CN112928959A - Permanent magnet synchronous motor position sensorless control method - Google Patents

Permanent magnet synchronous motor position sensorless control method Download PDF

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CN112928959A
CN112928959A CN202110136459.4A CN202110136459A CN112928959A CN 112928959 A CN112928959 A CN 112928959A CN 202110136459 A CN202110136459 A CN 202110136459A CN 112928959 A CN112928959 A CN 112928959A
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electromotive force
observer
back electromotive
permanent magnet
magnet synchronous
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CN112928959B (en
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高文根
张港
陆华才
倪天明
李鹏飞
何飞帆
陈东
程思锐
裴同
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Anhui Polytechnic University
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Anhui Polytechnic University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
    • H02P21/0007—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using sliding mode control
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
    • H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
    • H02P21/18—Estimation of position or speed
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P25/00—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details
    • H02P25/02—Arrangements or methods for the control of AC motors characterised by the kind of AC motor or by structural details characterised by the kind of motor
    • H02P25/022—Synchronous motors
    • H02P25/024—Synchronous motors controlled by supply frequency
    • H02P25/026—Synchronous motors controlled by supply frequency thereby detecting the rotor position
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P27/00—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage
    • H02P27/04—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage
    • H02P27/06—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters
    • H02P27/08—Arrangements or methods for the control of AC motors characterised by the kind of supply voltage using variable-frequency supply voltage, e.g. inverter or converter supply voltage using dc to ac converters or inverters with pulse width modulation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P6/00—Arrangements for controlling synchronous motors or other dynamo-electric motors using electronic commutation dependent on the rotor position; Electronic commutators therefor
    • H02P6/14—Electronic commutators
    • H02P6/16—Circuit arrangements for detecting position
    • H02P6/18—Circuit arrangements for detecting position without separate position detecting elements
    • H02P6/182—Circuit arrangements for detecting position without separate position detecting elements using back-emf in windings
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P2207/00—Indexing scheme relating to controlling arrangements characterised by the type of motor
    • H02P2207/05—Synchronous machines, e.g. with permanent magnets or DC excitation
    • H02P2207/055—Surface mounted magnet motors

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Control Of Motors That Do Not Use Commutators (AREA)
  • Control Of Ac Motors In General (AREA)

Abstract

The invention discloses a sensorless control method of a surface-mounted permanent magnet synchronous motor, which comprises the following steps: s1, constructing a sliding mode current observer based on a continuous, smooth and dynamically variable function under a mathematical model of a surface-mounted permanent magnet synchronous motor under alpha and beta coordinates; s2, observing error S of stator currenthTaking the value of 0 as a sliding mode surface of the sliding mode current observer, and outputting a back electromotive force signal which changes along with time under the sliding mode surface; s3. output inversion of RLS adaptive filter to sliding mode current observerFiltering the potential pre-signal and outputting a back electromotive force pre-estimated value; and S4, estimating the rotating speed and the rotor position of the motor based on the back electromotive force estimated value. The high-frequency buffeting phenomenon of the system is greatly improved, and the amplitude of phase delay of the system is reduced; the problems that a slip film observer cannot extract a back electromotive force signal and estimate the rotating speed and the rotor position of the motor when the permanent magnet synchronous motor runs at a low speed are solved.

Description

Permanent magnet synchronous motor position sensorless control method
Technical Field
The invention belongs to the technical field of motor control, and particularly relates to a position sensorless control method for a permanent magnet synchronous motor.
Background
The surface-mounted permanent magnet synchronous motor becomes the main drive of the electric automobile due to the advantages of small volume, high stability, simple structure, high power density and the like. However, in the control of the traditional surface-mounted permanent magnet synchronous motor, the rotating speed and the position information of a motor rotor need to be extracted by means of components such as a photoelectric encoder and a rotary transformer, so that the economic cost and the installation difficulty are improved, and a mechanical sensor is easily interfered by the external environment to generate a detection error, so that safety accidents are easily caused, and the safety performance of the whole vehicle is influenced. Therefore, the research on the control strategy of the sensorless surface-mounted permanent magnet synchronous motor has great significance.
