CN111313774A - Permanent magnet synchronous motor parameter online identification method based on NLMS algorithm - Google Patents
Permanent magnet synchronous motor parameter online identification method based on NLMS algorithm Download PDFInfo
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Abstract
The invention discloses a permanent magnet synchronous motor parameter online identification method based on NLMS algorithm, comprising the following steps: 1) constructing an Adaline neural network identification system, and updating the weight of the Adaline neural network identification system by adopting an NLMS algorithm; 2) considering nonlinear factors of an inverter, constructing a discrete domain mathematical model of a permanent magnet synchronous motor control system, and combining an identification principle of an Adaline neural network identification system to simplify the discrete domain mathematical model to obtain identification equations respectively used for iterative calculation of motor stator resistance, inductance and rotor flux linkage; 3) and calculating each vector of the Adaline neural network identification system according to the identification equation of the stator resistance, the inductance and the rotor flux linkage of the motor, and constructing a parameter identifier based on an NLMS algorithm for identifying the values of the stator resistance, the inductance and the rotor flux linkage of the motor. The invention considers the nonlinear factor of the inverter, combines the self-adaptive neural network and the normalized least mean square algorithm, and can effectively identify the parameters of the permanent magnet synchronous motor.
Description
Technical Field
The invention relates to the technical field of motor control, in particular to a permanent magnet synchronous motor parameter online identification method based on an NLMS algorithm.
Background
The Permanent Magnet Synchronous Motor (PMSM) has the advantages of high specific power, energy conservation, high efficiency, accurate control and the like, and is widely applied to various fields. The high-performance control method of the PMSM mainly includes vector control, direct torque control, and the like. In a control system of a permanent magnet synchronous motor, motor parameters are often required for auxiliary design of the parameters of the controller (such as no-speed-sensor control, vector control optimal controller parameter design and the like), so the quality of the control performance depends on the accuracy of the motor parameters to a certain extent. In the running process of the motor, parameters such as stator resistance, stator inductance and rotor flux linkage amplitude of the permanent magnet synchronous motor can change along with the change of temperature, load and magnetic saturation degree, and if the controller is designed according to the nominal parameters of the motor in different running states, the control performance of the motor is difficult to ensure. Therefore, in order to adjust the parameters of the controller and optimize the control performance of the motor on line according to the change of the parameters of the motor in the normal operation process of the motor, the online parameter identification method of the motor is researched greatly.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provides a permanent magnet synchronous motor parameter online identification method based on an NLMS (normalized least mean square) algorithm.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: an online identification method for parameters of a permanent magnet synchronous motor based on an NLMS algorithm comprises the following steps:
1) constructing an Adaline neural network identification system, and updating the weight of the Adaline neural network identification system by adopting an NLMS algorithm;
2) considering nonlinear factors of an inverter, constructing a discrete domain mathematical model of a permanent magnet synchronous motor control system, and combining the identification principle of an Adaline neural network identification system in the step 1), simplifying the discrete domain mathematical model of the permanent magnet synchronous motor control system to obtain identification equations respectively used for iterative calculation of motor stator resistance, inductance and rotor flux linkage;
3) calculating each vector of the Adaline neural network identification system according to the identification equation of the motor stator resistance, the inductance and the rotor flux linkage obtained in the step 2), and constructing a parameter identifier based on an NLMS algorithm for identifying the values of the motor stator resistance, the inductance and the rotor flux linkage.
In step 1), the Adaline neural network identification system is called an adaptive neural network identification system, and the input and output relationships thereof are as follows:
y＝WX＝∑WiXi(1)
in the formula: x, y and W are respectively the input, output and weight of the adaptive linear neural network identification system, Wi、XiThe weight and the ith component of the input are respectively; in the adaptive linear neural network identification system, an NLMS algorithm is adopted for iterative updating of weights, and the identification system equation is as follows:
in the formula: x (k), y (k), W (k) are input vectors, output vectors and weight vectors of the adaptive linear neural network identification system at the kth sampling moment; d (k) is the desired output vector for the kth sample; ε (k) is the deviation of the adaptive linear neural network identification system output from the expected output; w (k +1) is a weight vector of the (k +1) th sampling moment; xT(k) The method comprises the steps of taking a transpose matrix of an input signal X (k), η as a step length of weight calculation, wherein the value range is 0 & lt η & lt 2, delta is a small integer introduced for preventing the change of the weight step length caused by the fact that the inner product of an input vector X (k) is too small, taking 0.0001, and through continuous iterative calculation, updating a weight vector W (k +1) by adopting an NLMS algorithm according to a target output value and a deviation epsilon (k) output by an Adaline neural network identification system in each iteration until the epsilon (k) is smaller than a required value.
