Hydraulic fan driven high pressure water pump speed control method based on fuzzy adaptive PID

Abstract

The present application belongs to the technical field of seawater desalination equipment control, and relates to a hydraulic fan driven high-pressure water pump speed control method based on fuzzy adaptive PID, which comprises: S1, obtaining the high-pressure water pump speed error of the hydraulic fan, and determining the input variable of the fuzzy adaptive PID controller; S2, analyzing the stability of the hydraulic fan, establishing a high-pressure water pump variable motor swing angle control model, and determining the output variable of the fuzzy adaptive PID controller; S3, determining the fuzzy control set of the fuzzy adaptive PID controller of the hydraulic fan; S4, using the Mamdani method to perform fuzzy reasoning on the fuzzy reasoning table, and calculating the fuzzy control subset by the barycenter method; and S5, controlling the swing angle adjusting variable motor displacement of the high-pressure water pump variable motor according to the output value of the fuzzy control subset. The present application is based on the hydraulic fan driven high-pressure water pump passing through the reverse osmosis membrane to produce fresh water, and uses fuzzy control and PID control theory to improve the rapidity, accuracy and stability of the water pump speed control.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of seawater desalination equipment control, and particularly relates to a hydraulic fan driven high-pressure water pump rotating speed control method based on fuzzy adaptive PID. BACKGROUND

[0002] Reverse osmosis (RO) is a common and mature method for seawater desalination, and about 69% of the installed seawater desalination plants in the world use RO. However, seawater desalination is a high-energy process, and using fossil fuels to power seawater desalination plants greatly increases carbon dioxide emissions. Wind energy can meet the high energy consumption of seawater desalination, reduce costs and carbon dioxide emissions. Therefore, wind energy RO is one of the most promising solutions for renewable energy seawater desalination, especially in coastal areas and islands. Wind RO can also be divided into two subcategories: using electrical energy for battery energy storage (BES) to power the water pump, or directly mechanically pumping water to overcome the membrane osmotic pressure. Most small-scale seawater desalination systems based on wind energy use batteries as energy storage systems, but this greatly increases the construction and operating costs.

[0003] As an alternative, hydraulic wind turbines (HWTs) provide a more viable option for direct wind-powered desalination (D-WPD) systems. HWTs provide fast response and high reliability, and allow continuous variable speed operation, which can maintain high overall efficiency. The hydraulic transmission system separates the wind turbine from the high-pressure water pump, allowing for more modular and flexible layouts. However, the RO system needs to be operated under relatively stable conditions, and with wind speed fluctuations, the high-pressure water pump must quickly and accurately adjust its rotating speed to stabilize the reverse osmosis system flow and pressure. All of these pose additional challenges to the control technology of hydraulic wind-powered desalination.

[0004] In order to quickly and accurately adjust the rotating speed of the hydraulic wind turbine driven high-pressure water pump under complex disturbances and ensure the stable operation of the reverse osmosis system, an efficient controller design is needed. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a hydraulic fan driven high-pressure water pump rotating speed control method based on fuzzy adaptive PID. The present application is based on the production of fresh water by the hydraulic fan driven high-pressure water pump passing through the reverse osmosis membrane, and uses fuzzy control and PID control theory to improve the rapidity, accuracy and stability of water pump rotating speed control.

[0006] To achieve the above-mentioned purpose, the present application discloses the following technical scheme:

[0007] A hydraulic fan driven high-pressure water pump rotating speed control method based on fuzzy adaptive PID, comprising:

[0008] S1: Obtain the speed error of the hydraulic fan high-pressure water pump, and determine that the input variable of the fuzzy adaptive PID controller is the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error;

[0009] S2: Analyze the stability of the hydraulic fan, establish a high-pressure water pump variable motor swing angle control model, and determine the output variable and value range of the fuzzy adaptive PID controller;

[0010] S21: The transfer function of the high-pressure water pump speed to the high-pressure water pump variable motor swing angle control model is:

[0011]

[0012] Where ω m is the set speed of the high-pressure water pump; γ m is the swing angle of the variable motor in a stable state; p h0 is the high-pressure pipeline pressure in a stable state; C tp is the total leakage coefficient of the hydraulic main transmission system; V is the oil volume in the high-pressure pipeline; β e is the oil volume elastic modulus; s is the high-pressure water pump speed input control parameter; K m is the variable motor displacement gradient coefficient; γ m0 is the initial value of the variable motor swing angle in a stable state; ω m0 is the variable motor speed in a stable state; J m is the variable motor moment of inertia; η m is the variable motor transmission efficiency; B m is the variable motor viscous damping coefficient;

[0013] S22: The output variable of the fuzzy adaptive PID controller is set as: the proportional control parameter K1, the integral control parameter K2, and the differential control parameter K3, the characteristic equation and the value range of the output variable of the fuzzy adaptive PID controller are determined, and the fuzzy adaptive PID controller is used to control the swing angle of the high-pressure water pump variable motor in combination with the transfer function of step S21;

[0014] S3: Determine the fuzzy control set of the fuzzy adaptive PID controller of the hydraulic fan driving high-pressure water pump, determine the fuzzy control subsets of the input variable in step S1 and the output variable in step S2 according to the Z-shaped membership function, the triangular membership function and the S-shaped membership function; use fuzzy language to combine the fuzzy control subsets of the input variable and the output variable to obtain the fuzzy inference table of the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3;

[0015] S4: Use the Mamdani inference method to perform fuzzy inference on the fuzzy inference table in step S3, and calculate the fuzzy control subsets of the fuzzy adaptive PID controller by the center of gravity method:

[0016]

[0017] Among them, K n-final For the output value of the fuzzy control subset; K n-k C represents a point in the fuzzy control universe of discourse. n-final (k) represents the fuzzy set of the fuzzy control output; k is the sampling data point number; m is the total number of sampling data points;

[0018] S5: Based on the fuzzy control subset output value K in step S4 n-final The swing angle of the variable motor of the high-pressure water pump is controlled to adjust the displacement of the variable motor, thereby achieving a fast, stable and accurate output speed of the high-pressure water pump.

