A method and system for full-rank identification of electromagnetic parameters of surface-mounted permanent magnet synchronous motors

Abstract

The present invention discloses a method and system for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor, which belongs to the field of motor control technology. The identification method comprises: using a rotor position sliding mode observer to perform position sensorless control on the SPMSM, and simultaneously using the rotor position sliding mode observer to calculate the motor q-axis back electromotive force estimation value and the rotor electrical angular speed estimation value; using the q-axis back electromotive force estimation value and the rotor electrical angular speed estimation value as the rotor flux linkage ψ f The input quantity of the estimation algorithm is used to solve the estimated value of the rotor permanent magnet flux; the estimated value of the rotor permanent magnet flux and various state quantities of the motor are used as the input of the nonlinear Kalman filter algorithm, and the winding resistance and winding inductance of the SPMSM are fully identified to obtain the estimated value of the winding resistance and winding inductance; the estimated value of the winding resistance and winding inductance are used as the basis for the self-adjustment of the parameters of the sliding mode rotor position observer, and the parameters are adjusted to realize the online estimation of all electromagnetic parameters of the SPMSM.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor control, and in particular relates to a method and system for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor. Background Art

[0002] When implementing high-performance servo control of a permanent magnet synchronous motor (SPMSM), the actual electromagnetic parameters of the SPMSM need to be used to guide the design of the controller and observer. The electromagnetic parameters mainly include: winding resistance Rs, quadrature and direct axis inductance L q 、L d and rotor permanent magnet flux ψ f When the working conditions of the SPMSM change, the electromagnetic parameters of the SPMSM will change. For example, when the temperature of the SPMSM increases during operation, the stator winding resistance R s and AC and DC inductance L q 、L d The error will increase, leading to a mismatch between the controller or observer parameters designed offline and the electromagnetic parameters of the SPMSM during operation, ultimately affecting the control performance and stability of the servo system. Therefore, performing online identification of the SPMSM electromagnetic parameters during system operation to achieve real-time updates of the controller and observer parameters is one of the effective means to ensure efficient and stable operation of the system under different operating conditions.

[0003] The universal mathematical model of a permanent magnet synchronous motor is a second-order function, meaning that parameter identification using this model can only accurately identify two electromagnetic parameters simultaneously. Consequently, using traditional methods to perform online simultaneous identification of multiple electromagnetic parameters (greater than two) for a SPPMSM often results in an underranked equation, making the identification results prone to false convergence. Furthermore, parameter identification can easily fall into local optima when system operating conditions change. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention aims to provide a method and system for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor comprises the following steps:

[0007] The rotor position sliding mode observer is used to perform position sensorless control of the SPMSM. The estimated value of the motor's q-axis back electromotive force and the estimated value of the rotor's electrical angular speed are also calculated using the rotor position sliding mode observer.

[0008] The estimated value of q-axis back electromotive force and the estimated value of rotor electrical angular speed are used as the rotor flux ψ fEstimating the input of the algorithm and solving the estimated value of the rotor permanent magnet flux;

[0009] The estimated value of the rotor permanent magnet flux and the various state variables of the motor are used as the input of the nonlinear Kalman filter algorithm to perform full-rank identification on the winding resistance and winding inductance of the SPMSM to obtain the estimated value of the winding resistance and winding inductance;

[0010] The estimated values of winding resistance and winding inductance are used as the basis for self-adjustment of the parameters of the sliding mode rotor position observer. Parameter adjustment is performed to achieve online estimation of all electromagnetic parameters of the SPMSM.

[0011] Furthermore, the rotor position sliding mode observer includes a back electromotive force estimation module and a rotor position estimation module.

[0012] Furthermore, the steps of estimating the q-axis back electromotive force estimation value and the rotor electrical angular speed estimation value include:

[0013] S11, the back EMF estimation value obtained by the back EMF estimation module can be expressed as follows after filtering by a low-pass filter:

[0014]

[0015] Where, and are the estimated back electromotive force values of the motor d and q axes respectively; and are the observed values of the motor d-axis and q-axis currents respectively; is the estimated value of the rotor electrical angular velocity; i d 、i q are the motor d and q axis currents respectively; k sild is the sliding mode gain coefficient; f is the rotor flux.

