A parameter identification method for a strong coupling double-branch permanent magnet synchronous motor

Abstract

The application discloses a parameter identification method of a strong coupling double-branch permanent magnet synchronous motor, and belongs to the technical field of permanent magnet synchronous motor control. The method is as follows: motor driving and control; motor information acquisition; establishment of a motor mathematical model, inductance relation assumption, preliminary parameter identification by using a least square principle, rewriting of the motor mathematical model, iterative calculation, direct output of the flux linkage as a final parameter identification result, input of the self-inductance, mutual inductance and resistance as initial values to next step parameter identification; obtaining of an adaptive regulation rate of secondary parameter identification by using a Lyapunov method; obtaining of a secondary parameter identification result by using the adaptive regulation rate; and result output. The application is executed by a digital signal processor program, realizes parameter identification without additional cost and energy consumption, is simple to operate, is consistent with original system maintenance, can effectively monitor motor states and improve control system precision, and is especially suitable for strong coupling double-branch permanent magnet synchronous motor control and other fields.

Description

TECHNICAL FIELD

[0001] The application relates to a parameter identification method of a strong coupling double-branch permanent magnet synchronous motor, and belongs to the technical field of permanent magnet synchronous motor control. BACKGROUND

[0002] The strong coupling double-branch motor is a kind of innovative multi-winding structure motor, each phase being equipped with two independent stator windings. When one of the windings fails, the other winding can be immediately connected as a backup to realize load reduction operation, thereby meeting the strict requirements of key fields such as aerospace and national defense on high reliability of motor systems. In order to realize efficient driving control, such a motor is usually constructed based on an isolated interleaved parallel topology architecture, each branch independently adopting a control strategy of i d = 0, and being driven through a space vector pulse width modulation (SVPWM) technology. In addition, the use of carrier phase shift technology can significantly reduce the noise generated by PWM (pulse width modulation), further improving the operation quality of the motor.

[0003] Although the strong coupling double-branch permanent magnet synchronous motor exhibits the significant advantages of low noise and high reliability, its complex motor structure and mathematical model bring unprecedented challenges to parameter identification. Parameter identification is a basic work in the field of motor control, which not only can directly obtain the key parameters of the motor, but also is a prerequisite for realizing high-precision control strategies. However, due to the particularity of the strong coupling double-branch motor, there is no mature and effective parameter identification method proposed at present, which to some extent limits the development of the control algorithm of this type of motor and the performance optimization in practical application. SUMMARY

[0004] To solve the problems in the background art, the application provides a parameter identification method of a strong coupling double-branch permanent magnet synchronous motor.

[0005] To achieve the above-mentioned purpose, the application adopts the following technical solution: a parameter identification method of a strong coupling double-branch permanent magnet synchronous motor, the method comprising the following steps:

[0006] S1: driving and controlling the motor;

[0007] S101: driving the motor by using an isolated interleaved parallel topology structure, the two branches being separately powered;

[0008] S102: controlling the motor by using a double-loop strategy;

[0009] S103: the two branches of the motor both adopting a current given of i d = 0;

[0010] S104: adopting an SVPWM modulation algorithm;

[0011] S105: Adopting the control strategy of shifting the carrier wave by half a cycle.

[0012] S2: Obtaining motor information;

[0013] S201: Measuring the current of the motor;

[0014] S20101: Measuring the A-phase and B-phase current signals of the two branches;

[0015] S20102: Calculating the C-phase current signal according to the current relationship of the star-connected motor.

[0016] S202: Current signal coordinate transformation;

[0017] S20201: Calculating the actual values of the A-phase, B-phase, and C-phase three-phase currents according to the sampling values;

[0018] S20202: Converting the three-phase current signals into current component signals in the two-phase stationary coordinate system using Clark transformation;

[0019] S20203: Converting the current component signals in the two-phase stationary coordinate system into current signals in the synchronous rotating coordinate system using Park transformation.

