CN111734533A - Turbofan engine-based model prediction method and system - Google Patents
Turbofan engine-based model prediction method and system 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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Abstract
The invention discloses a model prediction method based on a turbofan engine, which comprises the following steps: establishing a turbofan engine model according to an actual turbofan engine; carrying out linearization processing on the turbofan engine model to obtain a linear model; designing a thrust predictive controller by using the linear model as a predictive model; validating the thrust predictive controller on a non-linear model; verifying the thrust predictive controller on the actual turbofan engine. The invention can improve the accuracy and reliability of control output.
Description
Technical Field
The invention relates to the field of aircraft engine simulation and control. And more particularly, to a turbofan engine based model prediction method and system.
Background
Model predictive control is one of the most popular branches of the current control field development. Design and realization of Liupingwang model prediction control[M]2009' provides the basic definition and idea of the control method, that is, a reference model is used to predict the control result in the future for a period of time, the optimal control strategy is found out as much as possible, and then the optimal control strategy is output. Advanced Hanz Richter turbofan engine control[M]2013 "indicates that the control method is very suitable for being applied to the control problems with multivariable and needing limit protection.
The control of the aero-engine is essentially a constraint optimal control problem, and the method is naturally suitable for controller design.
The operation mechanism of model predictive control can be briefly summarized into continuous circulation of an online optimization problem, and the biggest problems caused by the continuous circulation are huge calculation amount and high calculation difficulty, which are main reasons that model prediction cannot be widely applied in the past.
Predictive control was originally derived from chemical control and has found widespread use in this area. In recent years, with the increasing computing power of electronic microprocessors, predictive control is beginning to be applied to other control fields such as electric power, distributed, autopilot, aerospace, and the like. The related research organization in the united states has successfully applied model predictive control to the controller design of aircraft engines and achieved superior results compared to conventional PID controllers.
The model predictive control has the following advantages. Firstly, model prediction is suitable for processing multivariable control problems, and the purpose that one controller is used for cooperatively outputting a plurality of control variables can be achieved, so that the multivariable control performance is guaranteed. And secondly, model prediction control can well introduce a limiting protection measure, and control performance and the protection measure are processed at the same time. The predictive control can well perform control work under fault tolerance and fault states. The prediction control has definite internal logic, and the adjustment of the control performance can be easily realized by adjusting the key parameters.
In controllers for aircraft engines, the more common control object is the speed, since the highest and lowest speeds directly correspond to the maximum and minimum thrust of the engine in the current state. However, in the air, the working state of the aircraft engine changes with some external environmental conditions, if the rotation speed control is adopted, in order to ensure that the specified thrust can be output, an additional control rule is required to adjust the target control rotation speed, the design difficulty is increased by the design mode, and design improvement is required.
The next generation of aircraft engine controllers are required to have the functions of multivariable cooperation, active fault tolerance when a fault occurs, active response to performance change of a controlled engine, more accurate output of a control result and the like. At present, a mainstream engine controller basically adopts a PID control algorithm, and the algorithm can obtain a good control effect when the actual engine is matched with an ideal engine model, but cannot well cope with the performance change or fault of an actual system, and also has the problem of high design difficulty when a multivariable controller is designed. The traditional engine adopts a mode of controlling the rotating speed, if the corresponding rotating speed is obtained, a corresponding control rule needs to be designed for conversion, and the mode has large workload demand and is difficult to ensure the accuracy in all operating environments.
Disclosure of Invention
In order to solve the technical problems in the background art, a first aspect of the present invention provides a model prediction method based on a turbofan engine, including the following steps:
establishing a turbofan engine model according to an actual turbofan engine;
carrying out linearization processing on the turbofan engine model to obtain a linear model;
designing a thrust predictive controller by using the linear model as a predictive model;
validating the thrust predictive controller on a non-linear model;
verifying the thrust predictive controller on the actual turbofan engine.
Optionally, the establishing a turbofan engine model from an actual turbofan engine comprises:
acquiring operation data of each component in the actual turbofan engine;
completing model building of each component according to the operation data;
and connecting the established models of the components to obtain the turbofan engine model.
Optionally, the linearizing the turbofan engine model to obtain a linear model includes:
extracting steady-state points of the turbofan engine model at each state point;
and completing the extraction of the corresponding linear model under the state point based on the steady-state point.