The traditional synovial membrane observer algorithm has strong robustness and is welcomed by people due to the characteristics of insensitivity to parameter change and disturbance of a system and the like. However, in the conventional synovial algorithm, the sign function is taken as the switching function of the system, and the high-frequency buffeting phenomenon occurs in the system due to the sudden change of the switching function at the zero point. And the use of a low pass filter of a fixed cutoff frequency for extracting the discrete back electromotive force signal causes a phase delay phenomenon. In addition, the slip film observer needs to rely on the back electromotive force signal of the motor to estimate the motor speed and the rotor position. Since the motor operates at low speed, especially near zero speed, the back emf signal is extremely weak. At the moment, the traditional synovial observer cannot extract a back electromotive force signal, so that the traditional synovial observer fails when the system runs at a low speed.
Disclosure of Invention
The invention provides a position sensorless control method of a permanent magnet synchronous motor, aiming at improving the problems.
The invention is realized in this way, a surface-mounted permanent magnet synchronous motor sensorless control method, the method specifically includes the following steps:
s1, constructing a sliding mode current observer based on a continuous, smooth and dynamically variable function under a mathematical model of a surface-mounted permanent magnet synchronous motor under alpha and beta coordinates;
s2, observing error S of stator currenthTaking the value of 0 as a sliding mode surface of the sliding mode current observer, and outputting a back electromotive force signal which changes along with time under the sliding mode surface;
s3, filtering an output back electromotive force pre-signal of the sliding mode current observer by using an RLS self-adaptive filter, and outputting a back electromotive force pre-estimated value;
and S4, estimating the rotating speed and the rotor position of the motor based on the back electromotive force estimated value.
Further, a synovial membrane current observer is constructed based on a sigmoid function, and a control equation of the synovial membrane current observer is as follows:
Figure BDA0002926878220000021
in the formula: i.e. iα、iβAs measured values of two-phase stator currents, eα、eβTwo-phase back electromotive force which changes along with time; rSIs a stator resistor; l isSIs a stator inductance;
Figure BDA0002926878220000022
a predicted value of two-phase stator current, t representing time; a is a normal number, KSIs the optimized function gain.
Further, corner
Figure BDA0002926878220000023
The final estimate of (d) is:
Figure BDA0002926878220000024
in the disclosure:
Figure BDA0002926878220000025
for the two-phase back electromotive force estimate of the RLS adaptive filter output, Ψ is the rotor flux linkage, K1Is a compensation factor for the phase angle.
Further, a synovial membrane current observer is constructed based on a hyperbolic tangent function tanh, and a control equation of the synovial membrane current observer is as follows:
Figure BDA0002926878220000031
in the formula: i.e. iα、iβAs measured values of two-phase stator currents, eα、eβTwo-phase back electromotive force which changes along with time; rSIs a stator resistor; l isSIs a stator inductance;
Figure BDA0002926878220000032
for a predicted value of the two-phase stator current, t represents time, KSIs the optimized function gain.
Further, if the synovial current observer is constructed based on the hyperbolic tangent function tanh, the method further includes, before step S3:
and amplifying the counter potential signal output by the synovial membrane current observer, and inputting the amplified counter potential signal into the RLS adaptive filter.
Further, corner
Figure BDA0002926878220000033
Is the final estimate of
Figure BDA0002926878220000034
In the disclosure:
Figure BDA0002926878220000035
for the two-phase back electromotive force pre-estimation value output by the RLS adaptive filter, psi is rotor flux linkage, N is the compensation coefficient of the phase angle, K2Is an amplification factor.
The improved slide film observer has the following advantages that 1) the high-frequency buffeting phenomenon of the system is greatly improved; 2) the amplitude of the phase delay of the system is greatly reduced; 3) the convergence rate of the synovial membrane observer is improved; 4) the problems that a slip film observer cannot extract a back electromotive force signal and estimate the rotating speed and the rotor position of the motor when the permanent magnet synchronous motor runs at a low speed are solved; 5) the rotating speed and the rotor position of the permanent magnet synchronous motor are estimated in a full-speed section through a single novel improved SMO observer.