[0020] S203: Obtaining the voltage of the motor;

[0021] S204: Obtaining the rotor position information of the motor through the precision position sensor;

[0022] S205: Calculating the motor speed signal by differentiating the current period and the previous period speed signal in the controller.

[0023] S3: Motor parameter identification.

[0024] S301: Establishing the mathematical model of the motor:

[0025]

[0026] In formula (1):

[0027] u d represents the output voltage of the current regulator d-axis;

[0028] u q represents the output voltage of the current regulator q-axis;

[0029] L d represents the synchronous self-inductance of the current regulator d-axis, which is a parameter to be identified;

[0030] L q represents the synchronous self-inductance of the current regulator q-axis, which is a parameter to be identified;

[0031] M d represents the mutual inductance of the current regulator d-axis after transformation, which is a parameter to be identified;

[0032] M q represents the mutual inductance of the current regulator q-axis after transformation, which is a parameter to be identified;

[0033] p represents a differential symbol;

[0034] i d represents the measured current of the current regulator d-axis;

[0035] i q represents the measured current of the current regulator q-axis;

[0036] ω e represents the rotor electric angular velocity;

[0037] ψ f represents the permanent magnet flux linkage of the motor, which is a parameter to be identified;

[0038] R represents the resistance of the motor, which is a parameter to be identified;

[0039] S302: Inductance relationship assumption: assuming that the motor is an ideal motor, i.e. a surface-mounted motor with uniform air gap, the motor mutual inductance M and the motor self-inductance L only contain constant components, so the assumption that the motor mutual inductance M and the motor self-inductance L are linearly proportional, i.e. M=kL, k is the self-inductance and mutual inductance coefficient, is made;

[0040] S303: Preliminary parameter identification is performed using the least squares principle, and the mathematical model of the motor is rewritten as follows by substituting M=kL:

[0041]

[0042] S304: Iterative calculation is performed:

[0043]

[0044] S305: The flux linkage is directly output as the final parameter identification result, and the self-inductance, mutual inductance and resistance are input as initial values to the next parameter identification;

[0045] S306: The Lyapunov method is used to obtain the adaptive regulation rate of the second parameter identification;

[0046] S30601: The mathematical model of the control object is established as a reference model for the second identification:

[0047]

[0048] It can be obtained that:

[0049]

[0050] For simplicity of notation, let the ratio of resistance to inductance be set to unity Then equation (5) can be written in the form of a current state equation as follows:

[0051]

[0052] S30602: Establish the system equation of the adjustable model:

[0053]

[0054] In equation (7):

[0055] represents the estimated value of the adjustable model of the measured current of the d-axis of the current regulator;

[0056] represents the estimated value of the adjustable model of the measured current of the q-axis of the current regulator;

[0057] represents the estimated value of the adjustable model of the self-inductance of the motor;

[0058] represents the estimated value of the adjustable model of the mutual inductance of the motor;

[0059] represents the estimated value of the ratio of resistance to inductance;

[0060] S30603: Define:

[0061]

[0062] In equations (8)-(9):

[0063] I, A, B, U, W, and are all dummy symbols and have no meaning;

[0064] S30604: Select the difference between the current state variable of the d-axis of the current regulator and the current state variable of the q-axis of the current regulator:

[0065]

[0066] S30605: Differentiate the difference:

[0067]

[0068] S30606: Set:

[0069]

[0070] In formula (12):

[0071] a, b, φ T and s are all negative signs, meaningless;

[0072] It can be obtained:

[0073]

[0074] S30607: Construct scalar function V(e, t):

[0075]

[0076] S30608: Solve the non-zero vector scalar function Negative condition.