Optionally, after the step of completing the extraction of the linear model based on the steady-state point, the method further includes:
and carrying out accuracy verification on the extracted linear model.
Optionally, the verifying the accuracy of the extracted linear model comprises:
adding an input to both the turbofan engine model and the linear model;
observing whether the response curves of the turbofan engine model and the linear model are matched or not;
if the degree of coincidence is not sufficient,
returning to the step of extracting steady-state points at the respective state points of the turbofan engine model.
The invention provides a model prediction system based on a turbofan engine in a second aspect, which comprises:
the model establishing module is used for establishing a turbofan engine model according to an actual turbofan engine;
the linearization processing module is used for carrying out linearization processing on the turbofan engine model to obtain a linear model;
a design module for designing a thrust predictive controller using the linear model as a predictive model;
a first verification module that verifies the thrust predictive controller on a nonlinear model;
a second validation module that validates the thrust predictive controller on the actual turbofan engine.
Optionally, the establishing a turbofan engine model from an actual turbofan engine comprises:
acquiring operation data of each component in the actual turbofan engine;
completing model building of each component according to the operation data;
and connecting the established models of the components to obtain the turbofan engine model.
Optionally, the linearizing the turbofan engine model to obtain a linear model includes:
extracting steady-state points of the turbofan engine model at each state point;
and completing the extraction of the corresponding linear model under the state point based on the steady-state point.
Optionally, after the step of completing the extraction of the linear model based on the steady-state point, the method further includes:
and carrying out accuracy verification on the extracted linear model.
Optionally, the verifying the accuracy of the extracted linear model comprises:
adding an input to both the turbofan engine model and the linear model;
observing whether the response curves of the turbofan engine model and the linear model are matched or not;
if the degree of coincidence is not sufficient,
returning to the step of extracting steady-state points at the respective state points of the turbofan engine model.
The invention has the following beneficial effects:
in summary, compared with the prior art, the technical scheme of the invention has the following advantages:
(1) the technical scheme of the invention can better meet the requirement of the aircraft engine on multivariable cooperative control, and because of the inherent advantages of the algorithm, the multivariable control can be realized easily, and the workload is reduced to a great extent compared with the traditional PID control;
(2) the model prediction method controls the embedded airborne model, so that fault-tolerant control and the situation of performance change can be easily realized, timely adjustment can be made through adjustment of the internal airborne model after a fault or performance degradation occurs, expected control under the current situation is given, and the model prediction method has very remarkable advantages compared with the traditional PID;
(3) the model prediction method can better coordinate the relationship between the control performance and the limit guarantee by taking the limit protection and the control algorithm into consideration cooperatively. The prior PID control introduces limit protection through a high-low selection strategy, limit protection logic and control logic are independently calculated, certain defects exist in cooperativity, and a model prediction method has obvious algorithm advantages compared with a model prediction method;
(4) compared with the rotating speed control, the direct thrust control saves the intermediate conversion of a control rule, reduces the development complexity, and improves the accuracy and the reliability of control output by omitting the step of intermediate conversion.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 illustrates a flow chart of a model prediction method based on a turbofan engine according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing a basic algorithm of the model prediction method in the present embodiment;
FIG. 3 is a block diagram of a model prediction system for a turbofan-based engine according to another embodiment of the present invention.
Detailed Description
In order to make the technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Fig. 1 shows a flowchart of a model prediction method based on a turbofan engine according to an embodiment of the present invention, as shown in fig. 1, the prediction method includes the following steps:
s100, establishing a turbofan engine model according to an actual turbofan engine;
s200, carrying out linearization processing on the turbofan engine model to obtain a linear model;
s300, designing a thrust predictive controller by using the linear model as a predictive model;
s400, verifying the thrust predictive controller on a nonlinear model;
and S500, verifying the thrust predictive controller on the actual turbofan engine.
Specifically, in an example of the present embodiment, the establishing a turbofan engine model based on an actual turbofan engine further includes: acquiring operation data of each component in the actual turbofan engine; completing model building of each component according to the operation data; and connecting the established models of the components to obtain the turbofan engine model.
Further, in this embodiment, the linearizing the turbofan engine model to obtain a linear model further includes: extracting steady-state points of the turbofan engine model at each state point; and completing the extraction of the corresponding linear model under the state point based on the steady-state point.