[0016] S12, after the d-axis back electromotive force estimation value is subtracted from the given value 0, it is input into a PI link as the input value of the rotor position estimation module to obtain the rotor speed information, and then the rotor position information is obtained through the first-order integration link.

[0017] Furthermore, the transfer function of the rotor position estimation module is:

[0018]

[0019] Where K pll-p , K pll-i are the proportional coefficient and integral coefficient of the PI regulator respectively; E q is the motor q-axis back electromotive force; ω n is the expected bandwidth of the closed-loop system.

[0020] Furthermore, the calculation expression of the estimated value of the rotor permanent magnet flux is:

[0021]

[0022] Where, is the estimated value of q-axis back electromotive force, is the estimated value of the rotor electrical angular velocity, is the estimated value of the rotor permanent magnet flux.

[0023] Furthermore, the nonlinear Kalman filter algorithm expression is:

[0024]

[0025] in, t k time, t k-1 time, t k-1 Time to t k The estimated state at the moment; U k-1 is the system deterministic control; T s is the sampling time; Φ k / k-1 t k-1 Time to t k The transfer matrix at time t; H k is the measurement matrix; Q is the non-negative variance matrix of the system noise sequence; R is the positive definite variance matrix of the measurement noise; P k / k-1 is the state variable mean square error estimation matrix; P k is the state variable mean square error update matrix; K k is the gain array; Z k is the measurement equation.

[0026] Furthermore, the full-rank identification means that after the motor state variables are input into the SPMSM voltage equation, the voltage equation contains only two unknowns: the winding resistance Rs and the winding inductance Ls, and the voltage equation is a second-order system, that is, the second-order equation is full-rank when solving two unknowns;

[0027] Among them, the SPMSM voltage equation is:

[0028]

[0029] Where u d 、u q 、i d 、i q are the motor dq axis voltage and current respectively; L d 、L q are the dq axis inductances respectively; R s is the winding resistance; ω e is the rotor electrical angular velocity; E qis the motor q-axis back electromotive force.

[0030] Furthermore, the self-adjustment of the sliding mode rotor position observer parameters refers to: substituting the estimated value of the winding resistance and the estimated value of the winding inductance into the design function of the sliding mode rotor position observer to ensure that the design parameters of the sliding mode rotor position observer are always consistent with the online operation parameters of the SPMSM.

[0031] Furthermore, the design function is:

[0032]

[0033]

[0034] Where, L s is the inductance of the surface-mount permanent magnet synchronous motor. In an ideal surface-mount permanent magnet synchronous motor, the d-axis and q-axis inductances are equal, that is, L s =L d =L q ; V d 、V q They are the dq-axis back electromotive force of the surface-mounted permanent magnet synchronous motor.

[0035] A full-rank identification system for electromagnetic parameters of a surface-mounted permanent magnet synchronous motor, comprising:

[0036] Potential and speed estimation module: The rotor position sliding mode observer is used to perform position sensorless control of the SPMSM. At the same time, the rotor position sliding mode observer is used to calculate the estimated value of the motor's q-axis back electromotive force and the estimated value of the rotor's electrical angular speed.

[0037] Flux estimation module: The q-axis back electromotive force estimation value and the rotor electrical angle speed estimation value are used as the rotor flux ψ f Estimating the input of the algorithm and solving the estimated value of the rotor permanent magnet flux;

[0038] Resistance and inductance estimation module: The rotor permanent magnet flux estimation value and various motor state variables are used as inputs to the nonlinear Kalman filter algorithm to perform full-rank identification on the SPMSM winding resistance and winding inductance to obtain the winding resistance and winding inductance estimation values;

[0039] And, parameter adjustment module: the estimated value of winding resistance and winding inductance are used as the basis for self-adjustment of the parameters of the sliding mode rotor position observer, and the parameters are adjusted to realize the online estimation of all electromagnetic parameters of the SPMSM.