[0077] S3060801: Differentiate the scalar function V(e, t):

[0078]

[0079] In formula (15):

[0080]

[0081] It can be obtained:

[0082]

[0083] Because:

[0084]

[0085] Therefore This item must be negative;

[0086] S3060802: If:

[0087]

[0088] Then the non-zero vector scalar function Negative

[0089] S3060803: Expand formula (18):

[0090]

[0091] S3060804: Solve the differential equation group:

[0092]

[0093] It can be obtained to make the non-zero vector scalar function The self-adaptive adjustment rhythm which must be negative is:

[0094]

[0095] In formula (20):

[0096] An estimated value of an adjustable model representing resistance;

[0097] An estimated value of an adjustable model representing inductance, i.e. an inductance value obtained through secondary identification;

[0098] M0 represents an initial value of motor mutual inductance identification;

[0099] R0 represents an initial value of resistance identification;

[0100] K L represents an adjustment gain corresponding to motor self-inductance;

[0101] K R represents an adjustment gain corresponding to resistance;

[0102] S307: obtaining secondary parameter identification results by using adaptive adjustment rate;

[0103] S308: outputting results.

[0104] S30801: calculating motor mutual inductance:

[0105]

[0106] S30802: calculating a new proportional coefficient k1:

[0107]

[0108] S30803: setting conditions for parameter output;

[0109]

[0110] S30704: determining whether the conditions for parameter output are met;

[0111] If the conditions for parameter output are met, the final results are outputted, i.e. the flux linkage and motor self-inductance obtained through primary identification, the resistance obtained through secondary identification, and the motor mutual inductance obtained by subtracting the motor self-inductance from the motor mutual inductance are outputted.

[0112] If the conditions for parameter output are not met, the obtained self-inductance and mutual inductance example coefficient k1 are iterated again until the conditions are met.

[0113] Compared with the prior art, the present application has the following advantages:

[0114] The application realizes parameter identification without additional cost and energy consumption on the basis of not changing the original control system structure, and through digital signal processor program execution. The operation is simple, the maintenance is consistent with the original system, the motor state can be effectively monitored and the control system precision can be improved, and the application is especially suitable for strong coupling double branch permanent magnet synchronous motor control and other fields. Through the use of the special structure of the motor, the full rank and the inductance and mutual inductance characteristics are combined, the equation is simplified and the differential term under the steady state is ignored, the operation speed and convergence of the parameter identification are improved, and an efficient and accurate solution is provided for motor control. BRIEF DESCRIPTION OF DRAWINGS

[0115] Figure 1 is a topology diagram based on which the parameter identification algorithm of the application is based;

[0116] Figure 2 is a flowchart of the application;

[0117] Figure 3 is a parameter value diagram obtained by initial parameter identification, wherein (a) is a resistance value diagram of initial identification, (b) is a self-inductance value diagram of initial identification, (c) is a flux linkage value of initial identification, and the dashed lines in (a), (b) and (c) represent the true values of the corresponding parameters of the motor;

[0118] Figure 4 is a parameter value diagram obtained by secondary parameter identification, wherein (a) is a sum of inductance value diagram of secondary identification, (b) is a resistance value of secondary identification, and the dashed lines in (a) and (b) represent the true values of the corresponding parameters of the motor. DETAILED DESCRIPTION

[0119] The technical solutions in the application will be described clearly and completely below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0120] A parameter identification method of a strong coupling double branch permanent magnet synchronous motor, based on the special structure of the strong coupling double branch motor, adopts a linearization processing method of coupled inductance, and obtains the parameters of the motor by using two-step identification, on the basis of the traditional three-phase surface-mounted permanent magnet synchronous motor parameter identification algorithm. The method comprises the following steps:

[0121] S1: driving and controlling the motor;

[0122] S101: driving the motor by using an isolated interleaved parallel topology structure, and separately supplying power to the two branches;

[0123] S102: controlling the motor by using a double closed loop strategy;

[0124] S103: Both branches of the motor use i d = 0 current given;

[0125] S104: SVPWM modulation algorithm is used;

[0126] S105: The control strategy of carrier phase shift half a cycle is used to reduce the sum of the currents of the two branches and the current harmonics of each branch.

[0127] S2: Obtain motor information and then accurately feedback control the motor;

[0128] S201: Measure the current of the motor;

[0129] S20101: Use high-precision current sensors to measure the A-phase and B-phase current signals of the two branches respectively;

[0130] S20102: According to the current relationship of the star-connected motor (i.e. the sum of the three-phase currents is zero), the C-phase current signal is calculated; and then the double-branch three-phase current signal of the motor is obtained.

