Method for controlling rotating speed of high-pressure water pump driven by hydraulic fan based on fuzzy self-adaptive PID (proportion integration differentiation)

Abstract

The invention belongs to the technical field of seawater desalination equipment control, and relates to a hydraulic fan driving high-pressure water pump rotating speed control method based on fuzzy self-adaptive PID, which comprises the following steps: S1, acquiring a rotating speed error of a hydraulic fan high-pressure water pump, and determining an input variable of a fuzzy self-adaptive PID controller; s2, analyzing the stability of the hydraulic fan, establishing a high-pressure water pump variable displacement motor swing angle control model, and determining output variables of a fuzzy self-adaptive PID (Proportion Integration Differentiation) controller; s3, determining a fuzzy control set of a hydraulic fan driving fuzzy self-adaptive PID (Proportion Integration Differentiation) controller; s4, performing fuzzy reasoning on the fuzzy reasoning table by using a Mamdan i method, and calculating a fuzzy control subset through a gravity center method; and S5, controlling the swing angle of the high-pressure water pump variable displacement motor to adjust the displacement of the variable displacement motor according to the output value of the fuzzy control subset. The hydraulic fan drives the high-pressure water pump to penetrate through the reverse osmosis membrane to produce fresh water, and fuzzy control and PID control theories are applied to improve rapidity, accuracy and stability of water pump rotating speed control.

Description

Technical Field

[0001] The present invention belongs to the technical field of seawater desalination equipment control, and particularly relates to a method for controlling the rotational speed of a high-pressure water pump driven by a hydraulic fan based on fuzzy adaptive PID. Background Art

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

[0003] As an alternative, the hydraulic wind turbine (HWT) provides a more feasible option for the direct wind-powered seawater desalination (D-WPD) system. The HWT provides fast response and high reliability, and allows continuous variable speed operation, enabling a relatively high overall efficiency. The hydraulic transmission system separates the wind turbine from the high-pressure water pump, allowing a more modular and flexible layout. However, the RO system needs to operate under relatively stable conditions. With the fluctuation of wind speed, the high-pressure water pump must quickly and accurately adjust its rotational speed to stabilize the flow rate and pressure of the reverse osmosis system. All of these pose additional challenges to the control technology of hydraulic wind-powered seawater desalination.

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

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method for controlling the rotational speed of a high-pressure water pump driven by a hydraulic fan based on fuzzy adaptive PID. The present invention is based on the hydraulic fan driving the high-pressure water pump to produce fresh water through the reverse osmosis membrane, and uses fuzzy control and PID control theories to improve the rapidity, accuracy and stability of the water pump rotational speed control.

[0006] To achieve the above object, the present invention discloses the following technical solutions:

[0007] A method for controlling the rotational speed of a high-pressure water pump driven by a hydraulic fan based on fuzzy adaptive PID, which includes:

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

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

[0010] S21: Establish the transfer function of the rotational speed of the high-pressure water pump to the control model of the swashplate angle of the variable motor of the high-pressure water pump as:

[0011]

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

[0013] S22: Set the output variables of the fuzzy adaptive PID controller as: the proportional control parameter K 1 , the integral control parameter K 2 and the derivative control parameter K 3 of, determine the characteristic equation and value range of the output variables of the fuzzy adaptive PID controller, and use the fuzzy adaptive PID controller to combine the transfer function in step S21 to control the swashplate angle of the variable motor of the high-pressure water pump;

[0014] S3: Determine the fuzzy control set of the fuzzy adaptive PID controller for driving the high-pressure water pump of the hydraulic fan, 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, triangular membership function, and S-shaped membership function; Use fuzzy language to combine the fuzzy control subsets of the input variables and output variables to obtain the proportional control parameter K 1 , the integral control parameter K 2 and the derivative control parameter K 3 of the fuzzy inference table;

[0015] S4: Use the Mamdani inference method to perform fuzzy inference on the fuzzy inference table in step S3, and calculate the fuzzy control subset of the fuzzy adaptive PID controller by the centroid method as follows:

[0016]

[0017] where, 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 control output fuzzy set; k is the sampling data point number; m is the total number of sampling data points;

[0018] S5: According to the output value K n-final of the fuzzy control subset in step S4, control the swing angle of the variable motor of the high-pressure water pump to adjust the displacement of the variable motor, so as to achieve a fast, stable and accurate output speed of the high-pressure water pump.

