CN111313774A - An online parameter identification method of permanent magnet synchronous motor based on NLMS algorithm - Google Patents

Abstract

The invention discloses an on-line identification method for permanent magnet synchronous motor parameters based on NLMS algorithm. The nonlinear factor of the inverter is used to construct the discrete domain mathematical model of the permanent magnet synchronous motor control system. Combined with the identification principle of the Adaline neural network identification system, the discrete domain mathematical model is simplified, and the discrete domain mathematical model is obtained for iterative calculation of the motor stator resistance, inductance and rotor flux linkage. 3) The various vectors of the Adaline neural network identification system are calculated from the identification equations of the motor stator resistance, inductance, and rotor flux linkage, and a parameter identifier based on the NLMS algorithm is constructed to identify the motor stator resistance, inductance, rotor magnetic chain value. The invention takes into account the nonlinear factors of the inverter, and combines the adaptive neural network and the normalized least mean square algorithm to effectively identify the parameters of the permanent magnet synchronous motor.

Description

An online parameter identification method of permanent magnet synchronous motor based on NLMS algorithm

technical field

The invention relates to the technical field of motor control, in particular to an online parameter identification method of a permanent magnet synchronous motor based on an NLMS algorithm.

Background technique

Permanent magnet synchronous motor (PMSM) has the advantages of high specific power, energy saving and high efficiency, and precise control, and has been widely used in various fields. The high-performance control methods of PMSM mainly include vector control and direct torque control. In the control system of the permanent magnet synchronous motor, the parameters of the controller often need the motor parameters to assist the design (such as speed sensorless control, vector control optimal controller parameter design, etc.), so the control performance depends to a certain extent. depends on the accuracy of the motor parameters. During the operation of the motor, the parameters of the permanent magnet synchronous motor, such as stator resistance, stator inductance, and rotor flux linkage amplitude, will change with the changes of temperature, load and magnetic saturation. It is difficult to ensure the control performance of the motor if it is called a parameter design controller. Therefore, in order to adjust the controller parameters online and optimize the control performance of the motor according to the changes of the motor parameters during the normal operation of the motor, a lot of researches have been done on the online parameter identification method of the motor.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the deficiencies and shortcomings of the prior art, and proposes an online parameter identification method of permanent magnet synchronous motor based on NLMS algorithm (normalized least mean square algorithm). The identification equation is full rank, and the multi-parameter (stator resistance, inductance and rotor flux linkage and other parameters) of the permanent magnet synchronous motor can be accurately identified online through the NLMS algorithm.

In order to achieve the above object, the technical solution provided by the present invention is: a method for online parameter identification of permanent magnet synchronous motor based on NLMS algorithm, comprising the following steps:

1) Build the Adaline neural network identification system, and use the NLMS algorithm to update the weights of the Adaline neural network identification system;

2) Considering the nonlinear factors of the inverter, construct the discrete domain mathematical model of the permanent magnet synchronous motor control system, and combine the identification principle of the Adaline neural network identification system in step 1) to simplify the discrete domain mathematical model of the permanent magnet synchronous motor control system, The identification equations for iterative calculation of stator resistance, inductance and rotor flux linkage are obtained respectively;

3) Calculate the various vectors of the Adaline neural network identification system from the identification equations of the motor stator resistance, inductance, and rotor flux linkage obtained in step 2), and build a parameter identifier based on the NLMS algorithm to identify the motor stator resistance, inductance, rotor The value of the flux linkage.

In step 1), described Adaline neural network identification system is called adaptive neural network identification system, and its input and output relation are as follows:

y=WX=∑W _i X _i (1)

In the formula: X, y, W are the input, output and weight of the adaptive linear neural network identification system, respectively, Wi, X _i are the weight and the _ith component of the input; in the adaptive linear neural network identification In the system, the NLMS algorithm is used to iteratively update the weights, and the identification system equation is as follows:

In the formula: X(k), y(k), W(k) are the input vector, output vector and weight vector of the adaptive linear neural network identification system at the kth sampling time; d(k) is the kth sampling time The expected output vector of ; ε(k) is the deviation between the output of the adaptive linear neural network identification system and the expected output; W(k+1) is the weight vector at the k+1th sampling time; X ^T (k) is the input The transposed matrix of the signal X(k); η is the step size of the weight calculation, and the value range is 0<η<2; δ is to prevent the weight step size from changing because the inner product of the input vector X(k) is too small The small integer introduced because it is too large is taken as 0.0001; through continuous iterative calculation, each iteration is based on the target output value and the deviation ε(k) of the output of the Adaline neural network identification system, and the NLMS algorithm is used to update the weight vector W(k+ 1), and continue iterative calculation until ε(k) is less than the required value.