[0019] Preferably, in step S1, the speed error of the hydraulic blower's high-pressure water pump is obtained, and the input variables of the fuzzy adaptive PID controller are determined to be the high-pressure water pump speed error e and the rate of change ec of the high-pressure water pump speed error, specifically:

[0020] S11: Obtain the actual output speed ω of the high-pressure water pump through a speed sensor. p The actual output speed ω p With the set speed ω m The speed error e of the high-pressure water pump is obtained by comparison:

[0021] e = ω p -ω m ;

[0022] Where e is the high-pressure water pump speed error; ω p ω represents the actual output speed of the high-pressure water pump. m This is the set speed for the high-pressure water pump;

[0023] S12: The rate of change (ec) of the high-pressure water pump speed error is obtained as follows:

[0024]

[0025] Where ec is the rate of change of the high-pressure water pump speed error; t is the time parameter.

[0026] Preferably, in step S22, the characteristic equation and value range of the output variable of the fuzzy adaptive PID controller are determined as follows:

[0027] Based on the proportional control parameter K1, integral control parameter K2, and derivative control parameter K3 of the output variable of the fuzzy adaptive PID controller, the characteristic equation of the closed-loop system of the fuzzy adaptive PID controller is obtained as follows:

[0028]

[0029] According to the Routh criterion, the stability of the fuzzy adaptive PID controller is analyzed, and the condition for the system to be stable is obtained as:

[0030]

[0031] Preferably, the fuzzy control set of the fuzzy adaptive PID controller of the hydraulic blower driving high-pressure water pump in step S3 is determined, specifically:

[0032] The fuzzy adaptive PID controller of the hydraulic blower driving high-pressure water pump is set to a two-input, three-output structure; the input variables are: high-pressure water pump speed error e and high-pressure water pump speed error change rate ec; the output variables are: proportional control parameter K1, integral control parameter K2, and differential control parameter K3.

[0033] The fuzzy control set of the input variables and output variables is set to: first-level fuzzy control subset NB, second-level fuzzy control subset NM, third-level fuzzy control subset NS, fourth-level fuzzy control subset ZO, fifth-level fuzzy control subset PS, sixth-level fuzzy control subset PM, and seventh-level fuzzy control subset PB.

[0034] Preferably, the Z-shaped membership function in step S3 is used to determine the first-level fuzzy control subset NB of the high-pressure water pump speed error e, specifically:

[0035]

[0036] where NB(e(t)) is the Z-shaped membership function vector of the first-level fuzzy control subset; e(t) is the variable of the high-pressure water pump speed error with respect to time parameter t; x is the actual value of the input variable.

[0037] Similarly, the Z-shaped membership function vector of the first-level fuzzy control subset of the high-pressure water pump speed error change rate ec is NB(ec(t)).

[0038] Preferably, the triangular membership function in step S3 is used to determine the fuzzy control subset, specifically:

[0039] The triangular membership functions of the second-level fuzzy control subset NM, third-level fuzzy control subset NS, fourth-level fuzzy control subset ZO, fifth-level fuzzy control subset PS, and sixth-level fuzzy control subset PM of the high-pressure water pump speed error e and high-pressure water pump speed error change rate ec are all:

[0040]

[0041] Wherein, f(x, a, b, c) is a triangular membership function vector of the fuzzy control subset; a is the first subsection parameter of the triangular membership function; b is the second subsection parameter of the triangular membership function; c is the third subsection parameter of the triangular membership function;

[0042] Similarly, each subset of the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3 is also determined according to the triangular membership function.

[0043] Preferably, the S-shaped membership function in step S3 is used to determine the seven-level fuzzy control subset PB of the high-pressure water pump speed error e, specifically as follows:

[0044]

[0045] Wherein, PB(e(t)) is an S-shaped membership function vector of the seven-level fuzzy control subset;

[0046] Similarly, the S-shaped membership function vector of the one-level fuzzy control subset of the change rate ec of the high-pressure water pump speed error amount is PB(ec(t)).

[0047] Preferably, the fuzzy reasoning table in step S3 is subjected to fuzzy reasoning using the Mamdani reasoning method in step S4, specifically as follows:

[0048] The reasoning rule of the Mamdani reasoning method is: E∩EC→K; wherein, E is the fuzzy control subset of the high-pressure water pump speed error e, EC is the fuzzy control subset of the change rate ec of the high-pressure water pump speed error amount, K is the output variable, including the fuzzy control subset of the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3; specifically including the following steps:

[0049] S41: input fuzzification of the fuzzy adaptive PID controller, according to the membership function in step S3, the accurate value of the input variable is converted into the membership function vector of each variable;