[0019] Preferably, in step S1, obtain the speed error of the high-pressure water pump of the hydraulic fan, and determine that the input variables of the fuzzy adaptive PID controller are the speed error e of the high-pressure water pump and the change rate ec of the speed error amount of the high-pressure water pump, specifically:

[0020] S11: Obtain the actual output speed ω p of the high-pressure water pump through a speed sensor, and compare the actual output speed ω p with the set speed ω m to obtain the speed error e of the high-pressure water pump as:

[0021] e = ω p - ω m ;

[0022] where, e is the speed error of the high-pressure water pump; 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;

[0023] S12: Obtain the change rate ec of the speed error amount of the high-pressure water pump as:

[0024]

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

[0026] Preferably, in step S22, determine the characteristic equation and value range of the output variable of the fuzzy adaptive PID controller, specifically:

[0027] According to the proportional control parameter K 1 of the output variable of the fuzzy adaptive PID controller, integral control parameter K2 and the differential control parameter K 3 , 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 analysis of the fuzzy adaptive PID controller is carried out, and the condition for the system to be stable is obtained as follows:

[0030]

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

[0032] Set the fuzzy adaptive PID controller for the hydraulic fan driving the high-pressure water pump to have a two-input and three-output structure; where the input variables are: the rotational speed error e of the high-pressure water pump and the change rate ec of the rotational speed error of the high-pressure water pump; the output variables are: the proportional control parameter K 1 , the integral control parameter K 2 and the differential control parameter K 3 ;

[0033] Set the fuzzy control sets of the input variables and output variables to be: the first-level fuzzy control subset NB, the second-level fuzzy control subset NM, the third-level fuzzy control subset NS, the fourth-level fuzzy control subset ZO, the fifth-level fuzzy control subset PS, the sixth-level fuzzy control subset PM, and the 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 rotational speed error e of the high-pressure water pump, specifically as follows:

[0035]

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

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

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

[0039] The triangular membership functions of the secondary fuzzy control subset NM, the tertiary fuzzy control subset NS, the quaternary fuzzy control subset ZO, the quinary fuzzy control subset PS, and the senary 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 amount are all as follows:

[0040]

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

[0042] Similarly, each subset of the proportional control parameter K 1 、the integral control parameter K 2 and the derivative control parameter K 3 is also determined according to the triangular membership function.

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

[0044]

[0045] where PB(e(t)) is the vector of the S-shaped membership function of the seventh-level fuzzy control subset;

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

[0047] Preferably, in step S4, the Mamdani inference method is used to perform fuzzy inference on the fuzzy inference table in step S3, specifically:

[0048] The inference rule of the Mamdani inference method is: E∩EC→K; where 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, and K is the output variable, including the fuzzy control subsets of the proportional control parameter K 1 、the integral control parameter K 2 and the derivative control parameter K 3 ; Specifically, it includes the steps:

[0049] S41: Fuzzification of the input of the fuzzy adaptive PID controller. According to the membership function described in step S3, the exact value of the input variable is converted into the membership function vector of each variable;

[0050] S42: Evaluate the fuzzy control rules of the fuzzy adaptive PID controller to obtain the activation intensity matrix; express the fuzzy control rule base in matrix form, where each fuzzy control rule corresponds to a combination of input fuzzy sets, and the element R n-ij represents the consequent of the fuzzy control rule, where n takes values of 1, 2, and 3, corresponding to the proportional control parameter K 1 , the integral control parameter K 2 and the derivative control parameter K 3 of the fuzzy control rule matrix, i is the abscissa of the element in the fuzzy control rule matrix, and j is the ordinate 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 vectors, and the acquisition method is:

[0052]

[0053] where, 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;

[0054] S43: The fuzzy set C n-ij ' n of each consequent R -ij of the fuzzy control rule is adjusted by the activation intensity matrix W n-ij , and the acquisition method is:

[0055]

[0056] where, C n ' -ij is the fuzzy set of each consequent of the fuzzy control rule; R n-ij is the consequent of the fuzzy control rule;

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

[0058]

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

[0060] S45: Use the centroid method to calculate the centroid of the fuzzy set to determine the exact output value and implement the defuzzification operation.