In the step 2), the consideration of the nonlinear factors of the inverter means that the magnetic saturation and the iron loss of the surface-mounted permanent magnet synchronous motor are ignored; the identification principle of the Adaline neural network identification system is that the resistance, the inductance and the rotor flux linkage of a motor stator are respectively used as weight vectors of the Adaline neural network identification system for iterative computation; the identification equation of the motor stator resistance, the inductance and the rotor flux linkage is obtained by the following steps:
2.1) the voltage equation of the permanent magnet synchronous motor under the d-q synchronous rotating coordinate system is as follows:
in the formula: u. ofd、uqD and q-axis components of the stator voltage, respectively; i.e. id、iqD and q-axis components of the stator current, respectively; r is the resistance of the stator winding; l issIs a motor inductor; omega is the electrical angular velocity of the motor; ΨmIs the rotor flux linkage amplitude;
2.2) when the nonlinear factor of the inverter is considered, the discrete domain mathematical model of the formula (3) is:
wherein,
in the formula: vdeadAn equivalent compensation voltage for considering the nonlinear factor of the inverter; k is the sampling frequency; θ is the rotor position; i.e. ias、ibs、icsThe three-phase current of the motor is obtained; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is the 6 th harmonic containing the dc component, and the function sgn (i) is defined as:
in the short time period from the starting of the motor to the stable rotating speed, the resistance of the stator does not change greatly, and the primary identification of the resistance of the stator of the motor can be realized by injecting d-axis current;
2.3) when the motor speed is 0, that is, ω is 0, d-axis current is injected, and equation (4) is simplified as follows:
in the formula: u. ofd0(k)、uq0(k) And id0(k)、iq0(k) D-axis voltage and q-axis voltage and current are obtained for the kth sampling in the static state of the motor respectively; and (3) converting the equation (7) and eliminating the error voltage to obtain:
ud0(k)Dq0(k)-uq0(k)Dd(k)＝Rid0(k)Dq(k)-Riq0(k)Dd(k) (8)
the stator resistance is preliminarily identified through a formula (8);
2.4) in idUnder the control strategy of 0, equation (4) is simplified as follows:
averaging the first equation in equation (9) yields:
in the formula:
are each ud(k)、ω(k)、iq(k) A filtered direct current component; dd(k) Is the 6 th harmonic, V, with a mean value of 0deadDd(k) Has a direct current component of 0; the formula (10) does not contain error voltage and has only L as unknown parametersUsing the formula (10) as an identification equation for iteratively calculating the inductance;
2.5) transforming the second equation in the formula (7), and eliminating the error voltage to obtain:
ud(k)Dq(k)-uq(k)Dd(k)＝-Lsω(k)iq(k)Dq(k)-Riq(k)Dd(k)-ψmω(k)Dd(k) (11)
the stator resistance in equation (11) has been calculated by means of current injection when the motor is at rest, and the inductance LsAlso calculated by equation (10), therefore, equation (11) can be used as an iterative calculation of the rotor permanent magnet flux linkage ψmThe identification equation of (1).