[0050] S42: fuzzy control rule evaluation of the fuzzy adaptive PID controller, to obtain an activation intensity matrix; the fuzzy control rule base is expressed in the form of a matrix, and each fuzzy control rule corresponds to a combination of input fuzzy sets, wherein the element R n-ij represents the fuzzy control rule consequent, wherein n is 1, 2 or 3, corresponding to the fuzzy control rule matrix of the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3 respectively, i is the horizontal coordinate of the element in the fuzzy control rule matrix, and j is the vertical coordinate of the element in the fuzzy control rule matrix;

[0051] The activation intensity matrix is calculated by the outer product of the input membership function vector, and the obtaining method is as follows:

[0052]

[0053] wherein W n-ij is the activation strength matrix; is the input membership function vector of the high-pressure water pump speed error e; is the input membership function vector of the change rate ec of the high-pressure water pump speed error amount; is the conjunction operation;

[0054] S43: the fuzzy set C n-ij ′ n of each fuzzy control rule consequent R -ij is activated by the activation strength matrix W n-ij , and the acquisition method is:

[0055]

[0056] wherein C n ′ -ij is the fuzzy set of each fuzzy control rule consequent; R n-ij is the fuzzy control rule consequent;

[0057] S44: fuzzy control output aggregation, the maximum value of each fuzzy control rule consequent fuzzy set is taken point by point to generate the final fuzzy control output fuzzy set, and the acquisition method is:

[0058]

[0059] wherein C n-final is the fuzzy control output fuzzy set;

[0060] S45: the centroid method is used to calculate the centroid of the fuzzy set to determine the accurate output value, and the defuzzification operation is realized.

[0061] Preferably, in step S5, the swing angle adjusting variable motor displacement of the high-pressure water pump variable motor is controlled according to the fuzzy control subset output value K n-final in step S4, so that the high-pressure water pump output speed is quickly, stably and accurately realized, and specifically:

[0062] According to the fuzzy control subset output value K n-final in step S4, the actuator control signal output amount u k is calculated as:

[0063]

[0064] wherein u k is the actuator control signal output amount at the kth sampling; e k is the input deviation amount at the kth sampling; e k-1The deviation amount input at the k-1 sampling time; T is an integral time constant; T d The differential time constant; u0 is the initial value of the actuator control signal output.

[0065] Compared with the prior art, the present application has the following beneficial effects:

[0066] (1) The present application is based on the hydraulic fan driven high pressure water pump to produce fresh water through the reverse osmosis membrane, using fuzzy control theory and traditional PID control theory to realize the fast and accurate adjustment of the hydraulic wind turbine driven high pressure water pump speed under complex disturbance and ensure the stable operation of the reverse osmosis system, which does not depend on the mathematical model of the controlled object, and the calculation is simple, easy to realize and strong in adaptability.

[0067] (2) The present application overcomes the defects of traditional PID control that cannot handle external input disturbance, changing working point and internal parameter coupling and other internal and external disturbances, effectively improves the rapidity, accuracy and stability of the hydraulic wind driven high pressure water pump speed control. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 The flow chart of the hydraulic fan driven high pressure water pump speed control method based on fuzzy adaptive PID of the present application;

[0069] Figure 2 The principle of the hydraulic fan driven high pressure water pump speed control method based on fuzzy adaptive PID of the present application;

[0070] Figure 3 The membership function schematic diagram of the input parameters e and ec of the present application;

[0071] Figure 4 The membership function schematic diagram of the output parameter K1 of the present application;

[0072] Figure 5 The membership function schematic diagram of the output parameter K2 of the present application;

[0073] Figure 6 The membership function schematic diagram of the output parameter K3 of the present application;

[0074] Figure 7 The change rule of the output parameter K1 of the present application with error e and error change rate ec;

[0075] Figure 8 The change rule of the output parameter K2 of the present application with error e and error change rate ec;

[0076] Figure 9 The change rule of the output parameter K3 of the present application with error e and error change rate ec;

[0077] Figure 10 Response curve of the fuzzy adaptive controller of the present application;

[0078] Figure 11 Response curve of the output parameter K1 of the fuzzy adaptive controller of the present application;

[0079] Figure 12 Response curve of the output parameter K2 of the fuzzy adaptive controller of the present application;

[0080] Figure 13 Response curve of the output parameter K3 of the fuzzy adaptive controller of the present application. DETAILED DESCRIPTION

[0081] The exemplary embodiments, features and aspects of the present application will be described in detail below with reference to the accompanying drawings. The same reference numbers in the drawings represent the same or similar elements. Although various aspects of the embodiments are illustrated in the drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0082] The present application provides a hydraulic fan driven high-pressure water pump speed control method based on fuzzy adaptive PID, as shown in Figure 1 The speed error of the hydraulic fan high-pressure water pump is obtained to determine the input variable of the fuzzy adaptive PID controller. The stability of the hydraulic fan is analyzed to establish a high-pressure water pump variable motor swing angle control model to determine the output variable of the fuzzy adaptive PID controller. The fuzzy control set of the fuzzy adaptive PID controller of the hydraulic fan driven high-pressure water pump is determined. The Mamdani method is used for fuzzy reasoning of the fuzzy reasoning table, and the barycentric method is used for calculation of the fuzzy control subset. According to the output value of the fuzzy control subset, the swing angle of the high-pressure water pump variable motor is adjusted to control the displacement of the motor. As shown in Figure 2 The principle diagram of the hydraulic fan driven high-pressure water pump speed control method based on fuzzy adaptive PID is shown in the figure, which specifically includes a PID controller 1, a fuzzy controller 2, an actuator 3, a wind wheel 4, a variable motor 5, a quantitative pump 6, and a high-pressure water pump 7. The specific steps include:

[0083] Step S1: The speed error of the hydraulic fan high-pressure water pump is obtained to determine the input variable of the fuzzy adaptive PID controller as the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error.