[0061] Preferably, in step S5, according to the output value K of the fuzzy control subset in step S4n-final , control the swing angle of the variable motor of the high-pressure water pump to adjust the displacement of the variable motor, and achieve a fast, stable and accurate output speed of the high-pressure water pump. Specifically:

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

[0063]

[0064] where u k is the output quantity of the actuator control signal at the k-th sampling; e k is the input deviation quantity at the k-th sampling; e k-1 is the deviation quantity input at the (k - 1)-th sampling moment; T is the integral time constant; T d is the differential time constant; u 0 is the initial value of the output quantity of the actuator control signal.

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

[0066] (1) The present invention is based on a hydraulic fan driving a high-pressure water pump to produce fresh water through a reverse osmosis membrane, and uses the fuzzy control theory and the traditional PID control theory in combination to achieve fast and accurate adjustment of the rotational speed of the hydraulic wind turbine driving the high-pressure water pump under complex disturbances and ensure the stable operation of the reverse osmosis system. It does not depend on the mathematical model of the controlled object, and has simple calculation, easy implementation and strong adaptability.

[0067] (2) The present invention overcomes the defects of the traditional PID control that it cannot handle the negative impacts of internal and external disturbances such as interference from the external input end, continuous change of the working point and internal parameter coupling on the system, and effectively improves the rapidity, accuracy and stability of the rotational speed control of the hydraulic wind-driven high-pressure water pump. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a flow chart of the method for controlling the rotational speed of a high-pressure water pump driven by a hydraulic fan based on fuzzy adaptive PID of the present invention;

[0069] Figure 2 is the principle of the method for controlling the rotational speed of a high-pressure water pump driven by a hydraulic fan based on fuzzy adaptive PID of the present invention;

[0070] Figure 3 is a schematic diagram of the membership function of the input parameters e and ec of the present invention;

[0071] Figure 4 is the membership function schematic diagram of the output parameter K 1 of the present invention;

[0072] Figure 5 is the membership function diagram of the output parameter K of the present invention 2 ;

[0073] Figure 6 is the membership function diagram of the output parameter K of the present invention 3 ;

[0074] Figure 7 is the membership function diagram of the output parameter K of the present invention 1 along with the variation rules of the error e and the error change rate ec;

[0075] Figure 8 is the membership function diagram of the output parameter K of the present invention 2 along with the variation rules of the error e and the error change rate ec;

[0076] Figure 9 is the membership function diagram of the output parameter K of the present invention 3 along with the variation rules of the error e and the error change rate ec;

[0077] Figure 10 is the response curve diagram of the fuzzy adaptive controller of the present invention;

[0078] Figure 11 is the response curve of the output parameter K of the fuzzy adaptive controller of the present invention 1 ;

[0079] Figure 12 is the response curve of the output parameter K of the fuzzy adaptive controller of the present invention 2 ;

[0080] Figure 13 is the response curve of the output parameter K of the fuzzy adaptive controller of the present invention 3 ; Specific Embodiments

