A permanent magnet synchronous motor position sensorless control method and storage medium

Abstract

The present invention discloses a method and storage medium for controlling a permanent magnet synchronous motor without a position sensor, and belongs to the technical field of permanent magnet synchronous motor control. The method comprises: obtaining a first reference signal of the permanent magnet synchronous motor based on the state equation of the extended Kalman; constructing a state identification model of the permanent magnet synchronous motor based on the current reference model of the permanent magnet synchronous motor to obtain a second reference signal of the permanent magnet synchronous motor; and then performing weighted averaging processing on the first reference signal machine and the second reference signal to obtain an effective reference signal. The present invention can reduce the problem of large estimation error, improve estimation accuracy, strong robustness, and has good real-time performance, stability, and is convenient for controlling a permanent magnet synchronous motor without a position sensor.

Description

Technical Field

[0001] The present invention relates to the technical field of permanent magnet synchronous motor control, and more particularly to a position sensorless control method and storage medium for a permanent magnet synchronous motor. Background Art

[0002] Permanent magnet synchronous motor (PMSM) has many advantages such as small size, light weight, high power density, high power factor, high efficiency, high torque / inertia ratio, high reliability, and easy maintenance, and is widely used. However, installing position sensors in traditional PMSMs will bring a series of problems such as difficult installation and limited use environment. Therefore, position sensorless control technology has emerged, which promotes the wider application of PMSM and is of great significance. Permanent magnet synchronous motor drive systems based on position sensorless control technology have many advantages such as eliminating mechanical position sensors, reducing system weight and cost, avoiding installation and high-speed vibration and easy damage, etc., and have been increasingly used in automotive electronics, home appliances, industrial fields and other occasions. It is used for loads such as fans, pumps, and compressors.

[0003] At present, the control methods of permanent magnet synchronous motors mainly include:

[0004] 1) Vector control, including current vector control, magnetic field oriented control, direct torque control and flux linkage control, etc. These control strategies have high control accuracy and high dynamic response performance. 2) Scalar control, including conventional standard V / f control, V / f control with stable correction loop, etc., which are relatively simple to implement, but have low dynamic responsiveness. In the prior art, PMSM vector control systems with position sensors are still commonly used in various fields. Among them, the widely used position sensors include photoelectric encoders, rotary transformers and magnetoelectric encoders. Photoelectric encoders have high accuracy and small size, but are expensive, and are susceptible to interference from pollutant particles and easy to break due to high-speed vibration. Rotary transformers are mainly used in high-power or harsh environment applications. The manufacturing process is complex and requires a special position resolution chip. The magnetoelectric encoder has a simple structure, is impact-resistant, has high reliability and is low in price, but its accuracy, resolution, consistency, etc. still need to be further improved. The position sensorless control system omits the position sensor, making the system simpler and more reliable, greatly reducing the cost and volume of the device, and avoiding the interference in direct measurement and high-speed vibration. Therefore, the research on PMSM drive system based on position sensorless control technology has very important practical and economic significance. It can be seen that the traditional speed identification accuracy in the existing position sensorless control technology is not high, which affects the accuracy of the vector control method.

[0005] Therefore, how to provide a position sensorless control method and storage medium for a permanent magnet synchronous motor is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0006] In view of this, the present invention provides a position sensorless control method and storage medium for a permanent magnet synchronous motor, so as to solve the technical problems existing in the above-mentioned prior art.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] In one aspect, the present invention provides a position sensorless control method for a permanent magnet synchronous motor, comprising the following steps:

[0009] Step 1: Establish a voltage mathematical equation in a coordinate system where the two phases a and β of the permanent magnet synchronous motor are stationary, and convert the voltage mathematical equation into a voltage state equation and an observation equation;

[0010] Step 2: Establishing an extended Kalman state equation by combining the voltage state equation and the observation equation;

[0011] Step 3: Based on the extended Kalman equation of state, the permanent magnet synchronous motor a and β phases are stationary in the coordinate system i after Park transformation α 、i β As input, iterative calculation is used to estimate the state, and its output is the first reference signal of the permanent magnet synchronous motor;

[0012] Step 4: Establish a current mathematical equation in the permanent magnet synchronous motor d and q two-phase rotating coordinate system, and transform the current mathematical equation to obtain a current reference model of the permanent magnet synchronous motor;

[0013] Step 5: constructing a state identification model of the permanent magnet synchronous motor based on the current reference model of the permanent magnet synchronous motor, and calculating a second reference signal of the permanent magnet synchronous motor based on the state identification model of the permanent magnet synchronous motor;

[0014] Step 6: Perform weighted average processing on the second reference signal of the first reference signal machine to obtain a valid reference signal.

