Parameter prediction and control method and system of sensorless motor

Abstract

The invention relates to the technical field of power systems, in particular to a parameter prediction and control method and system of a sensorless motor. The method comprises the following steps: acquiring a sampling stator current and a sampling stator voltage of a motor and a motor rotating speed observation value obtained through an orthogonal phase-locked loop, and performing coordinate transformation on the sampling stator current to obtain a two-phase static coordinate system current; inputting the sampled stator voltage, the two-phase static coordinate system current and the motor rotating speed observation value into a sliding mode observer to obtain a back electromotive force observation value; inputting the back electromotive force observation value into a pre-constructed cross feedback frequency adaptive filter, eliminating harmonic waves and extracting a fundamental component to obtain a filtered fundamental wave back electromotive force observation value; and normalizing the fundamental wave back electromotive force observation value, and inputting the normalized fundamental wave back electromotive force observation value into an orthogonal phase-locked loop to obtain a motor rotor position prediction value and a rotating speed prediction value. By adopting the cross feedback frequency adaptive filter, the quality of the back electromotive force signal is improved, and the accuracy of rotor position and speed estimation is also remarkably improved.

Description

Technical Field

[0001] This application relates to the technical field of power systems, and particularly to a method and system for parameter prediction and control of a sensorless motor. Background Art

[0002] Due to its advantages such as high efficiency, high power density, and low operating noise, the permanent magnet synchronous motor has become the core power source component of high-end equipment such as advanced rail transit equipment and aerospace equipment. The high-performance control of the interior permanent magnet synchronous motor (IPMSM) system requires real-time acquisition of the rotor position information of the motor. A mechanical position sensor is often used to obtain the real-time position. However, the use of such a sensor will result in disadvantages such as an increase in the volume of the motor system, a decrease in reliability, and an increase in cost.

[0003] The sensorless control technology abandons the mechanical position sensor and only uses voltage and current signals to extract position and speed information, which is suitable for occasions where the structure is compact, such as electric vehicles, or where the position sensor cannot be applied. Currently, in the sensorless control field, the model-based method is mostly used to estimate the rotor position and speed in the medium and high-speed operating range, mainly including the sliding mode observer method, the full-order Luenberger observer method, the disturbance observer method, etc. The sliding mode observer method realizes the convergence of the observation error signal through variable structure control, and the algorithm is simple and easy to implement. However, the sliding mode observer method will have an obvious chattering phenomenon due to the influence of variable structure control. Secondly, due to the nonlinearity of the inverter and the magnetic field space harmonics, sub-harmonic error pulsations will occur in the rotor position detection based on the sliding mode observer method, affecting the accuracy of rotor position estimation and reducing the performance of the IPMSM sensorless control system. Therefore, it is necessary to suppress the chattering phenomenon and the back electromotive force harmonics.

[0004] The commonly used suppression methods are mostly implemented based on additional filters, such as low-pass filters, band-pass filters, etc. However, the use of a low-pass filter will cause a phase lag in the observed back electromotive force, and additional compensation measures are required, reducing the accuracy of rotor position estimation and deteriorating the dynamic performance of the system; while a simple band-pass filter usually cannot eliminate the specified harmonic components, reducing the accuracy of eliminating specific frequency harmonics of the back electromotive force. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and system for parameter prediction and control of a sensorless motor to solve the problem that additional compensation measures are required to eliminate the harmonic components in the back electromotive force in the prior art, introducing a phase delay.

[0006] A method for parameter prediction of a sensorless motor includes:

[0007] Obtain the sampled stator current of the motor, the sampled stator voltage, and the motor speed observation value obtained through an orthogonal phase-locked loop, and perform coordinate transformation on the sampled stator current to obtain the two-phase stationary coordinate system current;

[0008] Input the sampled stator voltage, the two-phase stationary coordinate system current, and the motor speed observation value into a sliding mode observer to obtain the back electromotive force observation value;

[0009] Input the back electromotive force observation value into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component, obtaining the filtered fundamental back electromotive force observation value;

[0010] After normalizing the fundamental back electromotive force observation value, input it into the orthogonal phase-locked loop to obtain the motor rotor position prediction value and the speed prediction value.

[0011] Preferably, input the back electromotive force observation value into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component, obtaining the filtered fundamental back electromotive force observation value, satisfying:

[0012]

[0013] Wherein, is the fundamental back electromotive force vector after filtering the back electromotive force observation value in the two-phase stationary coordinate system by the cross-feedback frequency adaptive filter; z αβ is the back electromotive force vector observation value in the α,β directions observed by the sliding mode observer; W1, W5, W7 are the transfer functions corresponding to the fundamental component and the 5th and 7th harmonic observation values respectively.

[0014] Preferably, the transfer functions corresponding to the fundamental component and the 5th and 7th harmonic observation values satisfy:

[0015]

[0016] Wherein, ζ h is the damping coefficient for the cross-feedback frequency adaptive filter to select the hth back electromotive force component; h = 1, 5, 7; is the motor speed observation value.

