Parameter identification method of strong-coupling double-branch permanent magnet synchronous motor

Abstract

The invention discloses a parameter identification method for a strong-coupling double-branch permanent magnet synchronous motor, and belongs to the technical field of permanent magnet synchronous motor control. The method comprises the following steps: driving and controlling the motor; acquiring motor information; the method comprises the following steps: establishing a mathematical model of a motor, assuming an inductance relation, carrying out preliminary parameter identification by using a least square principle, rewriting the mathematical model of the motor, carrying out iterative calculation, directly outputting a flux linkage as a final parameter identification result, and inputting self-inductance, mutual inductance and resistance as initial values to parameter identification in the next step; using a Lyapunov method to obtain a self-adaptive adjustment rate of secondary parameter identification; obtaining a secondary parameter identification result by using the self-adaptive adjustment rate; and outputting a result. According to the invention, through program execution of the digital signal processor, parameter identification without extra cost and energy consumption is realized. Operation is simple, maintenance is consistent with that of an original system, the motor state can be effectively monitored, the precision of a control system is improved, and the method is especially suitable for the fields of strong-coupling double-branch permanent magnet synchronous motor control and the like.

Description

Technical Field

[0001] The invention relates to a parameter identification method of a strongly coupled double-branch permanent magnet synchronous motor, belonging to the technical field of permanent magnet synchronous motor control. Background Art

[0002] As an innovative multi-winding structure motor, the strongly coupled dual-branch motor is equipped with two independent stator windings per phase. When one winding fails, the other winding can be immediately connected as a backup to achieve load reduction operation, thus meeting the strict requirements of high reliability of motor systems in key fields such as aerospace and national defense. In order to achieve efficient drive control, this type of motor is usually built based on an isolated interleaved parallel topology architecture, with each branch independently using i d = 0 control strategy, and driven by 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 running quality of the motor.

[0003] Although the strongly coupled dual-branch permanent magnet synchronous motor has 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. It can not only directly obtain the key parameters of the motor, but also is the premise for realizing high-precision control strategy. However, due to the particularity of the strongly coupled dual-branch motor, no mature and effective parameter identification method has been proposed, which to some extent limits the development of this type of motor control algorithm and its performance optimization in practical applications. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the present invention provides a parameter identification method for a strongly coupled dual-branch permanent magnet synchronous motor.

[0005] To achieve the above object, the present invention adopts the following technical solution: a parameter identification method for a strongly coupled dual-branch permanent magnet synchronous motor, the method comprising the following steps:

[0006] S1: Motor drive and control;

[0007] S101: The motor is driven by an isolated interleaved parallel topology, with two branches powered separately;

[0008] S102: Control the motor using a double closed-loop strategy;

[0009] S103: Both branches of the motor use i d =0 current setting;

[0010] S104: adopt SVPWM modulation algorithm;

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

[0012] S2: Acquisition of motor information;

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

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

[0015] S20102: According to the current relationship of the star-connected motor, the C phase current signal is calculated.

[0016] S202: current signal coordinate transformation;

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

[0018] S20202: Use Clark transformation to convert the three-phase current signal into a current component signal in a two-phase stationary coordinate system;

[0019] S20203: Use Park transformation to convert the current component signal in the two-phase stationary coordinate system into the current signal in the synchronous rotating coordinate system.

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

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

[0022] S205: The speed signal of the motor is obtained by performing differential calculation on the speed signal of the current cycle and the speed signal of the previous cycle 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 d-axis of the current regulator;

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

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

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

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

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

[0033] p represents the differential symbol;

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

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

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

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

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

[0039] S302: Inductance relationship assumption: Assume that the motor is an ideal motor, that is, a surface-mounted motor with uniform air gap, and the motor mutual inductance M and the motor self-inductance L only contain constant components, so it is assumed that the motor mutual inductance M and the motor self-inductance L are in linear proportion, that is, M=kL, k is the coefficient of self-inductance and mutual inductance;

[0040] S303: Perform preliminary parameter identification using the least squares principle, and substitute M=kL to rewrite the mathematical model of the motor as follows:

[0041]

[0042] S304: Perform iterative calculation:

[0043]

[0044] S305: directly outputting the flux linkage as the final parameter identification result, and inputting the self-inductance, mutual inductance and resistance as initial values ​​to the next step of parameter identification;

[0045] S306: Obtaining an adaptive adjustment rate of secondary parameter identification using the Lyapunov method;

[0046] S30601: Establish a mathematical model of the controlled object as a reference model for secondary identification:

[0047]

[0048] We can get:

[0049]

[0050] To simplify the notation, the ratio of resistance to inductance is set to Then equation (5) can be written as the current state equation as follows:

[0051]

[0052] S30602: Establishing the system equations of the adjustable model:

[0053]

[0054] In formula (7):

[0055] An estimate of the adjustable model representing the measured current of the d-axis of the current regulator;

[0056] An estimate of the adjustable model representing the measured current of the q-axis of the current regulator;

[0057] An estimate of the adjustable model representing the motor's self-inductance;

[0058] An estimate of an adjustable model representing the mutual inductance of the motor;

[0059] Represents an estimate of the ratio of resistance to inductance;

[0060] S30603: Definition:

[0061]

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

[0063] I, A, B, U, W, as well as They are all transfer symbols and have no meaning;

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

[0065]

[0066] S30605: Differentiate the difference:

[0067]

[0068] S30606: Settings:

[0069]

[0070] In formula (12):

[0071] a, b, φ T and s are both transfer symbols and have no meaning;

[0072] We can get:

[0073]

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

[0075]

[0076] S30608: Finding non-zero vector scalar functions Negative conditions.

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

[0078]

[0079] In formula (15):

[0080]

[0081] We can get:

[0082]

[0083] because:

[0084]

[0085] therefore This term must be negative;

[0086] S3060802: If:

[0087]

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

[0089] S3060803: Expanded formula (18):

[0090]

[0091] S3060804: Solving Systems of Differential Equations:

[0092]

[0093] We can get a non-zero vector scalar function The necessarily negatively definite adaptive regulation law is:

[0094]

[0095] In formula (20):

[0096] An estimate of the adjustable model representing the resistance;

[0097] Represents the estimated value of the adjustable model of inductance, that is, the inductance value obtained by secondary identification; L0 represents the initial value of the motor self-inductance identification;

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

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

[0100] K L Represents the regulation gain corresponding to the motor self-inductance;

[0101] K R Represents the adjustment gain corresponding to the resistor;

[0102] S307: Obtaining secondary parameter identification results using the adaptive adjustment rate;

[0103] S308: Output the results.

[0104] S30801: Calculate the mutual inductance of the motor:

[0105]

[0106] S30802: Calculate the new proportionality coefficient k1:

[0107]

[0108] S30803: Set the conditions for parameter output:

[0109]

[0110] S30704: Determine whether the parameter output condition is met:

[0111] If the parameter output conditions are met, the final result is output, that is, the flux linkage and motor self-inductance obtained by the first identification, the resistance obtained by the second identification, and the motor mutual inductance obtained by difference;

[0112] If the parameter output condition is not met, the obtained self-inductance and mutual inductance coefficient k1 will be iterated again until the condition is met.

[0113] Compared with the prior art, the present invention has the following beneficial effects:

[0114] The present invention realizes parameter identification without additional cost and energy consumption through the execution of digital signal processor program without changing the original control system structure. It is easy to operate and the maintenance is consistent with the original system. It can effectively monitor the motor state and improve the accuracy of the control system. It is particularly suitable for the fields of strong coupling double-branch permanent magnet synchronous motor control. By utilizing the special structure of the motor, combining its full rank and inductance and mutual inductance characteristics, simplifying the equation and ignoring the differential terms in the steady state, the operation speed and convergence of parameter identification are improved, providing an efficient and accurate solution for motor control. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 It is a topological block diagram on which the parameter identification algorithm of the present invention is based;

[0116] Figure 2 is a flow chart of the present invention;

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

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

[0119] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0120] A parameter identification method for a strongly coupled dual-branch permanent magnet synchronous motor is based on a traditional three-phase surface-mounted permanent magnet synchronous motor parameter identification algorithm. Based on the special structure of the strongly coupled dual-branch motor, a method of linearizing the coupled inductance is adopted to obtain the motor parameters by two-step identification. The method comprises the following steps:

[0121] S1: Motor drive and control;

[0122] S101: The motor is driven by an isolated interleaved parallel topology, with two branches powered separately;

[0123] S102: Control the motor using a double closed-loop strategy;

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

[0125] S104: adopt SVPWM modulation algorithm;

[0126] S105: adopting a control strategy of carrier phase shifting for half a cycle to reduce the sum of the dual-branch currents and the current harmonics of each branch.

[0127] S2: Acquisition of motor information, and then precise feedback control of the motor;

[0128] S201: measuring 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 (ie, the sum of the three-phase currents is zero), the C-phase current signal is calculated; and then the dual-branch three-phase current signal of the motor is obtained.