In some optional implementations of this embodiment, after the step of completing the extraction of the linear model based on the steady-state point, the method further includes: and carrying out accuracy verification on the extracted linear model.
Further, in this embodiment, the verifying the accuracy of the extracted linear model includes: adding an input to both the turbofan engine model and the linear model; observing whether the response curves of the turbofan engine model and the linear model are matched or not; and if the matching degree is not enough, returning to the step of extracting the steady-state points of the turbofan engine model at each state point.
The model prediction method based on the turbofan engine provided by the embodiment can be summarized into the following parts;
firstly, turbofan engine modeling and linearization are carried out, and the output of an algorithm in a model prediction method needs to depend on a turbofan engine model, so whether the turbofan engine model is matched with an actual system enough or not directly determines the control effect. The modeling of the aircraft engine is divided according to components in a flow passage, response parameters are calculated in each component, if some parameters are difficult to perform simulation calculation by an algorithm, actual test run data need to be collected, physical parameters are given by using a data fitting mode, an execution mechanism on the engine also needs to be modeled, and the adopted mode can be physical modeling or system identification. And then, carrying out linearization operation, adopting a small deviation method, simulating a partial derivative by using a difference, removing a high-order component, only keeping a first-order linear component, and continuously verifying the linear model and the nonlinear model in the extraction process in order to ensure that the reference model used by the controller is accurate enough and the response requirements of the linear model and the nonlinear model in a certain range are accurate enough.
The second part is the algorithm design of the model prediction method, as shown in fig. 2, firstly, according to the algorithm under the condition of no limit protection, firstly, setting initial performance parameters according to needs, then, deriving according to a formula to obtain a final result expression, during the actual execution process, writing the algorithm according to the derived flow, and executing once calculation according to input and giving output at each step. When the condition of limiting protection exists, the problem is converted into a dynamic planning problem, a determined formula solution does not exist, and iterative calculation of an algorithm is required. When the related algorithm modules are developed more mature, the modules are directly introduced into the controller to complete the calculation.
The third part is the calculation of direct thrust, and the calculation is obtained by measuring various physical parameters of an inlet and an outlet according to the momentum theorem and combining with formula calculation and is sent to the controller as a feedback parameter. Because a large amount of noise exists in an actual system, the feedback signal needs to be filtered, and a Kalman filter which is developed more mature can be selected. And finally, a relatively gentle and accurate thrust feedback result can be obtained.
And the fourth part is experimental verification performed by the model and the controller, the control input and the engine feedback signal are accessed into the model prediction controller, the control output is connected to the execution mechanism and finally acts on the aero-engine, the experimental result is recorded, the controller effect is judged according to the expectation and the result, and corresponding modification and adjustment are performed. After the basic predictive controller is designed, through further development of a basic algorithm, external conditions of parameter change can be set, further, the execution effect of the engine is dynamically adjusted in the actual execution process, the expectation and the actual output are compared, the deviation degree of an actual system and a model is measured according to the deviation amount, adjustment is carried out, the effect of the controller is improved, the component increase and decrease of the model are introduced, and the method can be fully adapted when faults occur or a control plan is changed.
In summary, compared with the prior art, the technical scheme of the invention has the following advantages:
(1) the technical scheme of the invention can better meet the requirement of the aircraft engine on multivariable cooperative control, and because of the inherent advantages of the algorithm, the multivariable control can be realized easily, and the workload is reduced to a great extent compared with the traditional PID control.
(2) The model prediction method controls the embedded airborne model, can easily realize fault-tolerant control and cope with the situation of performance change, can make timely adjustment through the adjustment of the internal airborne model after a fault or performance degradation occurs, gives expected control under the current situation, and has very obvious advantages compared with the traditional PID.
(3) The model prediction method can better coordinate the relationship between the control performance and the limit guarantee by taking the limit protection and the control algorithm into consideration cooperatively. The prior PID control introduces limit protection through a high-low selection strategy, limit protection logic and control logic are independently calculated, certain defects exist in cooperativity, and the model prediction method has obvious algorithm advantages compared with the model prediction method.
(4) Compared with the rotating speed control, the direct thrust control saves the intermediate conversion of a control rule, reduces the development complexity, and improves the accuracy and the reliability of control output by omitting the step of intermediate conversion.
