Combination optimization method for dynamic control law of aero-engine

Abstract

The invention discloses an aero-engine dynamic control rule combination optimization method, and belongs to the field of aero-engine control rule optimization design. The method comprises the following steps: determining control parameters and controlled parameters of a starting point and an ending point of an engine dynamic process; dispersing the dynamic process of the engine into a plurality of sub-control points according to time step length or other independent variables; constructing an optimization objective function and constraints of discrete points; determining an optimization variable of the point-by-point optimization process as a control parameter; carrying out point-by-point optimization to solve a discrete optimization problem; fitting to obtain a dynamic control rule curve of the engine; and judging whether an optimization termination condition is met or not. The problems that in the prior art, the engine dynamic control rule design process is complex, the optimization problem solving difficulty is large when the engine dynamic control rule faces multiple adjustable parameters, and the obtained control rule has numerical fluctuation and is difficult to be directly applied to engineering are solved.

Description

Technical Field

[0001] The invention belongs to the field of optimization design of aircraft engine control laws, and in particular relates to a combined optimization method for aircraft engine dynamic control laws. Background Art

[0002] The maneuverability of an aircraft depends largely on the dynamic response capability of the engine. For the same engine, different dynamic control laws will make its dynamic response capabilities vary greatly. Therefore, optimizing the design of the engine dynamic control law is of great significance to the dynamic performance of the engine. At present, there are two main mainstream control law optimization methods: point-by-point optimization technology and global optimization technology. Point-by-point optimization technology simplifies the construction and solution difficulty of the optimization problem by discretizing the dynamic process. However, since point-by-point optimization technology only focuses on the fastest response of each independent discrete process, it is impossible to obtain the global optimal solution of the entire dynamic process. In addition, the optimization results obtained by point-by-point optimization technology may also have numerical fluctuations and cannot be used directly as a control law. Global optimization technology aims to minimize the response time of the entire dynamic process, which requires optimizing the time-arranged sequence of all adjustable parameters of the engine.

[0003] For a complex configuration like a variable cycle engine, there are more than 10 adjustable parameters. Assuming that each adjustable parameter is discretized into a sequence of 10 control points arranged in time, the number of optimization variables will reach more than 100. For this kind of high-dimensional nonlinear optimization problem, the conventional gradient-based optimization algorithm will no longer be applicable. Generally, a swarm intelligence optimization algorithm is used to solve it. Multiplying it by the number of populations, each generation needs to call the dynamic model of the engine thousands of times. Therefore, for a configuration like a variable cycle engine with many adjustable parameters, the optimization time and computational cost of the global optimization technology are extremely high.

[0004] Therefore, it is necessary to develop new optimization design methods for aircraft engine dynamic control laws in order to obtain the available optimal engine dynamic control laws while ensuring a certain global convergence and calculation speed. Summary of the invention

[0005] Technical issues to be solved:

[0006] In order to avoid the shortcomings of the prior art, the present invention provides a combined optimization method for the dynamic control laws of aircraft engines, which integrates the idea of ​​global optimization on the basis of traditional point-by-point optimization technology. The method of the present invention uses point-by-point optimization technology to reduce the difficulty of constructing and solving the optimization problem, integrates the global convergence of the global optimization technology, and improves the comprehensive performance of the dynamic control law optimization design method. This at least solves the problems in the prior art that the design process of the dynamic control law of the engine is complicated, the optimization problem is difficult to solve when facing multiple adjustable parameters, and the obtained control law has numerical fluctuations and is difficult to be directly applied to engineering.

[0007] The technical solution of the present invention is: a method for combining and optimizing dynamic control laws of an aircraft engine, and the specific steps are as follows:

[0008] Determine the control parameters and controlled parameters of the starting and ending points of the engine dynamic process; the control parameters include the fuel supply of the combustion chamber and the adjustable geometric parameters, and the controlled parameters are the performance parameters of the engine;

[0009] Discretize the dynamic process of the engine into several sub-control points according to the time step or other independent variables;

[0010] Construct optimization objective functions and constraints for discrete points;

[0011] Determine the optimization variables of the point-by-point optimization process as control parameters;

[0012] Carry out point-by-point optimization to solve discrete optimization problems; construct discrete optimization problems and use a series of quadratic programming algorithms to solve them point by point to obtain the dynamic control law data of the engine;

[0013] The dynamic control law curve of the engine is obtained by fitting; the dynamic control law curves of all control parameters are obtained by fitting the dynamic control law data of the engine;

[0014] Determine whether the optimization termination condition is met.