[0017] Preferably, the sliding mode observer is a sliding mode observer based on the sign function, and the observation formula of the sliding mode observer satisfies:

[0018]

[0019] Wherein, respectively represent the stator current observation values in the αβ directions in the two-phase stationary coordinate system; u α 、u βrespectively represent the stator voltages in the α and β directions of the two-phase stationary coordinate system; z α and z β respectively represent the observed values of the back electromotive force in the α and β directions of the two-phase stationary coordinate system; where i α and i β are the sampled values of the stator current; R s is the stator resistance; k smo is the sliding mode gain, set as a constant; L d and L q are the d-axis and q-axis inductances respectively.

[0020] Preferably, normalizing the observed value of the fundamental back electromotive force includes:

[0021] Multiplying the fundamental back electromotive forces corresponding to the α and β axes of the two-phase stationary coordinate system in the fundamental back electromotive force respectively by

[0022] where, is the fundamental back electromotive force component in the two-phase stationary αβ coordinate system after the observed value of the back electromotive force is filtered by the cross-feedback frequency adaptive filter.

[0023] A sensorless motor parameter prediction system, characterized in that it includes:

[0024] A data acquisition and processing module, configured to acquire the sampled stator current, sampled stator voltage of the motor, and the observed value of the motor speed obtained by an orthogonal phase-locked loop, and perform coordinate transformation on the sampled stator current to obtain the current in the two-phase stationary coordinate system;

[0025] A back electromotive force observation module, configured to input the sampled stator voltage, the current in the two-phase stationary coordinate system, and the observed value of the motor speed into a sliding mode observer to obtain the observed value of the back electromotive force;

[0026] A filtering module, configured to input the observed value of the back electromotive force into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component to obtain the filtered observed value of the fundamental back electromotive force;

[0027] A prediction module, configured to input the normalized observed value of the fundamental back electromotive force into the orthogonal phase-locked loop to obtain the predicted value of the motor rotor position and the predicted value of the speed.

[0028] Preferably, it further includes: a control module, configured to use the estimated predicted value of the rotor position and the predicted value of the speed for IPMSM vector control to obtain an SVPWM drive signal; the SVPWM drive signal controls the on and off of the inverter switching tubes to obtain an inverter voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM.

[0029] A sensorless motor control method, characterized in that it includes the following steps:

[0030] N1: Obtain the sampled stator current, sampled stator voltage of the motor, and the motor speed observation value obtained through an orthogonal phase-locked loop, and perform coordinate transformation on the sampled stator current to obtain the two-phase stationary coordinate system current;

[0031] N2: Input the sampled stator voltage, the two-phase stationary coordinate system current, and the motor speed observation value into a sliding mode observer to obtain the back electromotive force observation value;

[0032] N3: Input the back electromotive force observation value into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental wave component, and obtain the filtered fundamental wave back electromotive force observation value;

[0033] N4: After normalizing the fundamental wave back electromotive force observation value, input it into the orthogonal phase-locked loop to obtain the motor rotor position prediction value and the speed prediction value;

[0034] N5: Use the obtained motor rotor position prediction value and speed prediction value for IPMSM vector control to obtain the SVPWM drive signal; the SVPWM drive signal controls the on and off of the inverter switch tubes to obtain the inverter voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM; wherein, the rotor position prediction value is used for rotational coordinate transformation, and the speed prediction value is used as the feedback value of the speed outer loop.

[0035] A sensorless motor control system, including:

[0036] A sampling module, a sliding mode observer, a cross-feedback frequency adaptive filter, a normalization phase-locked loop, a current inner loop, a speed outer loop, a PI regulator, an SVPWM modulation module, an inverter, and a permanent magnet synchronous motor;

[0037] Wherein, the sampling module is connected to the permanent magnet synchronous motor, and is used to sample the permanent magnet synchronous motor to obtain the sampled stator current, and perform coordinate transformation on the sampled stator current to obtain the two-phase stationary coordinate system current; and is also used to obtain the sampled stator voltage;

[0038] Both the sampling module and the normalization phase-locked loop are connected to the sliding mode observer, wherein the sampled stator voltage, the two-phase stationary coordinate system current, and the motor speed observation value are input into the sliding mode observer to obtain the back electromotive force observation value;

[0039] The sliding mode observer is connected to the cross-feedback frequency adaptive filter, and the cross-feedback frequency adaptive filter filters the back electromotive force observation value to obtain the fundamental wave back electromotive force;

[0040] The cross-feedback frequency adaptive filter is connected to the normalized phase-locked loop, and the normalized phase-locked loop obtains the predicted rotor position and the predicted rotational speed based on the fundamental back electromotive force;

[0041] The normalized phase-locked loop is connected to the outer speed loop, and inputs the predicted rotational speed into the outer speed loop;

[0042] The outer speed loop, the speed loop PI regulator, the inner current loop, the inner current loop PI regulator, the SVPWM modulation module, and the inverter are connected in sequence to form an IPMSM vector control module, and finally generate an inverter voltage to drive the IPMSM, and the inverter voltage driving the IPMSM is input into the permanent magnet synchronous motor.