[0081] The following will describe in detail exemplary embodiments, features, and aspects of the present invention with reference to the accompanying drawings. Identical reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0082] The present invention provides a hydraulic fan-driven high-pressure water pump speed control method based on fuzzy adaptive PID, as Figure 1As shown, obtain the rotational speed error of the high-pressure water pump of the hydraulic fan, and determine the input variables of the fuzzy adaptive PID controller; analyze the stability of the hydraulic fan, establish a control model for the swing angle of the variable motor of the high-pressure water pump, and determine the output variables of the fuzzy adaptive PID controller; determine the fuzzy control set of the fuzzy adaptive PID controller for driving the high-pressure water pump by the hydraulic fan; use the Mamdani method to perform fuzzy inference on the fuzzy inference table, and calculate the fuzzy control subset by the centroid method; according to the output value of the fuzzy control subset, control the swing angle of the variable motor of the high-pressure water pump to adjust the displacement of the variable motor. As Figure 2 The figure shows the schematic diagram of the rotational speed control method for driving a high-pressure water pump by a hydraulic fan based on fuzzy adaptive PID, which specifically includes: a PID controller 1, a fuzzy controller 2, an actuator 3, a wind wheel 4, a variable motor 5, a fixed displacement pump 6, and a high-pressure water pump 7. The specific steps include:

[0083] Step S1: Obtain the rotational speed error of the high-pressure water pump of the hydraulic fan, and determine that the input variables of the fuzzy adaptive PID controller are the rotational speed error e of the high-pressure water pump and the change rate ec of the rotational speed error of the high-pressure water pump.

[0084] Step S11: Obtain the actual output rotational speed ω of the high-pressure water pump through a 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 as follows:

[0085] e = ω p - ω m ;

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

[0087] Step S12: Obtain the change rate ec of the rotational speed error of the high-pressure water pump as follows:

[0088]

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

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

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

[0092]

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

[0094] Taking a certain typical hydraulic fan as an example, the relevant parameters obtained from the actual unit parameter identification are summarized and organized as shown in the following table:

[0095] Table 1 Typical Hydraulic Fan Parameter Table

[0096]

[0097] Step S22: Set the output variables of the fuzzy adaptive PID controller as: the proportional control parameter K 1 , the integral control parameter K 2 and the derivative control parameter K 3 . Determine the characteristic equation and value range of the output variables of the fuzzy adaptive PID controller, specifically:

[0098] According to the output variables of the fuzzy adaptive PID controller, the proportional control parameter K 1 , the integral control parameter K 2 and the derivative control parameter K 3 , the closed-loop system characteristic equation of the fuzzy adaptive PID controller is obtained as:

[0099]

[0100] Perform stability analysis on the fuzzy adaptive PID controller according to the Routh criterion, and the condition for the system to be stable is obtained as:

[0101]

[0102] Use the fuzzy adaptive PID controller to control the swashplate angle of the high-pressure water pump variable motor in combination with the transfer function in Step S21.

[0103] Obtain the proportional control parameter K1 ranges from [-0.188, 0.005], then the integral control parameter K 2 and the derivative control parameter K 3 range from [-35.46, 0] and [-0.069, 0.007] respectively.

[0104] Step S3: Determine the fuzzy control set of the fuzzy adaptive PID controller for the hydraulic fan driving the 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, triangular membership function, and S-shaped membership function.

[0105] Set the fuzzy adaptive PID controller for the hydraulic fan driving the high-pressure water pump to a two-input and three-output structure; where 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; the output variables are: the proportional control parameter K 1 , the integral control parameter K 2 and the derivative control parameter K 3 .

[0106] Determine the fuzzy control universes of the input and output variables. Since the final output speed control target is 1500 r / min, if ±1500 r / min is used as its error range, the controller adjustment range is large and the accuracy is low. 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 universes of the high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error are {-1, -0.679, -0.333, 0, 0.333, 0.679, 1}.

[0107] Set the fuzzy control sets of the input variables and output variables as: the first-level fuzzy control subset NB, the second-level fuzzy control subset NM, the third-level fuzzy control subset NS, the fourth-level fuzzy control subset ZO, the fifth-level fuzzy control subset PS, the sixth-level fuzzy control subset PM, and the seventh-level fuzzy control subset PB.