Drawings
FIG. 1 is a block diagram of sensorless control of a surface-mounted permanent magnet synchronous motor based on an improved slip film observer provided by the invention;
FIG. 2 is a schematic block diagram of a conventional SMO observer provided in the practice of the present invention;
fig. 3 is a flowchart of a surface-mount permanent magnet synchronous motor sensorless control method according to an embodiment of the present invention;
FIG. 4 is a schematic block diagram of a synovium observer based on a sigmoid function provided in the implementation of the present invention;
FIG. 5 is a schematic block diagram of a slip film observer based on a hyperbolic tangent function tanh according to an embodiment of the present invention;
FIG. 6 is a graph comparing plots of functions provided by an embodiment of the present invention;
FIG. 7 is a diagram of the motor speed of the SMO observer at high speed, wherein (a) is a comparison diagram of the motor speed of the conventional SMO observer at high speed, (b) is a comparison diagram of the motor speed of the novel improved SMO observer at high speed,
FIG. 8 is a diagram of the rotor position of the motor at high speed for the SMO observer provided by the present invention; wherein, (a) is a motor rotor position comparison diagram of a traditional SMO observer at high speed, and (b) is a motor rotor position comparison diagram of a novel improved SMO observer at high speed;
FIG. 9 is a comparison of motor speeds at low speed for the new and improved SMO observer provided by the present invention;
FIG. 10 is a comparison of motor speeds at low speed for a conventional SMO observer provided by the present invention;
FIG. 11 is a comparison of rotor positions at low speed for a new and improved SMO observer provided by the present invention;
fig. 12 is a graph showing a variation of estimated values of two-phase back electromotive force of a conventional SMO observer at a low speed according to an embodiment of the present invention;
fig. 13 is a graph showing a variation of two-phase back electromotive force estimated values at a low speed by the new and improved SMO observer according to the embodiment of the present invention;
FIG. 14 is a comparison of motor speed at full speed for the new and improved SMO observer provided by an embodiment of the present invention;
FIG. 15 is a comparison of the rotor position of a motor in the full speed range for a new and improved SMO observer provided by an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be given in order to provide those skilled in the art with a more complete, accurate and thorough understanding of the inventive concept and technical solutions of the present invention.
Fig. 1 is a block diagram of sensorless control of a surface-mounted permanent magnet synchronous motor based on an improved slip film observer provided by the invention. The system comprises an SPMSM (surface-mounted permanent magnet synchronous motor), a three-phase inverter module, an SVPWM (space vector pulse width modulation) vector control module and an improved sliding-mode observer module. The control method adopts idThe three-phase current and voltage collected by the sensor are converted into the current component i on the alpha axis under the two-phase static coordinate system through Clark under the 0-vector controlαOn the beta axis of the current component iβVoltage component u on the alpha axisαVoltage component u on the beta axisβThen i isα、iβAnd uα、uβInput to an improved sliding mode observer module. The motor rotating speed and position information estimated by the improved sliding mode observer module are calibrated through a speed loop PI controller and a current loop controller, and the calibration output is a voltage component u on a d axis under a synchronous rotating coordinate systemdVoltage component u on the q-axisqThen, calculating voltage component u on alpha axis under two-phase static coordinate system by inverse Park coordinate transformationαVoltage component u on the beta axisβAnd after Space Vector Pulse Width Modulation (SVPWM), the SVPWM is input to an inverter, the voltage is converted into three-phase alternating current through the inverter and is supplied to a motor, and finally, a motor control system forms a closed-loop control loop.
Equation of state for stationary coordinate system (α β coordinate system) SPMSM:
Figure BDA0002926878220000051
wherein:
Figure BDA0002926878220000061
in the formula: u. ofα、uβIs the two-phase stator voltage; i.e. iα、iβIs a two-phase stator current; e.g. of the typeα、eβIs two-phase counter electromotive force; rSIs a stator resistor; theta is a rotor position angle; l isSIs a stator inductance; omega is the angular speed of the rotor; psi is the rotor flux linkage;
the position and the rotating speed of the SPMSM rotor can be deduced by the formula (2):
Figure BDA0002926878220000062
fig. 2 is a schematic block diagram of a conventional SMO observer provided in the implementation of the present invention, and for convenience of explanation, only the portions related to the embodiment of the present invention are shown.