[0043] A computer-readable storage medium stores program instructions thereon, and when the program instructions are executed by a computer, the computer is caused to execute the parameter prediction method of the sensorless motor as described above; or the sensorless motor control method as described above.

[0044] Compared with the prior art, the beneficial effects of the present application are as follows: The present application adopts a cross-feedback frequency adaptive filter, which not only improves the quality of the back electromotive force signal, but also significantly improves the accuracy of rotor position and speed estimation. In addition, the requirement for a mechanical position sensor is removed, further simplifying the system structure, reducing the cost and volume, and improving the reliability and dynamic performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a schematic flowchart of a parameter prediction method for a sensorless motor provided by an embodiment of the present application.

[0046] Figure 2 is a structural block diagram of a sliding mode observer based on the sign function provided by an embodiment of the present application.

[0047] Figure 3 is a structural block diagram of a frequency adaptive filter provided by an embodiment of the present application.

[0048] Figure 4 is a structural block diagram of a cross-feedback frequency adaptive filter provided by an embodiment of the present application.

[0049] Figure 5 is a schematic structural diagram of a sensorless motor parameter prediction system 500 provided by an embodiment of the present application.

[0050] Figure 6 is a schematic flowchart of a sensorless motor control method provided by an embodiment of the present application.

[0051] Figure 7 is a schematic structural diagram of a sensorless motor control system provided by an embodiment of the present application. Detailed implementation manners

[0052] To more clearly illustrate the technical solutions of the embodiments of the present application, the accompanying drawings required for use in the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative efforts.

[0053] As Figure 1 shown, Figure 1 is a schematic flowchart of a parameter prediction method for a sensorless motor provided by an embodiment of the present application. The parameter prediction method for the sensorless motor includes the following steps:

[0054] S101: Obtain the sampled stator current, sampled stator voltage of the motor, and the motor speed observation value obtained through an orthogonal phase-locked loop, and perform coordinate transformation on the sampled stator current to obtain the current in the two-phase stationary coordinate system.

[0055] Sample the three-phase stator current of the permanent magnet synchronous motor to obtain the stator current and sampled stator voltage, and perform coordinate transformation on the stator current to obtain the stator current in the two-phase stationary coordinate system; where the stator current i a , i b , i c , after the stationary coordinate transformation, such as (3s / 2s transformation), to obtain i α , i β .

[0056] S102: Input the sampled stator voltage, the current in the two-phase stationary coordinate system, and the motor speed observation value into a sliding mode observer to obtain the back electromotive force observation value.

[0057] A sliding mode observer (SMO) is a non-linear observer that can quickly converge the state error of the system to zero by designing a special switching surface (or sliding mode).

[0058] For the sampled stator voltage, it can be the actual voltage value obtained by sampling the voltage on the stator side of the motor. The sampled stator voltage is usually measured by a voltage sensor, and it can also be a reference voltage value generated according to the requirements of the control system. For the two-phase stationary coordinate system current (the current after αβ transformation), by performing αβ transformation (Clarke transformation) on the three-phase stator current, it is converted into the current components in the two-phase stationary coordinate system, which can simplify the subsequent calculation process and better match the motor model. The motor speed observation value can be the current motor speed value estimated by an orthogonal phase-locked loop (PLL) or other methods, which reflects the rotational speed of the motor rotor and participates in the calculation as important feedback information in the sliding mode observer.

[0059] Furthermore, the sliding mode observer in the embodiments of the present application is a sliding mode observer based on the sign function, as Figure 2 shown, Figure 2 is the structural block diagram of the sliding mode observer based on the sign function provided by the embodiments of the present application. Specifically, the steps to construct the sliding mode observer are as follows:

[0060] (1) First, based on the mathematical model of the interior permanent magnet synchronous motor (IPMSM), establish its extended back electromotive force model, and represent the state variables of the two-phase stationary coordinate system current in the state space form:

[0061]

[0062] where, R s is the stator resistance; L d , L q are the direct-axis (dq) inductances respectively; ω e is the actual electrical angular velocity of the motor; i α , i β are the stator currents in the two-phase stationary coordinate system; u α , u β represent the stator voltages in the αβ directions in the two-phase stationary coordinate system respectively;

[0063] are the extended back electromotive forces in the ɑβ directions in the two-phase stationary coordinate system, where, θ e is the actual rotor position, and ψ f is the permanent magnet flux linkage.