[0131] S202: Current signal coordinate transformation;

[0132] S20201: Calculate the actual values of the A-phase, B-phase and C-phase three-phase currents according to the sampling values;

[0133] S20202: Use Clark transformation to convert the three-phase current signals into current component signals in the two-phase stationary coordinate system (α-β coordinate system);

[0134] S20203: Use Park transformation to convert the current component signals in the two-phase stationary coordinate system into current signals in the synchronous rotating coordinate system (dq coordinate system).

[0135] S203: Obtain the voltage of the motor: the voltage obtained by PI regulator (current loop) of the current signal in the dq coordinate system as the voltage of the motor in the dq coordinate system;

[0136] S204: Obtain the rotor position information of the motor through a high-precision position sensor (such as an encoder, a Hall sensor, etc.);

[0137] S205: Obtain the speed signal of the motor by differentiating the speed signal of the current period and the last period in the controller.

[0138] S3: Motor parameter identification.

[0139] S301: Establish the mathematical model of the motor:

[0140]

[0141] In formula (1):

[0142] u d represents the output voltage (V) of the current regulator d-axis;

[0143] u q represents the output voltage (V) of the current regulator q-axis;

[0144] L d represents the synchronous self-inductance (H) of the current regulator d-axis, which is a parameter to be identified;

[0145] L q represents the synchronous self-inductance (H) of the current regulator q-axis, which is a parameter to be identified;

[0146] M d represents the mutual inductance (H) of the current regulator d-axis after transformation, which is a parameter to be identified;

[0147] M q represents the mutual inductance (H) of the current regulator q-axis after transformation, which is a parameter to be identified;

[0148] p represents a differential symbol;

[0149] i d represents the measured current (A) of the current regulator d-axis;

[0150] i q represents the measured current (A) of the current regulator q-axis;

[0151] ω e represents the rotor electric angular velocity (rad / s);

[0152] ψ f represents the permanent magnet flux linkage (Wb) of the motor, which is a parameter to be identified;

[0153] R represents the resistance (Ω) of the motor, which is a parameter to be identified;

[0154] S302: Inductance relationship assumption: Since the two sets of windings of the strong coupling double-branch motor are placed in the same slot, the mutual inductance between the windings of the same phase of the two sets of windings is close to the self-inductance. During the initial identification of the parameters of the motor, the motor can be assumed to be an ideal motor, i.e. a surface-mounted motor with uniform air gap, and the motor mutual inductance M and the motor self-inductance L only include constant components, so the assumption that the motor mutual inductance M and the motor self-inductance L are linearly proportional, i.e. M=kL, is made, where k is the self-inductance and mutual inductance coefficient, which can be obtained through finite element software;

[0155] Since the strong coupling double-branch motor of the application is a surface-mounted permanent magnet synchronous motor, the inductances of the d-axis and the q-axis are the same, so L d = L q = L, Md = M q = M;

[0156] S303: The conventional permanent magnet synchronous motor has two equations and three parameters to be identified, so the problem of under-rank will occur. When the least square method is used for parameter identification, some parameters will not converge. The strong coupling double branch motor has four equations and four parameters to be identified. The characteristic that the two branches of the motor can be powered separately makes the bus voltage different, so that the motor parameters can achieve full rank. For parameter identification, the redundancy design of the double branch has the advantages of full rank, so that the parameters converge when parameter identification is performed. On the other hand, the number of equations of the strong coupling double branch motor is twice that of the ordinary motor, and when the full rank advantage is used to construct the equation, a large amount of operation is brought, which is a challenge to the computing power of the digital signal processor. As can be seen from the mathematical model of the motor of S301, the motor has four constraint voltage equations and four parameters to be identified, which has the full rank characteristic. According to the full rank characteristic, the dq-axis voltage equation is rewritten in the form of the least square method. The least square principle is used for preliminary parameter identification. The present application makes full use of the special structure of the strong coupling double branch permanent magnet synchronous motor, and simplifies the equation by using the characteristics that the inductance and mutual inductance are close to and proportional to each other. At the same time, because the parameter identification is performed in the steady state, the differential term can be ignored for further simplification. The parameter identification by this algorithm has a high operation speed, and the calculation result can converge quickly. Substitute M=kL to rewrite the mathematical model of the motor as follows:

[0157]

[0158] S304: Four equations are selected from three of the four voltage equations and M=kL. After obtaining the motor speed, dq-axis voltage and current information, the recursive least square method is used for iterative calculation:

[0159]

[0160] S305: This method can realize parameter identification under the condition of full rank. In the above-mentioned case where the proportion of self-inductance and mutual inductance is relatively accurate, the inductance, resistance and flux linkage of the motor can be accurately obtained. However, since the resistance is more easily affected by the proportion relationship, and the flux linkage and inductance are almost not affected by the assumed nonlinear factors, further parameter identification is needed. The flux linkage obtained in this step is directly output as the final parameter identification result, and the self-inductance, mutual inductance and resistance are input as the initial values to the next step of parameter identification. The resistance and inductance parameters obtained are used as the initial values of the secondary parameter identification, and then more accurate resistance and inductance are obtained by further parameter identification.

[0161] S306: Obtain the adaptive regulation rate of the quadratic parameter identification by using Lyapunov method;

[0162] S30601: Establish the mathematical model of the control object as the reference model of the quadratic identification:

[0163]

[0164] It can be obtained that:

[0165]

[0166] In order to simplify the symbols, the ratio of resistance to inductance is set as Then, formula (5) is written in the form of current state equation as follows:

[0167]

[0168] S30602: Establish the system equation of the adjustable model:

[0169]

[0170] In formula (7):

[0171] represents the estimated value of the adjustable model of the measured current of the current regulator d-axis;

[0172] represents the estimated value of the adjustable model of the measured current of the current regulator q-axis;

[0173] represents the estimated value of the adjustable model of the self-inductance of the motor;

[0174] represents the estimated value of the adjustable model of the mutual inductance of the motor;

[0175] represents the estimated value of the ratio of resistance to inductance;

[0176] S30603: Define:

[0177]

[0178] In formulas (8)-(9):

[0179] I, A, B, U, W, and are all dummy symbols and meaningless;

[0180] S30604: Select the difference between the current regulator d-axis current state variable and the current regulator q-axis current state variable:

[0181]

[0182] S30605: Differentiate the difference:

[0183]

[0184] S30606: Set:

[0185]

[0186] In formula (12):

[0187] a, b, φ T And s are all the sign of the symbol, meaningless;

[0188] It can be obtained:

[0189]

[0190] S30607: Construct scalar function V(e, t):

[0191]

[0192] If this scalar function exists, it is positive definite, and its derivative is negative definite, and when ‖e‖→∞, V(e, t)→∞, then it can be guaranteed to be uniformly stable in a large range. This is the criterion for judging whether the parameter identification converges or not by using the second Lyapunov method.

[0193] S30608: Obtain non-zero vector scalar function Negative condition.

[0194] S3060801: Differentiate the scalar function V(e, t):

[0195]

[0196] In formula (15):

[0197]

[0198] It can be obtained:

[0199]

[0200] Since:

[0201]

[0202] Therefore This item must be negative;

[0203] S3060802: If:

[0204]

[0205] the non-zero vector scalar function negative definite, thus satisfying the Lyapunov second method, the difference e will be uniformly asymptotically stable to zero vector, that is, the adjustable model will tend to the reference model, and the identified parameters will converge to the true values;

[0206] S3060803: expansion (18):

[0207]

[0208] S3060804: solve the differential equation set:

[0209]

[0210] the non-zero vector scalar function The adaptive adjustment law is necessarily negative:

[0211]

[0212] In equation (20):

[0213] represents the estimated value of the adjustable model of the resistance;