From equation (3), it can be seen that the back electromotive force signal must be accurately estimated to obtain the rotor position and the rotation speed of the SPMSM. The equation for conventional SMO control is given below:
Figure BDA0002926878220000063
in the formula:
Figure BDA0002926878220000064
-two-phase stator current estimate; sign (x) -sign function; k is the gain factor.
And (3) obtaining a current error equation of the SPMSM by subtracting the formula (4) and the formula (1):
Figure BDA0002926878220000065
since the input control quantity is discrete signal, a low-pass filter is usually needed to extract continuous back electromotive force signal from the motor. Back electromotive force
Figure BDA0002926878220000071
The estimation equation of (a) is as follows:
Figure BDA0002926878220000072
in the formula:
Figure BDA0002926878220000073
ωcis the cut-off frequency of the low-pass filter;
estimates of SPMSM rotor position and speed can be derived from equation (3):
Figure BDA0002926878220000074
the phase delay phenomenon can be generated due to the fact that the low-pass filter is used for extracting the back electromotive force signal, and therefore a certain error exists between the estimated rotor position and the actual positionThe magnitude is inversely proportional to the cutoff frequency. Usually, a certain angle compensation is added after the estimated value of the rotor position to reduce the error between the estimated value and the actual value. Corner
Figure BDA0002926878220000075
Final estimate of (d):
Figure BDA0002926878220000076
fig. 3 is a flowchart of a surface-mount permanent magnet synchronous motor sensorless control method according to an embodiment of the present invention, where the method specifically includes the following steps:
s1, constructing a sliding mode current observer (SMO) based on a continuous, smooth and dynamically variable function under a mathematical model of a surface-mounted permanent magnet synchronous motor under alpha and beta coordinates;
s2, observing error S of stator currenthTaking the value of 0 as a sliding mode surface of the sliding mode current observer, and outputting a back electromotive force signal which changes along with time under the sliding mode surface;
s3, filtering an output back electromotive force pre-signal of the sliding mode current observer by using an RLS self-adaptive filter, and outputting a back electromotive force pre-estimated value;
and S4, estimating the rotating speed and the rotor position of the motor based on the back electromotive force estimated value.
Fig. 4 is a schematic block diagram of a synovium observer based on a sigmoid function provided in the implementation of the present invention, and only the part relevant to the embodiment of the present invention is given for convenience of explanation.
The switching function of the traditional sliding film control strategy is a sign function, and the high-frequency jitter phenomenon of the system occurs due to the discontinuity of the sign function at a zero point. In order to solve this problem,
a continuously smooth and dynamically variable sigmoid(s) is used herein to replace the sign function in the traditional synovial membrane control strategy. sigmoid(s) expression is as follows:
Figure BDA0002926878220000081
in the formula: a is a normal number, the value range is 1-300, and the change of the value a can directly influence the convergence speed of the function, so that the robustness of the system can be improved by selecting a proper value for a, and the phenomena of system buffeting and phase delay are reduced better.
The equations for designing improved SMO control are as follows:
Figure BDA0002926878220000082
in the formula: kSGain for the optimized function;
from equation (1) -equation (10), the current error equation for the SPMSM for improved SMO control can be derived:
Figure BDA0002926878220000083
equation (11) can also be expressed as follows:
Figure BDA0002926878220000091
selecting a slip form surface as follows:
Figure BDA0002926878220000092
according to the synovial variable structure control strategy, defining S h0 is the sliding mode surface of the system. When the system continuously carries out high-frequency and small-amplitude motion on the sliding mode surface and finally approaches to coincide with the sliding mode surface, the numerical value of the estimated current can be determined as an actual value. The key control factor is the back electromotive force signal generated when the system operates.