[0064] (2) Construct the sliding mode observer according to the above extended back electromotive force model as:

[0065]

[0066] where, represent the observed values of the stator currents in the αβ directions in the two-phase stationary coordinate system respectively; u ɑ , uβ respectively represent the stator voltages in the α and β directions in the two-phase stationary coordinate system; z α and z β respectively represent the observed values of the back electromotive force in the α and β directions in the two-phase stationary coordinate system; where i α and i β are the sampled values of the stator current; R s is the stator resistance; k smo is the sliding mode gain, set as a constant; L d and L q are the d-axis and q-axis inductances respectively.

[0067] According to the input signal and the design of the sliding mode observer, the observed value of the back electromotive force is calculated. Specifically, the estimated back electromotive force (observed value) component is calculated using the constructed sliding mode observer.

[0068] S103: Input the observed value of the back electromotive force into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component, obtaining the filtered fundamental back electromotive force observed value.

[0069] The cross-feedback frequency adaptive filter (Cross-Feedback Frequency Adaptive Filter, CFFAF) is a filter specifically designed to dynamically adjust its parameters to adapt to the frequency characteristics of the input signal. It can not only effectively extract the fundamental component but also suppress harmonic components of specific frequencies (such as the 5th and 7th harmonics), thereby improving the performance of the motor control system.

[0070] Constructing the cross-feedback frequency adaptive filter includes: First, determine the frequency adaptive filter (Frequency Adaptive Filter, FAF), such as Figure 3 the structural block diagram of the frequency adaptive filter shown. Specifically:

[0071] Taking as the state variable, the state equation can be obtained from the structural block diagram of the frequency adaptive filter shown in Figure 3 as:

[0072]

[0073] where ζ is the damping coefficient; ω is the fundamental frequency, that is, the center resonance frequency of the FAF.

[0074] Therefore, the transfer function of the output relative to the input Z can be expressed as:

[0075]

[0076] Simplifying gives:

[0077]

[0078] It can be seen from the transfer function that the band-pass transfer function characteristic it has is the basis for extracting the fundamental sine component, and it has an inhibitory effect on all components except the fundamental frequency ω; can be regarded as the cascaded combination with a phase-shifting all-pass filter, that is, the estimated is obtained by shifting the phase by 90°; and can be regarded as the cascaded combination of a notch filter and a low-pass filter, which can not only estimate the DC component in the input signal, but also attenuate the harmonic components. In addition, it is easy to find through the final value theorem that this filter can completely eliminate the DC component in the input signal, that is:

[0079]

[0080] where z dc represents the DC component.

[0081] When the center frequency ω can track the back electromotive force frequency, it can be used for the extraction of the fundamental back electromotive force, that is when, the back electromotive force obtained by the sliding mode observer can be filtered to extract the fundamental back electromotive force without distortion, that is:

[0082]

[0083] where: ζ is the damping coefficient of the frequency adaptive filter.

[0084] Such as Figure 4 shown is the structural block diagram of the cross-feedback frequency adaptive filter provided by the embodiment of the present application. Specifically, the filters for selecting frequencies for multiple different frequency back electromotive force components work in a parallel and cooperative manner, and the output of each structure is fed back to the input to eliminate the 5th and 7th harmonics and ensure the extraction of the fundamental back electromotive force. Among them, during the operation of the motor, due to asymmetries, non-ideal factors, and electromagnetic interference in the motor design or manufacturing process, additional harmonic components may be introduced, especially the 5th and 7th harmonics, which will appear in the stator current and voltage signals of the motor, and if not suppressed, will have a negative impact on the motor control performance, such as increasing torque ripple and reducing efficiency. Therefore, in the present application, it is preferably to suppress the 5th and 7th harmonics.

[0085] Figure 4 In, FAF-1, FAF-5, and FAF-7 respectively represent the frequency selection structures with center frequencies of and aiming to extract the fundamental component from the equivalent back electromotive force obtained by the sliding mode observer and the 5th and 7th harmonic components

[0086] The feedback coupling relationship between the filtered fundamental back electromotive force and the 5th and 7th harmonic components can be obtained:

[0087]

[0088] Among them, are the fundamental component of the back electromotive force and the 5th and 7th harmonic components of the back electromotive force respectively; W1(s), W5(s), and W7(s) are the corresponding transfer functions, that is

[0089]

[0090] In the formula: ζ h is the damping coefficient for the cross-feedback frequency adaptive filter to select the hth back electromotive force component. h = 1, 5, 7; is the observed value of the motor speed.

[0091] Therefore, according to equations (11), (12), and (13), the output of the cross-feedback frequency adaptive filter can be obtained and the transfer function between the input z αβ is:

[0092]

[0093] Input the observed value of the back electromotive force into the pre-built cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component, and obtain the observed value of the filtered fundamental back electromotive force, satisfying:

[0094]

[0095] Among them, is the fundamental back electromotive force vector after the observed value of the back electromotive force in the two-phase stationary coordinate system is filtered by the cross-feedback frequency adaptive filter; z αβ is the observed value of the back electromotive force vector in the α and β directions obtained by the sliding mode observer; W1, W5, and W7 are the transfer functions corresponding to the observed values of the fundamental wave and the 5th and 7th harmonics of the back electromotive force respectively. Among them, ζ h is the damping coefficient for the cross-feedback frequency adaptive filter to select the hth back electromotive force component.

