Permanent magnet synchronous motor speed regulation system variable universe fuzzy PI control method and system

Abstract

The invention provides a permanent magnet synchronous motor speed regulation system variable universe fuzzy PI control method and system, and relates to the technical field of motor speed regulation system control. The motor rotating speed deviation is determined; current controller parameters are adjusted based on the rotating speed deviation, and a q-axis current given value is obtained; converting the three-phase current into a two-phase static coordinate system; converting the static coordinate system current into a rotating coordinate system; d-axis current and q-axis current are adjusted respectively, and d-axis reference voltage and q-axis reference voltage are obtained; converting the d-axis reference voltage and the q-axis reference voltage into a static coordinate system; modulating the voltage of the static coordinate system to obtain an inverter control signal; and outputting an inverter control signal to the three-phase voltage inverter to drive the permanent magnet synchronous motor to operate. Compared with a traditional fuzzy PI controller, the method has the advantages of being rapid in speed response, small in torque pulsation, high in control precision and high in disturbance rejection capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of motor speed regulation system control, and in particular to a variable domain fuzzy PI control method and system for a permanent magnet synchronous motor speed regulation system. Background Art

[0002] Permanent magnet synchronous motor (PMSM) has been widely used in CNC machine tools, electric vehicles, robots and other fields due to its simple structure, small size, high efficiency and high reliability. With the development of industrial automation and intelligence, the requirements for the dynamic response, steady-state accuracy, reliability and anti-disturbance capability of the permanent magnet synchronous motor speed control system are becoming more and more stringent. The permanent magnet synchronous motor is a multivariable, nonlinear, strongly coupled system that is very sensitive to its own parameter perturbations and external disturbances. Therefore, it is particularly important to adopt a suitable control strategy.

[0003] As a linear control method, traditional PI control is widely used in motor control because of its simple algorithm, strong adaptability and certain robustness. However, the setting of PI parameters depends on the accurate mathematical model of permanent magnet synchronous motor. Once set, the parameters will no longer change and belong to the local optimal value. This limits the dynamic and anti-interference performance of the system when facing parameter changes and external disturbances. For example, the permanent magnet synchronous motor speed control system and its control method described in patent CN115118184A, although a fuzzy PID control strategy is proposed, it is still limited in adaptive adjustment ability.

[0004] As a nonlinear intelligent control method, fuzzy control does not rely on the precise mathematical model of the object, is insensitive to changes in system parameters, and has the characteristics of fast dynamic response and strong robustness. However, when the internal parameters or external disturbances of the permanent magnet synchronous motor system change, the traditional fuzzy control cannot make adjustments to the fuzzy division and fuzzy rules, resulting in poor system adaptability. Especially when the load torque changes, it directly affects the regulation characteristics of the permanent magnet synchronous motor, and puts forward higher requirements on the adaptive regulation ability of the PI control parameters. It is difficult for traditional fuzzy PI control to achieve good control effects. For example, the dual closed-loop control system and control method of the speed and current of the permanent magnet synchronous linear motor described in patent CN110138297B, although it enhances the robustness of the system, still needs to be further optimized in practical applications.

[0005] In view of the limitations of the prior art, the present application provides a variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system. Summary of the invention

[0006] In order to solve the problems existing in traditional fuzzy PI control technology, the present invention provides a variable domain fuzzy PI control method and system for a permanent magnet synchronous motor speed control system. The variable domain fuzzy control idea is used to adjust the fuzzy domain in real time. When the input variable error is small, the domain is compressed so that the fuzzy division is concentrated in a smaller domain range, thereby increasing the number of available fuzzy rules. The expansion factor of the input domain and the output domain are designed separately using a function form expansion factor. When the system is subjected to load disturbance or parameter changes, the expansion degree of the domain can change in real time according to the input variable, so that the system has better adaptive ability. Compared with the traditional fuzzy PI controller, this method has the advantages of rapid speed response, small torque pulsation, high control accuracy and strong anti-disturbance ability.