In order to obtain the back electromotive force signal, a low-pass filter with fixed cut-off frequency is mostly adopted in the traditional mode to extract an estimated value of the back electromotive force, and then the back electromotive force estimated value is subjected to arc tangent operation to obtain an estimated value [ formula (6) -formula (8) ] of the position and the rotating speed of the SPMSM rotor. However, the use of a low-pass filter with a fixed cutoff frequency to extract the back electromotive force signal is not accurate because it cannot perform appropriate filtering adjustment in real time in accordance with changes in the input signal. The extracted signals contain more interference signals, and the required back electromotive force signals cannot be accurately extracted, so that the problems of high-frequency buffeting, phase delay and observation failure at a low-speed stage of the traditional SMO observer occur. In order to solve the problems, the invention adopts a recursive least square algorithm RLS (recursive least-squares) adaptive filter to replace a low-pass filter in the traditional sliding mode control strategy.
The RLS adaptive filter calculates and updates the weight and the coefficient of an FIR (finite Impulse response) filter in real time through a recursive least square algorithm, and filters out an optimal waveform through a signal input into the filter through an adaptive weight control strategy. The matrix equation for the RLS adaptive filter is expressed as follows:
Figure BDA0002926878220000093
y(n)＝ω(n-1)u(n) (13)
e(n)＝d(n)-y(n) (14)
ω(n)＝ω(n-1)+kH(n)e(n) (15)
P(n)＝λ-1P(n-1)-λ-1k(n)uHP(n-1) (16)
in the formula: n-current time index; u (n) -the nth step buffers the vector of input samples; p (n) -the inverse covariance matrix of step n; k (n) -the gain vector at step n; ω (n) -the vector of the filter tap estimates of step n; y (n) -the filtered output at step n; e (n) -n steps of estimation error; d (n) -expected response at step n; λ -forgetting factor;
the RLS adaptive filter comprises two input ends and one output end, and Z isαInputting the input end u (n) and d (n) of an RLS adaptive filter, and the output end y (n) outputting the back electromotive force estimated value
Figure BDA0002926878220000101
Will ZβInputting another RLS adaptive filterThe input ends u (n) and d (n) of the filter, and the output end y (n) output the back electromotive force estimated value
Figure BDA0002926878220000102
u (n) and d (n) correspond to the current error input samples in the synovial observer, and y (n) is the back emf estimate output by the RLS adaptive filter, with λ taken to be 1.
Although the RLS adaptive filter is greatly improved compared with the conventional low-pass filter, some phase delay problems are inevitably caused. In order to reduce the influence of the phase delay, an angle compensation is added to the estimated value of the rotor position to reduce the error between the estimated value and the actual value after the arctan operation of the formula (7). Corner
Figure BDA0002926878220000103
Final estimate of (d):
Figure BDA0002926878220000104
Figure BDA0002926878220000105
in the formula:
Figure BDA0002926878220000106
for the two-phase back electromotive force estimate of the RLS adaptive filter output, Ψ is the rotor flux linkage, K1Are the common coefficients for filter phase angle compensation.
Compared with the traditional sliding mode control strategy, the improved sliding mode control strategy based on the sigmoid function and the RLS adaptive filter reduces the buffeting phenomenon of the switching function in the transition stage, and meanwhile, the adaptive filtering method is adopted to improve the extraction precision of the back electromotive force signal, so that the problems of high-frequency buffeting, phase delay, difficulty in extracting fundamental wave signals in a low-speed stage and the like are obviously improved.
A simulation traditional SMO algorithm model is built in Matlab/Simulink. The simulation model takes a surface-mounted permanent magnet synchronous motor as an example, and specific parameters of the motor are shown in table 1. The system simulation time length is set to be 0.1s, and the full-speed section is subjected to mutation at multiple moments to further verify the stability and the tracking performance of the system.
TABLE 1 SPMSM parameters
Parameter(s) Unit of Value taking
Stator resistor R/Ω 2.875
Magnetic linkage ψfWb 0.175
Moment of inertia J/(kg·m2) 0.001
Number of pole pairs Pn 4
Stator inductance Ls/mH 8.5
Moment of inertia J/kg·m2 0.001
FIGS. 7(a) and 8(a) are graphs comparing actual values and estimated values of motor speed and rotor position of a conventional SMO observer, respectively, and FIGS. 7(b) and 8(b) are graphs comparing actual values and estimated values of motor speed and rotor position of a novel improved SMO observer, respectively; firstly, the rotating speed is suddenly set to 1000r/min when the rotating speed of the motor is 500r/min and 0.05 s. Comparing (a) and (b) of fig. 7 and (a) and (b) of fig. 8, respectively, it can be seen that the new and improved SMO observer greatly reduces the high-frequency buffeting and phase delay phenomena when the system operates at high speed, and meanwhile, the convergence speed of the system is faster and the tracking performance is better.