The present embodiment is further described in detail with reference to the practical application scenarios as follows:
a model prediction method based on a turbofan engine comprises the following steps:
the first step is as follows: establishing a turbofan engine model according to a specific type of turbofan engine;
specifically, corresponding operation data are obtained through tests and experiments on engine components, subsystems and the whole engine, particularly, attention is paid to the experimental work of the rotating component, and accuracy is guaranteed. The invention adopts a component method for modeling, firstly completes the establishment of a model of a single component for main components in an engine flow passage according to the modes of a physical formula, data fitting, system identification and the like, and compares the model with actual data to ensure better coincidence. The various actuators of the engine are then modeled in a similar manner. And connecting the input and output values of each part according to actual conditions to form a complete engine model. Because some dynamic quantities which are difficult to accurately describe exist among all parts of the engine, the whole system always has a certain degree of deviation, and the conservation principle of flow, energy and the like needs to be introduced into the whole system, so that parameters among all parts are constrained, and a balance equation set is solved, so that the final model output result is more accurate.
The second step is that: carrying out linearization treatment on the established turbofan engine model in a full flight envelope;
the model prediction method needs to depend on a linear model, so that the model in the first step is required to be subjected to linearization processing, and the processing process can be divided into the following four steps. The method comprises the steps of firstly, extracting a steady state point of a model, selecting a specific environment and a specific running state as input, and recording the current input and output values as the steady state value of the state point after the output value is kept stable. And secondly, performing linearization processing on the model, approximately simulating the derivative value of each state quantity and input quantity of the steady-state point by the principle of a small deviation method on the basis of the extracted steady-state point, and splicing into an ABCD matrix of a linear space after completing calculation by a programmed program to finish the extraction of the linear model. And thirdly, verifying the accuracy of the extracted linear model, adding a small input to the original model and the linear model at the same time, observing whether the response curves of the original model and the linear model are sufficiently matched, if the matching degree is not sufficient, returning to the previous two steps, adjusting system parameters, re-obtaining the linear model and verifying.
It should be noted that, because the turbofan engine belongs to a typical highly nonlinear system, it is difficult for a linearization model extracted at a certain state point to achieve better coincidence with all nonlinear states of the full flight state, and the solution is to select a plurality of state points in the entire flight envelope to perform linearization operation, and select a linear model of a state point with the closest performance as a reference model according to the proximity of an actual operating point and the state point. The extraction of all state points is completed according to the above-mentioned process.
The third step: design of model predictive controller using linear model
The execution flow of the model predictive controller is as follows. Firstly, the existing model needs to be expanded, the original state is changed into an incremental form, and the output is taken as a state quantity and is introduced into a state vector. And then, predicting the state and output of P step lengths in the future by using the expanded ABCD matrix, substituting the control input into a formula on the assumption that the control input at C moments in the future exists, obtaining a determined formula solution under the condition of no limitation, defining a performance function, solving the minimum value of the performance function, and further obtaining the control output. When restrictive protection exists, an inequality equation set needs to be listed, and the subsequent problem can be converted into a dynamic programming problem.
According to the algorithm, a model predictive controller code aiming at a steady-state point is developed, the obtained linearized model is used as a reference model of model predictive control, parameters in the model predictive control are set according to expected performance, if the engine has a requirement of limiting protection, the limiting protection and the control requirement are combined in the model predictive calculation algorithm according to actual output limitation, and finally an optimal solution solving problem of multiple limiting conditions, namely a QP problem, is formed.
And then, taking all the extracted linear models as reference models, and repeating the steps to complete the design of the model predictive controllers of all the state points. And setting a switching rule to ensure that the controller can always find a linear model with the closest performance in the full flight envelope as a reference.
The fourth step: verifying controller usage effects on a non-linear model
The method is characterized in that unmodeled dynamics and uncertainty exist in an actual system, problems are easy to occur when the controller is directly verified, and in order to guarantee the safety of an engine and the success probability of an experiment to the maximum degree, the designed controller is verified on a nonlinear model closest to the actual system. Verification is first performed in the vicinity of all state points, starting with a steady state value at each state point, giving small increments, observing whether the results can respond as expected. If there is an unexpected situation, the controller of the state point needs to be redesigned, including parameter adjustment and part of limiting protection.