In step 2), the consideration of the nonlinear factor of the inverter refers to ignoring the magnetic saturation and iron loss of the surface-mounted permanent magnet synchronous motor; the identification principle of the Adaline neural network identification system refers to the separation of the motor stator resistance, The inductance and rotor flux linkage are iteratively calculated as the weight vector of the Adaline neural network identification system; the identification equations of the motor stator resistance, inductance and rotor flux linkage are obtained by the following steps:

2.1) The voltage equation of the permanent magnet synchronous motor in the d-q synchronous rotating coordinate system is:

In the formula: _ud and u _q are the d and q axis components of the stator voltage, respectively; id , i _q are the _d and q axis components of the stator current, respectively; R is the resistance of the stator winding; L _s is the motor inductance; ω is the The electrical angular velocity of the motor; Ψ _m is the amplitude of the rotor flux linkage;

2.2) When considering the nonlinear factors of the inverter, the discrete domain mathematical model of equation (3) is:

in,

In the _formula : V _dead is the equivalent compensation voltage considering the nonlinear factor of the inverter; k is the sampling times; θ is the rotor position; i _as , i _bs , and _ics are the three-phase currents of the motor; The 6th harmonic with mean 0; D _q (k) is the 6th harmonic with a DC component, and the function sgn(i) is defined as:

The stator resistance will not change greatly during the short period of time from the start of the motor to the stable speed of the motor, and the initial identification of the stator resistance of the motor can be realized by injecting the d-axis current;

2.3) When the motor speed is 0, that is, ω=0, the d-axis current is injected, and equation (4) is simplified as:

In the formula: u _d0 (k), u _q0 (k) and i _d0 (k), i _q0 (k) are the d and q axis voltages and currents obtained from the kth sampling of the motor in the static state, respectively; for formula (7) Transform and eliminate the error voltage to get:

u _d0 (k)D _q0 (k)-u _q0 (k)D _d (k)=Ri _d0 (k)D _q (k)-Ri _q0 (k)D _d (k) (8)

The stator resistance is initially identified by formula (8);

2.4) Under the control strategy of id = 0, _formula (4) is simplified as:

The first equation in equation (9) is averaged to get:

分别是u_d(k)、ω(k)、i_q(k)经过滤波后的直流分量；D_d(k)是均值为0的6次谐波，V_deadD_d(k)的直流分量为0；式(10)中不含误差电压且其未知参数只有L_s，将式(10)用作迭代计算电感的辨识方程；where:

are the filtered DC components of u _d (k), ω(k), and i _q (k) respectively; D _d (k) is the 6th harmonic with a mean value of 0, and the DC component of V _dead D _d (k) is 0; there is no error voltage in equation (10) and its unknown parameter is only L _s , and equation (10) is used as the identification equation for iterative calculation of inductance;

2.5) Transform the second equation in equation (7) and eliminate the error voltage to get:

u _d (k)D _q (k)-u _q (k)D _d (k)=-L _s ω(k)i _q (k)D _q (k)-Ri _q (k)D _d (k) -ψ _m ω(k)D _d (k) (11)

The stator resistance in equation (11) has been calculated by the current injection method when the motor is stationary, and the inductance L _s is also calculated by equation (10). Therefore, equation (11) can be used to iteratively calculate the rotor permanent magnet flux linkage ψ _m identification equation.