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

[0109]

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

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

[0112] The triangular membership function is used to determine the fuzzy control subsets, specifically as follows:

[0113] As Figure 3 is the schematic diagram of the membership functions of the input parameters e and ec of the present invention. The triangular membership functions of the secondary fuzzy control subset NM, the tertiary fuzzy control subset NS, the quaternary fuzzy control subset ZO, the quinary fuzzy control subset PS, and the senary 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 amount are all:

[0114]

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

[0116] As shown in Table 2 is the value table of the segmented parameters of the three triangular membership functions.

[0117] Table 2 Value Table of Segmented Parameters of Triangular Membership Function of Input Variables

[0118]

[0119] Similarly, each subset of the proportional control parameter K 1 , the integral control parameter K 2 and the derivative control parameter K 3 is also determined according to the triangular membership function. The value ranges of the proportional control parameter K 1 , the integral control parameter K 2 and the derivative control parameter K 3 are [-0.188, 0.005], [-35.46, 0] and [-0.069, 0.007] respectively. As Figure 4 is the schematic diagram of the membership function of the output parameter K 1 of the present invention. The universe of discourse of the proportional control parameter K 1 is set as {-0.188, -0.153, -0.124, -0.091, -0.059, -0.027, 0.005}; as Figure 5 is the schematic diagram of the membership function of the output parameter K 2 of the present invention. The universe of discourse of the integral control parameter K 2 is set as {-35.46, -29.55, -23.64, -17.73, -11.82, -5.911, 0}; as Figure 6 is the schematic diagram of the membership function of the output parameter K 3Schematic diagram of the membership function to obtain the differential control parameter K 3 The universe of discourse of is {-0.069, -0.057, -0.044, -0.031, -0.019, -0.006, 0.007}, and the piecewise parameter values of the triangular membership functions corresponding to the membership functions of each subset are shown in the following table:

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

[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] Among them, PB(e(t)) is the vector of the S-shaped membership function of the seven-level fuzzy control subset.

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

[0126] Using fuzzy language and combining the fuzzy control subsets of the input variable and the output variable to obtain the proportional control parameter K 1 and the integral control parameter K 2 and the differential control parameter K 3 fuzzy inference table.

[0127] According to the above rules and combining the actual scenario of the hydraulic fan driving the high-pressure water pump, a fuzzy rule base is formulated. Its inference rule is "IF e and ec then K", and 49 double-input triple-output rules are established. The obtained fuzzy rule base is shown in Table 4.

[0128] Table 4 Output parameters K 1 、K 2 、K 3 Fuzzy rule base

[0129]

[0130]

[0131] Step S4: Use the Mamdani inference method to perform fuzzy inference on the fuzzy inference table in step S3.

[0132] The inference rule of the Mamdan i inference method is: E ∩ EC → K; where 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 proportional control parameter K 1 , the integral control parameter K 2 and the differential control parameter K 3 of the fuzzy control subset; specifically including the steps:

[0133] Step S41: Fuzzification of the input of the fuzzy adaptive PID controller. According to the membership function in Step S3, convert the exact value of the input variable into the membership function vector of each variable. For example:

[0134] The membership vector of the 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 the input variable change rate ec of the high-pressure water pump speed error is: μEC = [μEC-NB(ec), μEC-NM(ec), μEC-NS(ec), μEC-ZO(ec), μEC-PS(ec), μEC-PM(ec), μEC-PB(ec)].

[0137] Step S42: Evaluation of the fuzzy control rules of the fuzzy adaptive PID controller to obtain the activation intensity matrix; express the fuzzy control rule base in matrix form. Each fuzzy control rule corresponds to a combination of input fuzzy sets, where the element R n-ij represents the consequent of the fuzzy control rule, where n takes values of 1, 2, 3, corresponding to the proportional control parameter K 1 , the integral control parameter K 2 and the differential control parameter K 3 of the fuzzy control rule matrix, i is the abscissa of the element in the fuzzy control rule matrix, and j is the ordinate of the element in the fuzzy control rule matrix.

