameter alpha and the health parameter h, and then controlling the system by using the obtained interpolated controller.
Two adjacent peripheral working points alpha are selected according to the current scheduling parameter alpha of the engineiAnd alphai+1And obtaining two set operating points alphaiAnd alphai+1Controller K for engine without component failureiAnd Ki+1Various typical component failures Δ hbase_jController (2)
Figure BDA0002539521620000101
Δhbase_jThe value of the jth element representing the vector Δ h is Δ hbaseThe value of the other element is 0, i.e. Δ hbase_jIndicating 10 different component failures, e.g. Δ hbase_1Indicates that the fan has failed and the amount of change in fan efficiency is Δ hbase. The working point alpha can be obtained by linear interpolationiController for gas circuit component fault h
Figure BDA0002539521620000102
Likewise, the operating point α can be obtained i+1Controller for gas circuit component fault h
Figure BDA0002539521620000103
We use the piecewise linear interpolation method to derive K from the PID controller set1,K2,…,KqLinear interpolation is performed between each pair of controllers. A linear interpolation controller K (alpha) at the current degradation degree h of the current scheduling parameter alpha is obtained, i is 1,2, …, and q-1 is
Figure BDA0002539521620000104
According to the formula, a corresponding controller under the condition that a certain air path component has a fault at a certain working point can be obtained, and the engine is effectively controlled.
Based on the above process, the fault-tolerant controller for gain scheduling of a failure of a gas circuit component of an aircraft engine provided in this embodiment is given below, and as shown in fig. 1, the fault-tolerant controller mainly includes a fault-tolerant control module of a PID controller group and a fault diagnosis module of the gas circuit component.
Wherein, the fault-tolerant control module of the PID controller group, the gas circuit component fault diagnosis module, the aircraft engine body and a plurality of sensors on the aircraft engine form a gas circuit component fault scheduling control loop 10.
The PID controller group fault-tolerant control 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 the gas circuit component fault diagnosis module, the gas circuit component fault diagnosis module resolves to obtain a health parameter h of the aircraft engine, and outputs the health parameter h to the PID controller group fault-tolerant control module.
The PID controller group fault-tolerant control module, the aircraft engine body and a plurality of sensors on the aircraft engine also form a scheduling parameter scheduling control loop 20; and outputting the scheduling parameter alpha to a PID controller group fault-tolerant control module by a sensor.
The fault-tolerant control module of the PID controller group is internally provided with a plurality of PID controllers, the PID controllers are respectively designed by utilizing a plurality of linearization models, and the linearization models are obtained by linearizing nonlinear models of the aircraft engine under different set working points and different gas circuit component faults.
In a preferred embodiment, the PID controllers are designed by the following process: selecting q working points in the full flight envelope according to the scheduling parameter alpha to linearize the engine nonlinear model containing the health parameters to obtain q linearized models containing the health parameters, obtaining 11q linearized models at the positions of the engine without air channel component failure and the specific air channel component failure by adjusting the values of the health parameters, and designing corresponding PID controllers for the 11q linearized models respectively to form a PID controller group.
The fault-tolerant control module of the PID controller group calculates and obtains an adaptive PID controller by utilizing a plurality of PID controllers designed in the PID controller according to the input health parameter h and the scheduling parameter alpha, and the PID controller generates a control input vector u according to a difference value e between a reference input r and a measurement parameter y.
In a preferred specific implementation manner, the adaptive PID controller may be obtained by interpolation according to the input health parameter h and the scheduling parameter α:
firstly, two adjacent set working points alpha are selected according to the current scheduling parameter alpha of the aeroengineiAnd alphai+1And obtaining two set operating points alphaiAnd alphai+1Controller K for engine without component failureiAnd Ki+1Various typical component failures Δ hbase_jController (2)
Figure BDA0002539521620000111
Δhbase_jThe value of the jth element representing the vector Δ h is Δ hbaseThe value of the other element is 0, i.e. Δ hbase_jIndicating 10 different component failures, e.g. Δ hbase_1Indicates that the fan has failed and the amount of change in fan efficiency is Δ hbase. According to the formula
Figure BDA0002539521620000112
Figure BDA0002539521620000113
Calculating to obtain the selected working point alpha of the aeroengineiAnd alphai+1PID controller K under the current component fault degree (health parameter is h) of the engineiAnd Ki+1(wherein. DELTA.hjIs the jth element of the vector Δ h; only if the | | delta h | | | is less than or equal to | | | delta hmaxFault condition of engine gas path component, when | | | delta h | | non-woven hair>||ΔhmaxThe engine has failed);according to the formula
Figure BDA0002539521620000121
And calculating to obtain the current adaptive fault-tolerant PID controller K (alpha) of the aero-engine.