In step 3), each vector of the Adaline neural network identification system refers to an input vector, an output vector, an expected output vector and a weight vector respectively; a parameter identifier based on the NLMS algorithm is constructed to identify the motor stator resistance, The value of inductance, rotor flux linkage, including the following steps:

3.1) From the formula u _d0 (k)D _q0 (k)-u _q0 (k)D _d (k)=Ri _d0 (k)D _q (k)-Ri _q0 (k)D _d (k), the motor The preliminary identifier for the stator resistance R is:

In the formula: k represents the sampling times; u _d0 (k), u _q0 (k) and i _d0 (k), i _q0 (k) are the d and q axis voltages and currents obtained from the kth sampling when the motor is stationary; The D _d (k) function is the 6th harmonic with mean 0; D _q (k) is the 6th harmonic with a DC component;

After the motor is started, the resistance of the motor remains unchanged in a short-term running state; after the motor runs to a stable speed, the resistance value is updated and identified by the identified inductance L _s and rotor flux linkage Ψ _m . The motor based on NLMS algorithm The stator resistance identifier is:

In the formula: k represents the sampling times; X(k) is the input vector at the kth moment; _ud (k), u _q (k) and _id (k), i _q (k) are the kth sampling respectively Obtain the d and q axis voltages and currents; D _d (k) is the 6th harmonic with a mean value of 0; D _q (k) is the 6th harmonic with a DC component; O(k) is the kth sampling time The Adaline neural network identifies the system output vector; ε(k) is the error signal of the kth sampling; d(k) is the expected output vector of the kth sampling; ω(k) is the angular velocity of the kth sampling; Ψ _m is the rotor flux linkage; η is the step size of the weight calculation, and the value range is 0<η<2; δ is introduced to prevent the inner product of the input vector X(k) from being too small to cause the weight step size to change too much Small integer, take 0.0001; R(k) and R(k+1) are the identification values of the motor stator resistance R of the kth sampling and the k+1th sampling respectively;

得，基于NLMS算法的电感辨识器为3.2) By the formula

So, the inductance identifier based on NLMS algorithm is

分别是u_d(k)、ω(k)、i_q(k)经过滤波后的直流分量；In the formula: L _s (k) and L _s (k+1) are the inductance identification values of the kth sampling and the k+1th sampling, respectively;

are the filtered DC components of _ud (k), ω(k), and i _q (k), respectively;

3.3) By the formula u _d (k)D _q (k)-u _q (k)D _d (k)=-L _s ω(k)i _q (k)D _q (k)-Ri _q (k)D _d (k)-ψ _m ω(k)D _d (k), the rotor flux linkage identifier based on NLMS algorithm is:

In the formula: ψ _m (k) and ψ _m (k+1) are the identification values of the rotor flux linkage at the k-th sampling time and the k+1-th sampling time, respectively.

Compared with the prior art, the present invention has the following advantages and beneficial effects:

1. The NLMS algorithm used in the present invention normalizes the input signal according to its own ability, solves the problem of sudden change of system coefficients in the LMS (least mean square) algorithm due to the sudden change of the input signal, and effectively improves the performance of the LMS algorithm.

2. When the system has white noise and colored noise, the NLMS algorithm used in the present invention has better convergence speed and steady-state performance than the traditional LMS algorithm.

3. The present invention takes into account the nonlinear factors of the inverter when establishing the motor vector control equation, so that higher identification accuracy can be obtained in the online identification of motor parameters.

Description of drawings

FIG. 1 is a basic architecture diagram of the Adline neural network used in the present invention.

FIG. 2 is a system to be identified for verifying the validity of the NLMS algorithm according to the present invention.

Figure 3 shows the effect of the NLMS algorithm compared to the LMS algorithm.

Detailed ways

The present invention will be further described below in conjunction with specific embodiments.

The method for online parameter identification of permanent magnet synchronous motor based on NLMS algorithm provided in this embodiment is a motor vector control system based on three-phase inverter control, and uses Adaline neural network and NLMS algorithm to identify the parameters of the motor online, specifically including: The following steps:

1) Construct Adaline neural network identification system based on NLMS algorithm

The Adaline neural network identification system is also called an adaptive linear neural network identification system, and its network structure is shown in Figure 1, and its input and output relationship is as follows:

y=WX=∑W _i X _i (16)

Among them, X, y and W are the input, output and weight of the adaptive linear neural network identification system respectively, and Wi and X _i are the weight and the _ith component of the input, respectively. In this adaptive linear neural network identification system, the NLMS algorithm (normalized least mean square algorithm) is used to iteratively update the weights, and the update formula is as follows:

Among them, X(k), y(k), W(k) are the input, output and weights of the adaptive linear neural network identification system at the kth sampling time; d(k) is the expected output of the kth sampling; ε(k) is the deviation between the output of the adaptive linear neural network identification system and the expected output; W(k+1) is the k+1th sampling time; X ^T (k) is the transposed matrix of the input signal X(k) ; η is the step size of the weight calculation, and the value range is 0<η<2; δ is a small integer introduced to prevent the inner product of the input data vector X(k) from being too small to cause the weight step size to change too much, Generally take 0.0001. Through continuous iterative calculation, each iteration identifies the deviation ε(k) of the system output according to the target output value and the Adaline neural network, uses the NLMS algorithm to update the weight W(k+1), and continues the iterative calculation until ε( k) is less than the required value.

In order to verify the effectiveness of the NLMS algorithm, a simulation model is established using MATLAB, as shown in Figure 2. Among them, v ₁ (k) is a white noise signal with a mean value of 0 and a variance of 1. Through the AR autoregressive model G ₁ (z)=1+0.5z ^-1 , the input signal x(k) is obtained, and the model G to be identified is input. ₂ (z)=2+z ^-1 +0.5z ^-2 -0.2z ^-3 , where z is a variable in the z domain, v(k) is a white noise with a mean of 0 and a variance of 0.3 as the measurement noise, using LMS And NLMS two algorithms into the weight vector W identification. The identification results are shown in Figure 3. It can be seen that the NLMS algorithm has faster convergence speed and better steady-state performance than the LMS algorithm.

2) Considering the nonlinear factors of the inverter, construct the discrete domain mathematical model of the permanent magnet synchronous motor control system, and combine the identification principle of the Adaline neural network identification system in step 1) to simplify the discrete domain mathematical model of the permanent magnet synchronous motor control system, The identification equations used to iteratively calculate the stator resistance, inductance and rotor flux linkage of the motor are obtained.

The non-linear factor of the inverter is considered, which means that the magnetic saturation and iron loss of the surface-mounted permanent magnet synchronous motor are ignored. The weight vector of the Adaline neural network identification system is iteratively calculated; the identification equations of the motor stator resistance, inductance, and rotor flux linkage are obtained by the following steps:

2.1) The voltage equation of the permanent magnet synchronous motor in the d-q synchronous rotating coordinate system is:

where _ud and u _q are the d and q axis components of the stator voltage, respectively; id and i _q are the _d and q axis components of the stator current, respectively; R is the resistance of the stator winding; L _s is the motor inductance; ω is the motor The electrical angular velocity; Ψ _m is the rotor flux amplitude;

2.2) When considering the nonlinear factor of the inverter, the stable discrete domain equation of equation (16) is:

in

In the _formula , V _dead is the equivalent compensation voltage considering the nonlinear factors of the inverter; k is the sampling times; θ is the rotor position; i _as , i _bs , and _ics are the three-phase currents of the motor; The 6th harmonic with mean 0; D _q (k) is the 6th harmonic with a DC component, and the function sgn(i) is defined as

During the short period of time from the start of the motor to the stable speed of the motor, the stator resistance will not change greatly. Therefore, the initial identification of the stator resistance can be realized by injecting the d-axis current when the rotational speed is 0.

2.3) When the motor speed is 0 (ω=0), the d-axis current is injected, and equation (17) can be simplified as

In the formula, u _d0 (k), u _q0 (k) and i _d0 (k), i _q0 (k) are the d and q axis voltages and currents obtained by sampling under the static state of the motor, respectively. Transforming equation (20) and eliminating the error voltage can be obtained

u _d0 (k)D _q0 (k)-u _q0 (k)D _d (k)=Ri _d0 (k)D _q (k)-Ri _q0 (k)D _d (k) (23)

The stator resistance can be preliminarily identified by equation (21).

2.4) Under the control strategy of _id = 0, equation (17) can be simplified as

Averaging the first equation in Eq. (22) yields

分别是u_d(k)、ω(k)、i_q(k)经过滤波后的直流分量。由式(18)中表达式可知，D_d(k)是均值为0的6次谐波，所以V_deadD_d(k)的直流分量为0。因此，式(23)中不含误差电压且其未知参数只有L_s，因此将式(23)作为电感的辨识模型。In the formula,

are the filtered DC components of _ud (k), ω(k), and i _q (k), respectively. It can be known from the expression in equation (18) that D _d (k) is the 6th harmonic with an average value of 0, so the DC component of V _dead D _d (k) is 0. Therefore, there is no error voltage in equation (23) and its unknown parameter is only L _s , so equation (23) is used as the identification model of the inductance.