[0015] A further technical solution of the present invention is: the control parameters refer to parameters that can control the working state of the engine, wherein the adjustable geometric parameters include the fan inlet guide vane angle, the fan inlet stator blade angle, the compressor inlet guide vane angle, the turbine inlet guide device area, and the nozzle throat area; the controlled parameters include thrust and high-pressure / low-pressure rotor physical speed.

[0016] A further technical solution of the present invention is: the dynamic process of the engine is discretized according to time steps, and its direct optimization target is to minimize the dynamic response time. The optimization target after discretization becomes the fastest response within each time step.

[0017] A further technical solution of the present invention is: the optimization objective function expression of the discrete points is as follows:

[0018]

[0019] Where J[k] represents the objective function of the kth time step; P[k+1] represents the performance parameter value after the kth time step; P obj Indicates the target value of the performance parameter; P start represents the starting value of the performance parameter; X[j] k+1 represents the value of the jth adjustable parameter after the kth time step; X[j]obj represents the target value of the jth adjustable parameter, which is equal to the value of the steady-state adjustable parameter corresponding to the target value of the performance parameter; X[j] up and X[j] low represent the upper and lower limits of the jth adjustable parameter respectively; D represents the number of adjustable parameters; ω represents the weight factor; the first item on the right side of the equal sign in the expression represents that P[k+1] needs to be as close to its target value as possible in each time step, and the second item on the right side of the equal sign represents that X[j] k+1 As close to its target value as possible; when X[j] k+1 When it approaches its target value, it will also cause P[k+1] to approach its target value.

[0020] A further technical solution of the present invention is: the optimization constraints of the discrete points are: the rotation speed is not higher than a given value, the combustion chamber outlet temperature is not higher than a given value, the compression component surge margin is not lower than a given value, and the control parameter change rate is not higher than a given value.

[0021] A further technical solution of the present invention is: the process of performing point-by-point optimization to solve the discrete optimization problem is:

[0022] A series of quadratic programming algorithms are used to solve the optimization objective function of each discrete point in turn until both the control parameters and the controlled parameters reach a steady state and last for a sufficient time or the simulation time reaches the upper limit of the dynamic process, that is, the optimization result of each discrete point is obtained;

[0023] Arrange the optimization results of each discrete point in chronological order, which is the discrete dynamic control law data sequence;

[0024] If the point-by-point optimization counting variable i=1, it is the first time to carry out point-by-point optimization. At this time, the optimization variables are the fuel supply to the combustion chamber and all adjustable geometric parameters. The optimization problem is solved point by point to obtain the discrete dynamic control law data sequence of the fuel supply to the combustion chamber and all adjustable geometric parameters; otherwise, after global optimization, the adjustable geometric parameters have become control variables known in advance and are no longer used as optimization variables for point-by-point optimization. At this time, the only optimization variable left is the fuel supply to the combustion chamber. Therefore, after solving the optimization problem point by point, only the discrete dynamic control law data sequence of the fuel supply to the combustion chamber can be obtained.

[0025] A further technical solution of the present invention is: the process of fitting to obtain the dynamic control law curve of the engine is: if the point-by-point optimization counting variable i=1, the discrete dynamic control law data sequence is used to fit the dynamic control law curve of the combustion chamber fuel supply and the dynamic control law curve of the adjustable geometric parameters; otherwise, the adjustable geometric parameters have been used as control variables, and the dynamic control law curve of the adjustable geometric parameters has been obtained before the point-by-point optimization, so it is only necessary to fit the dynamic control law curve of the combustion chamber fuel supply.

[0026] A further technical solution of the present invention is: the process of judging whether the optimization termination condition is reached is:

[0027] Determine whether the optimization termination condition is met. If so, complete the optimization; if not, go to the next step;

[0028] If it is judged that the optimization termination condition is not met, the differential evolution algorithm is used to carry out global optimization, optimize the position of the characteristic points of the adjustable geometric parameter control law, and use the linear fitting method to fit the new characteristic points to obtain a new adjustable geometric parameter control law curve;

[0029] The new adjustable geometric parameter control law curve is transmitted to the engine model. At this time, the adjustable geometric parameter is no longer used as an optimization parameter in the next point-by-point optimization, but as a known control parameter. The control law data of the fuel supply of the combustion chamber is obtained by using the point-by-point optimization technology, and the cycle is repeated until the termination condition is reached;

[0030] After the optimization is completed, the dynamic control law curves of the combustion chamber fuel supply and adjustable geometric parameters are obtained.