[0084] Step S11: The actual output speed ω p of the high-pressure water pump is obtained by a speed sensor. p Step S12: The actual output speed ω m is compared with the set speed ω

[0085] e=ωp -ω m ;

[0086] wherein e is the high-pressure water pump speed error; ω p is the actual output speed of the high-pressure water pump; ω m is the set speed of the high-pressure water pump.

[0087] Step S12: Obtain the rate of change ec of the speed error amount of the high-pressure water pump, which is:

[0088]

[0089] wherein ec is the rate of change of the speed error amount of the high-pressure water pump; t is the time parameter.

[0090] Step S2: Analyze the stability of the hydraulic fan, establish a high-pressure water pump variable motor swing angle control model, and determine the output variable and value range of the fuzzy self-adaptive PID controller.

[0091] Step S21: Establish the transfer function of the high-pressure water pump speed to the high-pressure water pump variable motor swing angle control model, which is:

[0092]

[0093] wherein ω m is the set speed of the high-pressure water pump; γ m is the variable motor swing angle in a stable state; p h0 is the high-pressure pipeline pressure in a stable state; C tp is the total leakage coefficient of the hydraulic main transmission system; V is the oil volume in the high-pressure pipeline; β e is the oil volume elastic modulus; s is the high-pressure water pump speed input control parameter; K m is the variable motor displacement gradient coefficient; γ m0 is the initial value of the variable motor swing angle in a stable state; ω m0 is the variable motor speed in a stable state; J m is the variable motor moment of inertia; η m is the variable motor transmission efficiency; B m is the variable motor viscous damping coefficient.

[0094] Taking a certain typical hydraulic fan as an example, the related parameters obtained by identifying the actual unit parameters are summarized and shown in the following table:

[0095] Table 1: Parameters of a typical hydraulic fan

[0096]

[0097] Step S22: Set the output variables of the fuzzy adaptive PID controller as the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3, determine the characteristic equation and the value range of the output variables of the fuzzy adaptive PID controller, specifically as follows:

[0098] According to the output variables of the fuzzy adaptive PID controller, the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3, the closed-loop system characteristic equation of the fuzzy adaptive PID controller is obtained as follows:

[0099]

[0100] According to the Routh criterion, the stability of the fuzzy adaptive PID controller is analyzed, and the condition for the system to be stable is obtained as follows:

[0101]

[0102] The fuzzy adaptive PID controller is used to control the swing angle of the variable motor of the high-pressure water pump in combination with the transfer function of step S21.

[0103] The value range of the proportional control parameter K1 is obtained as [-0.188, 0.005], and the value ranges of the integral control parameter K2 and the differential control parameter K3 are [-35.46, 0] and [-0.069, 0.007], respectively.

[0104] Step S3: Determine the fuzzy control set of the fuzzy adaptive PID controller of the hydraulic fan driven high-pressure water pump, and determine the fuzzy control subsets of the input variables in step S1 and the output variables in step S2 according to the Z-shaped membership function, the triangular membership function and the S-shaped membership function.

[0105] The fuzzy adaptive PID controller of the hydraulic fan driven high-pressure water pump is set as a two-input and three-output structure; the input variables are the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error; and the output variables are the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3.

[0106] Determine the fuzzy control domain of the input and output variables. Since the final output speed control target is 1500 r / min, if ±1500 r / min is taken as the error range, the controller has a large adjustment range and low precision. Therefore, the error is normalized in the system, and the error range of ±1500 r / min is set to the range of [-1, 1]. At this time, the fuzzy control domain of the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error is {-1, -0.679, -0.333, 0, 0.333, 0.679, 1}.

[0107] The fuzzy control set of the input variable and the output variable is: a first fuzzy control subset NB, a second fuzzy control subset NM, a third fuzzy control subset NS, a fourth fuzzy control subset ZO, a fifth fuzzy control subset PS, a sixth fuzzy control subset PM and a seventh fuzzy control subset PB.

[0108] A Z-shaped membership function is used to determine the first fuzzy control subset NB of the high-pressure water pump speed error e, and specifically,

[0109]

[0110] Wherein, NB(e(t)) is the Z-shaped membership function vector of the first fuzzy control subset; e(t) is the variable of the high-pressure water pump speed error with time parameter t; x is the actual value of the input variable.

[0111] Similarly, the Z-shaped membership function vector of the first fuzzy control subset of the change rate ec of the high-pressure water pump speed error is NB(ec(t)).

[0112] A triangular membership function is used to determine the fuzzy control subset, and specifically,

[0113] As Figure 3 The membership function of the input parameter e and ec of the application is shown in the figure, and the triangular membership function of the second fuzzy control subset NM, the third fuzzy control subset NS, the fourth fuzzy control subset ZO, the fifth fuzzy control subset PS and the sixth fuzzy control subset PM of the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error is:

[0114]

[0115] Wherein, f(x,a,b,c) is the triangular membership function vector of the fuzzy control subset; a is the first section parameter of the triangular membership function; b is the second section parameter of the triangular membership function; c is the third section parameter of the triangular membership function.

[0116] As shown in Table 2, the section parameter value table of the three triangular membership functions is shown in Table 2.

