Permanent magnet synchronous motor parameter identification method based on fuzzy adaptive control

Abstract

The permanent magnet synchronous motor parameter identification method based on fuzzy adaptive control has the following steps: inputting a frequency value f0, an amplitude value, and a bias value of a d-axis current signal at a set angle, performing PI setting of a current loop, and obtaining K p , K i of the current loop; giving a d-axis step current instruction, adopting a mean value and difference method to obtain an identification resistance; giving a d-axis sinusoidal current instruction, and utilizing a least square method with a forgetting factor to obtain an identification d-axis inductance; giving a q-axis sinusoidal current instruction, and obtaining an identification q-axis inductance according to a q-axis voltage, a q-axis feedback current and the identification resistance; and giving a q-axis step current instruction, and obtaining a flux according to the q-axis voltage, the q-axis feedback current, a motor rotating angular velocity and the identification resistance. The method is a closed-loop offline parameter identification method, and has the advantages of simple and reliable identification process, universality, controllable current, high safety, no motor rotation except for identification of the flux, and applicability to different occasions.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet synchronous motor control technology, and in particular to an offline parameter identification method for permanent magnet synchronous motors based on fuzzy control. Background Technology

[0002] High-precision servo control systems often utilize permanent magnet synchronous motors (PMSMs), and the accuracy of motor parameters is crucial for a stable and efficient drive system. Advanced control strategies such as predictive control, current loop self-tuning, and sensorless operation require prior knowledge of the motor's intrinsic parameters. Therefore, parameter identification of permanent magnet synchronous motors has always been a key research focus for scholars both domestically and internationally.

[0003] Due to limitations imposed by industrial applications (such as limit switches), online parameter identification is not widely applicable to servo control systems. Existing offline parameter identification methods often employ open-loop voltage injection, which makes current control difficult. Excessive current may cause motor magnetic saturation; insufficient current leads to inaccurate data acquisition due to nonlinearity, inevitably affecting the accuracy of the identified parameters. Furthermore, the injection amplitude is uncertain for different motors, and the identification intensity cannot be adjusted, hindering universality. Therefore, a safe, reliable, and universal offline parameter identification strategy is urgently needed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to address the shortcomings of the prior art by providing an offline closed-loop parameter identification method for permanent magnet synchronous motors based on fuzzy adaptive PI control, which can reliably and accurately identify the parameters of permanent magnet synchronous motors and has universality.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A parameter identification method for a permanent magnet synchronous motor based on fuzzy adaptive control is characterized by including: based on fuzzy adaptive PI control, with the input frequency value f0 and amplitude value f0 at a set angle. Bias is The d-axis current signal is used for current loop PI tuning to obtain the current loop K. p K i Where f0 is approximately the rated operating frequency of the permanent magnet synchronous motor, and A0 is a current value that can be set manually.

[0007] Preferably, the fuzzy adaptive PI control employs a two-dimensional fuzzy controller, and the specific steps include the following:

[0008] 1) Blurring process

[0009] Take the d-axis current error e and the rate of change of error. As two inputs to the controller, e, and Kp K i The fuzzy set of linguistic values ​​is defined as follows: e, K p K i ={NB, NM, NS, ZO, PS, PM, PB}, denoted by B0 to B6, with corresponding universes of discourse values ​​of {-3, -2, -1, 0, 1, 2, 3}. Let the variables e, To unify the actual scope with the domain of discourse, the transformation formula is:

[0010]

[0011] In the formula: x is the actual input precise quantity, ranging from [a,b], and y is the continuous quantity in the universe of discourse, ranging from [-3,3].

[0012] 2) Perform fuzzy inference based on fuzzy rules

[0013] Triangular membership functions are used to describe fuzzy sets, and e and are calculated respectively. Membership degree of fuzzy variables:

[0014] Based on the principles of PI control, K is formulated. p K i In different e, The control rule table is as follows; the smaller value of the membership degree of the input variable in the rule table is taken as the membership degree of the output variable, and K is obtained respectively. p K i Membership degree u of each fuzzy variable kp (B i ), u ki (B i );

[0015] 3) Deblurring yields K p K i value

[0016] The centroid method is used for defuzzification, and K is determined by the following formula. p K i value,

[0017]

[0018]

[0019] Preferably, using the obtained K p K i Input the amplitude value at the set angle. The d-axis stepped current command is used for current closed-loop control.

[0020] Where A0 is a current value that can be set manually.