[0214] represents the estimated value of the adjustable model of the inductance (the sum of self-inductance and mutual inductance), that is, the inductance value obtained by secondary identification;

[0215] L0 represents the initial value of the motor self-inductance identification;

[0216] M0 represents the initial value of the motor mutual inductance identification;

[0217] R0 represents the initial value of the resistance identification;

[0218] K L represents the adjustment gain corresponding to the motor self-inductance;

[0219] K R represents the adjustment gain corresponding to the resistance, and appropriate selection of the adjustment gain can make the convergence speed and accuracy at a relatively optimal value;

[0220] The Lyapunov stability law is used to design the approach rate. Since the flux value of the motor has been obtained, according to the equation of the motor, only the resistance and the sum of self-inductance and mutual inductance need to be calculated, and full rank is realized in this step.

[0221] S307: obtain the secondary parameter identification result (resistance and sum of inductance) using the adaptive adjustment rate;

[0222] S308: result output.

[0223] S30801: Calculate the mutual inductance of the motor:

[0224]

[0225] S30802: Calculate the new proportional coefficient k1:

[0226]

[0227] S30803: Set the condition for outputting the parameters:

[0228]

[0229] S30704: Determine whether the condition for outputting the parameters is met:

[0230] If the condition for outputting the parameters is met, the final result is output, i.e., the flux linkage and the self-inductance of the motor obtained by primary identification, and the resistance obtained by secondary identification, and the motor mutual inductance obtained by subtraction are output.

[0231] If the condition for outputting the parameters is not met, the obtained self-inductance and mutual inductance coefficient k1 are iterated again until the condition is met.

[0232] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent conditions of the claims are intended to be included in the present application. Any reference signs in the claims should not be considered as limiting the claims involved.

[0233] Furthermore, it should be understood that although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that those skilled in the art can understand.

Claims

1. A parameter identification method of a strongly coupled two-branch permanent magnet synchronous motor, characterized in that: The method comprises the following steps: S1: driving and controlling of the motor; S2: acquisition of motor information; S3: motor parameter identification; The S3 comprises the following steps: S301: establishing a mathematical model of the motor: （1） In formula (1): This indicates the output voltage of the d-axis of the current regulator; Vq represents an output voltage of the current regulator q-axis; Lsd represents the synchronous self-induction of the current regulator d-axis, which is the parameter to be identified; Lq represents the synchronous inductance of the current regulator q-axis, which is a parameter to be identified; Xd represents the mutual inductance of the current regulator d-axis after transformation, which is the parameter to be identified; represents the mutual inductance of the current regulator q-axis after transformation, which is the parameter to be identified; d / dt represents a differential symbol; id represents the measured current of the current regulator d-axis; represents the measured current of the current regulator q-axis; Indicates the rotor's electrical angular velocity; φpm represents the permanent magnet flux linkage of the motor, which is a parameter to be identified; R represents the resistance of the motor, which is the parameter to be identified; S302: Inductive relationship assumption: assuming that the motor is an ideal motor, i.e. a surface-mounted motor with uniform air gap, the mutual inductance M and the self-inductance L of the motor only include constant components, so the assumption that the mutual inductance M and the self-inductance L of the motor are linearly proportional is made, i.e. , are the self-inductance and mutual inductance coefficients; S303: Preliminary parameter identification is performed using the least square principle, and the following is substituted The mathematical model of the motor is rewritten as follows: （2） S304: performing iterative calculation: （3） S305: directly outputting the flux linkage as the final parameter identification result, and inputting the self-inductance, mutual inductance and resistance as initial values into the next step of parameter identification; S306: obtaining an adaptive adjustment rate of secondary parameter identification by using the Lyapunov method; The S306 comprises the following steps: S30601: establishing a mathematical model of the control object as a reference model of secondary identification: （4） It can be obtained that: （5） For simplicity of notation, set the ratio of resistance to inductance , then equation (5) is written in the form of the current state equation as follows: （6） S30602: establishing a system equation of the adjustable model: （7） In formula (7): an estimated value of an adjustable model representing the measured current of the d-axis of the current regulator; an estimated value of an adjustable model representing the measured current of the q-axis of the current regulator; an estimate of an adjustable model representing the self-induction of the electric machine; an estimate of an adjustable model representing the mutual inductance of the motor; an estimate of the ratio of resistance to inductance; S30603: defining: （8） （9） In formula (8)-(9): Both are presented as symbols, meaningless; S30604: selecting the difference between the d-axis current state variable of the current regulator and the q-axis current state variable of the current regulator: （10） S30605: differentiating the difference: （11） S30606: setting: （12） In formula (12): Both are presented as symbols, meaningless; It can be obtained that: （13） S30607: Constructing a scalar function : （14） S30608: finding non-zero vector scalar function Negative condition; S307: obtaining the result of secondary parameter identification by using the adaptive adjustment rate; S308: result output.