In step 3), each vector of the Adaline neural network identification system respectively refers to an input vector, an output vector, an expected output vector and a weight vector; the method comprises the following steps of constructing a parameter identifier based on an NLMS algorithm, and identifying values of motor stator resistance, inductance and rotor flux linkage, wherein the parameter identifier comprises the following steps:
3.1) from the formula ud0(k)Dq0(k)-uq0(k)Dd(k)＝Rid0(k)Dq(k)-Riq0(k)Dd(k) The preliminary identifier of the motor stator resistor R is obtained as follows:
in the formula: k represents the number of samples; u. ofd0(k)、uq0(k) And id0(k)、iq0(k) D-axis voltage and q-axis voltage and current are obtained for the kth sampling in the static state of the motor respectively; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is the 6 th harmonic containing a dc component;
after the motor is started, the resistance of the motor is kept unchanged in a short-time running state; after the motor runs to a stable rotating speed, the inductance L is obtained by identificationsAnd rotor flux linkage ΨmThe resistance value is updated and identified, and the motor stator resistance identifier based on the NLMS algorithm is as follows:
in the formula: k represents the number of samples; x (k) is the input vector at time k; u. ofd(k)、uq(k) And id(k)、iq(k) D-axis voltage and q-axis voltage and current are obtained for the kth sampling respectively; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is the 6 th harmonic containing a dc component; o (k) is an Adaline neural network identification system output vector at the kth sampling moment; ε (k) is the error signal of the kth sample; d (k) is the desired output vector for the kth sample; ω (k) is the angular velocity of the kth sample; Ψmη is a rotor flux linkage, wherein the value range is 0 & lt η & lt 2 for the step length of weight calculation, delta is a small integer introduced for preventing the change of the weight step length caused by the over-small inner product of an input vector X (k), and 0.0001 is taken, and R (k), (k) and R (k +1) are respectively the identification values of the motor stator resistance R of the kth sampling and the kth +1 sampling;
3.2) preparation of
The inductor identifier based on NLMS algorithm is
In the formula: l iss(k) And Ls(k +1) are inductance identification values of the kth sampling and the kth +1 sampling respectively;
are each ud(k)、ω(k)、iq(k) A filtered direct current component;
3.3) from the formula ud(k)Dq(k)-uq(k)Dd(k)＝-Lsω(k)iq(k)Dq(k)-Riq(k)Dd(k)-ψmω(k)Dd(k) The rotor flux linkage identifier based on the NLMS algorithm is as follows:
in the formula: psim(k) And psim(k +1) is the kth sampling instant and the (k +1) th sampling, respectivelyAnd identifying the rotor flux linkage at the sample time.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the NLMS algorithm used by the invention carries out normalization processing on the input signal according to the self capacity, solves the problem of system coefficient mutation caused by input signal mutation in the LMS (least mean square) algorithm, and effectively improves the performance of the LMS algorithm.
2. When white noise and colored noise exist in the system, compared with the traditional LMS algorithm, the NLMS algorithm used by the invention has better convergence rate and steady-state performance.
3. According to the method, when a motor vector control equation is established, the nonlinear factors of the inverter are considered, so that higher identification precision can be obtained in the aspect of online identification of motor parameters.
Drawings
FIG. 1 is a basic architecture diagram of an Adline neural network used in the present invention.
Fig. 2 is a system to be identified for verifying the validity of NLMS algorithm according to the present invention.
Fig. 3 is an effect graph of NLMS algorithm versus LMS algorithm.
Detailed Description
The present invention will be further described with reference to the following specific examples.
The method for identifying the parameters of the permanent magnet synchronous motor on line based on the NLMS algorithm is based on a motor vector control system controlled by a three-phase inverter, adopts an Adaline neural network and the NLMS algorithm to identify the parameters of the motor on line, and specifically comprises the following steps:
1) adaline neural network identification system constructed based on NLMS algorithm
The Adaline neural network identification system is also called as an adaptive linear neural network identification system, the network structure of the Adaline neural network identification system is shown in fig. 1, and the input and output relationship of the Adaline neural network identification system is as follows:
y＝WX＝∑WiXi(16)
wherein X, y and W are respectively the input, output and weight of the adaptive linear neural network identification system,Wi、Xithe weight and the ith component of the input, respectively. In the adaptive linear neural network identification system, an NLMS algorithm (normalized least mean square algorithm) is adopted for iterative updating of the weight, and an updating formula is as follows:
wherein, X (k), y (k), W (k) are the input, output and weight of the adaptive linear neural network identification system at the kth sampling moment; d (k) is the desired output of the kth sample; ε (k) is the deviation of the adaptive linear neural network identification system output from the expected output; w (k +1) is the (k +1) th sampling moment; xT(k) The method comprises the steps of calculating a transposed matrix of an input signal X (k), η, wherein the value range of step length of weight calculation is 0 & lt η & lt 2, delta is a small integer introduced for preventing the change of the weight step length caused by the over-small inner product of an input data vector X (k), and is generally 0.0001. through continuous iterative calculation, the weight W (k +1) is updated by adopting an NLMS algorithm according to a target output value and the deviation epsilon (k) output by an Adaline neural network identification system in each iteration, and the iterative calculation is continued until the epsilon (k) is smaller than a required value.