[0031] An aircraft engine dynamic control law combined optimization system, including an engine dynamic process simulation model, a discrete model, a discrete point optimization model, a point-by-point optimization model and a fitting model;

[0032] Determine the control parameter values ​​and the controlled parameter values ​​of the starting point and the ending point of the engine dynamic process based on the engine dynamic process simulation model;

[0033] The engine dynamic process is discretized into a plurality of control points according to the time step by using the discrete model;

[0034] Constructing the optimization objective function and constraints of discrete points through the discrete point optimization model;

[0035] The discrete optimization problem can be solved by performing point-by-point optimization through the point-by-point optimization model.

[0036] The dynamic control law curve of the engine is obtained by fitting the fitting model.

[0037] Beneficial Effects

[0038] The beneficial effects of the present invention are as follows: the combined optimization method for dynamic control laws of aircraft engines of the present invention integrates the idea of ​​global optimization on the basis of traditional point-by-point optimization technology, uses a differential evolution algorithm to perform global optimization on the characteristic point positions of the adjustable geometric parameter control law curve, and directly obtains the adjustable geometric parameter control law curve by data fitting. It can overcome the problem that the point-by-point optimization design method cannot guarantee the acquisition of the global optimal control law and the difficulty in solving the optimization model in the global optimization design method, and solves the technical difficulty that the optimization results have numerical fluctuations and are difficult to directly apply to engineering when using the traditional point-by-point optimization design method. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flow chart of a combined optimization method for dynamic control laws of an aircraft engine which can be selected in an embodiment of the present invention;

[0040] Figure 2 It is a detailed flow chart of the combination of point-by-point technology and global technology in a combined optimization method of dynamic control law of an aircraft engine which can be selected in an embodiment of the present invention;

[0041] Figure 3 The dynamic control law curve of the fuel supply amount in the combustion chamber of a mixed turbofan engine during deceleration is obtained by performing point-by-point optimization only once using the method of the present invention;

[0042] Figure 4 The dynamic control law curve of the nozzle throat area during the deceleration process of the mixed turbofan engine is obtained by performing point-by-point optimization only once using the method of the present invention;

[0043] Figure 5 The thrust variation curve of the mixed turbofan engine during deceleration process obtained by performing point-by-point optimization only once using the method of the present invention;

[0044] Figure 6 The dynamic control law curve of the fuel supply amount in the combustion chamber of the mixed turbofan engine during the deceleration process is obtained by optimizing the method of the present invention;

[0045] Figure 7 The dynamic control law curve of the nozzle throat area of ​​the mixed turbofan engine during the deceleration process is obtained by optimizing the method of the present invention;

[0046] Figure 8 It is a curve of thrust variation over time during the deceleration process of a mixed turbofan engine optimized by the method of the present invention. DETAILED DESCRIPTION

[0047] The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0048] Based on the configuration of variable cycle engine with many adjustable parameters, there is a problem of extremely high optimization time and calculation cost of global optimization technology. The present invention provides a combined optimization method of dynamic control law of aircraft engine, and the specific steps are as follows:

[0049] Step 1: Determine the control parameters and controlled parameters of the starting point and the end point of the engine dynamic process; the control parameters include the fuel supply of the combustion chamber and the adjustable geometric parameters, and the controlled parameters are the performance parameters of the engine;

[0050] Step 2: Discretize the dynamic process of the engine into several sub-control points according to the time step or other independent variables;

[0051] Step 3: Construct the optimization objective function and constraints of discrete points;

[0052] Step 4: Determine the optimization variables of the point-by-point optimization process as control parameters;

[0053] Step 5: Carry out point-by-point optimization to solve the discrete optimization problem; construct the discrete optimization problem and use a series of quadratic programming algorithms to solve it point by point to obtain the dynamic control law data of the engine;

[0054] Step 6: fitting to obtain the dynamic control law curve of the engine; using the dynamic control law data of the engine to fit to obtain the dynamic control law curves of all control parameters;

[0055] Step 7: Determine whether the optimization termination condition is met.