[0138] The proportional control parameter K 1 corresponding rule matrix R 1 (7×7) is shown in the following table:

[0139] Table 5 Consequent of Fuzzy Control Rule R 1 Data Table

[0140]

[0141] The integral control parameter K 2 corresponding rule matrix R2 (7×7) is shown in the following table:

[0142] Table 6 Consequent R of Fuzzy Control Rules 2 Data Table

[0143]

[0144]

[0145] Differential control parameter K 3 Corresponding rule matrix R 3 (7×7) is shown in the following table:

[0146] Table 7 Consequent R of Fuzzy Control Rules 3 Data Table

[0147]

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

[0149]

[0150] where, 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 quantity; is the conjunction operation.

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

[0152]

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

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

[0155]

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

[0157] Step S45: Calculate the centroid of the fuzzy set using the centroid method to determine the exact output value, implement the defuzzification operation, and obtain the fuzzy control subset of the fuzzy adaptive PID controller as follows:

[0158]

[0159] where, 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 control output fuzzy set; 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 present invention is 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: Rule 32 (IF e = PS and ec = ZO, THEN K 1 = NS, K 2 = PS, K 3 = PS); Rule 33 (IF e = PS and ec = PS, THEN K 1 = NS, K 2 = PS, K 3 = ZO); Rule 39 (IF e = PM and ec = ZO, THEN K1 = NM, K 2 = PM, K 3 = PM); Rule 40 (IF e = PM and ec = PS, THEN K 1 = NM, K 2 = PS, K 3 = PB).

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

[0166]

[0167] Step S463: Output Fuzzification: The activation strength is applied to the fuzzy set in the conclusion part using the truncation method, i.e., the output membership function is truncated to the height of the activation strength. Rule 32 outputs "K 1 = NS, K 2 = PS, K 3 = PS" is truncated to 0.150; Rule 33 outputs "K 1 = NS, K 2 = PS, K 3 = ZO" is truncated to 0.147; Rule 39 outputs "K 1 = NM, K 2 = PM, K 3 = PM" is truncated to 0.150; Rule 40 outputs "K 1 = NM, K 2 = PS, K 3 = PB" is truncated to 0.147; that is:

[0168]

[0169] Step S464: Result Composition: The output fuzzy sets of all rules are combined through the maximum operation to form the final output fuzzy set, and the elements of the unactivated consequent fuzzy sets 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 output value K of the fuzzy control subset in Step S4 n-final , control the swing angle of the variable motor of the high-pressure water pump to adjust the displacement of the variable motor, and achieve a fast, stable and accurate output speed of the high-pressure water pump. Specifically:

[0176] According to the output value K of the fuzzy control subset in Step S4 n-final , calculate the output quantity u of the actuator control signal k as:

[0177]

[0178] where u k is the output quantity of the actuator control signal at the k-th sampling; e k is the input deviation quantity at the k-th sampling; e k-1 is the deviation quantity input at the (k - 1)-th sampling moment; T is the integral time constant; T d is the differential time constant; u 0 is the initial value of the output quantity of the actuator control signal.

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

[0180] As Figure 7 the output parameter K of the present invention 1 changes with the error e and the error change rate ec, specifically, the proportional control parameter K obtained from the input high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error quantity 1 . As Figure 8 the output parameter K of the present invention 2 changes with the error e and the error change rate ec, specifically, the integral control parameter K obtained from the input high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error quantity 2 . As Figure 9 the output parameter K of the present invention 3 changes with the error e and the error change rate ec, specifically, the differential control parameter K obtained from the input high-pressure water pump speed error e and the change rate ec of the high-pressure water pump speed error quantity 3 . It can be observed that the change curves of the output parameters K 1 , K 2 , K 3 with the error e and the error change rate ec are relatively smooth, and there is no 90-degree horizontal fold angle, which can prove that the fuzzy adaptive PID controller meets the requirements of system control stability.