[0015] Preferably, it also includes:

[0016] Step 7: Use the effective reference signal as the input of the PI regulator to perform sensorless vector control of the PMSM with the inverter and the power supply.

[0017] Preferably, in the step 1, a voltage mathematical equation is established in a coordinate system in which the two phases a and β of the permanent magnet synchronous motor are stationary, and the voltage mathematical equation is converted into a voltage state equation and an observation equation, including:

[0018] S1.1 The voltage mathematical equation of the permanent magnet synchronous motor a and β phases in the stationary coordinate system is:

[0019]

[0020]

[0021] In the formula, i α 、i β is the component of the current in the coordinate system where phases a and β are stationary, ω e is the rotor electrical angular velocity, θ e is the rotor position angle;

[0022] S1.2 Convert the voltage mathematical equation into the voltage state equation:

[0023]

[0024]

[0025] In the formula, R s is the phase resistance of the permanent magnet synchronous motor, L s is the phase inductance of the permanent magnet synchronous motor, ψ f is the permanent magnet flux of the permanent magnet synchronous motor rotor, N p is the number of pole pairs of the permanent magnet synchronous motor, ω e is the rotor electrical angular velocity.

[0026] Preferably, in the step 2, the extended Kalman state equation is established by combining the voltage state equation and the observation equation, including:

[0027] S2.1 Linearize and discretize the voltage mathematical equation and voltage state equation to obtain the state equation and observation equation as follows:

[0028]

[0029] In the formula, X (t) is the system error state vector matrix, where t = [i α i β ω e θ e ] T , is the system error state vector X (t) The derivative of , F(t) is the system state transfer matrix, H(t) is the measurement matrix, W(t) is the system process noise, V(t) is the measurement noise, Z (t) is the measurement vector, Z (t) =[i α i β ] T ;

[0030] S2.2: Establish the state equation of the extended Kalman:

[0031]

[0032] In the formula, is the estimated value of the state variable, is the estimated value of output, T s is the sampling period, and t|t-1 is the state from time t-1 to time t.

[0033] Preferably, in the step 4, a current mathematical equation in the permanent magnet synchronous motor d and q two-phase rotating coordinate system is established, and the current mathematical equation is transformed to obtain a current reference model of the permanent magnet synchronous motor, including:

[0034] S4.1 Establish the current mathematical equation of the permanent magnet synchronous motor in the d and q two-phase rotating coordinate system:

[0035]

[0036]

[0037] In the formula, u d 、u q are the d-axis and q-axis components of the stator voltage, respectively, d 、i q are the d-axis and q-axis components of the stator current, R s is the phase resistance of the permanent magnet synchronous motor, L q is the q-axis component of the stator winding self-inductance, L d is the d-axis component of the stator winding self-inductance, ψ f is the permanent magnet flux of the permanent magnet synchronous motor rotor, ω e is the rotor electrical angular velocity

[0038] S4.2 Establish a reference model of permanent magnet synchronous motor:

[0039]

[0040] in,

[0041]

[0042]

[0043]

[0044]

[0045] Preferably, in step 5, constructing a permanent magnet synchronous motor state identification model based on the current reference model of the permanent magnet synchronous motor, and calculating a second reference signal of the permanent magnet synchronous motor based on the state identification model of the permanent magnet synchronous motor include:

[0046] 5.1 The permanent magnet synchronous motor state identification model is constructed based on the current reference model of the motor:

[0047]

[0048] 5.2 Calculate the second reference signal of the permanent magnet synchronous motor based on the state identification model of the permanent magnet synchronous motor.

[0049] In one aspect, the present invention further provides a computer storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for controlling a permanent magnet synchronous motor without a position sensor are implemented.