[0131] S202: current signal coordinate transformation;

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

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

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

[0135] S203: Obtaining the voltage of the motor: The voltage obtained by passing the current signal in the dq coordinate system through the PI regulator (current loop) is used as the voltage of the motor in the dq coordinate system;

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

[0137] S205: The speed signal of the motor is obtained by performing differential calculation on the speed signal of the current cycle and the speed signal of the previous cycle in the controller.

[0138] S3: Motor parameter identification.

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

[0140]

[0141] In formula (1):

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

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

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

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

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

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

[0148] p represents the differential symbol;

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

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

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

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

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

[0154] S302: Inductance relationship assumption: Since the coils of the two sets of windings of the strongly coupled dual-branch motor are placed in the same slot, the mutual inductance and self-inductance of the two sets of windings in the same phase are close. When performing initial parameter identification on the motor, it can be assumed that the motor is an ideal motor, that is, a surface-mounted motor with uniform air gap. The motor mutual inductance M and the motor self-inductance L only contain constant components. Therefore, it is assumed that the motor mutual inductance M and the motor self-inductance L are in linear proportion, that is, M=kL, k is the coefficient of self-inductance and mutual inductance, which can be obtained through finite element software;

[0155] Since the strongly coupled dual-branch motor of the present invention is a surface-mounted permanent magnet synchronous motor, the inductance of its d-axis and q-axis is the same, so L d =L q =L,Md =M q =M;

[0156] S303: The traditional permanent magnet synchronous motor has two equations and three parameters to be identified, so it will cause the problem of lack of rank. When using the least squares method for parameter identification, some parameters will not converge. The strongly coupled dual-branch motor has four equations and four parameters to be identified. By using the characteristics that the two branches of the motor can be powered separately, the bus voltage is different, and the motor parameters can achieve full rank. For parameter identification, the redundancy design of the dual branch has the advantage of full rank on the one hand, so that the parameters converge during parameter identification. On the other hand, the number of equations of the strongly coupled dual-branch motor is twice that of the ordinary motor. When constructing equations using the advantage of full rank, a large number of operations are brought, which is a challenge for the computing power of the digital signal processor. From the mathematical model of the motor in S301, it can be seen that the motor has four constrained voltage equations and four parameters to be identified, which has a full rank characteristic. According to the full rank characteristic, the dq axis voltage equation is rewritten in the form of the least squares method. The least squares principle is used for preliminary parameter identification. The present invention makes full use of the special structure of the strongly coupled dual-branch permanent magnet synchronous motor. While taking advantage of the full rank of its four equations, it also uses the characteristics that its inductance and mutual inductance are close and proportional to simplify the equations. At the same time, because the parameter identification is performed in a steady state, the equations can be further simplified by ignoring the differential terms. The algorithm performs parameter identification with a high computation speed and the calculation results can converge quickly. Substituting M=kL, the mathematical model of the motor is rewritten as follows:

[0157]

[0158] S304: Select three of the four voltage equations and M=kL to form four equations. After obtaining the motor speed, dq axis voltage and current information, use the recursive least squares method to perform iterative calculation:

[0159]

[0160] S305: This method can be used to achieve parameter identification while satisfying full rank. When the ratio of the self-inductance and mutual inductance is accurate, the inductance, resistance and flux of the motor can be accurately obtained. However, since resistance is more easily affected by the proportional relationship, while flux and inductance are almost unaffected by the assumed nonlinear factors, further parameter identification is required. The flux obtained in this step 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 step of parameter identification. The obtained resistance and inductance parameters are used as the initial values ​​of the secondary parameter identification, and then further parameter identification is used to obtain more accurate resistance and inductance.

[0161] S306: Obtaining an adaptive adjustment rate of secondary parameter identification using the Lyapunov method;

[0162] S30601: Establish a mathematical model of the controlled object as a reference model for secondary identification:

[0163]

[0164] We can get:

[0165]

[0166] To simplify the notation, the ratio of resistance to inductance is set to Then equation (5) can be written as the current state equation as follows:

[0167]

[0168] S30602: Establishing the system equations of the adjustable model:

[0169]

[0170] In formula (7):

[0171] An estimate of the adjustable model representing the measured current of the d-axis of the current regulator;

[0172] An estimate of the adjustable model representing the measured current of the q-axis of the current regulator;

[0173] An estimate of the adjustable model representing the motor's self-inductance;

[0174] An estimate of an adjustable model representing the mutual inductance of the motor;

[0175] Represents an estimate of the ratio of resistance to inductance;

[0176] S30603: Definition:

[0177]

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

[0179] I, A, B, U, W, as well as They are all transfer symbols and have no meaning;

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

[0181]

[0182] S30605: Differentiate the difference:

[0183]

[0184] S30606: Settings:

[0185]

[0186] In formula (12):

[0187] a, b, φ T and s are both transfer symbols and have no meaning;

[0188] We can get:

[0189]

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

[0191]

[0192] If such a scalar function exists, it is positive definite, and its derivative is negative definite, and when ‖e‖→∞, then V(e,t)→∞, which can ensure that it is asymptotically stable in a large range. This is the criterion for judging whether the parameter identification has converged using Lyapunov's second method.

