A method for automatically adjusting blade angles of a dual-axis dual-duct compressor tester

Abstract

The present application provides a method for automatic adjustment of the blade angle of a dual-axis dual-duct compressor tester, which belongs to aviation engine testing. The method includes: obtaining a speed-angle relationship curve of the compressor blade speed and the blade angle, and obtaining a target blade angle according to the current speed of the compressor blade and the relationship curve; constructing a blade angle automatic adjustment system model based on the target blade angle as output and the given blade angle as input, and constructing a blade angle given angle transfer function in the blade angle automatic adjustment system model based on PID control; constructing a multi-objective optimization function of the blade angle given angle transfer function; optimizing the multi-objective optimization function based on a genetic algorithm to obtain the PID control optimal control parameters of the blade angle given angle transfer function, and automatically adjusting the blade angle of the compressor tester based on the PID control optimal control parameters.

Description

Technical Field

[0001] The present application relates to the field of aero-engine testing, and in particular to a method for automatically adjusting the blade angle of a dual-axis dual-duct compressor tester. Background Art

[0002] The dual-shaft, dual-shroud compressor tester is used to study the matching characteristics and aerodynamic stability of the fan and compressor. It is an important means of studying the aerodynamic matching of high- and low-pressure compressor components and plays a vital role in promoting the rapid design of aircraft engines. As a key component of the dual-shaft, dual-shroud compressor tester, the compressor blade angle control system must be adapted to the blade angle control actuators and angle and speed control patterns of different test pieces. The stability and dynamics of the angle control will affect the aerodynamic characteristics of the test piece and have a significant impact on the test.

[0003] The multi-stage blade angle control adjustment carried out by existing single-axis single-culvert testers generally includes two stages: During the test preparation stage, the angle deviation ε range, output factor k and other control parameters in the angle control program are adjusted according to the blade angle control actuator of different test pieces; During the test process, the blade angle control mode is adjusted to automatic, and the control program converts the target blade angle θ0 according to the set angle and speed relationship curve based on the current compressor speed N0. The angle deviation ε1 between the current angle θ1 and the target angle θ0 is entered into the fuzzy table MHL (Hierarchical Linguistic Model) to calculate the output factor. When the speed and blade angle control are stable, the blade angle control mode is adjusted to manual, and the operator manually adjusts the blade angle to the target angle θ0 according to the pointer on the mechanical angle dial of the blade angle. If the angle and speed relationship curve of a test piece is steep, the blade angle control mode needs to be adjusted to manual, and the operator will perform manual control and adjustment throughout the process. The specific blade angle control process is as follows:

[0004] Step 1: Click on automatic blade angle control, θ0=f(N0), and go to step 2;

[0005] Step 2: ε1 = θ1 - θ0. If ε1 > ε, proceed to step 3. If ε1 ≤ ε, proceed to step 6.

[0006] Step 3: The angle deviation is stored in the fuzzy loop queue, i.e., ε1 = HLM, and step 4 is executed;

[0007] Step 4: Calculate the output factor based on the fuzzy matrix and proceed to step 5;

[0008] Step 5: Execute step 1 every 500ms;

[0009] Step 6: Exit fuzzy control;

[0010] Step 7: The operator manually adjusts the blade angle according to the mechanical angle dial on the test piece to eliminate the angle deviation ε1 in the steady state.

[0011] The blade angle adjustment control process carried out by existing single-axis, single-duct testers is supplemented by program control adjustment, and ultimately relies on the operator to observe the position of the blade angle dial on the outside of the test piece and manually adjust the blade angle to eliminate angle errors. However, for dual-axis, dual-duct compressor test pieces, the dual-axis test is limited by the test piece structure and cannot set a linkage dial on the high-pressure compressor blade angle. The corresponding angle is completely dependent on program closed-loop control. Therefore, compared with the blade angle control system of the single-axis tester, the blade angle control system of the dual-axis tester requires higher control accuracy of the blade angle. In addition, due to the influence of the control algorithm and hardware, the control system execution cycle is 500ms. When the angle-speed relationship curve is steep, the blade angle load changes too quickly, causing the blade angle to oscillate during the compressor follow-up process, increasing the risk of test piece surge. Summary of the Invention

[0012] The purpose of the present application is to provide a method for automatically adjusting the blade angle of a dual-axis dual-duct compressor tester to solve or alleviate at least one problem in the background technology.