[0040] Beneficial effects of the present invention:

[0041] The identification method proposed in the present invention has accurate identification results for the full electromagnetic parameters of SPMSM; it has good tracking performance for the changes in the electromagnetic parameters of SPMSM; the sliding mode parameter self-adjustment algorithm used can not only improve the identification accuracy and tracking performance of the electromagnetic parameters of SPMSM, but also improve the accuracy of the rotor position sliding mode observer in identifying the rotor position, thereby improving the control performance of the servo system for SPMSM; under harsh working environments such as high temperature and strong electromagnetic interference, the present invention can still fully rank identify the full electromagnetic parameters of the motor with high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 This is the principle diagram of full rank identification of electromagnetic parameters;

[0044] Figure 2 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] like Figure 1 As shown, a method for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor includes the following steps:

[0047] S1, using the rotor position sliding mode observer to perform position sensorless control on the SPMSM, the control block diagram is as follows Figure 2 As shown, the rotor position sliding mode observer is used to calculate the estimated value of the motor q-axis back electromotive force and the estimated rotor electrical angular velocity

[0048] like Figure 2 As shown, the rotor position sliding mode observer includes a back electromotive force estimation module and a rotor position estimation module;

[0049] q-axis back EMF estimation and the estimated rotor electrical angular velocity The estimation steps include:

[0050] S11, the back EMF estimation value obtained by the back EMF estimation module can be expressed as follows after filtering by a low-pass filter:

[0051]

[0052] Where, and are the estimated back electromotive force values of the motor d and q axes respectively; is the estimated value of the rotor electrical angular velocity; i d 、i q are the motor d and q axis currents respectively; k sild is the sliding mode gain coefficient; f is the rotor flux.

[0053] S12, after the d-axis back electromotive force estimation value is subtracted from the given value 0, it is input into a PI link as the input value of the rotor position estimation module to obtain the rotor speed information, and then the rotor position information is obtained through a first-order integration link;

[0054] Among them, the transfer function of the rotor position estimation module can be expressed as:

[0055]

[0056] Where K pll-p , K pll-i are the proportional coefficient and integral coefficient of the PI regulator respectively; E q is the motor q-axis back electromotive force; ω n is the expected bandwidth of the closed-loop system.

[0057] S2, estimated value of q-axis back electromotive force and the estimated rotor electrical angular velocity As the rotor flux ψ f Estimation algorithm input, solve the estimated value of rotor permanent magnet flux

[0058] The solution steps include:

[0059] S21, estimate the rotor flux;

[0060] The rotor flux estimation algorithm expression is:

[0061]

[0062] S22, the estimated result of the above equation is low-pass filtered to obtain the estimated value of the rotor permanent magnet flux The expression is:

[0063]

[0064] Where, is the estimated value of q-axis back electromotive force, is the estimated value of the rotor electrical angular velocity, is the estimated value of the rotor permanent magnet flux.

[0065] S3, the estimated value of the rotor permanent magnet flux As well as the motor state variables as the input of the nonlinear Kalman filter algorithm, the winding resistance R s and winding inductance L s Perform full rank identification to obtain estimated values of winding resistance and winding inductance;

[0066] The motor state quantities include: dq axis voltage u d and u q , dq axis current i d and i q and the estimated rotor electrical speed

[0067] The nonlinear Kalman filter algorithm expression is:

[0068]

[0069] in, t k time, t k-1 time, t k-1 Time to t k The estimated state at the moment; U k-1 is the system deterministic control; T s is the sampling time; Φ k / k-1 t k-1 Time to t k The transfer matrix at time t; H k is the measurement matrix; Q is the non-negative variance matrix of the system noise sequence; R is the positive definite variance matrix of the measurement noise; P k / k-1 is the state variable mean square error estimation matrix; P k is the state variable mean square error update matrix; K k is the gain array; Z k is the measurement equation.

[0070] Applying the above formula to the mathematical model of SPMSM, we can get the differential of the state variable Linearized equation of state Transfer matrix Φ k / k-1 and measurement matrix The expressions are:

[0071]

[0072]

[0073]

[0074]

[0075] Where a = R s / (2L s ); b=1 / L s ; t k-1 The estimated value at time a; t k-1 The estimated value at time b; t k-1 Estimated values of the motor's d and q axis currents at time u dk-1 、u qk-1 t k-1 Motor d and q axis voltage at the moment; ω ek-1 t k-1 The electrical angular velocity of the rotor at the moment; ψ fk-1 Rotor flux; T s is the sampling time.