To verify the effectiveness of the NLMS algorithm, a simulation model was built using MATLAB, as shown in fig. 2. Wherein v is1(k) White noise signal with mean 0 and variance 1 is processed by AR autoregressive model G1(z)＝1+0.5z-1Obtaining an input signal x (k) and inputting the model G to be identified2(z)＝2+z-1+0.5z-2-0.2z-3Wherein z is a variable of a z domain, v (k) is white noise with a mean value of 0 and a variance of 0.3, and the weight vector W is identified by adopting two algorithms of LMS and NLMS. The identification result is shown in fig. 3, and it can be seen that the NLMS algorithm has a faster convergence rate and a better steady-state performance than the LMS algorithm.
2) Considering nonlinear factors of the inverter, constructing a discrete domain mathematical model of the permanent magnet synchronous motor control system, and combining the identification principle of the Adaline neural network identification system in the step 1), simplifying the discrete domain mathematical model of the permanent magnet synchronous motor control system to obtain identification equations respectively used for iterative calculation of motor stator resistance, inductance and rotor flux linkage.
Considering the nonlinear factors of the inverter means neglecting the magnetic saturation and iron loss of the surface-mounted permanent magnet synchronous motor; the identification principle of the Adaline neural network identification system is that the resistance, the inductance and the rotor flux linkage of a motor stator are respectively used as weight vectors of the Adaline neural network identification system for iterative computation; the identification equation of the motor stator resistance, the inductance and the rotor flux linkage is obtained by the following steps:
2.1) the voltage equation of the permanent magnet synchronous motor under the d-q synchronous rotating coordinate system is as follows:
in the formula ud、uqD and q-axis components of the stator voltage, respectively; i.e. id、iqD and q-axis components of the stator current, respectively; r is the resistance of the stator winding; l issIs a motor inductor; omega is the electrical angular velocity of the motor; ΨmIs the rotor flux linkage amplitude;
2.2) when considering the inverter nonlinearity factor, the stable discrete domain equation of equation (16) is:
wherein
In the formula, VdeadAn equivalent compensation voltage for considering the nonlinear factor of the inverter; k is the sampling frequency; θ is the rotor position; i.e. ias、ibs、icsThe three-phase current of the motor is obtained; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is a 6 th harmonic containing a DC component, and the function sgn (i) is defined as
The resistance of the stator can not change greatly in a short time period from the starting of the motor to the stable rotating speed. Therefore, the stator resistance can be preliminarily identified by injecting d-axis current when the rotating speed is 0.
2.3) when the motor rotation speed is 0 (ω ═ 0), d-axis current is injected, and equation (17) can be simplified to
In the formula ud0(k)、uq0(k) And id0(k)、iq0(k) And respectively sampling the static state of the motor to obtain d-axis voltage and q-axis voltage and current. By converting equation (20), the error voltage can be eliminated
ud0(k)Dq0(k)-uq0(k)Dd(k)＝Rid0(k)Dq(k)-Riq0(k)Dd(k) (23)
The stator resistance can be preliminarily identified by equation (21).
2.4) in idUnder the control strategy of 0, the formula (17) can be simplified to
Averaging the first equation in equation (22) yields
In the formula,
are each ud(k)、ω(k)、iq(k) The filtered dc component. From the expression in the formula (18), Dd(k) Is the 6 th harmonic with an average value of 0, so VdeadDd(k) The dc component of (a) is 0. Therefore, equation (23) does not contain an error voltage and its unknown parameter is only LsThus will formula(23) As an identification model of the inductance.