2. The parameter identification method of a strongly coupled two-leg permanent magnet synchronous motor according to claim 1, characterized in that: The S1 comprises the following steps: S101: driving the motor by using an isolated interleaved parallel topology structure, and separately supplying power to two branches; S102: controlling the motor by using a double-closed-loop strategy; S103: both branches of the motor are given a current of i d = 0. S104: adopting an SVPWM modulation algorithm; S105: adopting a control strategy of carrier phase shift by half a period.

3. The parameter identification method of a strongly coupled two-leg permanent magnet synchronous motor according to claim 1, characterized in that: The S2 comprises the following steps: S201: measuring the current of the motor; S202: current signal coordinate transformation; S203: acquiring the voltage of the motor; S204: acquiring the rotor position information of the motor by using a high-precision position sensor; S205: obtaining the speed signal of the motor by differentiating the speed signal in the current period and the last period in the controller.

4. The parameter identification method of a strongly coupled two-leg permanent magnet synchronous motor according to claim 3, characterized in that: The S201 comprises the following steps: S20101: measuring the A-phase and B-phase current signals of the two branches; S20102: calculating the C-phase current signal according to the current relationship of the star-connected motor.

5. The parameter identification method of a strongly coupled two-leg permanent magnet synchronous motor according to claim 3, characterized in that: The S202 comprises the following steps: S20201: calculating the actual values of the A-phase, B-phase and C-phase three-phase currents according to the sampling values; S20202: converting the three-phase current signals into current component signals in a two-phase static coordinate system by using Clark transformation; S20203: converting the current component signals in the two-phase static coordinate system into current signals in a synchronous rotating coordinate system by using Park transformation.

6. The method of claim 1, wherein: The S30608 comprises the following steps: S3060801: on scalar function Differential: （15） In formula (15): It can be obtained that: （16） Because: （17） Therefore This item is necessarily negative; S3060802: if: （18） then the non-zero vector scalar function negative definite; S3060803: expanding formula (18): （19） S3060804: solving the differential equation set: A non-zero vector scalar function The adaptive adjustment rule is necessarily negative （20） In formula (20): The estimated value of the adjustable model representing the resistance; the estimated value of the adjustable model representing the inductance, i.e. the inductance value obtained by the second identification; The initial value representing the self-inductance identification of the motor; The initial value represents the identification of mutual inductance between motors; The initial value represents the resistance identification; This represents the adjustment gain corresponding to the motor's self-inductance. represents the adjustment gain corresponding to the resistance.

7. The parameter identification method of a strongly coupled two-leg permanent magnet synchronous motor according to claim 6, characterized in that: The S308 comprises the following steps: S30801: calculating the mutual inductance of the motor: （21） S30802: calculating a new proportional coefficient k1: （22） S30803: setting the condition of parameter output: （23） S30704: determining whether the condition of parameter output is met: If the condition of parameter output is satisfied, the final result is output, i.e. the flux linkage and the self-inductance of the motor obtained by primary identification and the resistance obtained by secondary identification and the mutual inductance of the motor obtained by subtraction are output; If the condition of parameter output is not satisfied, the obtained self-inductance and mutual inductance coefficient k1 are iterated again until the condition is satisfied.

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