[0007] In order to achieve the above invention objectives, the present invention will adopt the following technical solutions:

[0008] A variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system comprises the following steps:

[0009] Compare motor given speed n ref and the actual speed n, and the speed deviation n is obtained ref -n;

[0010] The speed deviation n ref -n input variable domain fuzzy PI controller, output q axis current given value i qref ;

[0011] Three-phase current i A 、i B 、i C After Clark and Park transformation, the two-phase current i in the dq two-phase rotating coordinate system is obtained d and i q ;

[0012] The current loop d-axis reference current i dref =0 and current i d The difference i d Input the d-axis current regulator to get the d-axis reference voltage u d ;

[0013] will i qref With current i q The difference i qref -i q After inputting the q-axis current regulator, the q-axis reference voltage u is obtained. q ;

[0014] The dq axis reference voltage u d and u q After Park's inverse transformation, the voltage u of the αβ two-phase stationary coordinate system is obtained α and uβ ;

[0015] The obtained two-phase voltage u α and u β After space vector pulse width modulation, six pulse width waveforms are obtained;

[0016] Input the six-way pulse width waveform into the three-phase voltage inverter and output the three-phase voltage U A , U B , U C , driving a permanent magnet synchronous motor.

[0017] Optionally, the variable universe fuzzy PI controller includes a PI controller, a fuzzy controller and a variable universe scaling factor;

[0018] The PI controller is used to generate a control signal based on the rotation speed deviation to adjust the amplitude of the modulation wave;

[0019] The fuzzy controller is used to dynamically adjust the proportional and integral parameters of the PI controller according to the speed deviation and change rate detected in real time, so as to adapt to the control requirements of the permanent magnet synchronous motor system under different operating conditions;

[0020] The variable domain scaling factor adjusts the input and output domains in real time in a functional form, wherein the input domain scaling factor affects the quantization factor, and the output domain scaling factor affects the proportional factor.

[0021] Optionally, the fuzzy controller takes the real-time detected speed deviation e and deviation change rate ec as input, and outputs the correction values ​​of the PI parameter proportional coefficient and integral coefficient through three steps of fuzzification, fuzzy reasoning and defuzzification, and performs online tuning of the PI parameters, and its expression is:

[0022]

[0023] In the formula, K P , K I is the parameter of the modified fuzzy PI controller; K P0 , K I0 is the initial parameter of the traditional PI controller; ΔK P , ΔK I is the online correction amount.

[0024] Optionally, the fuzzification includes detecting the calculated input variable speed deviation e and the deviation change rate ec as clear quantities, performing quantization transformation and fuzzification processing to obtain the fuzzy quantity speed deviation E and the fuzzy quantity deviation change rate EC,

[0025] Optionally, the core of the fuzzy reasoning is the fuzzy control rule, and the formulation principle of the fuzzy control rule is as follows:

[0026] When the speed deviation |e| is large, select a larger proportional coefficient K P and a smaller integral coefficient K I ;

[0027] When the speed deviation |e| is medium, select a moderate proportional coefficient K P and the integration coefficient K I ;

[0028] When the speed deviation |e| is small, a smaller proportional coefficient K is selected P and a larger integral coefficient K I .

[0029] Optionally, the defuzzification converts the fuzzy output variables obtained by fuzzy reasoning into clear quantities and performs self-tuning of PI parameters, specifically:

[0030] Use the centroid method to defuzzify, the formula is:

[0031] u=∑x i μ(x i ) / ∑μ(x i )

[0032] In the formula, u is the clear value after defuzzification, x i is the value of the output variable, μ(x i ) is the fuzzy membership function.

[0033] Optionally, the input universe scaling factor is derived based on a function form, and its expression is:

[0034]

[0035] Where, 0<τ<1; ε is a sufficiently small positive number; E and EC are the range boundary values ​​of the input variable deviation e and the deviation change rate ec, respectively.