[0021] Preferably, the d-axis voltage u is obtained under each step steady-state condition. d (n) and d-axis current i d (n), the motor phase resistance R is determined by taking the average value after differentiation. s As shown below:

[0022]

[0023] Preferably, using the obtained K p K i Perform I at a set angle dref =A0·sin(2π·f1·t),I qref =0 current command for current closed-loop control;

[0024] The d-axis voltage signal u obtained from the feedback d and current signal i d Combined with the identified resistance value R s The d-axis inductance parameter L is determined using the recursive least squares method with a forgetting factor, according to the following formula. d :

[0025]

[0026] Wherein, f1 is the frequency of the d-axis current signal. The d-axis inductance value is calculated for the current interrupt cycle. The d-axis inductance value calculated in the previous interrupt cycle. The gain coefficient is calculated for the current period.

[0027] Preferably, using the obtained K p K i Perform I at a set angle dref =0,I qref =A0·sin(2π·f2·t) current command for current closed-loop control;

[0028] Based on the q-axis voltage signal u q and current signal i q Combined with the identified resistance value R s The q-axis inductance parameter L is determined using the recursive least squares method with a forgetting factor, according to the following formula. q :

[0029]

[0030] Wherein, f2 is the frequency of the q-axis current signal. The q-axis inductance value is calculated for the current interrupt cycle. The q-axis inductance value was calculated in the previous interrupt cycle. The gain coefficient is calculated for the current period.

[0031] Preferably, the frequency values ​​f1 of the d-axis current signal and f2 of the q-axis current signal are approximately the rated operating frequencies of the permanent magnet synchronous motor.

[0032] Preferably, using the obtained K p K i , to perform I dref =0,I qref =A0 current command for current closed-loop control;

[0033] In steady state, based on the q-axis voltage signal u q and current signal i q electric angular velocity ω e Based on the identified resistance values, the flux linkage parameters are determined using the recursive least squares method with a forgetting factor, according to the following formula.

[0034]

[0035] in, The flux linkage value calculated for the current interrupt cycle. The flux linkage value calculated in the previous interruption cycle. The gain coefficient is calculated for the current period.

[0036] Preferably, the current value A0 can be set manually and is less than the rated current value of the permanent magnet synchronous motor.

[0037] Preferably, the procedure specifically includes the following steps:

[0038] Step 1: Establish a mathematical model of the permanent magnet synchronous motor in the rotating coordinate system dq:

[0039]

[0040] Step 2: Establish a two-dimensional fuzzy adaptive PI controller, and input the following parameters at the set angle: The d-axis current signal is used for current loop PI tuning. The d-axis current error e and the rate of change of error are taken. As two inputs to the controller, fuzzification, fuzzy rule establishment, fuzzy inference, and defuzzification are performed sequentially to obtain the current loop K. p K i .

[0041] Step 3: Determine the current loop parameters, set angles of 0°, 120°, and 240° to correspond to the three phases A, B, and C of the motor respectively, give the stepped d-axis current command, and perform closed-loop control of the current loop. The motor is simultaneously positioned to the corresponding phase under the action of electromagnetic force.

[0042] Step 4: Obtain the d-axis voltage and d-axis current signals under steady state. After differential and average processing, the effects of inverter nonlinearity and current noise are effectively reduced. The motor phase resistance R is then calculated. s As shown in the formula below:

[0043]

[0044] Step 5: Establish the least squares mathematical model with forgetting factor as follows:

[0045]

[0046] in, Let K(k) be the observable input matrix, θ(k) be the matrix of parameters to be identified, where K(k) and P(k) are the gain matrices of the recursive formula, and λ is the forgetting factor with a value range of (0, 1).

[0047] Step Six: Given I dref =A0·sin(2π·f1·t),I qref =0 current command, the motor is stationary, and the d-axis voltage equation can be simplified to:

[0048]

[0049] Based on the d-axis voltage signal u d and current signal i d Based on the identified resistance parameters, the d-axis inductance L is calculated using a recursive least squares method with a forgetting factor. d The formula is as follows:

[0050]

[0051] Wherein, f1 is the frequency of the d-axis current signal. The d-axis inductance value is calculated for the current interrupt cycle. The d-axis inductance value calculated in the previous interrupt cycle. The gain coefficient is calculated for the current period.