Fig. 9 and fig. 11 are graphs comparing the motor speed and the rotor position of the new improved SMO observer in the low speed condition of the system, respectively, and fig. 10 is a graph comparing the motor speed and the rotor position of the conventional SMO observer in the low speed condition of the system, and first, the rotation speed is suddenly changed to 100r/min when the motor speed is 10r/min and 0.05s is given. Comparing fig. 9 with fig. 10, it can be seen intuitively that the new improved SMO observer based on the sigmoid function and the RLS adaptive filter not only solves the problem that the conventional SMO observer fails in the low-speed section, but also greatly improves the high-frequency buffeting phenomenon during the operation of the system. As can be seen from fig. 9, the new and improved SMO observer can perform accurate estimation on the position of the rotor of the motor when the system operates at a low speed. As can be seen from fig. 9 and 11, the new improved SMO observer based on the sigmoid function and the RLS adaptive filter solves the problem that the fundamental wave model control strategy, which takes the sliding mode control strategy as an example, is difficult to estimate information such as the rotation speed of the motor and the position of the rotor at a low speed.
In order to more intuitively show the improvement of the motor rotating speed and the rotor position estimation precision performance of the novel improved SMO observer compared with the traditional SMO observer, a low-speed section with small back electromotive force is selected for simulation test.
Fig. 12 and 13 are curves of the changes of the two estimated values of the opposite electromotive forces of the motor under the low-speed operation condition of the system by the conventional SMO observer and the novel improved SMO observer, respectively. The rotating speed of the motor is set to be 100 r/min. Comparing fig. 12 and fig. 13, it can be seen intuitively that the RLS adaptive filter adopted by the novel improved SMO observer greatly improves the filtering performance and can extract a more accurate back electromotive force signal, compared with a low-pass filter used by a conventional SMO observer. The method lays a foundation for the novel improved SMO observer to realize better observation effect on the rotation speed and the rotor position information of the motor in the full-speed section.
In order to better simulate the phenomenon that the rotating speed of the motor is continuously changed in the driving process of the electric automobile. Fig. 14 and 15 are graphs comparing actual values and estimated values of motor speed and rotor position of the new and improved SMO observer, respectively, when the system is operating in the full speed range. Firstly, the rotating speed of the motor is set to be 10r/min, the rotating speed is suddenly changed to be 300r/min when the rotating speed is 0.02s, the rotating speed is suddenly changed to be 1400r/min when the rotating speed is 0.04s, and the rotating speed is suddenly changed to be 600r/min when the rotating speed is 0.07 s. As can be seen from fig. 14 and fig. 15, the new and improved SMO observer based on the sigmoid function and the RLS adaptive filter can realize accurate estimation of the motor speed and the rotor position information in the full-speed section.
Fig. 5 is a schematic block diagram of a slip film observer based on a hyperbolic tangent function tanh provided in the implementation of the present invention, and for convenience of explanation, only the part related to the embodiment of the present invention is given.
The switching function of the traditional sliding film control strategy is a sign function, and the high-frequency jitter phenomenon of the system occurs due to the discontinuity of the sign function at a zero point. To solve this problem, the hyperbolic tangent function tanh(s) of the present invention replaces the sign function signum(s) in the conventional synovial membrane control strategy. A graph of the switching function is shown in fig. 6.
Research and improvement are carried out on the high-frequency buffeting phenomenon caused by the sudden change of the switching function at the zero point by a plurality of scholars at home and abroad. The sign function signum(s) in the conventional SMO observer is replaced by the functions of saturation function sat(s), continuous function theta(s), arctan function atan(s), hyperbolic tangent function tanh(s), sigmoid(s). A graph of various switching functions is shown in fig. 6.