[0036] Optionally, the output universe scaling factor is derived based on a function form, and its expression is:

[0037]

[0038] In the formula, K i is the integral parameter of the fuzzy PI controller, e i (τ) is the input variable error; p i is the input variable weight coefficient; β(0) is the initial value of the output domain scaling factor.

[0039] Optionally, the parameter setting step of the variable universe fuzzy PI controller includes:

[0040] Initial setting of the proportional coefficient K of the PI controller P and the integration coefficient KI ;

[0041] According to the real-time detection value of the speed deviation and the deviation change rate, the proportional coefficient K of the PI controller is dynamically adjusted through the fuzzy controller. P and the integration coefficient K I ;

[0042] The variable universe scaling factor is used to adjust the input and output universes in real time to adapt to the control requirements of the permanent magnet synchronous motor system under different operating conditions.

[0043] A variable domain fuzzy PI control system for a permanent magnet synchronous motor speed control system, comprising:

[0044] Speed ​​comparison unit, compares the motor given speed n ref and the actual speed n, and the speed deviation n is obtained ref -n;

[0045] The variable universe fuzzy PI controller is used to convert the speed deviation n ref -n input variable domain fuzzy PI controller, output q axis current given value i qref ;

[0046] The current conversion unit is used to convert the three-phase current i A 、i B 、i C After Clark and Park transformation, the two-phase current i in the dq two-phase rotating coordinate system is obtained d and i q ;

[0047] Current regulator, used to convert i qref With current i q The difference i qref -i q After inputting the q-axis current regulator, the q-axis reference voltage u is obtained. q ;

[0048] The voltage conversion unit is used to convert the current loop d-axis reference current i dref =0 and current i d The difference i d Input the d-axis current regulator to get the d-axis reference voltage u d , the dq axis reference voltage u d and u q After Park's inverse transformation, the voltage u of the αβ two-phase stationary coordinate system is obtained α and u β ;

[0049] SVPWM modulator is used to obtain the two-phase voltage u α and u βAfter space vector pulse width modulation, six pulse width waveforms are obtained;

[0050] The three-phase voltage inverter is used to receive six pulse width waveforms, input the six pulse width waveforms into the three-phase voltage inverter, and output a three-phase voltage U A , U B , U C , driving a permanent magnet synchronous motor.

[0051] The beneficial effects of adopting the above technical solution are:

[0052] The present invention provides a variable domain fuzzy PI control method for a permanent magnet synchronous motor speed regulation system. The variable domain fuzzy control is used to adjust the fuzzy domain in real time. When the input variable error is small, the domain is compressed so that the fuzzy division is concentrated in a smaller domain range, thereby increasing the number of available fuzzy rules. The expansion factor of the input domain and the output domain are designed respectively using a function form expansion factor. When the system is subjected to load disturbance or parameter changes, the expansion degree of the domain can change in real time according to the input variable, so that the system has better self-adaptation ability. Compared with the traditional fuzzy PI controller, this method has the advantages of rapid speed response, small torque pulsation, high control accuracy and strong anti-disturbance ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a control block diagram of the permanent magnet synchronous motor speed regulation system of the present invention;

[0054] Figure 2 It is a structural block diagram of the variable domain fuzzy PI controller of the present invention;

[0055] Figure 3 It is a structural block diagram of a two-dimensional fuzzy controller of the present invention;

[0056] Figure 4 It is the membership function curve diagram of the system input variables;

[0057] Figure 5 It is the membership function curve diagram of the system output variable;

[0058] Figure 6 The fuzzy reasoning output surface of the proportional coefficient correction value;

[0059] Figure 7 It is the fuzzy reasoning output surface of the integral coefficient correction value. DETAILED DESCRIPTION

[0060] To further illustrate the technical solution adopted by the present invention, the specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. The implementation is only used to illustrate and explain the present invention and does not constitute a limitation on the protection scope of the present invention.