[0096] S104: After normalizing the observed value of the fundamental back electromotive force, input it into the orthogonal phase-locked loop to obtain the predicted value of the motor rotor position and the predicted value of the speed.

[0097] Normalization refers to scaling a signal to a specific range (usually [-1, 1] or [0, 1]) to facilitate subsequent processing and calculations. For the normalization of the fundamental back electromotive force (EMF) observation value, its main purposes include ensuring that the back EMF signals obtained under different conditions have the same scale, facilitating comparison and processing; reducing numerical errors through normalization, and some algorithms will be more efficient and stable when processing normalized data.

[0098] Specifically, normalizing the fundamental back EMF observation value includes:

[0099] Multiplying the fundamental back EMF corresponding to the αβ axes in the two-phase stationary coordinate system in the fundamental back EMF by respectively, where is the fundamental back EMF component in the two-phase stationary αβ coordinate system after the back EMF observation value is filtered by the cross-feedback frequency adaptive filter.

[0100] In this application, the quadrature phase-locked loop may include: a phase detector (PD) for comparing the phase difference between the input signal (normalized fundamental back EMF) and the internally generated reference signal to generate an error signal; a loop filter (LF) for filtering the error signal output by the phase detector to eliminate high-frequency noise and smooth the output signal; a voltage-controlled oscillator (VCO) for adjusting the output frequency and phase according to the filtered error signal to synchronize it with the input signal; and a quadrature signal generator for generating two mutually orthogonal reference signals based on the output frequency of the VCO for use by the phase detector.

[0101] Specifically, the normalized fundamental back EMF is two orthogonal components E α and E β , and these two components can be directly provided as input signals to the quadrature phase-locked loop. The phase detector compares the phase differences between the input signals E α and E β and the internally generated reference signal. Assuming the currently estimated rotor position is , the reference signal can be expressed as: The phase detector calculates the phase error e phase :

[0102]

[0103] The phase error signal e output by the phase detector phaseIt contains high-frequency noise, so it needs to be smoothed by a loop filter. A PI controller can generate a smooth control signal u(t) by performing proportional and integral operations on the error signal.

[0104] Then, the voltage-controlled oscillator adjusts its output frequency and phase according to the control signal output by the loop filter. Its output frequency w r can be expressed as:

[0105] w r＝ w0 + K v ·u(t)

[0106] where, w0 represents the initial frequency, K v represents the gain coefficient of the voltage-controlled oscillator, and u(t) represents the control signal output by the loop filter. The output frequency w r of the voltage-controlled oscillator directly reflects the rotational speed of the motor rotor, and its phase reflects the position of the rotor.

[0107] Finally, according to the output frequency and phase of the voltage-controlled oscillator, the estimated values of the rotor position and speed are updated:

[0108]

[0109] where, represents the estimated value of the rotor position at the next moment; the estimated value of the rotor speed; Δt represents the sampling time interval.

[0110] In the embodiment of the present application, after normalization processing, it can avoid the influence of the motor speed on the PLL bandwidth, ensure that the PLL can accurately estimate the rotor position within a wide speed regulation range, has strong robustness to model uncertainty and external interference, and can accurately estimate the state variables (speed, position) of the motor without using sensors. In addition, it should be noted that performing normalization processing is the preferred embodiment of the present application. In other feasible embodiments, normalization processing may not be selected.

[0111] In summary, the technical solution provided by the embodiment of the present application can, through the cross-feedback frequency adaptive filter, accurately eliminate the harmonic components in the back electromotive force while avoiding introducing phase delay, without the need for additional compensation measures. In addition, by using the method of combining a sliding mode observer with a cross-feedback frequency adaptive filter, the fundamental wave component of the back electromotive force can be more accurately extracted, and the normalized orthogonal phase-locked loop (PLL) is used to estimate the position and speed information of the rotor, improving the accuracy of position and speed estimation.

[0112] Based on the same inventive concept, the present application also provides a sensorless motor parameter prediction system, as Figure 5As shown, Figure 5 is a schematic structural diagram of a sensorless motor parameter prediction system 500 provided by an embodiment of the present application. The sensorless motor parameter prediction system 500 includes:

[0113] A data acquisition and processing module 501, configured to acquire a sampled stator current, a sampled stator voltage of the motor, and an observed motor speed obtained through an orthogonal phase-locked loop, and perform coordinate transformation on the sampled stator current to obtain a two-phase stationary coordinate system current;

[0114] A back electromotive force observation module 502, configured to input the sampled stator voltage, the two-phase stationary coordinate system current, and the observed motor speed into a sliding mode observer to obtain an observed value of the back electromotive force;

[0115] A filtering module 503, configured to input the observed value of the back electromotive force into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract a fundamental wave component, obtaining a filtered observed value of the fundamental wave back electromotive force;

[0116] A prediction module 504, configured to normalize the observed value of the fundamental wave back electromotive force and then input it into the orthogonal phase-locked loop to obtain a predicted value of the motor rotor position and a predicted value of the speed.