As can be seen from fig. 6, the value of sign function signum(s) at the zero crossing point is suddenly changed from-1 to 1, and the function has a sudden change characteristic, so that the whole system generates a high-frequency buffeting phenomenon. To improve this, a function should be chosen that is much smoother at the zero point and converges at a faster rate. From the above figure, it can be seen that the sigmoid(s) function converges at the fastest rate and is overly relatively smooth compared to other curves. However, the sigmoid(s) function has high-order exponents, and a processor is required to perform a large number of exponential operations, which not only needs to be equipped with a high-performance processor in the system, but also has relatively long operation time.
Based on the above considerations, an analysis is performed in conjunction with FIG. 6. Finally, the hyperbolic tangent function tanh(s) which is relatively simple in function structure and smooth in transition is adopted to replace a symbolic function in the traditional slip film control strategy. The expression of the hyperbolic tangent function tanh(s) is as follows:
Figure BDA0002926878220000131
the mathematical equation for designing the new improved SMO observer is as follows:
Figure BDA0002926878220000141
in the formula: kSIs the optimized function gain.
The current error equation for the SPMSM for improved SMO control can be derived from equations (1) - (10):
Figure BDA0002926878220000142
equation (21) can also be expressed as follows:
Figure BDA0002926878220000143
selecting a slip form surface as follows:
Figure BDA0002926878220000144
according to the synovial variable structure control strategy, defining S h0 is the sliding mode surface of the system. When the system continuously carries out high-frequency and small-amplitude motion on the sliding mode surface and finally approaches to coincide with the sliding mode surface, the numerical value of the estimated current can be determined as an actual value. The key control factor is the back electromotive force signal generated when the system operates.
The method aims at the problem that the traditional SMO observer cannot estimate the rotating speed and the rotor position information of the motor due to the fact that a back electromotive force signal is weak and difficult to extract under the condition that the motor runs at a low speed. The invention adds an amplification factor K before the low-pass filter2Increasing the back emf signal for filter acquisition, K2The value of (a) is within a reasonable range not exceeding the system threshold value in engineering. At this time, two-phase counter electromotive force eα、eβThe equation of (a) is as follows:
Figure BDA0002926878220000145
in the formula: k2Is a positive real number; in addition, in order to extract a purer back electromotive force signal from a plurality of interference signals, the invention abandons a low-pass filter with fixed cut-off frequency used in the traditional SMO observer and adopts an adaptive filter of Recursive Least Square (RLS) algorithm.
The low-pass filter with fixed cut-off frequency can not carry out proper filtering adjustment according to the change of an input signal in real time, so that the extracted signal contains more interference signals, the system has buffeting and phase delay phenomena, and the convergence rate of the system is also seriously influenced. The RLS adaptive filter calculates and updates the weight and the coefficient of the FIR (finite Impulse response) filter in real time through a recursive least square algorithm, and filters out the optimal waveform of the signal input into the filter through an adaptive weight control strategy, thereby greatly improving the phenomena of high-frequency buffeting and phase delay of the system and simultaneously improving the convergence rate of the system.
From the RLS adaptive filter, the amplification factor K is extracted2The back emf signal after boosting. Then performing arc tangent operation on the obtained product, and introducing 1/K into the arc tangent operation module2The estimated value is restored, and the estimated values of the position and the rotating speed of the SPMSM rotor can be obtained as follows:
Figure BDA0002926878220000151
although the performance of the RLS adaptive filter is greatly improved compared with that of the conventional low-pass filter, some phase delay problems are inevitably caused. Usually, a certain angle compensation is added after the estimated value of the rotor position to reduce the error between the estimated value and the actual value. Corner
Figure BDA0002926878220000152
The final estimate of (d) is:
Figure BDA0002926878220000153
in the formula:
Figure BDA0002926878220000154
and N is a commonly used coefficient for compensating the phase angle of the filter in engineering.
