Intelligent control method of guider area measurement control system

Abstract

The invention belongs to the field of intelligent control, and particularly relates to an intelligent control method of a guider area measurement control system, a fuzzy controller is arranged between input variables and a PID (Proportion Integration Differentiation) controller, the input variables comprise deviation and deviation change rate, and three output variables input into the PID controller are arranged on the fuzzy controller; comprising a proportionality coefficient, an integral action coefficient and a differential coefficient; according to the fuzzy relation between three PID parameters and the deviation and the deviation change rate, the deviation and the deviation change rate are detected in real time in system operation, and the three PID parameters are modified online according to a fuzzy control rule so as to meet the requirements of different deviations and deviation change rates on PID control parameters, so that a controlled object has good dynamic and static characteristics; the problems that a guider area measurement system has high nonlinearity and time-varying characteristics, and the control effect is poor when a traditional PID control method is used are solved, the manpower and material resource cost is reduced, and the test efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the field of intelligent control, and in particular relates to an intelligent control method for a guide area measurement control system. Background Art

[0002] For aviation turbine engines, turbine guide vanes are key components that determine important parameters such as engine flow and power. The exhaust area of ​​the guide vane can significantly affect engine slip, surge margin, exhaust temperature and other characteristics, thereby affecting the overall performance of the engine. It is an important parameter that needs to be strictly controlled during engine design, production and installation. Domestic methods for measuring the flow area of ​​turbine guide vanes of large thrust engines have the disadvantages of large workload and long measurement cycle. In response to the existing guide vane area measurement needs, a guide vane area measurement and control system is designed, which can quickly, efficiently and accurately measure the exhaust area of ​​the turbine guide vane.

[0003] The principle of the guide area measurement is to control the constant pressure of the pressure regulator box by adjusting the motor speed. When the pressure is constant, the exhaust area of ​​the guide is a function of the motor speed measurement value. Therefore, the pressure control accuracy is the key to ensure the accuracy of the guide area measurement. The most widely used robust control algorithm in the industrial field is the traditional PID control, which is also widely used in various control loops in the field of aero-engine testing.

[0004] The existing technical solution uses PID control method to regulate the pressure of the pressure regulating box. Figure 1 The PID control structure is adopted. The guide area measurement control system is a complex nonlinear, time-varying control system, which is greatly affected by changes in parameters such as temperature, humidity, and load. PID control is based on a fixed model, and the PID parameters cannot be changed after being debugged and determined. The fixed PID parameters are not suitable for process control problems with time-varying control system parameters, and it is impossible to obtain good static and dynamic performance, and it is difficult to obtain satisfactory control effects.

[0005] Therefore, how to perform more stable PID control is a problem that needs to be solved. Summary of the invention

[0006] The purpose of this application is to provide an intelligent control method for a guide area measurement control system to solve the problem that the existing PID control cannot obtain good static and dynamic performance.

[0007] The technical solution of the present application is: an intelligent control method for a guide area measurement control system, comprising: A fuzzy controller is set between the input variable and the PID controller. The input variable includes the deviation. and the rate of change of deviation The fuzzy controller is set with three output variables that are input to the PID controller, including the proportional coefficient , integral action coefficient , differential coefficient ; Calculate the fuzzy sets and domains of input and output variables, and select the membership functions of input and output variables; Obtaining proportionality coefficients based on fuzzy sets, domains, and membership functions of input and output variables , integral action coefficient and the differential coefficient The parameter characteristics are based on the proportional coefficient , integral action coefficient and the differential coefficient Different fuzzy control rules are established based on the parameter characteristics, and the proportional coefficients are constructed respectively. Control rule table, integral action coefficient Control law table and differential coefficients Control rules table; According to the fuzzy control rules, the corresponding input-output relationship matrix is ​​obtained; the "and" operation of all input-output relationship matrices is performed to obtain the total fuzzy relationship of the fuzzy control rules of the control system, and the fuzzy output is obtained. After precise calculation by the weighted average method, the output value represented by the domain element is obtained. , , , and form a fuzzy control query table; The output value obtained by refining the calculation , , , after gain adjustment, they are used as PID proportional gain , integral gain and differential gain parameter increment; during online operation, the control system calls the proportional coefficient Control rule table, integral action coefficient Control rule table, differential coefficient The control rule table and three parameter increments are used for result processing, table lookup and operation, and online self-correction of PID parameters is performed; wherein the table lookup is a fuzzy control lookup table.

[0008] Preferably, the control system calls the proportionality coefficient Control rule table, integral action coefficient Control rule table, differential coefficient The specific methods for result processing, table lookup and calculation of the control rule table and three parameter increments are as follows: Get the current sample value from the control system entrance, and then calculate the initial deviation based on the control system set value r and output y and the rate of change of deviation , let k be the current control cycle; then let , perform fuzzy tuning, combined with the proportional coefficient , integral action coefficient and the differential coefficient Calculate the current proportional gain , integral gain and differential gain ; After the calculation is completed, PID control output is carried out and the next cycle of control is carried out, and this process is repeated.