[0050] It can be seen from the above technical solution that compared with the prior art, the present invention discloses a permanent magnet synchronous motor position sensorless control method and storage medium. The method does not require a position sensor and reduces the system cost. First, a first reference signal and a second reference signal are obtained to obtain an effective reference signal, thereby reducing the problem of large estimation errors, improving estimation accuracy, and having strong robustness, and having good real-time performance and stability, and facilitating the permanent magnet synchronous motor position sensorless control. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0052] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0054] See attached Figure 1 As shown, the embodiment of the present invention discloses a position sensorless control method for a permanent magnet synchronous motor, comprising the following steps:

[0055] Step 1: Establish a voltage mathematical equation in a coordinate system where the two phases a and β of the permanent magnet synchronous motor are stationary, and convert the voltage mathematical equation into a voltage state equation and an observation equation;

[0056] Step 2: Combine the voltage state equation and the observation equation to establish the extended Kalman state equation;

[0057] Step 3: Based on the extended Kalman equation of state, the permanent magnet synchronous motor a and β phases are stationary in the coordinate system i after Park transformation α 、i β As input, iterative calculation is used to estimate the state, and its output is the first reference signal of the permanent magnet synchronous motor;

[0058] Step 4: Establish a current mathematical equation in the permanent magnet synchronous motor d and q two-phase rotating coordinate system, and transform the current mathematical equation to obtain a current reference model of the permanent magnet synchronous motor;

[0059] Step 5: constructing a state identification model of the permanent magnet synchronous motor based on the current reference model of the permanent magnet synchronous motor, and calculating a second reference signal of the permanent magnet synchronous motor based on the state identification model of the permanent magnet synchronous motor;

[0060] Step 6: Perform weighted average processing on the second reference signal of the first reference signal machine to obtain a valid reference signal.

[0061] In a specific embodiment, it also includes:

[0062] Step 7: Use the effective reference signal as the input of the PI regulator to perform sensorless vector control of the PMSM with other modules such as the inverter and power supply.

[0063] In a specific embodiment, in step 1, a voltage mathematical equation in a coordinate system where the two phases a and β of the permanent magnet synchronous motor are stationary is established, and the voltage mathematical equation is converted into a voltage state equation and an observation equation, including:

[0064] S1.1 The voltage mathematical equation of the permanent magnet synchronous motor a and β phases in the stationary coordinate system is:

[0065]

[0066]

[0067] In the formula, i α 、i β is the component of the current in the coordinate system where phases a and β are stationary, ω e is the rotor electrical angular velocity, θ e is the rotor position angle;

[0068] S1.2 Convert the voltage mathematical equation into the voltage state equation:

[0069]

[0070]

[0071] In the formula, R s is the phase resistance of the permanent magnet synchronous motor, L s is the phase inductance of the permanent magnet synchronous motor, ψ f is the permanent magnet flux of the permanent magnet synchronous motor rotor, N p is the number of pole pairs of the permanent magnet synchronous motor, ω e is the rotor electrical angular velocity.

[0072] In a specific embodiment, in step 2, the extended Kalman state equation is established by combining the voltage state equation and the observation equation, including:

[0073] S2.1 Linearize and discretize the voltage mathematical equation and voltage state equation to obtain the state equation and observation equation as follows:

[0074]

[0075] In the formula, X (t) is the system error state vector matrix, where t = [i α i β ω e θ e ] T , is the system error state vector X (t) The derivative of , F(t) is the system state transfer matrix, H(t) is the measurement matrix, W(t) is the system process noise, V(t) is the measurement noise, Z (t) is the measurement vector, Z (t) =[i α i β ] T ;

[0076] S2.2: Establish the state equation of the extended Kalman:

[0077]

[0078] In the formula, is the estimated value of the state variable, is the estimated value of output, T s is the sampling period, and t|t-1 is the state from time t-1 to time t.

[0079] Specifically, firstly, the permanent magnet synchronous motor winding reverse electromotive force detection and rotor position characteristic points are obtained:

[0080] Each phase winding of the permanent magnet synchronous motor is equivalent to a resistance-inductance series branch (RL branch), and the voltage across the winding is composed of the inductance voltage drop and the resistance voltage drop. R, as the RL branch of the permanent magnet synchronous motor winding, will present an induced electromotive force during the rotation of the permanent magnet synchronous motor rotor, and obtain the three-phase reverse electromotive force U, V, and W of the permanent magnet synchronous motor.