[0052] Step 7: Given I dref =0,I qref =A0·sin(2π·f2·t) current command, f2 is a medium-high frequency, the motor is approximately stationary, and the q-axis voltage equation can be simplified to:

[0053]

[0054] Based on the q-axis voltage signal u q and current signal i q Based on the identified resistance parameters, the q-axis inductance L is calculated using a recursive least squares method with a forgetting factor. q The formula is as follows:

[0055]

[0056] Wherein, f2 is the frequency of the q-axis current signal. The q-axis inductance value is calculated for the current interrupt cycle. The q-axis inductance value was calculated in the previous interrupt cycle. The gain coefficient is calculated for the current period.

[0057] Step 8: Perform I dref =0,I qref =A0 current command performs current closed-loop control. After torque balance, the speed is stable, and in steady state, it is controlled according to the q-axis voltage signal u. q and current signal i q electric angular velocity ω e Combined with the identified resistance parameter R s The flux linkage parameters are determined using the recursive least squares method with a forgetting factor, according to the following formula.

[0058]

[0059] in, The flux linkage value calculated for the current interrupt cycle. The flux linkage value calculated in the previous interruption cycle. The gain coefficient is calculated for the current period.

[0060] The technical solution of this invention is: a parameter identification method for permanent magnet synchronous motors based on fuzzy adaptive control. Given a d-axis current command in the rotating coordinate system of the permanent magnet synchronous motor, fuzzy adaptive PI control is used to obtain the current loop parameters Kp and Ki, which are then used for closed-loop control for motor parameter identification. Given a d-axis step current command, the identification resistor is obtained using a mean and difference method based on the d-axis voltage and d-axis feedback current. Given a d-axis sinusoidal current command, the identification d-axis inductance is obtained using the least squares method with a forgetting factor based on the d-axis voltage, d-axis feedback current, and identification resistor. Similarly, given a q-axis sinusoidal current command, the identification q-axis inductance is obtained using the least squares method with a forgetting factor based on the q-axis voltage, q-axis feedback current, and identification resistor. Given a q-axis step current command, the flux linkage is obtained using the least squares method with a forgetting factor based on the q-axis voltage, q-axis feedback current, motor rotational angular velocity, and identification resistor.

[0061] Compared with the prior art, the offline closed-loop parameter identification method for permanent magnet synchronous motors described in this invention has the following advantages:

[0062] (1) The present invention adopts a parameter identification method for permanent magnet synchronous motor based on fuzzy adaptive PI control. The identification process is simple, reliable and universal.

[0063] (2) This invention is a closed-loop offline parameter identification, with controllable current and high safety. Except for identifying the magnetic flux, the motor will not rotate, making it suitable for different occasions.

[0064] (3) The present invention can perform adaptive PI tuning and parameter identification in an extremely wide current range (10% to 100% of rated current), the identification intensity is adjustable, and the identification results are consistent.

[0065] (4) The present invention uses the least squares method with forgetting factor to independently identify the d-axis inductance, q-axis inductance and magnetic flux in turn, avoiding the defect of underranked equations in traditional methods. Attached Figure Description

[0066] Figure 1 This is a parameter identification structure block diagram;

[0067] Figure 2 This is a vector control block diagram of a fuzzy adaptive PI controller;

[0068] Figure 3 It is a triangular membership function that describes a fuzzy set;

[0069] Figure 4 It is the fuzzy control current loop Kp control rule table;

[0070] Figure 5 It is the Ki control rule table for fuzzy control current loop;

[0071] Figure 6 This is the control flowchart of a fuzzy adaptive PI controller;

[0072] Figure 7 This is a block diagram of the resistor identification control.

[0073] Figure 8 This is an example of resistance identification, showing a comparison between the current command and feedback value for phase A.

[0074] Figure 9 This is the block diagram for d-axis inductance identification and control.

[0075] Figure 10 This is the block diagram for q-axis inductor identification control;

[0076] Figure 11 This is a block diagram of magnetic link identification and control. Detailed Implementation

[0077] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0078] The present invention discloses a parameter identification method for permanent magnet synchronous motors based on fuzzy adaptive control, the structure of which is as follows: Figure 1 As shown, the method specifically includes the following steps:

[0079] (1) Establish a mathematical model of the permanent magnet synchronous motor in a rotating coordinate system (dq system), and its voltage equation is:

[0080]

[0081] In the above formula: u d u q These are the d-axis and q-axis voltages obtained from feedback calculations, i d i q For the d-axis and q-axis currents obtained from feedback calculations, R s L is the phase resistance of the stator winding. d L q d-axis and q-axis inductance, ω e Electric angular velocity, It is a permanent magnet flux linkage.