[0009] Preferably, the input variable , and output variables , , The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB, NM, NS, ZO, PS, PM, PB are all linguistic variables; the input variables , and output variables , , The domain of is {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

[0010] Preferably, the input variable , and output variables , , Select the triangular membership function as the membership function.

[0011] Preferably, the control system constructs a proportionality factor based on the feedback of the response speed and the overshoot Control rules table.

[0012] Preferably, the control system constructs the integral action coefficient based on the feedback of the integral and static error Control rules table.

[0013] The intelligent control method of the guide area measurement control system of the present application is based on the three parameters of PID , , With deviation and the rate of change of deviation The fuzzy relationship between and According to the fuzzy control rules , , Online modification to meet different and The requirements for PID control parameters are to ensure that the controlled object has good dynamic and static characteristics; the guide area measurement system has highly nonlinear and time-varying characteristics, and the traditional PID control method has poor control effect. The design of this application improves the system control accuracy and convergence speed, reduces manpower and material costs, and improves test efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution provided by the present application, the following is a brief introduction to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present application.

[0015] Figure 1 It is a schematic diagram of the PID control structure in the background technology; Figure 2 This is the overall structure diagram of PID control for this application; Figure 3 This is the control flow chart of the control system for this application. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] An intelligent control method for a guide area measurement control system, such as Figure 1 As shown, the core of control is to find out the three parameters of PID , , With deviation and the rate of change of deviation The fuzzy relationship between and According to the fuzzy control rules , , Online modification to meet different and The requirements for PID control parameters are to ensure that the controlled object has good dynamic and static characteristics.

[0018] like Figure 2 As shown, the following steps are included: Step S100, determine input and output variables: A fuzzy controller is set between the input variable and the PID controller. The input variable includes the deviation. and the rate of change of deviation The fuzzy controller is set with three output variables that are input to the PID controller, including the proportional coefficient , integral action coefficient , differential coefficient The other parts including the motor, fan, voltage regulator, differential pressure sensor, etc. are all existing designs. The fuzzy sets and domains of the input variables and output variables are calculated, and the membership functions of the input variables and output variables are selected.

[0019] Depending on the accuracy and control requirements, input variables , and output variables , , The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB, NM, NS, ZO, PS, PM, PB are all linguistic variables, namely {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}. Input variables , and output variables , , The domain of is {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}. Input variables , and output variables , , Select the triangular membership function as the membership function.

[0020] Step S200, determine the fuzzy control rules: Obtaining proportionality coefficients based on fuzzy sets, domains, and membership functions of input and output variables , integral action coefficient and the differential coefficient The parameter characteristics are based on the proportional coefficient , integral action coefficient and the differential coefficient Different fuzzy control rules are established based on the parameter characteristics, and the proportional coefficients are constructed respectively. Control rule table, integral action coefficient Control law table and differential coefficients Control rules table.

[0021] The principle of determining fuzzy control rules is to achieve the best control effect. The size of is related to the response speed and adjustment accuracy of the control system. Increase the proportional coefficient The value of can improve the response speed of the control system and improve the adjustment accuracy; reduce the proportional coefficient The value of will reduce the overshoot, resulting in a longer adjustment time. The control system constructs the proportional coefficient based on the feedback of response speed and overshoot. The control rule table is shown in Table 1: Table 1 Control rules table

[0022] Integral action coefficient Related to the steady-state error of the control system.

[0023] Take the larger value, can increase the integration speed and speed up the elimination of static error of the control system; taking a smaller The value will weaken the effect of integration and slow down the elimination of static error of the control system, which will eventually lead to reduced regulation accuracy and weaker dynamic characteristics of the control system.

[0024] The control system constructs the integral action coefficient based on the feedback of integral and static error Control rules table.

[0025] The control rule table is shown in Table 2: Table 2 Control rules table

[0026] Differential coefficient Related to the dynamic characteristics of the control system. Appropriate The value can enhance the dynamic characteristics of the control system. The change of the deviation rate will cause When the deviation changes, braking can be performed in advance to reduce overshoot and keep the control system stable. If the value is too small, the control system will not be able to brake in time, resulting in an increase in overshoot. The fuzzy adjustment rules of parameters are: Table 3 Control rules table

[0027] Step S300, fuzzy reasoning and decision making: In obtaining the proportionality factor , integral action coefficient and the differential coefficient After the fuzzy control rules are obtained, the fuzzy logic reasoning method shows that 49 input-output relationship matrices can be obtained for the above 49 fuzzy control rules. Perform the "and" operation of all input-output relationship matrices to obtain the total fuzzy relationship of the fuzzy control rules of the control system, thereby obtaining the fuzzy output quantity. After precise calculation by the weighted average method, the output quantity value represented by the domain element is obtained. , , , and form a fuzzy control query table, as shown in fuzzy control query table 4 to table 6. In real-time control, the fuzzy control query table is queried according to the sampled data to obtain the corresponding output value , , .