[0193] S30608: Finding non-zero vector scalar functions Negative conditions.

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

[0195]

[0196] In formula (15):

[0197]

[0198] We can get:

[0199]

[0200] because:

[0201]

[0202] therefore This term must be negative;

[0203] S3060802: If:

[0204]

[0205] Then the non-zero vector scalar function Negative definite, thus satisfying Lyapunov's second method, the difference e will tend to the zero vector consistently and asymptotically stably over a large range, that is, at this time the adjustable model will tend to the reference model, and the parameters to be identified will converge to the true value;

[0206] S3060803: Expanded formula (18):

[0207]

[0208] S3060804: Solving Systems of Differential Equations:

[0209]

[0210] We can get a non-zero vector scalar function The necessarily negatively definite adaptive regulation law is:

[0211]

[0212] In formula (20):

[0213] An estimate of the adjustable model representing the resistance;

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

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

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

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

[0218] K L Represents the regulation gain corresponding to the motor self-inductance;

[0219] K R Represents the adjustment gain corresponding to the resistance. Proper selection of the adjustment gain can make the convergence speed and accuracy at a better value;

[0220] The approach rate is designed using Lyapunov's stability law. Since the magnetic flux value of the motor has been obtained, only the resistance and the sum of the self-inductance and mutual inductance need to be calculated according to the motor equation. Full rank is achieved in this step.

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

[0222] S308: Output the results.

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

[0224]

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

[0226]

[0227] S30803: Set the conditions for parameter output:

[0228]

[0229] S30704: Determine whether the parameter output condition is met:

[0230] If the parameter output conditions are met, the final result is output, that is, the flux linkage and motor self-inductance obtained by the first identification, the resistance obtained by the second identification, and the motor mutual inductance obtained by difference;

[0231] If the parameter output condition is not met, the obtained self-inductance and mutual inductance coefficient k1 will be iterated again until the condition is met.

[0232] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and range of equivalents of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0233] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A parameter identification method for a strongly coupled dual-branch permanent magnet synchronous motor, characterized in that: The method comprises the following steps: S1: Motor drive and control; S2: Acquisition of motor information; S3: Motor parameter identification.

2. The method for parameter identification of a strongly coupled dual-branch permanent magnet synchronous motor according to claim 1, characterized in that: The S1 comprises the following steps: S101: The motor is driven by an isolated interleaved parallel topology, with two branches powered separately; S102: Control the motor using a double closed-loop strategy; S103: Both branches of the motor use i d =0 current setting; S104: adopt SVPWM modulation algorithm; S105: adopting a control strategy of shifting the carrier phase by half a cycle.

3. The parameter identification method of a strongly coupled dual-branch permanent magnet synchronous motor according to claim 1 is characterized in that: The S2 comprises the following steps: S201: measuring the current of the motor; S202: current signal coordinate transformation; S203: Obtaining the voltage of the motor; S204: Acquire rotor position information of the motor through a precision position sensor; S205: The speed signal of the motor is obtained by performing differential calculation on the speed signal of the current cycle and the speed signal of the previous cycle in the controller.

4. The method for parameter identification of a strongly coupled dual-branch permanent magnet synchronous motor according to claim 1, characterized in that: The S201 includes the following steps: S20101: Measure the A-phase and B-phase current signals of the two branches; S20102: According to the current relationship of the star-connected motor, the C phase current signal is calculated.

5. The method for parameter identification of a strongly coupled dual-branch permanent magnet synchronous motor according to claim 1, characterized in that: The S202 comprises the following steps: S20201: Calculate the actual values ​​of the three-phase currents of phase A, phase B and phase C according to the sampled values; S20202: Use Clark transformation to convert the three-phase current signal into a current component signal in a two-phase stationary coordinate system; S20203: Use Park transformation to convert the current component signal in the two-phase stationary coordinate system into the current signal in the synchronous rotating coordinate system.