[0117] Table 2 is the section parameter table of the triangular membership function of the input variable

[0118]

[0119] Similarly, the subsets of the proportional control parameter K1, integral control parameter K2, and derivative control parameter K3 are also determined according to the triangular membership function. The value ranges of the proportional control parameter K1, integral control parameter K2, and derivative control parameter K3 are [-0.188, 0.005], [-35.46, 0], and [-0.069, 0.007], respectively. Figure 4 This is a schematic diagram of the membership function of the output parameter K1 of the present invention. The universe of discourse of the proportional control parameter K1 is set as {-0.188, -0.153, -0.124, -0.091, -0.059, -0.027, 0.005}; as shown Figure 5 The diagram illustrates the membership function of the output parameter K2 in this invention. The universe of discourse for the integral control parameter K2 is set as {-35.46, -29.55, -23.64, -17.73, -11.82, -5.911, 0}; (The diagram is incomplete and requires further context.) Figure 6 The diagram below illustrates the membership function of the output parameter K3 of this invention. The universe of discourse for the differential control parameter K3 is {-0.069, -0.057, -0.044, -0.031, -0.019, -0.006, 0.007}. The piecewise parameter values ​​of the triangular membership function corresponding to each subset membership function are shown in the table below:

[0120] Table 3. Piecewise parameter table of the membership function of the PID control parameter triangle

[0121]

[0122] The S-shaped membership function is used to determine the seven-level fuzzy control subset PB of the high-pressure water pump speed error e, specifically:

[0123]

[0124] Wherein, PB(e(t)) is the S-shaped membership function vector of the seventh-level fuzzy control subset.

[0125] Similarly, the S-shaped membership function vector of the first-order fuzzy control subset of the rate of change of the high-pressure water pump speed error ec is PB(ec(t)).

[0126] By using fuzzy language and combining the fuzzy control subsets of input and output variables, fuzzy inference tables for proportional control parameter K1, integral control parameter K2, and derivative control parameter K3 are obtained.

[0127] Based on the above rules and the actual scenario of hydraulic blower driving high-pressure water pump, a fuzzy rule base was formulated. The reasoning rule is "IF e and ec then K". 49 dual-input three-output rules were established, and the fuzzy rule base is shown in Table 4.

[0128] Table 4 output parameter K1, K2, K3 fuzzy rule base

[0129]

[0130]

[0131] Step S4: fuzzy reasoning is performed on the fuzzy inference table in step S3 using Mamdani reasoning method.

[0132] The reasoning rule of Mamdani reasoning method is: E∩EC→K; wherein, E is the fuzzy control subset of high-pressure water pump speed error e, EC is the fuzzy control subset of the change rate ec of high-pressure water pump speed error, K is the output variable, including the fuzzy control subset of proportional control parameter K1, integral control parameter K2 and differential control parameter K3; specifically including steps:

[0133] Step S41: input fuzzification of fuzzy adaptive PID controller, according to the membership function in step S3, the accurate value of input variable is converted into the membership function vector of each variable, for example:

[0134] The membership vector of input variable high-pressure water pump speed error e is: μE=[μE-NB(e),μE2-NM(e),μE-NS(e),

[0135] μE-ZO(e),μE-PS(e),μE-PM(e),μE-PB(e)].

[0136] The membership vector of input variable high-pressure water pump speed error e is: μE=[μE-NB(e),μE2-NM(e),μE-NS(e),

[0137] The membership vector of input variable high-pressure water pump speed error e is: μE=[μE-NB(e),μE2-NM(e),μE-NS(e), n-ij

[0138] The rule matrix R1 corresponding to the proportional control parameter K1 (7×7) is shown in the following table:

[0139] Table 5 fuzzy control rule consequent R1 data table ​

[0140]

[0141] The rule matrix R2 (7x7) corresponding to the integral control parameter K2 is shown in the following table:

[0142] Table 6 Data table of the back of the fuzzy control rule R2

[0143]

[0144]

[0145] The rule matrix R3 (7x7) corresponding to the differential control parameter K3 is shown in the following table:

[0146] Table 7 Data table of the back of the fuzzy control rule R3

[0147]

[0148] The activation intensity matrix is calculated by the outer product of the input membership function vector, and the method is as follows:

[0149]

[0150] Wherein, W n-ij is the activation intensity matrix; is the input membership function vector of the high-pressure water pump speed error e; is the input membership function vector of the change rate ec of the high-pressure water pump speed error; is the conjunction operation.

[0151] Step S43: The fuzzy set C n-ij ′ n of each back of the fuzzy control rule R -ij is adjusted by the activation intensity matrix W n-ij , and the method is as follows:

[0152]

[0153] Wherein, C n ′ -ij is the fuzzy set of each back of the fuzzy control rule; R n-ij is the back of the fuzzy control rule.

[0154] Step S44: Fuzzy control output aggregation, the maximum value of the fuzzy set of each back of the fuzzy control rule is taken point by point to generate the final fuzzy control output fuzzy set, and the method is as follows:

[0155]

[0156] Wherein, C n-final is the fuzzy control output fuzzy set.

[0157] Step S45: using the center of gravity method to calculate the centroid of the fuzzy set to determine the accurate output value, realizing the defuzzification operation, obtaining the fuzzy control subset of the fuzzy adaptive PID controller is:

[0158]

[0159] Wherein, K n-final is the output value of the fuzzy control subset; K n-k is the fuzzy control domain point; C n-final (k) is the fuzzy set of fuzzy control output; k is the sampling data point number; m is the total number of sampling data points.