2.5) Transform the second equation in equation (22) and eliminate the error voltage to get

u _d (k)D _q (k)-u _q (k)D _d (k)=-L _s ω(k)i _q (k)D _q (k)-Ri _q (k)D _d (k) -ψ _m ω(k)D _d (k) (26)

The stator resistance in equation (24) has been identified by the way of current injection when the motor is stationary, and the inductance L _s is also obtained by the identification model of equation (23), so equation (24) can be used as the identification model of rotor permanent magnet flux linkage _ψm .

3) Calculate the various vectors of the Adaline neural network identification system from the identification equations of the motor stator resistance, inductance, and rotor flux linkage obtained in step 2), and build a parameter identifier based on the NLMS algorithm to identify the motor stator resistance, inductance, rotor The value of the flux linkage, the steps are as follows:

3.1) From equation (6), the preliminary identifier of stator resistance R is

Among them, k represents the number of samplings; u _d0 (k), u _q0 (k) and i _d0 (k), i _q0 (k) are the d and q axis voltages and currents obtained from the kth sampling under the static state of the motor, respectively; D The _d (k) function is the 6th harmonic with mean 0; D _q (k) is the 6th harmonic with a DC component.

After the motor is started, the resistance of the motor remains basically unchanged in a short-term running state. After the motor runs to a stable speed, the resistance value can be updated and identified by the identified inductance L _s and flux linkage Ψ _m . The resistance identifier is:

Among them, k represents the sampling times; X(k) is the input signal vector at the kth moment; _ud (k), u _q (k) and _id (k), i _q (k) are the kth sampling respectively Obtain the d and q axis voltages and currents; D _d (k) is the 6th harmonic with a mean value of 0; D _q (k) is the 6th harmonic with a DC component; O(k) is the kth sampling time The adaptive linear neural network identifies the output value of the system; ε(k) is the error signal of the kth sampling; d(k) is the expected output of the kth sampling; ω(k) is the angular velocity of the kth sampling; Ψ _m is the rotor flux linkage; η is the step size of the weight calculation, the value range is 0 < η <2; The small integer introduced is generally 0.0001. R(k) and R(k+1) are the identification values of the stator resistance R of the kth sampling and the k+1th sampling, respectively;

得，电感L_s的辨识器为3.2) By the formula

So, the identifier of the inductance L _s is

分别是u_d(k)、ω(k)、i_q(k)经过滤波后的直流分量。Among them, L _s (k) and L _s (k+1) are the motor inductance identification values of the kth sampling and the k+1th sampling, respectively;

are the filtered DC components of _ud (k), ω(k), and i _q (k), respectively.

3.3) By the formula u _d (k)D _q (k)-u _q (k)D _d (k)=-L _s ω(k)i _q (k)D _q (k)-Ri _q (k)D _d (k)-ψ _m ω(k)D _d (k), the identifier of rotor permanent magnet flux linkage ψ _m is:

Among them, ψ _m (k) and ψ _m (k+1) are the identification values of the rotor permanent magnet flux linkage at the kth sampling time and the k+1th sampling time, respectively.

In order to verify the feasibility of the proposed permanent magnet synchronous motor parameter online identification, a double closed-loop speed control system based on field-oriented control is established. The test platform mainly includes a control system with TMS320F28069M as the main control chip and a power drive system with Fuji IGBT power module 7MBP50VFN060-50 as the core. The nominal parameters of SPMSM are shown in Table 1.

Table 1-Nominal values of SPMSM parameters

parameter Numerical value parameter Numerical value Rated power (Kw) 1.1 Resistance (Ω) 2.875 Rated voltage (V) 220 Inductance (mH) 8.5 Rated speed (r/min) 1500 Magnetic Link (Wb) 0.175 Rated frequency(Hz) 100 Number of pole pairs 4

Based on the present invention, the motor parameters are identified online, and the specific parameter identification results are shown in Table 2.