[0061] like Figure 1 As shown, it discloses a control block diagram of a variable domain fuzzy PI control method for a permanent magnet synchronous motor speed regulation system according to the present invention, and the specific steps are:

[0062] Step 1: Set the motor speed to n ref The actual speed value n measured by the photoelectric encoder is passed through the first comparator to obtain the speed deviation value n ref -n;

[0063] Step 2: The speed deviation value n in step 1 is ref -n is input to the variable domain fuzzy PI controller, and the output is the q-axis current given value i qref ;

[0064] Step 3: Collect the three-phase current value i of the stator of the permanent magnet synchronous motor A 、i B 、i C After Clark transformation, the two-phase current i in the αβ two-phase stationary coordinate system is obtained α and i β ;

[0065] Step 4: Convert the two-phase current i in step 3 α and i β After Park transformation, the two-phase current i in the dq two-phase rotating coordinate system is obtained d and i q ;

[0066] Step 5: Set the d-axis reference current in the current loop to i dref =0, and the current i in step 4 d The difference i is obtained by the second comparator d The difference passes through the d-axis current regulator and then outputs the d-axis reference voltage u d ;

[0067] Step 6: Set the q-axis current given value i in step 2 qref The current i in step 4 q The difference i is obtained by the third comparator qref -i q The difference passes through the q-axis current regulator and then outputs the q-axis reference voltage u q ;

[0068] Step 7: Set the dq axis reference voltage u in step 5 and step 6 d and u q After Park's inverse transformation, the voltage u of the αβ two-phase stationary coordinate system is obtained α and u β ;

[0069] Step 8: Convert the two-phase voltage u in step 7 α and u β After space vector pulse width modulation (SVPWM), six pulse width waveforms are obtained;

[0070] Step 9: Input the six pulse width waveforms in step 8 into the three-phase voltage inverter, and the inverter outputs a three-phase voltage U A , U B , U C , thereby driving the permanent magnet synchronous motor to operate.

[0071] like Figure 2 The structure diagram of the variable domain fuzzy PI controller in step 2 is shown, which includes a PI controller, a fuzzy controller and a variable domain expansion factor. The absolute value output by the PI controller directly determines the amplitude of the modulation wave, which has a great influence on the control effect; the fuzzy controller adopts a dual-input / dual-output two-dimensional fuzzy controller, and through continuous detection and feedback of the speed deviation and the deviation change rate, the two parameters of the PI controller are corrected according to the different operating conditions of the permanent magnet synchronous motor system; the variable domain expansion factor is obtained based on the function form, the quantization factor is divided by the input domain expansion factor, and the proportional factor is multiplied by the output domain expansion factor to enhance the adaptive ability of the permanent magnet synchronous motor system.

[0072] like Figure 3 The structure block diagram of the two-dimensional fuzzy controller is shown, which includes four parts: fuzzification, fuzzy reasoning, defuzzification and knowledge base. The input variables of the speed error e and the error change rate ec detected and calculated are clear quantities, which need to be quantized and fuzzified to obtain fuzzy quantities E and EC respectively before fuzzy reasoning can be performed; correspondingly, the output variable of fuzzy reasoning is a fuzzy quantity, which is expressed as the correction value ΔKP and ΔKP of the proportional and integral coefficients, and cannot directly participate in the operation of the controlled object. It needs to be defuzzified and proportionally transformed to obtain clear quantities ΔK P and ΔK I .

[0073] First, the two-dimensional fuzzy controller is designed, which includes the following six steps:

[0074] Step 1: Determination of fuzzy sets of input and output variables

[0075] The number of elements in the fuzzy set of input and output variables determines the performance of the fuzzy controller. The more elements there are, the better the performance of the fuzzy controller is, and the higher the accuracy is, but it is more complicated to implement. On the contrary, the fewer elements there are, the lower the performance and accuracy of the controller is, but it is easier to implement. In this embodiment, according to the range of change of the input and output variables and the control accuracy requirements of the actual system, the fuzzy set of the input and output variables is defined to contain seven elements, expressed as {negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), positive large (PB)}.