[0013] The technical solution of this application is: a method for automatically adjusting the blade angle of a dual-axis dual-duct compressor tester, comprising:

[0014] Obtaining a speed-angle relationship curve of the compressor blade speed and the blade angle, and obtaining a target blade angle according to the current speed of the compressor blade and the relationship curve;

[0015] Constructing a blade angle automatic adjustment system model based on a blade angle target angle as output and a blade angle given angle as input, and constructing a blade angle given angle transfer function in the blade angle automatic adjustment system model based on PID control;

[0016] Constructing a multi-objective optimization function of the blade angle given angle transfer function;

[0017] The multi-objective optimization function is optimized based on a genetic algorithm to obtain the PID control optimal control parameters of the blade angle given angle transfer function, and the blade angle of the compressor tester is automatically adjusted based on the PID control optimal control parameters.

[0018] In at least one embodiment of the present application, the rotational speed and angle relationship curve of the compressor blade rotational speed and blade angle is obtained through ground testing.

[0019] In at least one embodiment of the present application, the blade angle automatic adjustment system model is:

[0020] ;

[0021] Where, y is the target blade angle, x is the status of the blade angle control system, f(x) is the regulator transfer function, g(x) is the servo valve transfer function, u is the blade angle, d(t) For time t The disturbance on .

[0022] In at least one embodiment of the present application, the blade angle given angle transfer function in the blade angle automatic adjustment system model constructed based on PID control is:

[0023] ;

[0024] Where, u is the blade angle, K p 、K i 、K d The proportional coefficient, integral coefficient and differential coefficient in PID control respectively, e is the tracking error, t For time.

[0025] In at least one embodiment of the present application, the multi-objective optimization function is:

[0026] ;

[0027] Where, J is a comprehensive performance index, α, β, γ are weight coefficients, e is the tracking error, t s To adjust the time, T is the upper limit of time t.

[0028] In at least one embodiment of the present application, the present invention further includes:

[0029] An error dynamics model and an error-based stability function are constructed, and the ideal control parameters of PID control are obtained based on the error dynamics model and the stability function, thereby achieving anti-saturation and stability guarantee of the control parameters.

[0030] In at least one embodiment of the present application, the error dynamics model is:

[0031] Defining Tracking Error e=θ d -θ , the dynamic error equation is: , ;

[0032] Where, i d Set the angle for the target, i is the current actual feedback angle, Set the first derivative of the angle to the target, is the first-order derivative of the current actual feedback angle, Set the second derivative of the angle to the target, is the second-order derivative of the current actual feedback angle, e is the tracking error, is the first-order derivative of the tracking error, is the second-order derivative of the tracking error;

[0033] The error-based stabilization function is:

[0034] ;

[0035] Where, V is a Lyapunov stable function, K p * 、 K i * 、 K d * They are the optimal proportional coefficient, optimal integral coefficient and optimal differential coefficient in PID control respectively. K p ** 、 K i ** 、 K d ** are the ideal proportional coefficient, ideal integral coefficient and ideal differential coefficient of PID control respectively, γ p , γ i , γ d are the learning rates of the proportional coefficient, integral coefficient and differential coefficient respectively, γ p , γ i , γ d >0;

[0036] For Lyapunov stable functions V Derivate and substitute into the error dynamics equation and design the adaptive law: ;

[0037] Guaranteed Lyapunov stable function V After derivation , and ensure that the tracking error e approaches 0 while making the optimal proportional coefficient K p* Approaching the ideal proportional coefficient K p ** , optimal integration coefficient K i * Approaching the ideal integral coefficient K i ** , the optimal differential coefficient K d * Approaching the ideal differential coefficient K d ** , thus obtaining the ideal proportional coefficient, ideal integral coefficient and ideal differential coefficient of PID control K p ** 、 K i ** 、 K d ** .