[0056] Specifically, the control parameters refer to parameters that can control the working state of the engine, wherein the adjustable geometric parameters include the fan inlet guide vane angle, the fan inlet stator blade angle, the compressor inlet guide vane angle, the turbine inlet guide device area, and the nozzle throat area; the controlled parameters include thrust and high-pressure / low-pressure rotor physical speed.

[0057] Specifically, the dynamic process of the engine is discretized according to time steps, and its direct optimization goal is to minimize the dynamic response time (such as acceleration time, starting time). The optimization goal after discretization becomes the fastest response within each time step.

[0058] Specifically, the optimization objective function expression of the discrete points is as follows:

[0059]

[0060] Where J[k] represents the objective function of the kth time step; P[k+1] represents the performance parameter value after the kth time step; P obj Indicates the target value of the performance parameter; P start represents the starting value of the performance parameter; X[j] k+1represents the value of the jth adjustable parameter after the kth time step; X[j] obj represents the target value of the jth adjustable parameter, which is equal to the value of the steady-state adjustable parameter corresponding to the target value of the performance parameter; X[j] up and X[j] low represent the upper and lower limits of the jth adjustable parameter respectively; D represents the number of adjustable parameters; ω represents the weight factor; the first item on the right side of the equal sign in the expression represents that P[k+1] needs to be as close to its target value as possible in each time step, and the second item on the right side of the equal sign represents that X[j] k+1 As close to its target value as possible; when X[j] k+1 When it approaches its target value, it will also cause P[k+1] to approach its target value.

[0061] Specifically, the optimization constraints of the discrete points are: the rotation speed is not higher than a given value, the combustion chamber outlet temperature is not higher than a given value, the compression component surge margin is not lower than a given value, and the control parameter change rate is not higher than a given value.

[0062] Specifically, the process of performing point-by-point optimization to solve the discrete optimization problem is:

[0063] A series of quadratic programming algorithms are used to solve the optimization objective function of each discrete point in turn until both the control parameter and the controlled parameter reach a steady state and last for a sufficient time or the simulation time reaches the upper limit of the dynamic process T Dyn (i.e. dt×m>T Dyn ), that is, the optimization result of each discrete point is obtained;

[0064] Arrange the optimization results of each discrete point in chronological order, which is the discrete dynamic control law data sequence;

[0065] If the point-by-point optimization counting variable i=1, it is the first time to carry out point-by-point optimization. At this time, the optimization variables are the fuel supply to the combustion chamber and all adjustable geometric parameters. The optimization problem is solved point by point to obtain the discrete dynamic control law data sequence of the fuel supply to the combustion chamber and all adjustable geometric parameters; otherwise, after global optimization, the adjustable geometric parameters have become control variables known in advance and are no longer used as optimization variables for point-by-point optimization. At this time, the only optimization variable left is the fuel supply to the combustion chamber. Therefore, after solving the optimization problem point by point, only the discrete dynamic control law data sequence of the fuel supply to the combustion chamber can be obtained.

[0066] Specifically, the process of fitting to obtain the dynamic control law curve of the engine is: if the point-by-point optimization counting variable i=1, the discrete dynamic control law data sequence is used to fit the dynamic control law curve of the combustion chamber fuel supply and the dynamic control law curve of the adjustable geometric parameters; otherwise, the adjustable geometric parameters have been used as control variables, and the dynamic control law curve of the adjustable geometric parameters has been obtained before the point-by-point optimization, so it is only necessary to fit the dynamic control law curve of the combustion chamber fuel supply.

[0067] Specifically, the process of determining whether the optimization termination condition is met is as follows:

[0068] Determine whether the optimization termination condition is met. If so, complete the optimization; if not, go to the next step;

[0069] If it is judged that the optimization termination condition is not met, the differential evolution algorithm is used to carry out global optimization, optimize the position of the characteristic points of the adjustable geometric parameter control law, and use the linear fitting method to fit the new characteristic points to obtain a new adjustable geometric parameter control law curve;

[0070] The new adjustable geometric parameter control law curve is transmitted to the engine model. At this time, the adjustable geometric parameter is no longer used as an optimization parameter in the next point-by-point optimization, but as a known control parameter. The control law data of the fuel supply of the combustion chamber is obtained by using the point-by-point optimization technology, and the cycle is repeated until the termination condition is reached;

[0071] After the optimization is completed, the dynamic control law curves of the combustion chamber fuel supply and adjustable geometric parameters are obtained.