[0028] ; ; ;

[0029] Step S400: Accurately calculate the output value , , , after gain adjustment, they are used as PID proportional gain , integral gain and differential gain During online operation, the control system calls the proportional coefficient Control rule table, integral action coefficient Control rule table, differential coefficient The control rule table and three parameter increments are used for result processing, table lookup and calculation, and online self-correction of PID parameters is performed; among which, the table lookup is to query the fuzzy control query table. The workflow diagram is as follows Figure 3 shown.

[0030] like Figure 3 , the control system calls the proportional coefficient Control rule table, integral action coefficient Control rule table, differential coefficient The specific methods for result processing, table lookup and calculation of the control rule table and three parameter increments are as follows: Get the current sample value from the control system entrance, and then calculate the initial deviation based on the control system set value r and output y and the rate of change of deviation , let k be the current control cycle; then let , perform fuzzy tuning, combined with the proportional coefficient , integral action coefficient and the differential coefficient Calculate the current proportional gain , integral gain and differential gain ; After the calculation is completed, PID control output is carried out and the next cycle of control is carried out, and this process is repeated.

[0031] In summary, according to the three parameters of PID , , With deviation and the rate of change of deviation The fuzzy relationship between and According to the fuzzy control rules , , Online modification to meet different and The requirements for PID control parameters are to ensure that the controlled object has good dynamic and static characteristics; the guide area measurement system has highly nonlinear and time-varying characteristics, and the traditional PID control method has poor control effect. The design of this application improves the system control accuracy and convergence speed, reduces manpower and material costs, and improves test efficiency.

[0032] Finally, it should be noted that: the drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent control method for a guide area measurement control system, characterized in that: include: A fuzzy controller is set between the input variable and the PID controller. The input variable includes the deviation. and the rate of change of deviation The fuzzy controller is set with three output variables that are input to the PID controller, including the proportional coefficient , integral action coefficient , differential coefficient ; Calculate the fuzzy sets and domains of input and output variables, and select membership functions of input and output variables; Obtaining proportionality coefficients based on fuzzy sets, domains, and membership functions of input and output variables , integral action coefficient and the differential coefficient The parameter characteristics are based on the proportional coefficient , integral action coefficient and the differential coefficient Different fuzzy control rules are established based on the parameter characteristics, and the proportional coefficients are constructed respectively. Control rule table, integral action coefficient Control law table and differential coefficients Control rules table; According to the fuzzy control rules, the corresponding input-output relationship matrix is ​​obtained; the "and" operation of all input-output relationship matrices is performed to obtain the total fuzzy relationship of the fuzzy control rules of the control system, and the fuzzy output is obtained. After precise calculation by the weighted average method, the output value expressed in the domain element is obtained. , , , and form a fuzzy control query table; The output value obtained by the precise calculation , , , after gain adjustment, they are used as PID proportional gain , integral gain and differential gain Parameter increment; During online operation, the control system calls the proportional coefficient Control rule table, integral action coefficient Control rule table, differential coefficient The control rule table and three parameter increments are used for result processing, table lookup and operation, and online self-correction of PID parameters is performed; wherein the table lookup is a fuzzy control lookup table.

2. The intelligent control method for the guide area measurement and control system according to claim 1, characterized in that: The control system calls the proportional coefficient Control rule table, integral action coefficient Control rule table, differential coefficient The specific methods for result processing, table lookup and calculation of the control rule table and three parameter increments are as follows: Get the current sample value from the control system entrance, and then calculate the initial deviation based on the control system set value r and output y and the rate of change of deviation , let k be the current control cycle; then let , perform fuzzy tuning, combined with the proportional coefficient , integral action coefficient and the differential coefficient Calculate the current proportional gain , integral gain and differential gain ; After the calculation is completed, PID control output is carried out and the next cycle of control is carried out, and this process is repeated.

3. The intelligent control method for the guide area measurement and control system according to claim 1, characterized in that: The input variables , and output variables , , The fuzzy set is {NB, NM, NS, ZO, PS, PM, PB}, where NB, NM, NS, ZO, PS, PM, PB are all linguistic variables; the input variables , and output variables , , The domain of is {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}.

4. The intelligent control method for the guide area measurement and control system according to claim 1, characterized in that: The input variables , and output variables , , Select the triangular membership function as the membership function.

5. The intelligent control method for the guide area measurement and control system according to claim 1, characterized in that: The control system constructs the proportional coefficient based on the feedback of response speed and overshoot Control rules table.

6. The intelligent control method for the guide area measurement and control system according to claim 1, characterized in that: The control system constructs the integral action coefficient based on the feedback of integral and static error Control rules table.

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