[0038] This application proposes an automatic blade angle adjustment method for a dual-axis, dual-duct compressor tester. This method uses an optimization algorithm to automatically adjust the control parameters of PID control, addressing the challenges of hydraulic system nonlinearity, time-varying behavior, and interference resistance. Test results demonstrate that this method can achieve an adjustment time of less than 100ms, a steady-state error of less than 0.1°, and is suitable for testing different blade angles. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions provided by this application, the following is a brief introduction to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of this application.

[0040] Figure 1 Schematic diagram of the automatic blade angle adjustment system of the dual-axis dual-duct compressor tester of this application.

[0041] Figure 2 Schematic diagram of the automatic blade angle adjustment method of the dual-axis dual-duct compressor tester of this application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the implementation of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below in conjunction with the drawings in the embodiments of this application.

[0043] This application first provides a dual-axis dual-duct compressor tester blade angle automatic adjustment system, which relies on the Siemens S7-300 PLC central control module in conjunction with the Siemens FM355 PID control module, SM331 analog input module, SM332 analog output module, etc. to build a blade angle automatic adjustment system.

[0044] like Figure 1 As shown, the automatic blade angle adjustment system includes a regulator 11, a servo valve 12, and an actuator 13. The blade angle is the controlled object, and a target blade angle is output by setting a given blade angle. The PLC hardware system, including a central control module, a PID control module, an analog input module, and an analog output module, constitutes the regulator 11. The servo valve 12 constitutes the actuator, and the actuator 13 is used to output the blade angle. The automatic blade angle adjustment system of this application is stable and reliable, unaffected by computer freezes and power outages, and can be self-contained.

[0045] In some embodiments of the present application, the servo valve 12 is an electro-hydraulic proportional valve.

[0046] like Figure 2 As shown, based on the above-mentioned dual-axis dual-duct compressor tester blade angle automatic adjustment system, the present application also provides a dual-axis dual-duct compressor tester blade angle automatic adjustment method, which includes the following steps:

[0047] Step S10: Obtain a speed-angle relationship curve of the compressor blade speed and the blade angle, and convert the target blade angle according to the current speed of the compressor blade and the speed-angle relationship curve.

[0048] During the test preparation phase, based on the test requirements of different models, the speed-angle relationship curves obtained from ground testing are entered into the PLC central control module, generating two arrays: blade angle θ[buff] and blade speed N[buff]. "buff" represents the interval between blade angles or blade speeds in the array, and this interval can be set based on test requirements. For example, if the blade angle θ ranges from -30 degrees to 45 degrees, the blade angle interval buff can be set to 1 degree; if the blade speed N ranges from 8000 rpm to 15000 rpm, the blade speed interval buff can be set to 100 rpm.

[0049] The corresponding blade speed range is found according to the current speed of the compressor blade, and the target blade angle corresponding to the current speed of the compressor blade is calculated according to the linear relationship within the blade speed range.

[0050] Step S20 , constructing a blade angle automatic adjustment system model based on the blade angle target angle as output and the blade angle given angle as input, and constructing a blade angle given angle transfer function in the blade angle automatic adjustment system model based on PID control.

[0051] In this application, the blade angle automatic adjustment system model is:

[0052] ;

[0053] Where y is the output of the blade angle automatic adjustment system, that is, the target blade angle. x The status of the blade angle control system (such as hydraulic system pressure, temperature, flow, etc.), f(x) is the regulator transfer function, g(x) is the servo valve transfer function, u The input of the blade angle automatic adjustment system is the given blade angle. d(t) For time t The control goal is to make the hydraulic system output y Tracking reference signal yd , that is, tracking error e = yd-y Approaching zero.