[0072] The invention discloses a combined optimization system for dynamic control laws of an aero-engine, comprising an engine dynamic process simulation model, a discrete model, a discrete point optimization model, a point-by-point optimization model and a fitting model; the control parameter values ​​and the controlled parameter values ​​of the starting point and the ending point of the engine dynamic process are determined based on the engine dynamic process simulation model; the engine dynamic process is discretized into a plurality of control points according to a time step by the discrete model; the optimization objective function and constraints of the discrete points are constructed by the discrete point optimization model; the discrete optimization problem is solved by point-by-point optimization by the point-by-point optimization model, and the dynamic control law curve of the engine is obtained by fitting by the fitting model.

[0073] The above technical solution is further described below with reference to the accompanying drawings and examples:

[0074] Reference Figure 1 As shown, the present embodiment provides a method for combining and optimizing dynamic control laws of an aircraft engine, and the steps are as follows:

[0075] Step 1: Determine the starting and ending points of the engine dynamic process.

[0076] It is necessary to determine the control parameter values ​​and controlled parameter values ​​of the starting and ending points of the engine dynamic process. The control parameter refers to the parameter that can control the working state of the engine, and the controlled parameter is usually the performance parameter of the engine. This embodiment takes the mixed turbofan engine as an example, assuming that its adjustable geometric parameter is only the nozzle throat area, then the selected control parameters are the combustion chamber fuel supply and the nozzle throat area, and the controlled parameter is the thrust;

[0077] Step 2: The deceleration process of the discrete mixed turbofan engine.

[0078] The dynamic process of the engine is discretized into m sub-control points with a time step of 0.1s. It should be noted that the time and parameter value of the starting point are known, while the parameter value of the ending point is known but the time is unknown. Therefore, the sub-control points do not include the starting point, but include the ending point;

[0079] Step 3: Construct the optimization objective function and constraints of discrete points.

[0080] The direct optimization goal of the dynamic process is to minimize the dynamic response time (such as acceleration time, start-up time), and the optimization goal after discretization becomes the fastest response within each time step. In this embodiment, in order to ensure the stable and safe operation of the mixed turbofan engine during the deceleration process, there are some constraints in the optimization problem: the relative physical speed and relative conversion speed of the fan and compressor are not higher than 102%, the fan surge margin is not less than 10%, the compressor surge margin is not less than 15%, the total temperature at the combustion chamber outlet is not higher than 2050K, and the combustion chamber fuel supply change rate is not higher than 0.8kg / s 2 wait;

[0081] Step 4: Determine the optimization variables for the point-by-point optimization process.

[0082] The dynamic process of the engine is mainly regulated by the fuel supply to the combustion chamber. The adjustable geometric parameters are mainly to match the fuel supply to the combustion chamber so that the engine can work in the best state. The optimization variables for the first point-by-point optimization of the deceleration process of the mixed turbofan engine are the fuel supply to the combustion chamber and the nozzle throat area. The point-by-point optimization count variable i is assigned a value of 1, and this variable is used to record the number of point-by-point optimizations.

[0083] Step 5: Carry out point-by-point optimization to solve the discrete optimization problem.

[0084] Reference Figure 2As shown, a series of quadratic programming algorithms are used to solve the optimization objective function of each discrete point in turn until the control parameters and the controlled parameters reach a steady state and last for a sufficient time or the simulation time reaches the upper limit of the dynamic process (6s). The optimization results of each discrete point are obtained and arranged in chronological order as a discrete dynamic control law data sequence. It should be noted that if the point-by-point optimization counting variable i=1, it is the first time to carry out point-by-point optimization. At this time, the optimization variables of the deceleration process of the mixed turbofan engine are the fuel supply of the combustion chamber and the nozzle throat area. The discrete dynamic control law data sequence of the fuel supply of the combustion chamber and the nozzle throat area is obtained by solving the optimization problem point by point; otherwise, after global optimization, the nozzle throat area has become a control variable known in advance and is no longer used as an optimization variable for point-by-point optimization. At this time, the only optimization variable left is the fuel supply of the combustion chamber. Therefore, after solving the optimization problem point by point, only the discrete dynamic control law data sequence of the fuel supply of the combustion chamber can be obtained;

[0085] Step 6: Fit the dynamic control law curve of the mixed turbofan engine.