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

[0182] like Figure 11 The fuzzy adaptive PID based hydraulic fan driving high pressure water pump control method of the present invention is the output parameter proportional control parameter K of the fuzzy adaptive controller output parameter proportional control parameter K of the ... 1 Response curve diagram; such as Figure 12 is the fuzzy adaptive PID based hydraulic fan driving high pressure water pump control method of the present invention, the fuzzy adaptive controller output parameter integral control parameter K 2 Response curve diagram; such as Figure 13 The fuzzy adaptive PID based hydraulic fan driving high pressure water pump control method of the present invention is the output parameter of the fuzzy adaptive controller differential control parameter K 3 Response curve. During the parameter adjustment process, the maximum adjustment of the proportional link parameter P is 0.98%, the maximum adjustment of the integral link parameter I is 0.93%, and the maximum adjustment of the differential link is 0.66%. It can be seen that when dealing with sudden drop in wind speed, the proportional link and integral link in the PID controller are most effective.

[0183] The beneficial effects of the present invention are as follows: the present invention provides a method for controlling the speed of a hydraulic fan driven high-pressure water pump based on fuzzy adaptive PID, based on the hydraulic fan driving the high-pressure water pump to pass through the reverse osmosis membrane to produce fresh water, and uses a combination of fuzzy control theory and traditional PID control theory to achieve rapid and accurate adjustment of the speed of the hydraulic wind machine driven high-pressure water pump under complex disturbances and ensure the stable operation of the reverse osmosis system, without relying on the mathematical model of the controlled object, and has simple calculation, easy implementation, and strong adaptability, overcoming the defects of traditional PID that cannot handle the negative impact of internal and external disturbances on the system such as external input interference, constantly changing working points, and internal parameter coupling, and effectively improves the rapidity, accuracy and stability of the speed control of the hydraulic wind driven high-pressure water pump.

[0184] The embodiments described above are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the spirit of the present invention's design, various modifications and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for controlling the speed of a hydraulic fan driven high-pressure water pump based on fuzzy adaptive PID, characterized in that: It includes: S1: Obtain the speed error of the high-pressure water pump of the hydraulic fan, and determine that the input variables of the fuzzy adaptive PID controller are 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, and determine the output variables and value range of the fuzzy adaptive PID controller, which specifically includes 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 follows: Among them, ω m is the set speed of the high-pressure water pump; m is the variable motor swing angle in the 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 volume of oil in the high-pressure pipeline; β e is the bulk elastic modulus of the oil; s is the input control parameter of the high-pressure water pump speed; K m is the variable motor displacement gradient coefficient; γ m0 is the initial value of the variable motor swing angle in the stable state; ω m0 is the variable motor speed in a stable state; J m is the variable motor moment of inertia; η m B is the variable motor transmission efficiency; m is the variable motor viscous damping coefficient; S22: setting the output variables of the fuzzy adaptive PID controller to: proportional control parameter K1, integral control parameter K2 and differential control parameter K3, determining the characteristic equation and value range of the output variables of the fuzzy adaptive PID controller, and using the fuzzy adaptive PID controller in combination with the transfer function of step S21 to control the swing angle of the variable motor of the high-pressure water pump; S3: Determine the fuzzy control set of the fuzzy adaptive PID controller of the hydraulic fan driving the high-pressure water pump, 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 triangle membership function and the S-shaped membership function; use fuzzy language to combine the fuzzy control subsets of the input variables and the output variables 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 centroid method: Among them, 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 control output fuzzy set; k is the sampling data point number; m is the total number of sampling data points; S5: Output value K according to the fuzzy control subset in step S4 n-final , control the swing angle of the high-pressure water pump variable motor to adjust the displacement of the 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 is characterized in that: In step S1, the speed error of the high-pressure water pump of the hydraulic fan 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 change rate ec of the high-pressure water pump speed error, specifically: S11: Obtain the actual output speed ω of the high-pressure water pump through the speed sensor p , the actual output speed ω p With the set speed ω m By comparison, the speed error e of the high-pressure water pump is obtained as: e=ω p -oh m Where, e is the speed error of the high-pressure water pump; ω p is the actual output speed of the high-pressure water pump; ω m The set speed of the high-pressure water pump; S12: The change rate ec of the speed error of the high-pressure water pump is obtained as: Among them, ec is the rate of change of the high-pressure water pump speed error; t is the time parameter.