[0117] Further, it further includes: a control module 505, configured to use the estimated predicted value of the rotor position and the predicted value of the speed for IPMSM vector control to obtain a space vector pulse width modulation (Space Vector Pulse Width Modulation, SVPWM) drive signal; the SVPWM drive signal controls the on / off of the inverter switching tubes to obtain an inverter voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM. Among them, SVPWM is an optimized pulse width modulation technology, which realizes more efficient and precise control of the motor by intelligently selecting the switching states of the inverter. Wherein, the SVPWM drive signal controls the on / off of the inverter switching tubes to obtain an inverter voltage to drive the IPMSM, which means generating an appropriate control signal through the SVPWM technology to drive the inverter. This embodiment realizes a closed-loop control from parameter prediction to actual motor control, improving the overall performance of the system. The application of the SVPWM technology further improves the efficiency and dynamic response ability of the motor.

[0118] In this application, the data acquisition and processing module 501, back electromotive force observation module 502, filtering module 503, prediction module 504, and control module 505 can be used to execute the solutions of steps S101 - S104 and any optional embodiments thereof. Without using sensors, the state variables (speed, position) of the motor can be accurately estimated, and then appropriate control signals are generated through SVPWM technology to drive the inverter, thereby controlling the operation of the interior permanent magnet synchronous motor. For the specific implementation processes of each module, please refer to the content of the above method and will not be elaborated here. It should be understood that the above division of functional modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. At the same time, the above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0119] Based on the same inventive concept, this application also provides a sensorless motor control method, as Figure 6 shown, Figure 6 is a schematic flowchart of the sensorless motor control method provided by an embodiment of this application. The sensorless motor control method includes the following steps:

[0120] N1: Obtain the sampled stator current of the motor, the sampled stator voltage, and the motor speed observation value obtained through an orthogonal phase-locked loop, and perform coordinate transformation on the sampled stator current to obtain the two-phase stationary coordinate system current;

[0121] N2: Input the sampled stator voltage, the two-phase stationary coordinate system current, and the motor speed observation value into a sliding mode observer to obtain the back electromotive force observation value;

[0122] N3: Input the back electromotive force observation value into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental wave component, obtaining the filtered fundamental wave back electromotive force observation value;

[0123] N4: After normalizing the fundamental wave back electromotive force observation value, input it into the orthogonal phase-locked loop to obtain the motor rotor position prediction value and the speed prediction value;

[0124] N5: Use the obtained motor rotor position prediction value and speed prediction value for IPMSM vector control to obtain the SVPWM drive signal; the SVPWM drive signal controls the on / off of the inverter switching tubes to obtain the inverter voltage to drive the IPMSM, thereby realizing the sensorless control of the IPMSM; among them, the rotor position prediction value is used for rotational coordinate transformation, and the speed prediction value is used as the feedback value of the speed outer loop.

[0125] Among them, steps N1-N4 can refer to the relevant descriptions in the foregoing embodiments, and will not be elaborated in this embodiment. In step N5, the rotor position observation value is used for rotational coordinate transformation (2s / 2r transformation, 2r / 2s transformation). The rotational speed observation value is fed back to the input end of the outer loop of rotational speed, and the difference is taken with the reference rotational speed to obtain the rotational speed error. Then, the rotational speed error passes through the PI regulator of the rotational speed loop to obtain the shaft reference current. The obtained shaft reference current and the shaft given reference current are respectively subtracted from the actual shaft current obtained by the 2s / 2r transformation. The current error passes through the PI regulator of the current loop, and the obtained outputs are respectively added with the shaft feedforward decoupling component to obtain the shaft reference voltage. Then, the 2r / 2s transformation is used to transform the obtained voltage into the voltage under the shaft and input it into the SVPWM modulation module. After modulation, the PWM drive signal is output to control the on-off of the inverter switch tube, and the inverter voltage is obtained to drive the IPMSM, thereby realizing the sensorless control of the IPMSM.

[0126] Based on the same inventive concept, the present application also provides a sensorless motor control system, as Figure 7 shown, Figure 7 is the structural schematic diagram of the sensorless motor control system provided by the embodiment of the present application. The sensorless motor control system can be the sensorless vector control of the IPMSM based on the cross-feedback frequency adaptive filter.