[0086] If the point-by-point optimization counting variable i=1, the dynamic control law curve of the fuel supply to the combustion chamber is obtained by fitting the discrete dynamic control law data sequence in step 5 (such as Figure 3 ) and the dynamic control law curve of the nozzle throat area (as shown in Figure 4 As shown in Figure 2, if the optimization is terminated at this time, it is equivalent to using the traditional point-by-point optimization method to optimize the control law of the deceleration process of the mixed turbofan engine. At this time, the deceleration time calculated according to the thrust change of 95% is 3.3s (the thrust change law with time is shown in Figure 2). Figure 5 shown), by Figure 4 It can be seen that the control law curve of the nozzle throat area presents high-dimensional characteristics and is difficult to use as a control law; otherwise, if i≠1, the nozzle throat area has been used as a control variable, and the dynamic control law curve of the nozzle throat area has been obtained before point-by-point optimization, so it is only necessary to fit the dynamic control law curve of the combustion chamber fuel supply. Considering the feasibility of engineering, the linear fitting method is used in this step to fit the dynamic control law curve of the deceleration process of the mixed turbofan engine.

[0087] Step 7: Determine whether the optimization termination condition is met. If so, go to step 10, otherwise go to step 8.

[0088] Step 8: Since the nozzle throat area optimization results obtained by the point-by-point optimization technology are difficult to be directly applied in engineering, this step uses the differential evolution algorithm to carry out global optimization, optimize the position of the characteristic points of the nozzle throat area control law curve, and use the linear fitting method to fit the obtained characteristic points to obtain a new nozzle throat area control law curve.

[0089] Step 9: Pass the nozzle throat area control law curve obtained in step 8 to the engine model, so that the nozzle throat area is no longer used as an optimization parameter in the next point-by-point optimization, but as a known control parameter, point-by-point optimization counting variable i=i+1, and go to step 5.

[0090] Step 10: After the optimization is completed, the dynamic control law curve of the combustion chamber fuel supply and nozzle throat area is obtained (such as Figure 6-Figure 7 As shown in the figure, it can be seen that the dynamic control law curve of the combustion chamber fuel supply obtained by the method of the present invention is basically linear, and the dynamic control law curve of the nozzle throat area is completely linear, which is conducive to the realization of the control system. In addition, Figure 8 The thrust variation curve of the mixed turbofan engine deceleration process optimized by the method of the present invention is shown. The deceleration time calculated according to 95% thrust change is 3.2s, which is 0.1s shorter than the deceleration time obtained by the traditional point-by-point optimization technology.

[0091] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A combined optimization method for dynamic control laws of an aircraft engine, characterized in that The specific steps are as follows: Determine the control parameters and controlled parameters of the starting and ending points of the engine dynamic process; the control parameters include the fuel supply of the combustion chamber and the adjustable geometric parameters, and the controlled parameters are the performance parameters of the engine; Discretize the dynamic process of the engine into several sub-control points according to the time step or other independent variables; Construct optimization objective functions and constraints for discrete points; Determine the optimization variables of the point-by-point optimization process as control parameters; Perform point-by-point optimization to solve discrete optimization problems; Construct a discrete optimization problem and use a series of quadratic programming algorithms to solve it point by point to obtain the dynamic control law data of the engine; The dynamic control law curve of the engine is obtained by fitting; the dynamic control law curves of all control parameters are obtained by fitting the dynamic control law data of the engine; Determine whether the optimization termination condition is met.

2. The method for combined optimization of dynamic control laws of an aircraft engine according to claim 1, characterized in that: The control parameters refer to parameters that can control the working state of the engine, among which the adjustable geometric parameters include the fan inlet guide vane angle, the fan inlet stator blade angle, the compressor inlet guide vane angle, the turbine inlet guide device area, and the nozzle throat area; the controlled parameters include thrust and high-pressure / low-pressure rotor physical speed.

3. The method for combined optimization of dynamic control laws of an aircraft engine according to claim 2, characterized in that: The dynamic process of the engine is discretized according to the time step, and its direct optimization goal is to minimize the dynamic response time. The optimization goal after discretization becomes the fastest response within each time step.