[0160] The calculation example of the embodiment of the application is shown as follows:

[0161] Step S461: input fuzzification, the input is e=0.5, ec=0.1; calculate the membership degree according to the membership function:

[0162]

[0163] That is: μE=[0.246,0.250], μEC=[0.150,0.147].

[0164] The corresponding activation rules are: rule 32 (IF e=PS and ec=ZO, THEN K1=NS, K2=PS, K3=PS); rule 33 (IF e=PS and ec=PS, THEN K1=NS, K2=PS, K3=ZO); rule 39 (IF e=PM and ec=ZO, THEN K1=NM, K2=PM, K3=PM); rule 40 (IF e=PM and ec=PS, THEN K1=NM, K2=PS, K3=PB).

[0165] Step S462: that is, taking the minimum value of each condition membership degree as the activation strength of the rule. The activation strength of rule 32: min (0.246, 0.150) = 0.150; the activation strength of rule 33: min (0.246, 0.147) = 0.147; the activation strength of rule 39: min (0.250, 0.150) = 0.150; the activation strength of rule 40: min (0.250, 0.147) = 0.147; that is:

[0166]

[0167] Step S463: output fuzzification: using truncation method, the activation intensity is applied to the fuzzy set of the conclusion part, i.e. the output membership function is truncated to the height of the activation intensity. The rule 32 output "K1=NS, K2=PS, K3=PS" is truncated to 0.150; the rule 33 output "K1=NS, K2=PS, K3=ZO" is truncated to 0.147; the rule 39 output "K1=NM, K2=PM, K3=PM" is truncated to 0.150; the rule 40 output "K1=NM, K2=PS, K3=PB" is truncated to 0.147; i.e.:

[0168]

[0169] Step S464: result synthesis: the output fuzzy sets of all rules are combined by maximum operation to form the final output fuzzy set, and the unactivated consequent fuzzy set elements are set to zero: μK1_NS=max(0.150, 0.147)=0.150; μK1_NM=max(0.150, 0.147)=0.150; μK2_PS=max(0.150, 0.147, 0.147)=0.150; μK2_PM=0.150; μK3_PS=0.150; K3_ZO=0.147; μK3_PM=0.150; μK3_PB=0.147. Therefore

[0170] C 1-final =[0, 0.147, 0.147, 0, 0, 0, 0];

[0171] C 2-final =[0, 0, 0, 0, 0.147, 0.147, 0];

[0172] C 3-final =[0, 0, 0, 0.147, 0.150, 0.150, 0.174].

[0173] Step S465: defuzzification processing.

[0174]

[0175] Step S5: according to the fuzzy control subset output value K n-final in step S4, the swing angle adjusting variable motor displacement of the high-pressure water pump variable motor is controlled to realize fast, stable and accurate high-pressure water pump output speed, specifically:

[0176] According to the fuzzy control subset output value K n-final in step S4, the actuator control signal output u k is calculated as:

[0177]

[0178] wherein, u k is the actuator control signal output at the kth sampling; e k is the input deviation at the kth sampling; e k-1 is the input deviation at the k-1th sampling; T is an integral time constant; T d is a differential time constant; u0 is an initial value of the actuator control signal output.

[0179] The simulation results of the hydraulic fan-driven high-pressure water pump speed control system based on the fuzzy adaptive PID are as follows:

[0180] As Figure 7 is the change rule of the output parameter K1 of the application with the error e and the error rate ec, specifically, the proportional control parameter K1 obtained by the input high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error; as Figure 8 is the change rule of the output parameter K2 of the application with the error e and the error rate ec, specifically, the integral control parameter K2 obtained by the input high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error; as Figure 9 is the change rule of the output parameter K3 of the application with the error e and the error rate ec, specifically, the differential control parameter K3 obtained by the input high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error. It is observed that the change curves of the output parameters K1, K2 and K3 with the error e and the error rate ec are relatively smooth, and there is no 90-degree horizontal corner, which can prove that the fuzzy adaptive PID controller meets the requirements of system control stability.

[0181] Figure 10 The response curve (compared with the traditional PID) of the fuzzy adaptive controller of the hydraulic fan-driven high-pressure water pump control method based on the fuzzy adaptive PID provided by the application under external disturbance is observed. Figure 10 When the system is disturbed by a sudden drop in wind speed, that is, the quantitative pump speed changes from 465 r / min to 233 r / min, the system with fuzzy adaptive PID control can recover to the required high-pressure water pump speed in only 5.7 s, while the classical PID adjustment takes 14.9 s to reach the high-pressure water pump speed requirement. In this process, the maximum deviation speed of the system with fuzzy adaptive PID control is 7.8 r / min, while the maximum deviation of the system with classical PID controller is 29.0 r / min. Therefore, the system with fuzzy adaptive PID control has a much better control effect than the system with classical PID control when the input end is disturbed.

[0182] As Figure 11The figure is a response curve of a proportional control parameter K1 of a fuzzy adaptive controller output parameter of the hydraulic fan driven high-pressure water pump control method based on fuzzy adaptive PID of the application. Figure 12 The figure is a response curve of an integral control parameter K2 of the fuzzy adaptive controller output parameter of the hydraulic fan driven high-pressure water pump control method based on fuzzy adaptive PID of the application. Figure 13 The figure is a response curve of a differential control parameter K3 of the fuzzy adaptive controller output parameter of the hydraulic fan driven high-pressure water pump control method based on fuzzy adaptive PID of the application. In the parameter adjustment process, the proportional link parameter P is adjusted to a maximum degree of 0.98%, the integral link parameter I is adjusted to a maximum degree of 0.93%, and the differential link is adjusted to a maximum degree of 0.66%. Thus, the proportional link and the integral link in the PID controller can play the most effective role when responding to sudden wind speed interference.