Replacing the sign function with a hyperbolic tangent function tanh(s) to make it excessively smoother; the solution to the phase delay problem is to replace the low pass filter of the fixed cut-off frequency with a variable cut-off frequency, recursive least squares, RLS, adaptive filter; the solution of the problem that the counter electromotive force signal is weak and difficult to extract in the low-speed section and causes estimation distortion is to add a K before the RLS adaptive filter2The multiplying amplifying module amplifies the back electromotive force signal and filters out the needed back through the RLS adaptive filterAfter the electromotive force signal is estimated by the arc tangent algorithm to the rotor position and the rotating speed information, the obtained estimated value is reduced by K2The practicality of the estimated value is guaranteed by times, so that the novel improved SMO observer can accurately estimate the position and the rotating speed information of the motor rotor when the system runs at a low speed. Meanwhile, the function of estimating the rotating speed and the rotor position of the motor can be realized by using a single observer and being suitable for the full-speed section of the motor.
The invention has been described above with reference to the accompanying drawings, it is obvious that the invention is not limited to the specific implementation in the above-described manner, and it is within the scope of the invention to apply the inventive concept and solution to other applications without substantial modification.

Claims (6)

1. A surface-mounted permanent magnet synchronous motor sensorless control method is characterized by comprising the following steps:
s1, constructing a sliding mode current observer based on a continuous, smooth and dynamically variable function under a mathematical model of a surface-mounted permanent magnet synchronous motor under alpha and beta coordinates;
s2, observing error S of stator currenthTaking the value of 0 as a sliding mode surface of the sliding mode current observer, and outputting a back electromotive force signal which changes along with time under the sliding mode surface;
s3, filtering an output back electromotive force pre-signal of the sliding mode current observer by using an RLS self-adaptive filter, and outputting a back electromotive force pre-estimated value;
and S4, estimating the rotating speed and the rotor position of the motor based on the back electromotive force estimated value.
2. The sensorless control method of the surface-mounted permanent magnet synchronous motor according to claim 1, wherein a synovial current observer is constructed based on a sigmoid function, and a control equation of the synovial current observer is as follows:
Figure FDA0002926878210000011
in the formula: i.e. iα、iβAs measured values of two-phase stator currents, eα、eβTwo-phase back electromotive force which changes along with time; rSIs a stator resistor; l isSIs a stator inductance;
Figure FDA0002926878210000012
a predicted value of two-phase stator current, t representing time; a is a normal number, KSIs the optimized function gain.
3. The sensorless control method of a surface-mounted permanent magnet synchronous motor according to claim 2, wherein the rotation angle is
Figure FDA0002926878210000013
The final estimate of (d) is:
Figure FDA0002926878210000014
in the disclosure:
Figure FDA0002926878210000015
for the two-phase back electromotive force estimate of the RLS adaptive filter output, Ψ is the rotor flux linkage, K1Is a compensation factor for the phase angle.
4. The sensorless control method of the surface-mounted permanent magnet synchronous motor according to claim 1, wherein a synovial current observer is constructed based on a hyperbolic tangent function tanh, and a control equation of the synovial current observer is as follows:
Figure FDA0002926878210000021
in the formula: i.e. iα、iβAs measured values of two-phase stator currents, eα、eβIs two-phase counter-current with time variationA momentum; rSIs a stator resistor; l isSIs a stator inductance;
Figure FDA0002926878210000022
for a predicted value of the two-phase stator current, t represents time, KSIs the optimized function gain.
5. The sensorless control method of the surface-mounted permanent magnet synchronous motor according to claim 4, wherein if the synovial current observer is constructed based on the hyperbolic tangent function tanh, the method further comprises, before step S3:
and amplifying the counter potential signal output by the synovial membrane current observer, and inputting the amplified counter potential signal into the RLS adaptive filter.
6. The sensorless control method of a surface-mounted permanent magnet synchronous motor according to claim 5, wherein the rotation angle is
Figure FDA0002926878210000023
The final estimate of (d) is:
Figure FDA0002926878210000024
in the disclosure:
Figure FDA0002926878210000025
for the two-phase back electromotive force pre-estimation value output by the RLS adaptive filter, psi is rotor flux linkage, N is the compensation coefficient of the phase angle, K2Is an amplification factor.
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