[0082] (2) Based on the vector control of permanent magnet synchronous motor, a fuzzy adaptive PI controller is established, such as... Figure 2 As shown, the d-axis current error e and the rate of change of error are taken. The two inputs to the controller are fuzzified. Define e, and K p K i The fuzzy sets of linguistic values ​​are as follows: e. K p K i={NB, NM, NS, ZO, PS, PM, PB}, denoted by B0 to B6, with corresponding universes of discourse values ​​of {-3, -2, -1, 0, 1, 2, 3}. Let the variables e, To unify the actual scope with the domain of discourse, the transformation formula is:

[0083]

[0084] In the above formula: x is the actual input precise quantity, with a range of [a,b], and y is a continuous quantity in the universe of discourse, with a range of [-3,3].

[0085] (3) Use membership functions to characterize fuzzy sets, such as triangular membership functions, for example... Figure 3 As shown, the degree of correlation between the input quantity and the elements in the fuzzy set is called the membership degree. Calculate e and... Membership degree of fuzzy variables.

[0086] (4) Based on the control law of the PI controller, formulate K... p K i In different e, The control rule table below, such as Figure 4 and Figure 5 As shown.

[0087] (5) Each element in the rule table corresponds to e and For a fuzzy variable with two membership degrees, the smaller value of the membership degree of the input variable in the rule table is taken as the membership degree of the output variable, thus obtaining K. p K i Membership degree u of each fuzzy variable kp (B i ), u ki (B i ).

[0088] (6) Convert the fuzzy variable values ​​into actual output values ​​and use the centroid method for defuzzification. K is obtained using the following formula. p K i :

[0089]

[0090]

[0091] (7) At the set angle (0°, 120°, and 240° correspond to phases A, B, and C respectively), the input frequency value is f0 and the amplitude value is... Bias is The d-axis current command is used for current loop PI tuning. The fuzzy adaptive PI tuning control flowchart is as follows: Figure 6As shown. Among them, A0 is a current value that can be set manually. (8) Identify the resistance parameters of the permanent magnet synchronous motor, keep the set angle, and input the amplitude as The d-axis stepped current command is used for current closed-loop control, such as... Figure 7 As shown in the figure. Where N is the number of steps set. Figure 8 This is an example of resistance identification, showing a comparison between the d-axis stepped current command and the acquired A-phase current feedback value. When the electrical angle is 0°, the d-axis current is equal to the A-phase current.

[0092] (9) Collect the three-phase currents under each step steady-state condition, and obtain the d-axis current i through Clark and Park transformations. d (n), the d-axis voltage u is obtained by the output of the PI regulator. d (n).

[0093] (10) Obtain the d-axis voltage and d-axis current for each step. After mean filtering, obtain the steady-state d-axis voltage and d-axis current for each step. Then, perform differential processing on the voltages and currents of adjacent steps to effectively reduce the adverse effects of inverter nonlinearity. Next, perform mean processing on the values ​​obtained after differential calculation to effectively reduce the influence of noise such as current sampling, and obtain the identification resistance value R. s As shown in the formula below:

[0094]

[0095] (11) Since the controller is a discrete system, it is impossible to sample all feedback current values. Furthermore, due to noise and other interference, the sampled values ​​will fluctuate and contain errors. The least squares method can be used to easily obtain the unknown data, minimizing the sum of squared errors between the obtained data and the actual values. Based on fuzzy adaptive PI control, the least squares method can be combined to calculate the d-axis inductance L... d q-axis inductance L q Magnetic Link Identification.

[0096] (12) Establish a least squares method with a forgetting factor. The forgetting factor can speed up the convergence of the calculation. The mathematical model is shown in the following formula:

[0097]

[0098] in, Let K(k) be the observable input matrix, θ(k) be the matrix of parameters to be identified, where K(k) and P(k) are the gain matrices of the recursive formula, and λ is the forgetting factor with a value range of (0, 1).

[0099] (13) Identify the d-axis inductance parameters of the permanent magnet synchronous motor, and the control process is as follows: Figure 9 As shown. Given I dref=A0·sin(2π·f1·t),I qref =0 current command, f1 is a medium-low frequency, K is obtained using fuzzy adaptive PI control. p K i Current loop closed-loop control is performed. Since the d-axis current command is given, the q-axis current is 0, and the motor is stationary. The d-axis voltage equation can be simplified to:

[0100]

[0101] (14) Obtain the d-axis voltage signal u d and current signal i d Combined with the identified resistance parameter R s The d-axis inductance L is calculated using the recursive least squares method with a forgetting factor. d The formula is as follows:

[0102]

[0103] in, The d-axis inductance value is calculated for the current interrupt cycle. The d-axis inductance value calculated in the previous interrupt cycle. The gain coefficient is calculated for the current period.