[0081] The reverse electromotive force A, B, and C of the three-phase motor winding are detected. According to the zero crossing points and maximum points of the three reverse electromotive forces, one electrical angle cycle can be divided into 12 intervals with a step size of 30 degrees. The boundary of each interval is determined according to the zero crossing points and positive and negative maximum points of the three reverse electromotive forces. Since the three-phase winding is symmetrically arranged in space, the positive and negative maximum points of the reverse electromotive force of a certain phase winding are at the same position as the zero crossing point of the difference between the other two reverse electromotive forces. In the specific implementation process, the positive and negative maximum points of a certain phase can be obtained by the difference between the other two phases.

[0082] Then, the position estimation algorithm based on extended Kalman filter can obtain 12 accurate rotor position feature points through back electromotive force in one electrical cycle. The position information between these feature points is recursively estimated by extended Kalman filter.

[0083] In a specific embodiment, in step 4, a current mathematical equation in a two-phase rotating coordinate system of the permanent magnet synchronous motor d and q is established, and the current mathematical equation is transformed to obtain a current reference model of the permanent magnet synchronous motor, including:

[0084] S4.1 Establish the current mathematical equation of the permanent magnet synchronous motor in the d and q two-phase rotating coordinate system:

[0085]

[0086]

[0087] In the formula, u d 、u q are the d-axis and q-axis components of the stator voltage, respectively, d 、i q are the d-axis and q-axis components of the stator current, R s is the phase resistance of the permanent magnet synchronous motor, L q is the q-axis component of the stator winding self-inductance, L d is the d-axis component of the stator winding self-inductance, ψ f is the permanent magnet flux of the permanent magnet synchronous motor rotor, ω e is the rotor electrical angular velocity

[0088] S4.2 Establish a reference model of permanent magnet synchronous motor:

[0089]

[0090] in,

[0091]

[0092]

[0093]

[0094]

[0095] In a specific embodiment, in step five, a permanent magnet synchronous motor state identification model is constructed based on a current reference model of the permanent magnet synchronous motor, and a second reference signal of the permanent magnet synchronous motor is calculated based on the state identification model of the permanent magnet synchronous motor, including:

[0096] 5.1 The permanent magnet synchronous motor state identification model is constructed based on the current reference model of the motor:

[0097]

[0098] 5.2 Calculate the second reference signal of the permanent magnet synchronous motor based on the state identification model of the permanent magnet synchronous motor.

[0099] Specifically, an adaptive rule of exponential weighting coefficients is established, and adaptive weighted averaging is performed on the first reference signal machine and the second reference signal. The nearest new information covariance value can be selected to obtain an effective reference signal, thereby improving the robustness of the system.

[0100] On the other hand, the present invention further discloses a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for controlling a permanent magnet synchronous motor without a position sensor are implemented.

[0101] In summary, during the rotation of the permanent magnet synchronous motor rotor, the winding will generate reverse electromotive force due to cutting the magnetic lines of force, and the reverse electromotive force has a direct corresponding relationship with the rotor phase, so the reverse electromotive force information can be used to monitor the rotor phase. The method of determining the phases of the rotor by the amplitude of the reverse electromotive force during the operation of the permanent magnet synchronous motor is complex. On the one hand, there are high-frequency components in the current split of each phase, and on the other hand, when the speed is unstable, the reverse electromotive force itself is also unstable, so it is not suitable to directly convert the rotor phase from the amplitude of the reverse electromotive force. The signal processing method can be used to digitally process the change process of the three-phase winding reverse electromotive force, extract some relatively stable characteristic positions of the reverse electromotive force during the rotation of the permanent magnet synchronous motor, such as zero points and extreme points, and obtain position characteristic points. The extended Kalman filter method and the model reference adaptive method are used for the rotor position information between the characteristic points to obtain the first reference signal and the second reference signal, and then the final effective reference signal is obtained by weighted average. The effective reference signal is the accurate position of the rotor. The permanent magnet synchronous motor is controlled by the effective reference signal to effectively improve the control accuracy.