[0127] The sensorless motor control system may include:

[0128] a sampling module, a sliding mode observer, a cross-feedback frequency adaptive filter, a normalized phase-locked loop, an inner current loop, an outer rotational speed loop, a PI regulator, an SVPWM modulation module, an inverter, and a permanent magnet synchronous motor;

[0129] Among them, the sampling module is connected to the permanent magnet synchronous motor, and is used to sample the permanent magnet synchronous motor to obtain the sampled stator current, and perform coordinate transformation on the sampled stator current to obtain the two-phase stationary coordinate system current; and is also used to obtain the sampled stator voltage;

[0130] Both the sampling module and the normalized phase-locked loop are connected to the sliding mode observer. Among them, the sampled stator voltage, the two-phase stationary coordinate system current, and the motor rotational speed observation value are input into the sliding mode observer to obtain the back electromotive force observation value;

[0131] The sliding mode observer is connected to the cross-feedback frequency adaptive filter, and the cross-feedback frequency adaptive filter filters the back electromotive force observation value to obtain the fundamental wave back electromotive force;

[0132] The cross-feedback frequency adaptive filter is connected to the normalized phase-locked loop, and the normalized phase-locked loop obtains the rotor position prediction value and the rotational speed prediction value based on the fundamental wave back electromotive force;

[0133] The normalized phase-locked loop is connected to the outer speed loop and inputs the predicted speed value into the outer speed loop;

[0134] The outer speed loop, the speed loop PI regulator, the inner current loop, the inner current loop PI regulator, the SVPWM modulation module, and the inverter are connected in sequence to form an IPMSM vector control module, and finally generate an inverter voltage to drive the IPMSM. The inverter voltage driving the IPMSM is input into the permanent magnet synchronous motor.

[0135] In addition, an embodiment of the present application further provides a computer-readable storage medium, on which program instructions are stored. When the program instructions are executed by a computer, the computer is caused to execute the parameter prediction method of the sensorless motor as described above; or the sensorless motor control method as described above. It should be understood that the implementation process of some steps, as well as whether some steps are executed and the execution order, may refer to the implementation process of the foregoing embodiments.

[0136] The readable storage medium is a computer-readable storage medium, which may be an internal storage unit of the controller described in any of the foregoing embodiments, such as the hard disk or memory of the controller. For example, the terrain element model constructed in the present invention exists in the hard disk, and then the computer program for executing the fusion step is stored in the memory, so that the fusion process is realized relying on the memory. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk equipped on the controller, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the readable storage medium may also include both the internal storage unit of the controller and the external storage device. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store the data that has been output or will be output.

[0137] Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing readable storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., which can store program codes.

[0138] It should be emphasized that the examples described in the present invention are illustrative rather than restrictive. Therefore, the present invention is not limited to the examples described in the specific embodiments. Any other embodiments derived by those skilled in the art based on the technical solution of the present invention, whether modified or replaced, as long as they do not depart from the spirit and scope of the present invention, also fall within the protection scope of the present invention.

Claims

1. A parameter prediction method for a sensorless motor, characterized in that, Including: Obtain the sampled stator current, sampled stator voltage of the motor, and the motor speed observation value obtained through an orthogonal phase-locked loop, and perform coordinate transformation on the sampled stator current to obtain the two-phase stationary coordinate system current; Input the sampled stator voltage, the two-phase stationary coordinate system current, and the motor speed observation value into a sliding mode observer to obtain the back electromotive force observation value; Input the back electromotive force observation value into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component, obtaining the filtered fundamental back electromotive force observation value; After normalizing the fundamental back electromotive force observation value, input it into the orthogonal phase-locked loop to obtain the motor rotor position prediction value and speed prediction value.

2. The parameter prediction method of the sensorless motor according to claim 1, characterized in that Input the back electromotive force observation value into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component, obtaining the filtered fundamental back electromotive force observation value, satisfying: Among them, is the fundamental back electromotive force vector after filtering the observed value of the back electromotive force in the two-phase stationary coordinate system through a cross-feedback frequency adaptive filter; z αβ are the observed values of the back electromotive force vectors in the α and β directions obtained by the sliding mode observer; W1, W5, and W7 are the transfer functions corresponding to the observed values of the 1st, 5th, and 7th harmonics respectively.

3. The parameter prediction method for the sensorless motor according to claim 2, characterized in that, The transfer functions corresponding to the 1st, 5th, and 7th harmonic observation values satisfy: where ζ h is the damping coefficient of the h-th back electromotive force component selected for the cross-feedback frequency adaptive filter; h = 1, 5, 7; is the observed value of the motor speed.

4. The parameter prediction method of the sensorless motor according to claim 1, characterized in that: The sliding mode observer is a sliding mode observer based on the sign function, and the observation formula of the sliding mode observer satisfies: Among them, respectively represent the observed values of the stator current in the α and β directions in the two-phase stationary coordinate system; u α , u β respectively represent the stator voltages in the α and β directions in the two-phase stationary coordinate system; z α , z β respectively represent the observed values of the back electromotive force in the α and β directions in the two-phase stationary coordinate system; Among them, i α , i β are the sampled values of the stator current; R s is the stator resistance; k smo is the sliding mode gain, set as a constant; L d , L q are the d-axis and q-axis inductances respectively.