[0076] The initial matrix P0 of the nonlinear Kalman filter algorithm, the non-negative variance matrix Q of the system noise sequence, and the positive definite variance matrix R of the measurement noise are:

[0077]

[0078] The full-rank identification means that after the motor state variables are input into the SPMSM voltage equation, the voltage equation contains only two unknowns: the winding resistance Rs and the winding inductance Ls. The voltage equation is a second-order system, that is, the second-order equation is full-rank when solving two unknowns.

[0079] Among them, the SPMSM voltage equation is:

[0080]

[0081] Where u d 、u q 、i d 、i q are the motor dq axis voltage and current respectively; L d 、L q are the dq axis inductances respectively; R s is the winding resistance; ω e is the rotor electrical angular velocity; E q is the motor q-axis back electromotive force.

[0082] S4, the estimated value of winding resistance and winding inductance estimates As the basis for the self-adjustment of the sliding mode rotor position observer parameters, the sliding mode rotor position observer parameters are adjusted in real time to achieve online estimation of all electromagnetic parameters of the SPMSM;

[0083] The self-adjustment of the sliding mode rotor position observer parameters refers to substituting the estimated winding resistance and winding inductance obtained by S3 into the design function of the sliding mode rotor position observer to ensure that the design parameters of the sliding mode rotor position observer are always consistent with the online operation parameters of the SPMSM.

[0084] The design function is:

[0085]

[0086]

[0087] Where, L s is the inductance of the surface-mount permanent magnet synchronous motor. In an ideal surface-mount permanent magnet synchronous motor, the d-axis and q-axis inductances are equal, that is, L s =L d =L q ; V d 、V q They are the dq-axis back electromotive force of the surface-mounted permanent magnet synchronous motor.

[0088] SPMSM electromagnetic full parameters include: winding resistance R s , winding inductance L s and rotor permanent magnet flux ψ f .

[0089] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0090] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor, characterized in that: The following steps are involved: The rotor position sliding mode observer is used to perform position sensorless control of the SPMSM. The estimated value of the motor's q-axis back electromotive force and the estimated value of the rotor's electrical angular speed are also calculated using the rotor position sliding mode observer. The estimated value of q-axis back electromotive force and the estimated value of rotor electrical angular speed are used as the rotor flux ψ f Estimating the input of the algorithm and solving the estimated value of the rotor permanent magnet flux; The estimated value of the rotor permanent magnet flux and the various state variables of the motor are used as the input of the nonlinear Kalman filter algorithm to perform full-rank identification on the winding resistance and winding inductance of the SPMSM to obtain the estimated value of the winding resistance and winding inductance; The estimated values of winding resistance and winding inductance are used as the basis for self-adjustment of the parameters of the sliding mode rotor position observer, and the parameters are adjusted to achieve online estimation of all electromagnetic parameters of the SPMSM. The full-rank identification means that after the motor state variables are input into the SPMSM voltage equation, the voltage equation contains only two unknowns: the winding resistance Rs and the winding inductance Ls. The voltage equation is a second-order system, that is, the second-order equation is full-rank when solving two unknowns. Among them, the SPMSM voltage equation is: Where u d 、u q 、i d 、i q are the motor dq axis voltage and current respectively; L d 、L q are the dq axis inductances respectively; R s is the winding resistance; ω e is the rotor electrical angular velocity; E q is the motor q-axis back electromotive force; The self-adjustment of the sliding mode rotor position observer parameters refers to: substituting the estimated value of the winding resistance and the estimated value of the winding inductance into the design function of the sliding mode rotor position observer to ensure that the design parameters of the sliding mode rotor position observer are always consistent with the online operation parameters of the SPMSM; The design function is: Where, L s is the inductance of the surface-mount permanent magnet synchronous motor. In an ideal surface-mount permanent magnet synchronous motor, the d-axis and q-axis inductances are equal, that is, L s =L d =L q ; V d 、V q They are the dq-axis back electromotive force of the surface-mounted permanent magnet synchronous motor.

2. A method for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor according to claim 1, characterized in that: The rotor position sliding mode observer includes a back electromotive force estimation module and a rotor position estimation module.