The gas circuit component fault diagnosis module comprises a nonlinear onboard engine model and a piecewise linearization Kalman filter.
The nonlinear airborne engine model is an engine nonlinear model with health parameters:
Figure BDA0002539521620000122
y＝g(x,u,h)
wherein
Figure BDA0002539521620000123
In order to control the input vector,
Figure BDA0002539521620000124
in the form of a state vector, the state vector,
Figure BDA0002539521620000125
in order to output the vector, the vector is,
Figure BDA0002539521620000126
for the health 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 health parameter h of the previous period, and the output health steady-state reference value (x) of the nonlinear onboard engine modelaug,NOBEM,yNOBEM) As the estimated initial value of the current period of the piecewise linearization Kalman filter.
The inputs of the piecewise linearization Kalman filter are a measurement parameter y and a healthy steady-state reference value (x) output by a nonlinear airborne engine modelaug,NOBEM,yNOBEM) According to the formula
Figure BDA0002539521620000127
And calculating to obtain the health parameter h of the engine in the current period.
Wherein
Figure BDA0002539521620000128
K is the gain of Kalman filtering
Figure BDA0002539521620000129
P is the Ricini equation
Figure BDA00025395216200001210
The solution of (1); coefficient AaugAnd CaugAccording to the formula
Figure BDA00025395216200001211
Caug＝(C M)
Determining, and A, C, L, M is an augmented linear state variable model reflecting engine performance degradation obtained by regarding the health parameter h as the control input of the engine and linearizing the nonlinear on-board engine model at a healthy steady-state reference point
Figure BDA0002539521620000131
Coefficient (c):
Figure BDA0002539521620000132
Figure BDA0002539521620000133
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 (7)

1. The utility model provides an aeroengine gas circuit part failure gain scheduling fault-tolerant controller which characterized in that: the fault diagnosis device comprises a PID controller group fault-tolerant control module and a gas circuit component fault diagnosis module;
the fault-tolerant control module and the gas circuit component fault diagnosis module of the PID controller group, the aircraft engine body and a plurality of sensors on the aircraft engine form a gas circuit component fault scheduling control loop;
the PID controller group fault-tolerant control 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 the gas circuit component fault diagnosis module, the gas circuit component fault diagnosis module resolves to obtain a health parameter h of the aircraft engine and outputs the health parameter h to the PID controller group fault-tolerant control module;
The PID controller group fault-tolerant control module, the aircraft engine body and a plurality of sensors on the aircraft engine also form a scheduling parameter scheduling control loop; the sensor outputs a scheduling parameter alpha to a PID controller group fault-tolerant control module;
the fault-tolerant control module of the PID controller group is internally provided with a plurality of PID controllers which are respectively designed by utilizing a plurality of linearization models, and the linearization models are obtained by linearizing nonlinear models of the aircraft engine under different set working points and different gas circuit component faults of the aircraft engine;
the fault-tolerant control module of the PID controller group calculates and obtains an adaptive PID controller by utilizing a plurality of PID controllers designed in the PID controller according to the input health parameter h and the scheduling parameter alpha, and the PID controller generates a control input vector u according to a difference value e between a reference input r and a measurement parameter y.
2. The aeroengine gas circuit component fault gain scheduling fault-tolerant controller of claim 1, characterized in that: the process of designing a plurality of PID controllers in the PID controller group fault-tolerant control module is as follows: selecting q working points in the full flight envelope according to the scheduling parameter alpha to linearize the engine nonlinear model containing the health parameters to obtain q linearized models containing the health parameters, obtaining 11q linearized models at the positions of the engine without air channel component failure and the specific air channel component failure by adjusting the values of the health parameters, and designing corresponding PID controllers for the 11q linearized models respectively to form a PID controller group.