[0076] Step 2: Selection of the numerical range of the fuzzy domain

[0077] The selection of the fuzzy domain value range determines the smoothness of the controller. If the range is too large or too small, the desired adjustment effect will not be achieved. This embodiment starts from the actual motor parameters and selects the domain range of the input and output variables according to the expert experience method and the experimental trial and error method. The domain range of the input variables E and EC is [-10, 10], and the set is expressed as {-10, -6.67, -3.34, -1.5, 0, 1.5, 3.34, 6.67, 10}; the domain range of the output variables ΔKP and ΔKI is [-1, 1], and the set is expressed as {-1, -0.67, -0.34, 0, 0.34, 0.67, 1}.

[0078] Step 3: Selection of quantization factor and scaling factor

[0079] The actual range of input and output variables is called the basic domain of the variables, which is a clear quantity. In order to realize the fuzzification of input variables, it is necessary to introduce quantization factors to transform the variables from the basic domain scale to the fuzzy domain. The quantization factors of e and ec are defined as m and m respectively. e and m ec In order to make the output variable directly participate in the control action, it is necessary to introduce a proportional factor to complete the scale transformation of the control quantity to achieve precise control. Definition ΔK P and ΔK I The scaling factors are n p and n i According to the actual system parameters, select the appropriate quantization and scaling factors m e =-0.01, m ec =-0.0001, n p =7 and n i =3.5.

[0080] Step 4: Selection of membership function

[0081] This embodiment selects the most commonly used triangular membership function as the membership function of the input and output fuzzy variables, which has the characteristics of small calculation amount and high sensitivity. The regularity degree is taken as 1. The membership function of the input variables E, EC and the output variables ΔKP, ΔKI is as follows Figure 4 and Figure 5 shown.

[0082] Step 5: Formulation of fuzzy control rules

[0083] The core of fuzzy reasoning is fuzzy control rules, which are the basis for the correction of proportional and integral coefficients. Based on expert knowledge and actual engineering experience, and in view of the performance requirements of the speed control system in this embodiment, the principles for formulating fuzzy rules are as follows:

[0084] 1) When the deviation |e| is large, in order to speed up the system response and improve the system tracking performance, a larger K should be used. P At the same time, in order to avoid integral saturation, which may cause a large overshoot in the system response, a smaller K should be used. I ;

[0085] 2) When the deviation |e| is of medium size, in order to take into account the rapidity and stability of the system response and ensure that the system response has a small overshoot, a moderate K should be selected. P and K I ;

[0086] 3) When the deviation |e| is small, a smaller K should be used to ensure the stability and reliability of the system. P and a larger K I .

[0087] According to the above principles and after repeated simulation debugging, the PI parameter correction value ΔK is obtained P and ΔK I The fuzzy control rule table contains 49 rules in total, as shown in Table 1 and Table 2.

[0088] Table 1ΔK P Fuzzy rule table

[0089]

[0090] Table 2ΔK I Fuzzy rule table

[0091]

[0092] The fuzzy reasoning decision algorithm of this embodiment adopts the relatively simple Mamdani method. The reasoning process can be expressed as "IF A AND B THEN C". For example, the first rule is expressed as "IF E = NB AND EC = NB THEN ΔKP = PB AND ΔKI = NB". According to the Mamdani fuzzy reasoning method, the output surface of the proportional coefficient and integral coefficient correction value is as follows: Figure 6 and Figure 7 shown.