2.5) transforming the second equation in the formula (22) to eliminate the error voltage
ud(k)Dq(k)-uq(k)Dd(k)＝-Lsω(k)iq(k)Dq(k)-Riq(k)Dd(k)-ψmω(k)Dd(k) (26)
The stator resistance in equation (24) is identified by current injection when the motor is at rest, and the inductance LsAlso, the magnetic flux is obtained by identifying the model by equation (23), and thus equation (24) can be used as the rotor permanent magnet flux linkage ψmThe identification model of (1).
3) Calculating each vector of the Adaline neural network identification system according to the identification equation of the motor stator resistance, the inductance and the rotor flux linkage obtained in the step 2), and constructing a parameter identifier based on an NLMS algorithm for identifying the values of the motor stator resistance, the inductance and the rotor flux linkage, wherein the steps are as follows:
3.1) from equation (6) the preliminary identifier of the stator resistance R is
Wherein k represents the number of samples; u. ofd0(k)、uq0(k) And id0(k)、iq0(k) D-axis voltage and q-axis voltage and current are obtained for the kth sampling in the static state of the motor respectively; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is the 6 th harmonic containing the dc component.
After the motor is started, the resistance of the motor is basically kept unchanged in a short-time running state. The inductance L can be obtained by identification after the motor runs to a stable rotating speedsAnd the magnetic linkage ΨmThe resistance value is updated and identified, and the identifier of the resistance is as follows:
wherein k represents the number of samples; x (k) is the input signal vector at time kAn amount; u. ofd(k)、uq(k) And id(k)、iq(k) D-axis voltage and q-axis voltage and current are obtained for the kth sampling respectively; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is the 6 th harmonic containing a dc component; o (k) is the output value of the adaptive linear neural network identification system at the kth sampling moment; ε (k) is the error signal of the kth sample; d (k) is the desired output of the kth sample; ω (k) is the angular velocity of the kth sample; Ψmη is a rotor flux linkage, wherein the value range is 0 & lt η & lt 2 for the step length of weight calculation, delta is a small integer introduced for preventing the change of the weight step length caused by the over-small inner product of an input data vector X (k), and 0.0001. R (k) and R (k +1) are respectively the identification values of the stator resistance R of the kth sampling and the kth +1 sampling;
3.2) preparation of
To obtain an inductance LsThe identifier is
Wherein L iss(k) And Ls(k +1) are the motor inductance identification values of the kth sampling and the kth +1 sampling respectively;
are each ud(k)、ω(k)、iq(k) The filtered dc component.
3.3) from the formula ud(k)Dq(k)-uq(k)Dd(k)＝-Lsω(k)iq(k)Dq(k)-Riq(k)Dd(k)-ψmω(k)Dd(k) To obtain the magnetic linkage psi of the permanent magnet of the rotormThe identifier is as follows:
wherein psim(k) And psim(k +1) is the k-th sampling time, respectivelyAnd (4) identifying the flux linkage of the rotor permanent magnet at the moment and the (k +1) th sampling moment.
In order to verify the feasibility of online identification of the parameters of the permanent magnet synchronous motor, a double closed-loop speed regulating system based on magnetic field directional control is established. The test platform mainly comprises a control system taking TMS320F28069M as a main control chip and a power driving system taking a Fuji IGBT power module 7MBP50VFN060-50 as a core, wherein the SPMSM nominal parameters are shown in Table 1.
TABLE 1-SPMSM parameter nominal values
| Parameter(s) | Numerical value | Parameter(s) | Numerical value |
| Rated power (Kw) | 1.1 | Resistance (omega) | 2.875 |
| Rated voltage (V) | 220 | Inductor (mH) | 8.5 |
| Rated speed (r/min) | 1500 | Magnetic linkage (Wb) | 0.175 |
| Rated frequency (Hz) | 100 | Number of pole pairs | 4 |
The motor parameters are identified on line based on the method, and the specific parameter identification result is shown in a table 2.
TABLE 2-SPMSM parameter identification values
As can be seen from Table 2, the resistance parameter identification precision of the motor parameter identification method based on the NLMS algorithm is improved compared with that of the LMS algorithm, and the motor parameters can be effectively identified accurately on line.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.