After the independent testing of all the state points is completed, the task testing of the full-flight envelope is carried out next, the testing is carried out according to the Mach number and the height range of the task requirement, whether the controller can carry out normal logic switching in the full range is observed, and the limit protection logic is successfully started when the engine performance boundary operates. Observing the obtained operation result, if a part with undesirable performance exists, further modification needs to be carried out on a control system of the part, and if necessary, a compensation controller comprising feedforward compensation and a second degree of freedom can be introduced for ensuring the performance, and the performance of the controller is ensured by a comprehensive method.
The fifth step: verifying use effect of controller on actual aero-engine
After the verification of the controller on the nonlinear model is completed, the verification is carried out on an actual engine, the algorithm of the controller is copied into an actual controller of the engine, the verification is firstly carried out on a ground test bed, the whole process from the minimum rotating speed to the maximum rotating speed is included, and if the engine can obtain the expected thrust effect, the design of the controller is proved to be successful. And if the conditions allow, continuing to perform real-machine verification, and verifying whether the same effect can be achieved in the flight envelope by adopting a model prediction direct thrust controller on a certain engine of the airplane.
Fig. 3 is a block diagram illustrating a model prediction system based on a turbofan engine according to another embodiment of the present invention, as shown in fig. 3, the system includes:
the model establishing module is used for establishing a turbofan engine model according to an actual turbofan engine;
the linearization processing module is used for carrying out linearization processing on the turbofan engine model to obtain a linear model;
a design module for designing a thrust predictive controller using the linear model as a predictive model;
a first verification module that verifies the thrust predictive controller on a nonlinear model;
a second validation module that validates the thrust predictive controller on the actual turbofan engine.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations or modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and all obvious variations or modifications may be included within the scope of the present invention.
Claims (10)
1. A model prediction method based on a turbofan engine is characterized by comprising the following steps:
establishing a turbofan engine model according to an actual turbofan engine;
carrying out linearization processing on the turbofan engine model to obtain a linear model;
designing a thrust predictive controller by using the linear model as a predictive model;
validating the thrust predictive controller on a non-linear model;
verifying the thrust predictive controller on the actual turbofan engine.
2. The method of claim 1,
the building of the turbofan engine model from the actual turbofan engine comprises:
acquiring operation data of each component in the actual turbofan engine;
completing model building of each component according to the operation data;
and connecting the established models of the components to obtain the turbofan engine model.
3. The method of claim 1,
the step of carrying out linearization processing on the turbofan engine model to obtain a linear model comprises the following steps:
extracting steady-state points of the turbofan engine model at each state point;
and completing the extraction of the corresponding linear model under the state point based on the steady-state point.
4. The method of claim 3,
after the step of completing the extraction of the linear model based on the steady-state point, further comprising:
and carrying out accuracy verification on the extracted linear model.
5. The method of claim 4,
the verifying the accuracy of the extracted linear model comprises:
adding an input to both the turbofan engine model and the linear model;
observing whether the response curves of the turbofan engine model and the linear model are matched or not;
if the degree of coincidence is not sufficient,
returning to the step of extracting steady-state points at the respective state points of the turbofan engine model.
6. A turbofan engine based model prediction system, comprising:
the model establishing module is used for establishing a turbofan engine model according to an actual turbofan engine;
the linearization processing module is used for carrying out linearization processing on the turbofan engine model to obtain a linear model;
a design module for designing a thrust predictive controller using the linear model as a predictive model;
a first verification module that verifies the thrust predictive controller on a nonlinear model;
a second validation module that validates the thrust predictive controller on the actual turbofan engine.
7. The system of claim 6,
the building of the turbofan engine model from the actual turbofan engine comprises:
acquiring operation data of each component in the actual turbofan engine;
completing model building of each component according to the operation data;
and connecting the established models of the components to obtain the turbofan engine model.
8. The method of claim 6,
the step of carrying out linearization processing on the turbofan engine model to obtain a linear model comprises the following steps:
extracting steady-state points of the turbofan engine model at each state point;
and completing the extraction of the corresponding linear model under the state point based on the steady-state point.
9. The method of claim 8,
after the step of completing the extraction of the linear model based on the steady-state point, further comprising:
and carrying out accuracy verification on the extracted linear model.
10. The method of claim 9,
the verifying the accuracy of the extracted linear model comprises:
adding an input to both the turbofan engine model and the linear model;
observing whether the response curves of the turbofan engine model and the linear model are matched or not;
if the degree of coincidence is not sufficient,
returning to the step of extracting steady-state points at the respective state points of the turbofan engine model.
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