[0183] The application has the advantages that the application provides a hydraulic fan driven high-pressure water pump speed control method based on fuzzy adaptive PID, which is based on the production of fresh water by the hydraulic fan driven high-pressure water pump through a reverse osmosis membrane, and combines fuzzy control theory and traditional PID control theory to realize rapid and accurate adjustment of the speed of the hydraulic fan driven high-pressure water pump under complex disturbance and ensure stable operation of the reverse osmosis system. The application does not depend on the mathematical model of the controlled object, and has simple calculation, easy implementation, strong adaptability, and overcomes the defects of traditional PID, such as inability to handle external input interference, constantly changing working points, and internal parameter coupling and other internal and external disturbances on the negative impact on the system, and effectively improves the rapidity, accuracy and stability of the hydraulic fan driven high-pressure water pump speed control.

[0184] The above-described embodiments are only preferred embodiments of the application, and do not limit the scope of the application. Without departing from the design spirit of the application, various modifications and improvements to the technical solutions of the application made by those skilled in the art shall fall within the protection scope of the claims of the application.

Claims

1. A hydraulic fan-driven high-pressure water pump speed control method based on fuzzy adaptive PID, characterized in that, It comprises: S1: obtain the speed error of the high-pressure water pump of the hydraulic fan, determine the input variable of the fuzzy adaptive PID controller as the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error; S2: analyze the stability of the hydraulic fan, establish a high-pressure water pump variable motor swing angle control model, determine the output variable and value range of the fuzzy adaptive PID controller, specifically including the following sub-steps: S21: establish the transfer function of the high-pressure water pump speed to the high-pressure water pump variable motor swing angle control model as: wherein ω m is the set speed of the high-pressure water pump; γ m is the variable motor swing angle in the steady state; p h0 is the high-pressure pipeline pressure in the steady state; C tp is the total leakage coefficient of the hydraulic main transmission system; V is the oil volume in the high-pressure pipeline; β e is the oil volume elastic modulus; s is the high-pressure water pump speed input control parameter; K m is the variable motor displacement gradient coefficient; γ m0 is the initial value of the variable motor swing angle in the steady state; ω m0 is the variable motor speed in the steady state; J m is the variable motor moment of inertia; η m is the variable motor transmission efficiency; B m is the variable motor viscous damping coefficient; S22: set the output variable of the fuzzy adaptive PID controller as: the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3, determine the characteristic equation and value range of the output variable of the fuzzy adaptive PID controller, and use the fuzzy adaptive PID controller to control the swing angle of the high-pressure water pump variable motor in combination with the transfer function of step S21; S3: determine the fuzzy control set of the fuzzy adaptive PID controller for driving the high-pressure water pump of the hydraulic fan, determine the fuzzy control subset of the input variable in step S1 and the output variable in step S2 according to the Z-shaped membership function, the triangular membership function and the S-shaped membership function; use fuzzy language to combine the fuzzy control subset of the input variable and the output variable to obtain the fuzzy reasoning table of the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3; S4: use the Mamdani reasoning method to perform fuzzy reasoning on the fuzzy reasoning table in step S3, and calculate the fuzzy control subset of the fuzzy adaptive PID controller by the barycentric method as: where K n-final is the fuzzy control subset output value; K n-k is the fuzzy control universe point; C n-final (k) is the fuzzy control output fuzzy set; k is the sampling data point number; m is the total number of sampling data points; S5: outputting the fuzzy control subset value K according to step S4 n-final , controlling the swing angle adjusting variable motor displacement of the high-pressure water pump variable motor.

2. The method for controlling the speed of a hydraulic fan-driven high-pressure water pump based on fuzzy adaptive PID according to claim 1, characterized in that: In step S1, the speed error of the high-pressure water pump of the hydraulic fan is obtained, and the input variable of the fuzzy adaptive PID controller is determined as the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error, specifically: S11: Obtain the actual output rotational speed ω of the high-pressure water pump through the rotational speed sensor p , and compare the actual output rotational speed ω p with the set rotational speed ω m to obtain the rotational speed error e of the high-pressure water pump: e = ω p -ω m Wherein, e is the high-pressure water pump speed error; ω p is the actual output speed of the high-pressure water pump; ω m is the set speed of the high-pressure water pump; S12: obtain the change rate ec of the speed error of the high-pressure water pump as: Wherein, ec is the change rate of the high-pressure water pump speed error; t is the time parameter.

3. The method of claim 1, wherein the method is a method of controlling the rotation speed of a hydraulic fan-driven high-pressure water pump based on a fuzzy adaptive PID, characterized by: In step S22, the characteristic equation and value range of the output variable of the fuzzy adaptive PID controller are determined, specifically: According to the output variable proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3 of the fuzzy adaptive PID controller, the closed-loop system characteristic equation of the fuzzy adaptive PID controller is obtained as: (1.042K3+0.0720)s 3 +(1.042K1-191.2K3+0.124)s 2 +(1.042K2-191.2K1+1)s-191.2K2=0 According to the Routh criterion, the stability of the fuzzy adaptive PID controller is analyzed, and the condition for system stability is obtained as:

4. The method for controlling the speed of a hydraulic fan-driven high-pressure water pump based on fuzzy adaptive PID according to claim 1, characterized in that: In step S3, the fuzzy control set of the fuzzy adaptive PID controller for driving the high-pressure water pump of the hydraulic fan is specifically: The fuzzy adaptive PID controller for driving the high-pressure water pump of the hydraulic fan is set as a two-input and three-output structure; wherein the input variable is: the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error; The output variable is: the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3; The fuzzy control set of the input variable and the output variable is set as: a first fuzzy control subset NB, a second fuzzy control subset NM, a third fuzzy control subset NS, a fourth fuzzy control subset ZO, a fifth fuzzy control subset PS, a sixth fuzzy control subset PM and a seventh fuzzy control subset PB.