3. The fault-tolerant controller for gain scheduling of the failure of the gas path component of the aircraft engine according to claim 1 or 2, characterized in that: and the PID controller group fault-tolerant control module obtains an adaptive PID controller according to the input health parameter h and the scheduling parameter alpha by interpolation.
4. The fault-tolerant controller for gain scheduling of the failure of the gas path component of the aero-engine according to claim 3, wherein: the fault-tolerant control module of the PID controller group selects two adjacent set working points alpha according to the current scheduling parameter alpha of the aircraft engineiAnd alphai+1And obtaining two set operating points alphaiAnd alphai+1Controller K for engine without component failureiAnd Ki+1Various typical component failures Δ hbase_jController (2)
Figure RE-FDA0002688073500000021
Δhbase_jThe value of the jth element representing the vector Δ h is Δ hbaseThe value of the other element is 0, i.e. Δ hbase_jIndicating 10 different component failures, e.g. Δ hbase_1Indicates that the fan has failed and the amount of change in fan efficiency is Δ hbase. According to the formula
Figure RE-FDA0002688073500000022
Figure RE-FDA0002688073500000023
Calculating to obtain the selected working point alpha of the aeroengineiAnd alphai+1PID controller K under the current component fault degree (health parameter is h) of the engineiAnd Ki+1(wherein. DELTA.hjIs the jth element of the vector Δ h; only if the | | delta h | | | is less than or equal to | | | delta h maxFault condition of engine gas path component, when | | | delta h | | non-woven hair>||ΔhmaxThe engine has failed); according to the formula
Figure RE-FDA0002688073500000024
And calculating to obtain the current adaptive fault-tolerant PID controller K (alpha) of the aero-engine.
5. The aeroengine gas circuit component fault gain scheduling fault-tolerant controller of claim 1, characterized in that: the gas circuit component fault diagnosis module comprises a nonlinear onboard engine model and a piecewise linearization Kalman filter;
the nonlinear airborne engine model is an engine nonlinear model with health parameters:
Figure RE-FDA0002688073500000025
y＝g(x,u,h)
wherein
Figure RE-FDA0002688073500000026
In order to control the input vector,
Figure RE-FDA0002688073500000027
in the form of a state vector, the state vector,
Figure RE-FDA0002688073500000028
in order to output the vector, the vector is,
Figure RE-FDA0002688073500000029
for the health 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 health parameter h of the previous period, and the output health steady-state reference value (x) of the nonlinear onboard engine modelaug,NOBEM,yNOBEM) The method comprises the steps of taking the current period as an estimated initial value of a piecewise linearization Kalman filter;
the inputs of the piecewise linearization Kalman filter are a measurement parameter y and a healthy steady-state reference value (x) output by a nonlinear airborne engine model aug,NOBEM,yNOBEM) According to the formula
Figure RE-FDA0002688073500000031
Calculating to obtain a health parameter h of the engine in the current period; wherein
Figure RE-FDA0002688073500000032
K is the gain of Kalman filtering
Figure RE-FDA0002688073500000033
P is the Ricini equation
Figure RE-FDA0002688073500000034
The solution of (1); coefficient AaugAnd CaugAccording to the formula
Figure RE-FDA0002688073500000035
Caug＝(C M)
Determining, and A, C, L, M is an augmented linear state variable model reflecting engine performance degradation obtained by regarding the health parameter h as the control input of the engine and linearizing the nonlinear on-board engine model at a healthy steady-state reference point
Figure RE-FDA0002688073500000036
Coefficient (c):
Figure RE-FDA0002688073500000037
Figure RE-FDA0002688073500000038
w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R.
6. The aeroengine gas circuit component fault gain scheduling fault-tolerant controller of claim 1, characterized in that: the scheduling parameter alpha comprises the fan rotating speed or the compressor rotating speed of the aircraft engine.
7. The aeroengine gas circuit component fault gain scheduling fault-tolerant controller of claim 1, characterized in that: 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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