3. The method for controlling the speed of a hydraulic fan driven high-pressure water pump based on fuzzy adaptive PID according to claim 1 is characterized in that: 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, integral control parameter K2 and 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 follows: (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 Routh criterion, the stability analysis of fuzzy adaptive PID controller is carried out, and the condition for system stability is obtained as follows:

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 is characterized in that: The fuzzy control set of the fuzzy adaptive PID controller of the hydraulic fan driving the high-pressure water pump in step S3 is specifically: The fuzzy adaptive PID controller of the hydraulic fan driving the high-pressure water pump is set to a two-input, three-output structure; the input variables are: the high-pressure water pump speed error e and the change rate of the high-pressure water pump speed error ec; The output variables are: proportional control parameter K1, integral control parameter K2 and differential control parameter K3; The fuzzy control sets of input variables and output variables are set as: 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.

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 is characterized in that: 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: Among them, 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 time parameter t; x is the actual value of the input variable; Similarly, the Z-shaped membership function vector of the first-level 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 triangle membership function in step S3 is used to determine the fuzzy control subset, specifically: The triangular membership functions of the second-level fuzzy control subset NM, the third-level fuzzy control subset NS, the fourth-level fuzzy control subset ZO, the fifth-level fuzzy control subset PS and the sixth-level fuzzy control subset PM of the high-pressure water pump speed error e and the change rate of the high-pressure water pump speed error ec are: Wherein, f(x,a,b,c) is the triangle membership function vector of the fuzzy control subset; a is the first segment parameter of the triangle membership function; b is the second segment parameter of the triangle membership function; c is the third segment parameter of the triangle 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 triangle 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 seven-level fuzzy control subset PB of the high-pressure water pump speed error e, specifically: Among them, PB(e(t)) is the S-shaped membership function vector of the seven-level fuzzy control subset; Similarly, the S-shaped membership function vector of the first-level 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 is characterized in that: In step S4, the Mamdani reasoning method is used to perform fuzzy reasoning on the fuzzy reasoning table in step S3, specifically: The inference rule of the Mamdani inference 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, the steps are as follows: S41: fuzzifying the input of the fuzzy adaptive PID controller, converting the exact value of the input variable into the membership function vector of each variable according to the membership function in step S3; 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 matrix form, where each fuzzy control rule corresponds to a combination of input fuzzy sets, where the element R n-ij represents the fuzzy control rule consequent, where n is 1, 2, and 3, corresponding to the fuzzy control rule matrices of proportional control parameter K1, integral control parameter K2, and 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; The activation intensity matrix is ​​calculated by the outer product of the input membership function vector, and the method of obtaining it is: Among them, 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; It is a conjunction operation; S43: Each fuzzy control rule has a consequent R n-ij The fuzzy set C n ' -ij The activated intensity matrix W n-ij Adjustment, the acquisition method is: Among them, C n ' -ij is the fuzzy set of the consequent of each fuzzy control rule; R n-ij is the consequent of fuzzy control rules; S44: Fuzzy control output aggregation, taking the maximum value of the fuzzy set of each fuzzy control rule's consequent point by point, and generating the final fuzzy control output fuzzy set, the acquisition method is: Among them, C n-final Output fuzzy sets for fuzzy control; S45: Use the centroid method to calculate the centroid of the fuzzy set to determine the exact output value and implement the defuzzification operation.

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: In step S5, the fuzzy control subset output value K in step S4 is n-final , control the swing angle of the variable motor of the high-pressure water pump to adjust the displacement of the variable motor, and achieve fast, stable and accurate output speed of the high-pressure water pump, specifically: According to the fuzzy control subset output value K in step S4 n-final , calculate the actuator control signal output u k for: Among them, u k is the output of the actuator control signal at the kth sampling; e k The input deviation value for the kth sampling; e k-1 is the deviation input at the k-1th sampling moment; T is the integration time constant; T d is the differential time constant; u0 is the initial value of the actuator control signal output.

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