[0102] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0103] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A position sensorless control method for a permanent magnet synchronous motor, characterized in that: The following steps are involved: Step 1: Establish a voltage mathematical equation in a coordinate system where the two phases a and β of the permanent magnet synchronous motor are stationary, and convert the voltage mathematical equation into a voltage state equation and an observation equation; Step 2: Establishing an extended Kalman state equation by combining the voltage state equation and the observation equation; Step 3: Based on the extended Kalman equation of state, the permanent magnet synchronous motor a and β phases are stationary in the coordinate system i after Park transformation α 、i β As input, iterative calculation is used to estimate the state, and its output is the first reference signal of the permanent magnet synchronous motor; Step 4: Establish a current mathematical equation in the permanent magnet synchronous motor d and q two-phase rotating coordinate system, and transform the current mathematical equation to obtain a current reference model of the permanent magnet synchronous motor; Step 5: constructing a state identification model of the permanent magnet synchronous motor based on the current reference model of the permanent magnet synchronous motor, and calculating a second reference signal of the permanent magnet synchronous motor based on the state identification model of the permanent magnet synchronous motor; Step 6: Perform weighted average processing on the first reference signal and the second reference signal to obtain a valid reference signal; In the step 2, the extended Kalman state equation is established by combining the voltage state equation and the observation equation, including: S2.1 Linearize and discretize the voltage mathematical equation and voltage state equation to obtain the state equation and observation equation as follows: ； In the formula, is the system error state vector matrix, where , is the system error state vector The derivative of is the system state transfer matrix, is the measurement matrix, System process noise, Measuring noise, is the measurement vector, ; S2.2: Establish the state equation of the extended Kalman: ； In the formula, is the estimated value of the state vector, is the estimated value of the measurement vector, is the sampling period, is the state from time t-1 to time t; In the step 5, a permanent magnet synchronous motor state identification model is constructed based on the current reference model of the permanent magnet synchronous motor, and a second reference signal of the permanent magnet synchronous motor is calculated based on the state identification model of the permanent magnet synchronous motor, including: 5.1 The permanent magnet synchronous motor state identification model is constructed based on the current reference model of the motor: )； 5.2 Calculate the second reference signal of the permanent magnet synchronous motor based on the state identification model of the permanent magnet synchronous motor; In the formula, are the d-axis and q-axis components of the stator current respectively, is the phase resistance of the permanent magnet synchronous motor, is the q-axis component of the stator winding self-inductance, is the d-axis component of the stator winding self-inductance, is the permanent magnet flux of the permanent magnet synchronous motor rotor, is the rotor electrical angular velocity.

2. A position sensorless control method for a permanent magnet synchronous motor according to claim 1, characterized in that: Also includes: Step 7: Use the effective reference signal as the input of the PI regulator to perform sensorless vector control of the PMSM with the inverter and the power supply.

3. A position sensorless control method for a permanent magnet synchronous motor according to claim 1, characterized in that: In the step 1, a voltage mathematical equation is established in a coordinate system where the two phases a and β of the permanent magnet synchronous motor are stationary, and the voltage mathematical equation is converted into a voltage state equation and an observation equation, including: S1.1 The voltage mathematical equation of the permanent magnet synchronous motor a and β phases in the stationary coordinate system is: ； ； In the formula, , is the component of the current in the coordinate system where phases a and β are stationary, is the rotor electrical angular velocity, is the rotor position angle; S1.2 Convert the voltage mathematical equation into the voltage state equation: ； ； In the formula, is the phase resistance of the permanent magnet synchronous motor, is the phase inductance of the permanent magnet synchronous motor, is the permanent magnet flux of the permanent magnet synchronous motor rotor, , is the number of pole pairs of the permanent magnet synchronous motor, is the rotor electrical angular velocity.

4. A position sensorless control method for a permanent magnet synchronous motor according to claim 1, characterized in that: In the step 4, a current mathematical equation in the permanent magnet synchronous motor d and q two-phase rotating coordinate system is established, and the current mathematical equation is transformed to obtain a current reference model of the permanent magnet synchronous motor, including: S4.1 Establish the current mathematical equation of the permanent magnet synchronous motor in the d and q two-phase rotating coordinate system: ； ； In the formula, , are the d-axis and q-axis components of the stator voltage respectively, , are the d-axis and q-axis components of the stator current respectively, is the phase resistance of the permanent magnet synchronous motor, is the q-axis component of the stator winding self-inductance, is the d-axis component of the stator winding self-inductance, is the permanent magnet flux of the permanent magnet synchronous motor rotor, is the rotor electrical angular velocity; S4.2 Establish a reference model of permanent magnet synchronous motor: ； in, ； ； ; 。 5. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the position sensorless control method for a permanent magnet synchronous motor as claimed in any one of claims 1 to 4 are implemented.

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