6. The method for parameter identification of a strongly coupled dual-branch permanent magnet synchronous motor according to claim 1, characterized in that: The S3 comprises the following steps: S301: Establishing the mathematical model of the motor: In formula (1): u d represents the output voltage of the d-axis of the current regulator; u q represents the output voltage of the q-axis of the current regulator; L d represents the synchronous self-inductance of the d-axis of the current regulator, which is the parameter to be identified; L q represents the synchronous self-inductance of the q-axis of the current regulator, which is the parameter to be identified; M d represents the mutual inductance of the current regulator after d-axis transformation, which is the parameter to be identified; M q represents the mutual inductance of the current regulator after q-axis transformation, which is the parameter to be identified; p represents the differential symbol; i d represents the measured current of the d-axis of the current regulator; i q represents the measured current of the q-axis of the current regulator; ω e represents the rotor electrical angular velocity; ψ f Represents the permanent magnet flux of the motor, which is the parameter to be identified; R represents the resistance of the motor, which is the parameter to be identified; S302: Inductance relationship assumption: Assume that the motor is an ideal motor, that is, a surface-mounted motor with uniform air gap, and the motor mutual inductance M and the motor self-inductance L only contain constant components, so it is assumed that the motor mutual inductance M and the motor self-inductance L are in linear proportion, that is, M=kL, k is the coefficient of self-inductance and mutual inductance; S303: Perform preliminary parameter identification using the least squares principle, and substitute M=kL to rewrite the mathematical model of the motor as follows: S304: Perform iterative calculation: S305: directly outputting the flux linkage as the final parameter identification result, and inputting the self-inductance, mutual inductance and resistance as initial values ​​to the next step of parameter identification; S306: Obtaining an adaptive adjustment rate of secondary parameter identification using the Lyapunov method; S307: Obtaining secondary parameter identification results using the adaptive adjustment rate; S308: Output the results.

7. The method for parameter identification of a strongly coupled dual-branch permanent magnet synchronous motor according to claim 6, characterized in that: The S306 includes the following steps: S30601: Establish a mathematical model of the controlled object as a reference model for secondary identification: We can get: To simplify the notation, the ratio of resistance to inductance is set to Then equation (5) can be written as the current state equation as follows: S30602: Establishing the system equations of the adjustable model: In formula (7): An estimate of the adjustable model representing the measured current of the d-axis of the current regulator; An estimate of the adjustable model representing the measured current of the q-axis of the current regulator; An estimate of the adjustable model representing the motor's self-inductance; An estimate of an adjustable model representing the mutual inductance of the motor; Represents an estimate of the ratio of resistance to inductance; S30603: Definition: In formula (8)-(9): I, A, B, U, W, as well as They are all transfer symbols and have no meaning; S30604: Select the current state variable of the d-axis current of the current regulator and the current state variable of the q-axis current of the current regulator and make a difference: S30605: Differentiate the difference: S30606: Settings: In formula (12): a, b, φ T and s are both transfer symbols and have no meaning; We can get: S30607: Construct scalar function V(e,t): S30608: Finding non-zero vector scalar functions Negative conditions.

8. The method for parameter identification of a strongly coupled dual-branch permanent magnet synchronous motor according to claim 6, characterized in that: The S30608 includes the following steps: S3060801: Differentiate the scalar function V(e,t): In formula (15): We can get: because: therefore This term must be negative; S3060802: If: Then the non-zero vector scalar function Negative determination; S3060803: Expanded formula (18): S3060804: Solving Systems of Differential Equations: We can get a non-zero vector scalar function The necessarily negatively definite adaptive regulation law is: In formula (20): An estimate 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 secondary identification; L0 represents the initial value of the motor self-inductance identification; M0 represents the initial value of motor mutual inductance identification; R0 represents the initial value of resistance identification; K L Represents the regulation gain corresponding to the motor self-inductance; K R Represents the adjustment gain corresponding to the resistor.

9. The method for parameter identification of a strongly coupled dual-branch permanent magnet synchronous motor according to claim 8, characterized in that: The S308 includes the following steps: S30801: Calculate the mutual inductance of the motor: S30802: Calculate the new proportionality factor k1: S30803: Set the conditions for parameter output: S30704: Determine whether the parameter output condition is met: If the parameter output conditions are met, the final result is output, that is, the flux linkage and motor self-inductance obtained by the first identification, the resistance obtained by the second identification, and the motor mutual inductance obtained by difference; If the parameter output condition is not met, the obtained self-inductance and mutual inductance coefficient k1 will be iterated again until the condition is met.

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