CN112928959B - Permanent magnet synchronous motor position sensorless control method - Google Patents
Permanent magnet synchronous motor position sensorless control method Download PDFInfo
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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
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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
- H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
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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
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
- H02P21/18—Estimation of position or speed
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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
- 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
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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
- 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
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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
- 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
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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
- 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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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 current h The sliding mode surface of the sliding mode current observer is taken as 0, and a back electromotive force signal which changes along with time under the sliding mode surface is output; 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. 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 position of a rotor of the permanent magnet synchronous motor when the permanent magnet synchronous motor runs at a low speed are solved.
Description
Technical Field
The invention belongs to the technical field of motor control, and particularly relates to a position sensorless control method of 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, a sign function is used as a switching function of the system, and a 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 emf signal causes a phase delay phenomenon. In addition, the synovial 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 current h The sliding mode surface of the sliding mode current observer is taken as 0, and a back electromotive force signal which changes along with time under the sliding mode surface is output;
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:
in the formula: i all right angle α 、i β As measured values of two-phase stator currents, e α 、e β Two-phase back electromotive force which changes with time; r is S Is a stator resistor; l is a radical of an alcohol S A stator inductor;
a predicted value of two-phase stator current, t representing time; a is normal number, K S Is the optimized function gain.
Further, corner
The final estimate of (d) is:
in the disclosure:
for the two-phase back electromotive force estimate of the RLS adaptive filter output, Ψ is the rotor flux linkage, K 1 Is 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:
in the formula: i.e. i α 、i β As measured values of two-phase stator currents, e α 、e β Two-phase back electromotive force which changes along with time; r is S Is a stator resistor; l is a radical of an alcohol S Is a stator inductance;
for a predicted value of the two-phase stator current, t represents time, K S And obtaining the optimized function.
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
Is the final estimate of
In the disclosure:
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, K 2 Is 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 position of a rotor of the permanent magnet synchronous 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 diagram of a 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 by the implementation of the 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; the system comprises a motor, a SMO observer, a motor rotor and a motor rotor, wherein (a) is a comparison diagram of the motor rotor position of the traditional SMO observer at a high speed, and (b) is a comparison diagram of the motor rotor position of the novel improved SMO observer at the 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 variation curve of two-phase back electromotive force estimated values of the conventional SMO observer according to an embodiment of the present invention at a low speed;
fig. 13 is a variation curve of the two-phase back electromotive force estimated value of the new and improved SMO observer according to the embodiment of the present invention at a low speed;
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 diagram of the rotor position of the motor in the full speed section of the new and improved SMO observer provided by the embodiment of the invention.
Detailed Description
The following detailed description of the embodiments of the present invention is provided to help those skilled in the art to more fully, accurately and deeply understand the inventive concept and technical solution of the present invention by describing the embodiments with reference to the accompanying drawings.
Fig. 1 is a sensorless control block diagram 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 i d The 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 control α Beta on-axis current component i β Voltage component u on the alpha axis α Beta on-axis voltage component u β Then i is mixed α 、i β And u α 、u β Input to the modified 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 the voltage component u on the d axis under a synchronous rotating coordinate system d Voltage component u on the q-axis q Then, the voltage component u on the alpha axis under the two-phase static coordinate system is calculated through inverse Park coordinate transformation α Voltage component u on the beta axis β And 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:
wherein:
in the formula: u. of α 、u β Is a two-phase stator voltage; i.e. i α 、i β Is a two-phase stator current; e.g. of a cylinder α 、e β Is two-phase counter electromotive force; r is S Is a stator resistor; theta is the rotor position angle; l is a radical of an alcohol S A stator inductor; 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):
fig. 2 is a schematic block diagram of a conventional SMO observer provided in the implementation of the present invention, and for convenience of description, only the portions related to the embodiment of the present invention are shown.
From equation (3), it can be seen that the back emf signal must be accurately estimated to obtain the rotor position and rotation speed of the SPMSM. The equation for conventional SMO control is given below:
in the formula:
-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) from the formula (1):
since the input control quantity is a discrete signal which is discontinuous, a low-pass filter is usually required to be externally connected to extract a continuous back electromotive force signal from the motor. Back electromotive force
The estimation equation of (c) is as follows:
in the formula:
ω c is the cut-off frequency of the low-pass filter;
the estimated values of the position and the rotation speed of the SPMSM rotor can be obtained according to the formula (3):
since the low-pass filter is used for extracting the back electromotive force signal, a phase delay phenomenon is generated, so that the estimated rotor position has a certain error with the actual position, and the magnitude of the error is inversely proportional to the cut-off 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 of a vehicle
Final estimate of (d):
fig. 3 is a flowchart of a sensorless control method of a surface-mounted permanent magnet synchronous motor 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 current h Taking 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 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 slip 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 instead of the sign function in the traditional slip film control strategy. sigmoid(s) expression is as follows:
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 can be better reduced.