3. The method for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor according to claim 2, characterized in that: The steps for estimating the q-axis back electromotive force estimate and the rotor electrical angular speed estimate include: S11, the back EMF estimation value obtained by the back EMF estimation module can be expressed as follows after filtering by a low-pass filter: Where, and are the estimated back electromotive force values of the motor d and q axes respectively; and are the observed values of the motor d-axis and q-axis currents respectively; is the estimated value of the rotor electrical angular velocity; i d 、i q are the motor d and q axis currents respectively; k sild is the sliding mode gain coefficient; f is the rotor flux; S12, after the d-axis back electromotive force estimation value is subtracted from the given value 0, it is input into a PI link as the input value of the rotor position estimation module to obtain the rotor speed information, and then the rotor position information is obtained through the first-order integration link.

4. The method for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor according to claim 3, characterized in that: The transfer function of the rotor position estimation module is: Where K pll-p , K pll-i are the proportional coefficient and integral coefficient of the PI regulator respectively; E q is the motor q-axis back electromotive force; ω n is the expected bandwidth of the closed-loop system.

5. The method for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor according to claim 1, characterized in that: The calculation expression of the estimated value of the rotor permanent magnet flux is: Where, is the estimated value of q-axis back electromotive force, is the estimated value of the rotor electrical angular velocity, is the estimated value of the rotor permanent magnet flux.

6. The method for full-rank identification of electromagnetic parameters of a surface-mounted permanent magnet synchronous motor according to claim 1, characterized in that: The nonlinear Kalman filter algorithm expression is: in, t k time, t k-1 time, t k-1 Time to t k The estimated state at the moment; U k-1 is the system deterministic control; T s is the sampling time; Φ k / k-1 t k-1 Time to t k The transfer matrix at time t; H k is the measurement matrix; Q is the non-negative variance matrix of the system noise sequence; R is the positive definite variance matrix of the measurement noise; P k / k-1 is the state variable mean square error estimation matrix; P k is the state variable mean square error update matrix; K k is the gain array; Z k is the measurement equation.

7. A full-rank identification system for electromagnetic parameters of a surface-mounted permanent magnet synchronous motor, characterized in that: include: Potential and speed estimation module: The rotor position sliding mode observer is used to perform position sensorless control of the SPMSM. At the same time, the rotor position sliding mode observer is used to calculate the estimated value of the motor's q-axis back electromotive force and the estimated value of the rotor's electrical angular speed. Flux estimation module: The q-axis back electromotive force estimation value and the rotor electrical angle speed estimation value are used as the rotor flux ψ f Estimating the input of the algorithm and solving the estimated value of the rotor permanent magnet flux; Resistance and inductance estimation module: The rotor permanent magnet flux estimation value and various motor state variables are used as inputs to the nonlinear Kalman filter algorithm to perform full-rank identification on the SPMSM winding resistance and winding inductance to obtain the winding resistance and winding inductance estimation values; And, parameter adjustment module: the estimated values of winding resistance and winding inductance are used as the basis for self-adjustment of the parameters of the sliding mode rotor position observer, and the parameters are adjusted to achieve online estimation of all electromagnetic parameters of the SPMSM; The full-rank identification means that after the motor state variables are input into the SPMSM voltage equation, the voltage equation contains only two unknowns: the winding resistance Rs and the winding inductance Ls. The voltage equation is a second-order system, that is, the second-order equation is full-rank when solving two unknowns. Among them, the SPMSM voltage equation is: Where u d 、u q 、i d 、i q are the motor dq axis voltage and current respectively; L d 、L q are the dq axis inductances respectively; R s is the winding resistance; ω e is the rotor electrical angular velocity; E q is the motor q-axis back electromotive force; The self-adjustment of the sliding mode rotor position observer parameters refers to: substituting the estimated value of the winding resistance and the estimated value of the winding inductance into the design function of the sliding mode rotor position observer to ensure that the design parameters of the sliding mode rotor position observer are always consistent with the online operation parameters of the SPMSM; The design function is: Where, L s is the inductance of the surface-mount permanent magnet synchronous motor. In an ideal surface-mount permanent magnet synchronous motor, the d-axis and q-axis inductances are equal, that is, L s =L d =L q ; V d 、V q They are the dq-axis back electromotive force of the surface-mounted permanent magnet synchronous motor.

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