4. The method for combined optimization of dynamic control laws of an aircraft engine according to claim 3, characterized in that: The optimization objective function expression of the discrete points is as follows: Where J[k] represents the objective function of the kth time step; P[k+1] represents the performance parameter value after the kth time step; P obj Indicates the target value of the performance parameter; P start represents the starting value of the performance parameter; X[j] k+1 represents the value of the jth adjustable parameter after the kth time step; X[j] obj represents the target value of the jth adjustable parameter, which is equal to the value of the steady-state adjustable parameter corresponding to the target value of the performance parameter; X[j] up and X[j] low represent the upper and lower limits of the jth adjustable parameter respectively; D represents the number of adjustable parameters; ω represents the weight factor; the first item on the right side of the equal sign in the expression represents that P[k+1] needs to be as close to its target value as possible in each time step, and the second item on the right side of the equal sign represents that X[j] k+1 Get as close to its target value as possible; When X[j] k+1 When it approaches its target value, it will also cause P[k+1] to approach its target value.

5. The method for combined optimization of dynamic control laws of an aircraft engine according to claim 4, characterized in that: The optimization constraint conditions of the discrete points are that the rotation speed is not higher than a given value, the combustion chamber outlet temperature is not higher than a given value, the compression component surge margin is not lower than a given value, and the control parameter change rate is not higher than a given value.

6. The method for combined optimization of dynamic control laws of an aircraft engine according to claim 5, characterized in that: The process of performing point-by-point optimization to solve discrete optimization problems is as follows: A series of quadratic programming algorithms are used to solve the optimization objective function of each discrete point in turn until both the control parameters and the controlled parameters reach a steady state and last for a sufficient time or the simulation time reaches the upper limit of the dynamic process, that is, the optimization result of each discrete point is obtained; Arrange the optimization results of each discrete point in chronological order, which is the discrete dynamic control law data sequence; If the point-by-point optimization counting variable i=1, it is the first time to carry out point-by-point optimization. At this time, the optimization variables are the fuel supply to the combustion chamber and all adjustable geometric parameters. The optimization problem is solved point by point to obtain the discrete dynamic control law data sequence of the fuel supply to the combustion chamber and all adjustable geometric parameters; otherwise, after global optimization, the adjustable geometric parameters have become control variables known in advance and are no longer used as optimization variables for point-by-point optimization. At this time, the only optimization variable left is the fuel supply to the combustion chamber. Therefore, after solving the optimization problem point by point, only the discrete dynamic control law data sequence of the fuel supply to the combustion chamber can be obtained.

7. The method for combined optimization of dynamic control laws of an aircraft engine according to claim 6, characterized in that: The process of fitting the dynamic control law curve of the engine is as follows: if the point-by-point optimization counting variable i=1, the dynamic control law curve of the combustion chamber fuel supply and the dynamic control law curve of the adjustable geometric parameters are fitted using the discrete dynamic control law data sequence; otherwise, the adjustable geometric parameters have been used as control variables, and the dynamic control law curve of the adjustable geometric parameters has been obtained before the point-by-point optimization, so it is only necessary to fit the dynamic control law curve of the combustion chamber fuel supply.

8. The method for combined optimization of dynamic control laws of an aircraft engine according to claim 7, characterized in that: The process of judging whether the optimization termination condition is reached is as follows: Determine whether the optimization termination condition is met. If so, complete the optimization; if not, go to the next step; If it is judged that the optimization termination condition is not met, the differential evolution algorithm is used to carry out global optimization, optimize the position of the characteristic points of the adjustable geometric parameter control law, and use the linear fitting method to fit the new characteristic points to obtain a new adjustable geometric parameter control law curve; The new adjustable geometric parameter control law curve is transmitted to the engine model. At this time, the adjustable geometric parameter is no longer used as an optimization parameter in the next point-by-point optimization, but as a known control parameter. The control law data of the fuel supply of the combustion chamber is obtained by using the point-by-point optimization technology, and the cycle is repeated until the termination condition is reached; After the optimization is completed, the dynamic control law curves of the combustion chamber fuel supply and adjustable geometric parameters are obtained.

9. An aircraft engine dynamic control law combination optimization system, characterized by: Used to implement the combined optimization method of aircraft engine dynamic control laws as described in any one of claims 1 to 8; including an engine dynamic process simulation model, a discrete model, a discrete point optimization model, a point-by-point optimization model and a fitting model; Determine the control parameter values ​​and the controlled parameter values ​​of the starting point and the ending point of the engine dynamic process based on the engine dynamic process simulation model; The engine dynamic process is discretized into a plurality of control points according to the time step by using the discrete model; Constructing the optimization objective function and constraints of discrete points through the discrete point optimization model; The discrete optimization problem can be solved by performing point-by-point optimization through the point-by-point optimization model. The dynamic control law curve of the engine is obtained by fitting the fitting model.

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