[0054] In this application, the blade angle given angle transfer function in the blade angle automatic adjustment system model based on PID control is:

[0055] ;

[0056] Where, u is the input of the blade angle automatic adjustment system, that is, the blade angle is given. K p 、K i 、K d The control parameters in PID control are proportional coefficient, integral coefficient and differential coefficient, respectively. e is the tracking error, t For time.

[0057] Step S30: construct a multi-objective optimization function of the blade angle given angle transfer function, the multi-objective optimization function is:

[0058] ;

[0059] Where, J is a comprehensive performance index, α, β, γ are weight coefficients, e is the tracking error, t s To adjust the time, T is the upper limit of time t.

[0060] Step S40: Optimizing the multi-objective optimization function using a genetic algorithm to screen out the best control parameters for PID control. The specific process includes:

[0061] S41, determining the initial range of the control parameters of the PID control;

[0062] For example, in this embodiment of the present application, the initial range of each control parameter is set as:

[0063] K p ∈[0,50],K i ∈[0,10],K d ∈[0,5].

[0064] S42, randomly generating the initial state of the control parameters, wherein the initial state of the control parameters is represented by a combination of: {K p (0),K i (0),K d (0)}.

[0065] S43, select parents according to fitness ratio by roulette wheel method.

[0066] S44, Crossover: Adaptive arithmetic crossover (crossover rate , P is the total algebra, P0 is the current algebra).

[0067] S45, mutation: Gaussian mutation (mutation rate ).

[0068] S46, Elite Reserve:

[0069] The top 5% best individuals are retained in each generation. Multi-objective optimization function J Monotonically decreasing, iteratively converges to the global optimal solution, thereby obtaining the optimal control parameters of PID control - that is, the optimal proportional coefficient, the optimal integral coefficient and the optimal differential coefficient K p * 、 K i * 、 K d * .

[0070] Step S50 , constructing an error dynamics model and an error-based stability function, and obtaining ideal control parameters of PID control based on the error dynamics model and the stability function, thereby achieving anti-saturation and stability assurance of the control parameters.

[0071] S51, construct error dynamics model;

[0072] Defining Tracking Error e=θ d -θ , the dynamic error equation is: , ;

[0073] Where, i d Set the angle for the target, i is the current actual feedback angle, Set the first derivative of the angle to the target, is the first-order derivative of the current actual feedback angle, Set the second derivative of the angle to the target, is the second-order derivative of the current actual feedback angle, e is the tracking error, is the first-order derivative of the tracking error, is the second-order derivative of the tracking error.

[0074] S52, constructing Lyapunov stable functions V :

[0075] ;

[0076] Where, K p ** 、 K i ** 、 K d ** is the ideal control parameter of PID control, namely the ideal proportional coefficient, ideal integral coefficient and ideal differential coefficient, γ p , γ i , γ d are the learning rates of the proportional coefficient, integral coefficient and differential coefficient respectively, γ p , γ i , γ d >0.

[0077] S53, parameter adaptive law design:

[0078] For Lyapunov stable functions V Derivate and substitute into the error dynamics equation and design the adaptive law:

[0079] ;

[0080] Guaranteed Lyapunov stable function V Derivative , ensure that the tracking error e→0 (i.e. the tracking error e approaches zero) and satisfies K p * →K p ** , K i * →K i ** , K d * →K d ** (i.e. the optimal proportional coefficient, the optimal integral coefficient and the optimal differential coefficient Kp * 、 K i * 、 K d * Approaching the ideal proportional coefficient, ideal integral coefficient and ideal differential coefficient respectively K p ** 、 K i ** 、 K d ** ).

[0081] Finally, the ideal control parameters of PID control can be obtained { K p ** 、 K i ** 、 K d **}, the ideal control parameters { K p ** 、 K i ** 、 K d ** The result of} is stored in the corresponding pin of the FM355 PID control module, thus realizing the self-tuning adjustment of the PID control parameters of the blade angle control system.