5. The method for controlling the speed of a hydraulic fan-driven high-pressure water pump based on fuzzy adaptive PID according to claim 1, characterized in that: The Z-shaped membership function in step S3 is used to determine the first fuzzy control subset NB of the high-pressure water pump speed error e, and specifically: Wherein, NB(e(t)) is the Z-shaped membership function vector of the first fuzzy control subset; e(t) is the variable of the high-pressure water pump speed error with time parameter t; x is the actual value of the input variable; Similarly, the Z-shaped membership function vector of the first fuzzy control subset of the change rate ec of the high-pressure water pump speed error is NB(ec(t)).

6. The method for controlling the speed of a hydraulic fan-driven high-pressure water pump based on fuzzy adaptive PID according to claim 1, characterized in that: The triangular membership function in step S3 is used to determine the fuzzy control subset, and specifically: The triangular membership function of the second fuzzy control subset NM, the third fuzzy control subset NS, the fourth fuzzy control subset ZO, the fifth fuzzy control subset PS and the sixth fuzzy control subset PM of the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error is: Wherein, f(x,a,b,c) is the triangular membership function vector of the fuzzy control subset; a is the first section parameter of the triangular membership function; b is the second section parameter of the triangular membership function; c is the third section parameter of the triangular membership function; Similarly, each subset of the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3 is also determined according to the triangular membership function.

7. The method for controlling the speed of a hydraulic fan-driven high-pressure water pump based on fuzzy adaptive PID according to claim 1, characterized in that: The S-shaped membership function in step S3 is used to determine the seventh fuzzy control subset PB of the high-pressure water pump speed error e, and specifically: Wherein, PB(e(t)) is the S-shaped membership function vector of the seventh fuzzy control subset; Similarly, the S-shaped membership function vector of the first fuzzy control subset of the change rate ec of the high-pressure water pump speed error is PB(ec(t)).

8. The method for controlling the speed of a hydraulic fan-driven high-pressure water pump based on fuzzy adaptive PID according to claim 1, characterized in that: In step S4, the fuzzy reasoning table in step S3 is subjected to fuzzy reasoning using the Mamdani reasoning method, and specifically: The reasoning rule of the Mamdani reasoning method is: E∩EC→K; wherein, E is the fuzzy control subset of the high-pressure water pump speed error e, EC is the fuzzy control subset of the change rate ec of the high-pressure water pump speed error, and K is the output variable, including the fuzzy control subset of the proportional control parameter K1, the integral control parameter K2 and the differential control parameter K3; specifically including the steps of: S41: input fuzzification of the fuzzy adaptive PID controller, according to the membership function in step S3, the accurate value of the input variable is converted into the membership function vector of each variable; S42: evaluate the fuzzy control rules of the fuzzy adaptive PID controller to obtain an activation intensity matrix; express the fuzzy control rule base in a matrix form, and each fuzzy control rule corresponds to a combination of input fuzzy sets, wherein element R n-ij represents a fuzzy control rule consequent, wherein n takes values of 1, 2, and 3, respectively corresponding to fuzzy control rule matrices of proportional control parameters K1, integral control parameters K2, and differential control parameters K3, i is a horizontal coordinate of an element in the fuzzy control rule matrix, and j is a vertical coordinate of the element in the fuzzy control rule matrix; The activation strength matrix is calculated by the outer product of the input membership function vector, and the acquisition method is: wherein W n-ij is the activation intensity matrix; is the input membership function vector of the high-pressure water pump speed error e; is the input membership function vector of the change rate ec of the high-pressure water pump speed error amount; is the conjunction operation; S43: Each fuzzy control rule consequent R n-ij fuzzy set C n ′ -ij activated intensity matrix W n-ij adjustment, the acquisition method is: where C n ′ -ij is the fuzzy set for the consequent of each fuzzy control rule; R n-ij is the fuzzy control rule consequent; S44: fuzzy control output aggregation, the maximum value of the fuzzy set of each fuzzy control rule consequent is taken point by point to generate the final fuzzy control output fuzzy set, and the acquisition method is: wherein C n-final is the fuzzy control output fuzzy set; S45: the centroid method is used to calculate the centroid of the fuzzy set to determine the accurate output value, and the defuzzification operation is realized.

9. The method for controlling the speed of a hydraulic fan-driven high-pressure water pump based on fuzzy adaptive PID according to claim 1, characterized in that: The output value K of the fuzzy control subset in step S4 is output in step S5 n-final The swing angle adjusting variable motor displacement of the high-pressure water pump variable motor is controlled to realize fast, stable and accurate high-pressure water pump output rotating speed, specifically: Based on the fuzzy control subset output value K in step S4 n-final Calculate the actuator control signal output quantity u k for: where u k is the actuator control signal output at the kth sample; e k is the input error at the kth sample; e k-1 is the input error at the (k-1)th sample; T is the integral time constant; T d is the derivative time constant; and u0 is the initial value of the actuator control signal output.

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