[0104] The inductance value is calculated in each control cycle and continuously weighted and converged to approximate the true inductance value.

[0105] (15) Identify the q-axis inductance parameters of the permanent magnet synchronous motor, and the control process is as follows: Figure 10 As shown. Given I dref =0,I qref =A0·sin(2π·f2·t) current command, where f2 is a medium-to-high frequency signal, and K is obtained using fuzzy adaptive PI control. p K i Closed-loop current control is performed. Since the q-axis current is a medium-to-high frequency sinusoidal command, the motor is approximately stationary, and the q-axis voltage equation can be simplified to:

[0106]

[0107] (16) Obtain the q-axis voltage signal u q and current signal i q Combined with the identified resistance parameter R s The q-axis inductance L is calculated using the recursive least squares method with a forgetting factor. q The formula is as follows:

[0108]

[0109] in, The q-axis inductance value is calculated for the current interrupt cycle. The q-axis inductance value was calculated in the previous interrupt cycle. The gain coefficient is calculated for the current period.

[0110] (17) Identify the flux linkage parameters of the permanent magnet synchronous motor, and the control process is as follows: Figure 11 As shown. Given I dref =0,I qref =A0 current command, K obtained using fuzzy adaptive PI control p K i This enables closed-loop current loop control. Because i q With a fixed value, the motor will start rotating. After the torque balances, the speed will stabilize. The speed is related to the given current value A0 and can be set manually. The q-axis voltage equation can be simplified to:

[0111]

[0112] (18) Under steady state, based on the q-axis voltage signal u q and current signal i q electric angular velocity ω e Combined with the identified resistance parameter R s The flux linkage parameters are determined using the recursive least squares method with a forgetting factor, according to the following formula.

[0113]

[0114] in, The flux linkage value calculated for the current interrupt cycle. The flux linkage value calculated in the previous interruption cycle. The gain coefficient is calculated for the current period.

Claims

1. A method for parameter identification of a permanent magnet synchronous motor based on fuzzy adaptive control, characterized in that, include: Based on fuzzy adaptive PI control, the input frequency value is f0 and the amplitude value is f0 at a set angle. Bias is The d-axis current signal is used for current loop PI tuning to obtain the current loop K. p K i Where A0 is a manually set current value; The fuzzy adaptive PI control employs a two-dimensional fuzzy controller, and the specific steps include: 1) Blurring process Take the d-axis current error e and the rate of change of error. As two inputs to the controller, e, and K p K i The fuzzy set of linguistic values ​​is defined as follows: Let B0~B6 represent the values ​​of the universe of discourse: {-3, -2, -1, 0, 1, 2, 3}; let the variables e, To unify the actual scope with the domain of discourse, the transformation formula is: In the formula: x is the actual input precise quantity, ranging from [a,b], and y is the continuous quantity in the universe of discourse, ranging from [-3,3]. 2) Perform fuzzy inference based on fuzzy rules Triangular membership functions are used to describe fuzzy sets, and e and are calculated respectively. Membership degree of fuzzy variables: Based on the principles of PI control, K is formulated. p K i In different e, The control rule table is as follows; the smaller value of the membership degree of the input variable in the control rule table is taken as the membership degree of the output variable, and K is obtained respectively. p K i Membership degree u of each fuzzy variable kp (B i ), u ki (B i ); 3) Deblurring yields K p K i value The centroid method is used for defuzzification, and K is determined by the following formula. p K i value, 4) Using the obtained K p K i Input the amplitude value at the set angle. Current closed-loop control is performed using d-axis stepped current commands (n = 1, 2... N); Where A0 is a manually set current value. 5) Obtain the d-axis voltage u under each step steady-state condition. d (n) and d-axis current i d (n), the motor phase resistance R is determined by taking the average value after differentiation. s As shown below: 6) Using the obtained K p K i Perform I at a set angle dref =A0·sin(2π·f1·t),I qref =0 current command for current closed-loop control; The d-axis voltage signal u obtained from the feedback d and current signal i d Combined with the identified motor phase resistance value R s Using the recursive least squares method with a forgetting factor, the d-axis inductance parameter L is determined by the following formula. d : Wherein, f1 is the frequency of the d-axis current signal. The d-axis inductance value is calculated for the current interrupt cycle. The d-axis inductance value calculated in the previous interrupt cycle. The gain coefficient calculated for the current cycle; 7) Using the obtained K p K i Perform I at a set angle dref =0,I qref =A0·sin(2π·f2·t) current command for current closed-loop control; Based on the q-axis voltage signal u q and current signal i q Combined with the identified motor phase resistance value R s Using the recursive least squares method with a forgetting factor, the q-axis inductance parameter L is determined by the following formula. q : Wherein, f2 is the frequency of the q-axis current signal. The q-axis inductance value is calculated for the current interrupt cycle. The q-axis inductance value was calculated in the previous interrupt cycle. The gain coefficient calculated for the current cycle; 8) Using the obtained K p K i , to perform I dref =0,I qref =A0 current command for current closed-loop control; In steady state, based on the q-axis voltage signal u q and current signal i q electric angular velocity ω e Combined with the identified motor phase resistance value R s Using the recursive least squares method with a forgetting factor, the flux linkage parameter is determined by the following formula. in, The flux linkage value calculated for the current interrupt cycle. The flux linkage value calculated in the previous interruption cycle. The gain coefficient is calculated for the current period.