[0082] This application proposes an automatic blade angle adjustment method for a dual-axis, dual-duct compressor tester. This method uses an optimization algorithm to automatically adjust the control parameters of PID control, addressing the challenges of hydraulic system nonlinearity, time-varying behavior, and interference resistance. Test results demonstrate that this method can achieve an adjustment time of less than 100ms, a steady-state error of less than 0.1°, and is suitable for testing different blade angles.

[0083] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for automatically adjusting blade angles of a dual-axis dual-duct compressor tester, characterized in that: include: Obtaining a relationship curve between the compressor blade speed and the blade angle, and obtaining a target blade angle according to the current compressor blade speed and the relationship curve; Constructing a blade angle automatic adjustment system model based on a blade angle target angle as output and a blade angle given angle as input, and constructing a blade angle given angle transfer function in the blade angle automatic adjustment system model based on PID control; Constructing a multi-objective optimization function of the blade angle given angle transfer function; Optimizing the multi-objective optimization function based on a genetic algorithm to obtain optimal PID control parameters of the blade angle given angle transfer function, and automatically adjusting the blade angle of the compressor tester based on the optimal PID control parameters; An error dynamics model and an error-based stability function are constructed, and the ideal control parameters of PID control are obtained based on the error dynamics model and the stability function, thereby achieving anti-saturation and stability guarantee of the control parameters. The error dynamics model is: Define tracking error e = θ d -θ, the dynamic error equation is: Where θ d is the target setting angle, θ is the current actual feedback angle, Set the first derivative of the angle to the target, is the first-order derivative of the current actual feedback angle, Set the second derivative of the angle to the target, is the second-order derivative of the current actual feedback angle, e is the tracking error, is the first-order derivative of the tracking error, is the second-order derivative of the tracking error; The error-based stabilization function is: Where V is the Lyapunov stability function, K p * , K i * , K d * They are the optimal proportional coefficient, optimal integral coefficient and optimal differential coefficient in PID control, K p ** , K i ** , K d ** are the ideal proportional coefficient, ideal integral coefficient and ideal differential coefficient in PID control respectively, γ p , γ i , γ d are the learning rates of the proportional coefficient, integral coefficient and differential coefficient respectively, γ p , γ i , γ d >0; Derivate the Lyapunov stability function V and substitute it into the error dynamics equation, and design the adaptive law: After taking the derivative of the Lyapunov stable function V And make the tracking error e close to 0, and make the optimal proportional coefficient K p * Approaching the ideal proportional coefficient K p ** , optimal integration coefficient K i * Approaching the ideal integral coefficient K i ** , the optimal differential coefficient K d * Approaching the ideal differential coefficient K d ** , thus obtaining the ideal proportional coefficient, ideal integral coefficient and ideal differential coefficient K of PID control p ** , K i ** , K d ** .

2. The method for automatically adjusting blade angles of a dual-axis dual-duct compressor tester according to claim 1, characterized in that: The relationship curve between the compressor blade rotation speed and the blade angle is obtained through ground tests.

3. The method for automatically adjusting blade angles of a dual-axis dual-duct compressor tester according to claim 1 or 2, characterized in that: The blade angle automatic adjustment system model is: y=f(x)+g(x)u+d(t); Where y is the target blade angle, x is the state of the blade angle control system, f(x) is the regulator transfer function, g(x) is the servo valve transfer function, u is the given blade angle, and d(t) is the disturbance at time t.

4. The method for automatically adjusting blade angles of a dual-axis dual-duct compressor tester according to claim 3, wherein: The blade angle given angle transfer function in the blade angle automatic adjustment system model constructed based on PID control is: Where, u is the given angle of the blade, K p , K i , K d are the proportional coefficient, integral coefficient and differential coefficient in PID control respectively, e is the tracking error, and t is the time.

5. The method for automatically adjusting blade angles of a dual-axis dual-duct compressor tester according to claim 4, characterized in that: The multi-objective optimization function is: Where J is the comprehensive performance index, α, β, γ are weight coefficients, e is the tracking error, t s To adjust the time, T is the upper limit of time t.

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