[0093] Step 6: Deobfuscation

[0094] Defuzzification means that the output variable obtained by fuzzy reasoning is a fuzzy quantity, which cannot be used to control the system directly. Defuzzification is required to obtain a clear quantity of the output variable, thereby realizing PI parameter self-tuning. This embodiment selects the centroid method with the highest accuracy, and its expression is:

[0095] u=∑x i μ(x i ) / ∑μ(x i ) (1)

[0096] In the formula, u is the clear value after defuzzification, x i is the value of the output variable, μ(x i ) is the fuzzy membership function.

[0097] Then the input and output domain scaling factors are designed based on the functional form.

[0098] The input universe scaling factor is derived based on a function form, and its expression is:

[0099]

[0100] Where, 0<τ<1; ε is a sufficiently small positive number; E and EC are the range boundary values ​​of the input variable deviation e and the deviation change rate ec, respectively.

[0101] The output universe scaling factor is derived based on a function form, and its expression is:

[0102]

[0103] In the formula, e i (τ) is the input variable error; p i is the input variable weight coefficient; β(0) is the initial value of the output domain scaling factor.

[0104] In summary, the present invention provides a variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system. Variable domain fuzzy control is used to adjust the fuzzy domain in real time. When the input variable error is small, the domain is compressed so that the fuzzy division is concentrated in a smaller domain range, thereby increasing the number of available fuzzy rules. The expansion factor of the input domain and the output domain are designed separately using a function form expansion factor. When the system is subjected to load disturbance or parameter changes, the expansion degree of the domain can change in real time according to the input variable, so that the system has better adaptive ability. Compared with the traditional fuzzy PI controller, this method has the advantages of rapid speed response, small torque pulsation, high control accuracy and strong anti-disturbance ability.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A variable universe fuzzy PI control method for a permanent magnet synchronous motor speed control system, characterized in that: The following steps are involved: Compare motor given speed n ref and the actual speed n, and the speed deviation n is obtained ref -n; The speed deviation n ref -n input variable domain fuzzy PI controller, output q axis current given value i qref ; Three-phase current i A 、i B 、i C After Clark and Park transformation, the two-phase current i in the dq two-phase rotating coordinate system is obtained d and i q ; The current loop d-axis reference current i dref =0 and current i d The difference i d Input the d-axis current regulator to get the d-axis reference voltage u d ; will i qref With current i q The difference i qref -i q After inputting the q-axis current regulator, the q-axis reference voltage u is obtained. q ; The dq axis reference voltage u d and u q After Park's inverse transformation, the voltage u of the αβ two-phase stationary coordinate system is obtained α and u β ; The obtained two-phase voltage u α and u β After space vector pulse width modulation, six pulse width waveforms are obtained; Input the six-way pulse width waveform into the three-phase voltage inverter and output the three-phase voltage U A , U B , U C , driving a permanent magnet synchronous motor.

2. The variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system according to claim 1 is characterized in that: The variable universe fuzzy PI controller includes a PI controller, a fuzzy controller and a variable universe expansion factor; The PI controller is used to generate a control signal based on the rotation speed deviation to adjust the amplitude of the modulation wave; The fuzzy controller is used to dynamically adjust the proportional and integral parameters of the PI controller according to the speed deviation and change rate detected in real time, so as to adapt to the control requirements of the permanent magnet synchronous motor system under different operating conditions; The variable domain scaling factor adjusts the input and output domains in real time in a functional form, wherein the input domain scaling factor affects the quantization factor, and the output domain scaling factor affects the proportional factor.

3. The variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system according to claim 2 is characterized in that: The fuzzy controller takes the real-time detected speed deviation e and deviation change rate ec as input, and outputs the correction values ​​of the PI parameter proportional coefficient and integral coefficient through three steps of fuzzification, fuzzy reasoning and defuzzification, and performs online tuning of the PI parameters. The expression is: In the formula, K P , K I is the parameter of the modified fuzzy PI controller; K P0 , K I0 is the initial parameter of the traditional PI controller; ΔK P , ΔK I is the online correction amount.