5. The parameter prediction method of the sensorless motor according to claim 1, characterized in that, Normalizing the fundamental back electromotive force observation value includes: Multiply the fundamental back electromotive force corresponding to the α and β axes in the two-phase stationary coordinate system in the fundamental back electromotive force by respectively, where is the fundamental back electromotive force component in the two-phase stationary αβ coordinate system after the back electromotive force observation value is filtered by the cross-feedback frequency adaptive filter.

6. A sensorless motor parameter prediction system based on the parameter prediction method of the sensorless motor according to any one of claims 1-5, characterized in that: Including: A data acquisition and processing module for obtaining the sampled stator current, sampled stator voltage of the motor, and the motor speed observation value obtained through an orthogonal phase-locked loop, and performing coordinate transformation on the sampled stator current to obtain the two-phase stationary coordinate system current; A back electromotive force observation module for inputting the sampled stator voltage, the two-phase stationary coordinate system current, and the motor speed observation value into a sliding mode observer to obtain the back electromotive force observation value; A filtering module for inputting the back electromotive force observation value into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component, obtaining the filtered fundamental back electromotive force observation value; A prediction module for, after normalizing the fundamental back electromotive force observation value, inputting it into the orthogonal phase-locked loop to obtain the motor rotor position prediction value and speed prediction value.

7. The parameter prediction system of the sensorless motor according to claim 6, characterized in that, Also including: A control module for using the estimated rotor position prediction value and speed prediction value for IPMSM vector control to obtain an SVPWM drive signal; The SVPWM drive signal controls the on-off of the inverter switching tubes to obtain an inverter voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM.

8. A sensorless motor control method, characterized in that, Including the following steps: N1: Obtain the sampled stator current, sampled stator voltage of the motor, and the motor speed observation value obtained through an orthogonal phase-locked loop, and perform coordinate transformation on the sampled stator current to obtain the two-phase stationary coordinate system current; N2: Input the sampled stator voltage, the two-phase stationary coordinate system current, and the motor speed observation value into a sliding mode observer to obtain the back electromotive force observation value; N3: Input the back electromotive force observation value into a pre-constructed cross-feedback frequency adaptive filter to eliminate harmonics and extract the fundamental component, obtaining the filtered fundamental back electromotive force observation value; N4: After normalizing the fundamental back electromotive force observation value, input it into the orthogonal phase-locked loop to obtain the motor rotor position prediction value and speed prediction value; N5: The obtained motor rotor position prediction value and speed prediction value are used for IPMSM vector control to obtain an SVPWM drive signal; the SVPWM drive signal controls the on / off of the inverter switch tubes to obtain an inverted voltage to drive the IPMSM, thereby realizing sensorless control of the IPMSM; wherein, the rotor position prediction value is used for rotational coordinate transformation, and the speed prediction value is used as the feedback value of the speed outer loop.

9. A sensorless motor control system based on the sensorless motor control method according to claim 8, characterized in that, It includes: a sampling module, a sliding mode observer, a cross-feedback frequency adaptive filter, a normalized phase-locked loop, a current inner loop, a speed outer loop, a PI regulator, an SVPWM modulation module, an inverter, and a permanent magnet synchronous motor; wherein, the sampling module is connected to the permanent magnet synchronous motor and is used to sample the permanent magnet synchronous motor to obtain a sampled stator current, and perform coordinate transformation on the sampled stator current to obtain a two-phase stationary coordinate system current; and is also used to obtain a sampled stator voltage; both the sampling module and the normalized phase-locked loop are connected to the sliding mode observer, wherein the sampled stator voltage, the two-phase stationary coordinate system current, and the motor speed observation value are input into the sliding mode observer to obtain a back electromotive force observation value; the sliding mode observer is connected to the cross-feedback frequency adaptive filter, and the cross-feedback frequency adaptive filter filters the back electromotive force observation value to obtain a fundamental back electromotive force; the cross-feedback frequency adaptive filter is connected to the normalized phase-locked loop, and the normalized phase-locked loop obtains a rotor position prediction value and a speed prediction value based on the fundamental back electromotive force; the normalized phase-locked loop is connected to the speed outer loop, and the speed prediction value is input into the speed outer loop; the speed outer loop, the speed loop PI regulator, the current inner loop, the current inner loop PI regulator, the SVPWM modulation module, and the inverter are connected in sequence to form an IPMSM vector control module, and finally generate an inverted voltage to drive the IPMSM, and the inverted voltage driving the IPMSM is input into the permanent magnet synchronous motor.

10. A computer-readable storage medium, characterized in that, It stores program instructions, and when the program instructions are executed by a computer, the computer executes the parameter prediction method of the sensorless motor according to any one of claims 1 to 5; or the sensorless motor control method according to claim 6.

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