2. The parameter identification method for permanent magnet synchronous motors based on fuzzy adaptive control according to claim 1, characterized in that, The current value A0 is less than the rated current value of the permanent magnet synchronous motor.

3. The parameter identification method for permanent magnet synchronous motors based on fuzzy adaptive control according to claim 1, characterized in that, Specifically, the following steps are included: Step 1: Establish a mathematical model of the permanent magnet synchronous motor in the rotating coordinate system dq: Step 2: Establish a two-dimensional fuzzy adaptive PI controller, and input the following parameters at the set angle: The d-axis current signal is used for current loop PI tuning; the d-axis current error e and the error change rate are taken. As two inputs to the controller, fuzzification, fuzzy rule establishment, fuzzy inference, and defuzzification are performed sequentially to obtain the current loop K. p K i ; Step 3: Determine the current loop parameters, set the angles 0°, 120°, and 240° to correspond to the three phases A, B, and C of the motor respectively, give the stepped d-axis current command, and perform closed-loop control of the current loop. The motor is simultaneously positioned to the corresponding phase under the action of electromagnetic force. Step 4: Obtain the d-axis voltage and d-axis current signals under steady state. After differential and average processing, the influence of inverter nonlinearity and current noise is effectively reduced, and the motor phase resistance R is calculated. s As shown in the formula below: Step 5: Establish the least squares mathematical model with forgetting factor as follows: in, Let K(k) be the observable input matrix, θ(k) be the matrix of parameters to be identified, where K(k) and P(k) are the gain matrices of the recursive formula, and λ is the forgetting factor with a value range of (0, 1). Step Six: Given I dref =A0·sin(2π·f1·t),I qref =0 current command, the motor is stationary, and the d-axis voltage equation can be simplified to: Based on the d-axis voltage signal u d and current signal i d Based on the identified resistance parameters, the d-axis inductance L is calculated using a recursive least squares method with a forgetting factor. d The formula is as follows: Wherein, f1 is the frequency of the d-axis current signal. The d-axis inductance value is calculated for the current interrupt cycle. The d-axis inductance value calculated in the previous interrupt cycle. The gain coefficient calculated for the current cycle; Step 7: Given I dref =0,I qref =A0·sin(2π·f2·t) current command, the q-axis voltage equation simplifies to: Based on the q-axis voltage signal u q and current signal i q Based on the identified resistance parameters, the q-axis inductance L is calculated using a recursive least squares method with a forgetting factor. q The formula is as follows: Wherein, f2 is the frequency of the q-axis current signal. The q-axis inductance value is calculated for the current interrupt cycle. The q-axis inductance value was calculated in the previous interrupt cycle. The gain coefficient calculated for the current cycle; Step 8: For I dref =0,I qref =A0 current command performs current closed-loop control; after torque balance, the speed is stable, and in steady state, it is controlled according to the q-axis voltage signal u q and current signal i q electric angular velocity ω e Combined with the identified resistance parameter R s The flux linkage parameters are determined using the recursive least squares method with a forgetting factor, according to the following formula. in, The flux linkage value calculated for the current interrupt cycle. The flux linkage value calculated in the previous interruption cycle. The gain coefficient is calculated for the current period.

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