4. The variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system according to claim 3 is characterized in that: The fuzzification includes detecting and calculating the input variable rotation speed deviation e and the deviation change rate ec as clear quantities, performing quantization transformation and fuzzification processing, and obtaining the fuzzy quantity rotation speed deviation E and the fuzzy quantity deviation change rate EC.

5. The variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system according to claim 4 is characterized in that: The core of the fuzzy reasoning is the fuzzy control rule. The principles for formulating the fuzzy control rule are as follows: When the speed deviation |e| is large, select a larger proportional coefficient K P and a smaller integral coefficient K I ; When the speed deviation |e| is medium, select a moderate proportional coefficient K P and the integral coefficient K I ; When the speed deviation |e| is small, a smaller proportional coefficient K is selected P and a larger integral coefficient K I .

6. The variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system according to claim 3 is characterized in that: The defuzzification converts the fuzzy output variables obtained by fuzzy reasoning into clear quantities and performs self-tuning of PI parameters, specifically: Use the centroid method to defuzzify, the formula is: u=∑x i μ(x i ) / ∑μ(x i ) In the formula, u is the clear value after defuzzification, x i is the value of the output variable, μ(x i ) is the fuzzy membership function.

7. The variable universe fuzzy PI control method for a permanent magnet synchronous motor speed control system according to claim 2 is characterized in that: The input universe scaling factor is derived based on a function form, and its expression is: Where, 0<τ<1; ε is a sufficiently small positive number; E and EC are the range boundary values ​​of the input variable deviation e and the deviation change rate ec, respectively.

8. The variable domain fuzzy PI control method for a permanent magnet synchronous motor speed control system according to claim 2 is characterized in that: The output universe scaling factor is derived based on a function form, and its expression is: In the formula, K i is the integral parameter of the fuzzy PI controller, e i (τ) is the input variable error; p i is the input variable weight coefficient; β(0) is the initial value of the output domain scaling factor.

9. The variable universe fuzzy PI control method for a permanent magnet synchronous motor speed control system according to claim 1, characterized in that: The parameter setting steps of the variable universe fuzzy PI controller include: Initial setting of the proportional coefficient K of the PI controller P and the integral coefficient K I ; According to the real-time detection value of the speed deviation and the deviation change rate, the proportional coefficient K of the PI controller is dynamically adjusted through the fuzzy controller. P and the integral coefficient K I ; The variable universe scaling factor is used to adjust the input and output universes in real time to adapt to the control requirements of the permanent magnet synchronous motor system under different operating conditions.

10. A variable universe fuzzy PI control system for a permanent magnet synchronous motor speed control system, based on a variable universe fuzzy PI control method for a permanent magnet synchronous motor speed control system according to any one of claims 1 to 9, characterized in that: include, Speed ​​comparison unit, compares the motor given speed n ref and the actual speed n, and the speed deviation n is obtained ref -n; The variable universe fuzzy PI controller is used to convert the speed deviation n ref -n input variable domain fuzzy PI controller, output q axis current given value i qref ; The current conversion unit is used to convert the three-phase current i A 、i B 、i C After Clark and Park transformation, the two-phase current i in the dq two-phase rotating coordinate system is obtained d and i q ; Current regulator, used to convert i qref With current i q The difference i qref -i q After inputting the q-axis current regulator, the q-axis reference voltage u is obtained. q ; The voltage conversion unit is used to convert the current loop d-axis reference current i dref =0 and current i d The difference i d Input the d-axis current regulator to get the d-axis reference voltage u d , the dq axis reference voltage u d and u q After Park's inverse transformation, the voltage u of the αβ two-phase stationary coordinate system is obtained α and u β ; SVPWM modulator is used to obtain the two-phase voltage u α and u β After space vector pulse width modulation, six pulse width waveforms are obtained; The three-phase voltage inverter is used to receive six pulse width waveforms, input the six pulse width waveforms into the three-phase voltage inverter, and output a three-phase voltage U A , U B , U C , driving a permanent magnet synchronous motor.

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