Table 2-SPMSM parameter identification values

It can be seen from Table 2 that the accuracy of the resistance parameter identification based on the NLMS algorithm in the present invention is improved compared with the LMS algorithm, and the motor parameters can be effectively and accurately identified online.

The above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of implementation of the present invention. Therefore, any changes made according to the shape and principle of the present invention should be included within the protection scope of the present invention.

Claims (4)

1. a permanent magnet synchronous motor parameter online identification method based on NLMS algorithm, is characterized in that, comprises the following steps: 1) Build the Adaline neural network identification system, and use the NLMS algorithm to update the weights of the Adaline neural network identification system; 2) Considering the nonlinear factors of the inverter, construct the discrete domain mathematical model of the permanent magnet synchronous motor control system, and combine the identification principle of the Adaline neural network identification system in step 1) to simplify the discrete domain mathematical model of the permanent magnet synchronous motor control system, The identification equations for iterative calculation of stator resistance, inductance and rotor flux linkage are obtained respectively; 3) Calculate the various vectors of the Adaline neural network identification system from the identification equations of the motor stator resistance, inductance, and rotor flux linkage obtained in step 2), and build a parameter identifier based on the NLMS algorithm to identify the motor stator resistance, inductance, rotor The value of the flux linkage. 2. a kind of permanent magnet synchronous motor parameter online identification method based on NLMS algorithm according to claim 1, is characterized in that: in step 1), described Adaline neural network identification system is called adaptive neural network identification system , and its input and output relationship is as follows: y=WX=∑W _i X _i (1) In the formula: X, y, W are the input, output and weight of the adaptive linear neural network identification system, respectively, Wi, X _i are the weight and the _ith component of the input; in the adaptive linear neural network identification In the system, the NLMS algorithm is used to iteratively update the weights, and the identification system equation is as follows:

In the formula: X(k), y(k), W(k) are the input vector, output vector and weight vector of the adaptive linear neural network identification system at the kth sampling time; d(k) is the kth sampling time The expected output vector of ; ε(k) is the deviation between the output of the adaptive linear neural network identification system and the expected output; W(k+1) is the weight vector at the k+1th sampling time; X ^T (k) is the input The transposed matrix of the signal X(k); η is the step size of the weight calculation, and the value range is 0<η<2; δ is to prevent the weight step size from changing because the inner product of the input vector X(k) is too small The small integer introduced because it is too large is taken as 0.0001; through continuous iterative calculation, each iteration is based on the target output value and the deviation ε(k) of the output of the Adaline neural network identification system, and the NLMS algorithm is used to update the weight vector W(k+ 1), and continue iterative calculation until ε(k) is less than the required value. 3. a kind of permanent magnet synchronous motor parameter online identification method based on NLMS algorithm according to claim 1, is characterized in that: in step 2), described considering inverter nonlinear factor refers to ignoring surface-mounted permanent magnet. Magnetic saturation and iron loss of the magnetic synchronous motor; the identification principle of the Adaline neural network identification system refers to the iterative calculation of the motor stator resistance, inductance, and rotor flux linkage as the weight vector of the Adaline neural network identification system; the motor The identification equations of stator resistance, inductance and rotor flux linkage are obtained by the following steps: 2.1) The voltage equation of the permanent magnet synchronous motor in the d-q synchronous rotating coordinate system is:

In the formula: _ud and u _q are the d and q axis components of the stator voltage, respectively; id , i _q are the _d and q axis components of the stator current, respectively; R is the resistance of the stator winding; L _s is the motor inductance; ω is the The electrical angular velocity of the motor; Ψ _m is the amplitude of the rotor flux linkage; 2.2) When considering the nonlinear factors of the inverter, the discrete domain mathematical model of equation (3) is:

in,

In the _formula : V _dead is the equivalent compensation voltage considering the nonlinear factor of the inverter; k is the sampling times; θ is the rotor position; i _as , i _bs , and _ics are the three-phase currents of the motor; The 6th harmonic with mean 0; D _q (k) is the 6th harmonic with a DC component, and the function sgn(i) is defined as:

The stator resistance will not change greatly during the short period of time from the start of the motor to the stable speed of the motor, and the initial identification of the stator resistance of the motor can be realized by injecting the d-axis current; 2.3) When the motor speed is 0, that is, ω=0, the d-axis current is injected, and equation (4) is simplified as:

In the formula: u _d0 (k), u _q0 (k) and i _d0 (k), i _q0 (k) are the d and q axis voltages and currents obtained from the kth sampling of the motor in the static state, respectively; for formula (7) Transform and eliminate the error voltage to get: u _d0 (k)D _q0 (k)-u _q0 (k)D _d (k)=Ri _d0 (k)D _q (k)-Ri _q0 (k)D _d (k) (8) The stator resistance is initially identified by formula (8); 2.4) Under the control strategy of id = 0, _formula (4) is simplified as:

The first equation in equation (9) is averaged to get:

where:

are the filtered DC components of u _d (k), ω(k), and i _q (k) respectively; D _d (k) is the 6th harmonic with a mean value of 0, and the DC component of V _dead D _d (k) is 0; there is no error voltage in equation (10) and its unknown parameter is only L _s , and equation (10) is used as the identification equation for iterative calculation of inductance; 2.5) Transform the second equation in equation (7) and eliminate the error voltage to get: u _d (k)D _q (k)-u _q (k)D _d (k)=-L _s ω(k)i _q (k)D _q (k)-Ri _q (k)D _d (k) -ψ _m ω(k)D _d (k) (11) The stator resistance in equation (11) has been calculated by the current injection method when the motor is stationary, and the inductance L _s is also calculated by equation (10). Therefore, equation (11) can be used to iteratively calculate the rotor permanent magnet flux linkage ψ _m identification equation. 4. a kind of permanent magnet synchronous motor parameter online identification method based on NLMS algorithm according to claim 1, is characterized in that: in step 3) in, each vector of described Adaline neural network identification system refers to input vector respectively , output vector, expected output vector and weight vector; build a parameter identifier based on NLMS algorithm to identify the values of motor stator resistance, inductance, and rotor flux linkage, including the following steps: 3.1) From the formula u _d0 (k)D _q0 (k)-u _q0 (k)D _d (k)=Ri _d0 (k)D _q (k)-Ri _q0 (k)D _d (k), the motor The preliminary identifier for the stator resistance R is:

In the formula: k represents the sampling times; u _d0 (k), u _q0 (k) and i _d0 (k), i _q0 (k) are the d and q axis voltages and currents obtained from the kth sampling when the motor is stationary; The D _d (k) function is the 6th harmonic with mean 0; D _q (k) is the 6th harmonic with a DC component; After the motor is started, the resistance of the motor remains unchanged in a short-term running state; after the motor runs to a stable speed, the resistance value is updated and identified by the identified inductance L _s and rotor flux linkage Ψ _m . The motor based on NLMS algorithm The stator resistance identifier is:

In the formula: k represents the sampling times; X(k) is the input vector at the kth moment; _ud (k), u _q (k) and _id (k), i _q (k) are the kth sampling respectively Obtain the d and q axis voltages and currents; D _d (k) is the 6th harmonic with a mean value of 0; D _q (k) is the 6th harmonic with a DC component; O(k) is the kth sampling time The Adaline neural network identifies the system output vector; ε(k) is the error signal of the kth sampling; d(k) is the expected output vector of the kth sampling; ω(k) is the angular velocity of the kth sampling; Ψ _m is the rotor flux linkage; η is the step size of the weight calculation, and the value range is 0<η<2; δ is introduced to prevent the inner product of the input vector X(k) from being too small to cause the weight step size to change too much Small integer, take 0.0001; R(k) and R(k+1) are the identification values of the motor stator resistance R of the kth sampling and the k+1th sampling respectively; 3.2) By the formula

So, the inductance identifier based on NLMS algorithm is

In the formula: L _s (k) and L _s (k+1) are the inductance identification values of the kth sampling and the k+1th sampling, respectively;

are the filtered DC components of _ud (k), ω(k), and i _q (k), respectively; 3.3) By the formula u _d (k)D _q (k)-u _q (k)D _d (k)=-L _s ω(k)i _q (k)D _q (k)-Ri _q (k)D _d (k)-ψ _m ω(k)D _d (k), the rotor flux linkage identifier based on the NLMS algorithm is:

In the formula: ψ _m (k) and ψ _m (k+1) are the identification values of the rotor flux linkage at the k-th sampling time and the k+1-th sampling time, respectively.

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