Claims (4)
1. An online identification method for parameters of a permanent magnet synchronous motor based on an NLMS algorithm is characterized by comprising the following steps:
1) constructing an Adaline neural network identification system, and updating the weight of the Adaline neural network identification system by adopting an NLMS algorithm;
2) considering nonlinear factors of an inverter, constructing a discrete domain mathematical model of a permanent magnet synchronous motor control system, and combining the identification principle of an Adaline neural network identification system in the step 1), simplifying the discrete domain mathematical model of the permanent magnet synchronous motor control system to obtain identification equations respectively used for iterative calculation of motor stator resistance, inductance and rotor flux linkage;
3) calculating each vector of the Adaline neural network identification system according to the identification equation of the motor stator resistance, the inductance and the rotor flux linkage obtained in the step 2), and constructing a parameter identifier based on an NLMS algorithm for identifying the values of the motor stator resistance, the inductance and the rotor flux linkage.
2. The permanent magnet synchronous motor parameter online identification method based on the NLMS algorithm according to claim 1, characterized in that: in step 1), the Adaline neural network identification system is called an adaptive neural network identification system, and the input and output relationships thereof are as follows:
y＝WX＝∑WiXi(1)
in the formula: x, y and W are respectively the input, output and weight of the adaptive linear neural network identification system, Wi、XiThe weight and the ith component of the input are respectively; in the adaptive linear neural network identification system, an NLMS algorithm is adopted for iterative updating of weights, and the identification system equation is as follows:
in the formula: x (k), y (k), W (k) are input vectors, output vectors and weight vectors of the adaptive linear neural network identification system at the kth sampling moment; d (k) is the desired output vector for the kth sample; ε (k) is the deviation of the adaptive linear neural network identification system output from the expected output; w (k +1) is a weight vector of the (k +1) th sampling moment; xT(k) The method comprises the steps of taking a transpose matrix of an input signal X (k), η as a step length of weight calculation, wherein the value range is 0 & lt η & lt 2, delta is a small integer introduced for preventing the change of the weight step length caused by the fact that the inner product of an input vector X (k) is too small, taking 0.0001, and through continuous iterative calculation, updating a weight vector W (k +1) by adopting an NLMS algorithm according to a target output value and a deviation epsilon (k) output by an Adaline neural network identification system in each iteration until the epsilon (k) is smaller than a required value.
3. The permanent magnet synchronous motor parameter online identification method based on the NLMS algorithm according to claim 1, characterized in that: in the step 2), the consideration of the nonlinear factors of the inverter means that the magnetic saturation and the iron loss of the surface-mounted permanent magnet synchronous motor are ignored; the identification principle of the Adaline neural network identification system is that the resistance, the inductance and the rotor flux linkage of a motor stator are respectively used as weight vectors of the Adaline neural network identification system for iterative computation; the identification equation of the motor stator resistance, the inductance and the rotor flux linkage is obtained by the following steps:
2.1) the voltage equation of the permanent magnet synchronous motor under the d-q synchronous rotating coordinate system is as follows:
in the formula: u. ofd、uqD and q-axis components of the stator voltage, respectively; i.e. id、iqD and q-axis components of the stator current, respectively; r is the resistance of the stator winding; l issIs a motor inductor; omega is the electrical angular velocity of the motor; ΨmIs the rotor flux linkage amplitude;
2.2) when the nonlinear factor of the inverter is considered, the discrete domain mathematical model of the formula (3) is:
wherein,
in the formula: vdeadAn equivalent compensation voltage for considering the nonlinear factor of the inverter; k is the sampling frequency; θ is the rotor position; i.e. ias、ibs、icsThe three-phase current of the motor is obtained; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is the 6 th harmonic containing the dc component, and the function sgn (i) is defined as:
in the short time period from the starting of the motor to the stable rotating speed, the resistance of the stator does not change greatly, and the primary identification of the resistance of the stator of the motor can be realized by injecting d-axis current;
2.3) when the motor speed is 0, that is, ω is 0, d-axis current is injected, and equation (4) is simplified as follows:
in the formula: u. ofd0(k)、uq0(k) And id0(k)、iq0(k) D-axis voltage and q-axis voltage and current are obtained for the kth sampling in the static state of the motor respectively; and (3) converting the equation (7) and eliminating the error voltage to obtain:
ud0(k)Dq0(k)-uq0(k)Dd(k)＝Rid0(k)Dq(k)-Riq0(k)Dd(k) (8)
the stator resistance is preliminarily identified through a formula (8);
2.4) in idUnder the control strategy of 0, equation (4) is simplified as follows:
averaging the first equation in equation (9) yields:
in the formula:
are each ud(k)、ω(k)、iq(k) A filtered direct current component; dd(k) Is the 6 th harmonic, V, with a mean value of 0deadDd(k) Has a direct current component of 0; the formula (10) does not contain error voltage and has only L as unknown parametersUsing the formula (10) as an identification equation for iteratively calculating the inductance;
2.5) transforming the second equation in the formula (7), and eliminating the error voltage to obtain:
ud(k)Dq(k)-uq(k)Dd(k)＝-Lsω(k)iq(k)Dq(k)-Riq(k)Dd(k)-ψmω(k)Dd(k) (11)
the stator resistance in equation (11) has been calculated by means of current injection when the motor is at rest, and the inductance LsAlso calculated by equation (10), therefore, equation (11) can be used as an iterative calculation of the rotor permanent magnet flux linkage ψmThe identification equation of (1).
4. The permanent magnet synchronous motor parameter online identification method based on the NLMS algorithm according to claim 1, characterized in that: in step 3), each vector of the Adaline neural network identification system respectively refers to an input vector, an output vector, an expected output vector and a weight vector; the method comprises the following steps of constructing a parameter identifier based on an NLMS algorithm, and identifying values of motor stator resistance, inductance and rotor flux linkage, wherein the parameter identifier comprises the following steps:
3.1) from the formula ud0(k)Dq0(k)-uq0(k)Dd(k)＝Rid0(k)Dq(k)-Riq0(k)Dd(k) The preliminary identifier of the motor stator resistor R is obtained as follows:
in the formula: k represents the number of samples; u. ofd0(k)、uq0(k) And id0(k)、iq0(k) D-axis voltage and q-axis voltage and current are obtained for the kth sampling in the static state of the motor respectively; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is the 6 th harmonic containing a dc component;
after the motor is started, the resistance of the motor is kept unchanged in a short-time running state; after the motor runs to a stable rotating speed, the inductance L is obtained by identificationsAnd rotor flux linkage ΨmThe resistance value is updated and identified, and the motor stator resistance identifier based on the NLMS algorithm is as follows:
in the formula: k represents the number of samples; x (k) is the input vector at time k; u. ofd(k)、uq(k) And id(k)、iq(k) D-axis voltage and q-axis voltage and current are obtained for the kth sampling respectively; dd(k) The function is 6 th harmonic with a mean value of 0; dq(k) Is the 6 th harmonic containing a dc component; o (k) is an Adaline neural network identification system output vector at the kth sampling moment; ε (k) is the error signal of the kth sample; d (k) is the desired output vector for the kth sample; ω (k) is the angular velocity of the kth sample; Ψmη is a rotor flux linkage, wherein the value range is 0 & lt η & lt 2 for the step length of weight calculation, delta is a small integer introduced for preventing the change of the weight step length caused by the over-small inner product of an input vector X (k), and 0.0001 is taken, and R (k), (k) and R (k +1) are respectively the identification values of the motor stator resistance R of the kth sampling and the kth +1 sampling;
3.2) preparation of
The inductor identifier based on NLMS algorithm is
In the formula: l iss(k) And Ls(k +1) are inductance identification values of the kth sampling and the kth +1 sampling respectively;
are each ud(k)、ω(k)、iq(k) A filtered direct current component;
3.3) from the formula ud(k)Dq(k)-uq(k)Dd(k)＝-Lsω(k)iq(k)Dq(k)-Riq(k)Dd(k)-ψmω(k)Dd(k) The rotor flux linkage identifier based on the NLMS algorithm is as follows:
in the formula: psim(k) And psim(k +1) is the identification value of the rotor flux linkage at the kth sampling time and the kth +1 sampling time, respectively.
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