The equation for designing improved SMO control is as follows:
in the formula: k is S Gain for the optimized function;
from equation (1) -equation (10), the current error equation for the SPMSM for improved SMO control can be derived:
equation (11) can also be expressed as follows:
selecting a slip form surface as follows:
according to the synovial variable structure control strategy, defining S h 0 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 approximates to be coincident with the sliding mode surface, the 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 conventional method mostly adopts a low-pass filter with fixed cut-off frequency to extract an estimated value of the back electromotive force, and then performs arc tangent operation on the estimated value of the back electromotive force 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 above problems, the present invention uses a Recursive Least Square (RLS) adaptive filter to replace the low-pass filter in the conventional 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 from a signal input into the filter through an adaptive weight control strategy. The matrix equation for the RLS adaptive filter is expressed as follows:
y(n)＝ω(n-1)u(n) (13)
e(n)＝d(n)-y(n) (14)
ω(n)＝ω(n-1)+k H (n)e(n) (15)
P(n)＝λ -1 P(n-1)-λ -1 k(n)u H P(n-1) (16)
in the formula: n-current time index; u (n) -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) -the expected response at step n; λ — forgetting factor;
the RLS adaptive filter comprises two input terminals and an output terminal, and Z is α Inputting 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
Will Z β Input to input ends u (n) and d (n) of another RLS adaptive filter, and output end y (n) output the back electromotive force estimation value
u (n) and d (n) correspond to current error input samples in the synovial observer, y (n) is the back emf estimate of the RLS adaptive filter output, λ is 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 brought about. 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
Final estimate of (c):
in the formula:
for the two-phase back electromotive force estimate of the RLS adaptive filter output, Ψ is the rotor flux linkage, K 1 Are 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 extraction precision of the back electromotive force signal is improved by adopting the adaptive filtering method, 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 | Value taking |
| Stator resistor | R/Ω | 2.875 |
| Magnetic linkage | ψ f W b | 0.175 |
| Moment of inertia | J/(kg·m 2 ) | 0.001 |
| Number of pole pairs | P n | 4 |
| Stator inductance | L s /mH | 8.5 |
| Moment of inertia | J/kg·m 2 | 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 of the motor is set to be 500r/min and is suddenly set to be 1000r/min when the rotating speed is 0.05 s. Comparing (a) and (b) of fig. 7 and (a) and (b) of fig. 8, respectively, it can be seen that the high-frequency buffeting and phase delay phenomena of the novel improved SMO observer are greatly reduced when the system runs at a 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 stage, but also greatly improves the high-frequency buffeting phenomenon during system operation. 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 carrying out 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 a motor is continuously changed in the driving process of an 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 a full speed section. Firstly, the rotating speed of the motor is set to be 10r/min and is suddenly changed to be 300r/min when the rotating speed is 0.02s, 1400r/min when the rotating speed is 0.04s, and 600r/min when the rotating speed is 0.07 s. As can be seen from fig. 14 and 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 slip 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 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.
From fig. 6, it can be seen that the sign function signum(s) has a value suddenly changed from-1 to 1 at the zero-crossing point, and the function itself 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 transitions are relatively smooth compared to other curves. However, the sigmoid(s) function has high-order exponentials, and a processor is required to perform a large number of exponential operations, which not only requires 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:
the mathematical equation for designing the new and improved SMO observer is as follows:
in the formula: k is S And obtaining the optimized function.
The current error equation for the SPMSM for improved SMO control can be derived from equations (1) - (10):
equation (21) can also be expressed as follows:
selecting a slip form surface as follows:
according to the synovial variable structure control strategy, defining S h 0 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 approximates to be coincident with the sliding mode surface, the value of the estimated current can be determined as an actual value. The key control factor is the back electromotive force signal generated during the operation of the system.
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 filter 2 Increase the counter electromotive forceSignal to facilitate filter acquisition, K 2 The value of (a) is within a reasonable range which does not exceed a system threshold value in engineering. At this time, two-phase counter electromotive force e α 、e β The equation of (c) is as follows:
in the formula: k 2 Is 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 perform 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 generates 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 extracted 2 The back emf signal after boosting. Then performing arc tangent operation on the obtained product, and introducing 1/K into the arc tangent operation module 2 The estimated value is restored, and the estimated values of the position and the rotating speed of the SPMSM rotor can be obtained as follows:
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. Typically at rotor position estimationAfter the value, a certain angle compensation is added to reduce the error between the estimated value and the actual value. Corner of a vehicle
The final estimate of (d) is:
in the formula:
and N is a commonly used coefficient for compensating the phase angle of the filter in engineering.
Replacing the sign function by a hyperbolic tangent function tanh(s) to make the transition 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 to the problem of estimation distortion caused by weak and difficult extraction of back electromotive force signals in the low-speed section is to add a K before an RLS adaptive filter 2 The power amplification module amplifies the back electromotive force signal, filters out the needed back electromotive force signal through the RLS self-adaptive filter, estimates the rotor position and the rotating speed information through the arc tangent algorithm, and reduces the obtained estimated value by K 2 The 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 present invention has been described in detail with reference to the accompanying drawings, and it is to be understood that the invention is not limited to the specific embodiments described above, and that various insubstantial modifications of the inventive concepts and solutions, or their direct application to other applications without modification, are intended to be covered by the scope of the invention.
Claims (3)
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 current h The sliding mode surface of the sliding mode current observer is taken as 0, and a back electromotive force signal which changes along with time under the sliding mode surface is output;
s3, multiplying a back electromotive force signal output by the synovial membrane current observer by an amplification factor K 2 Amplifying, and inputting the amplified back electromotive force signal into an RLS adaptive filter;
s4, filtering the amplified back electromotive force pre-signal by the RLS self-adaptive filter, and outputting a back electromotive force pre-estimated value;
s5, predicting the rotating speed and the rotor position of the motor based on the back electromotive force predicted value and predicting the rotor position
The final estimate of (d) is:
wherein,
for the estimation of the two-phase back electromotive force output by the RLS adaptive filter, psi is the rotor flux linkage, N is the compensation coefficient of the phase angle, K 2 Is an amplification factor.
2. The sensorless control method of the surface-mounted permanent magnet synchronous motor according to claim 1, characterized in that a synovial current observer is constructed based on a sigmoid function, and a control equation of the synovial current observer is as follows:
in the formula: i.e. i α 、i β As measured values of two-phase stator currents, e α 、e β Two-phase back electromotive force which changes along with time; r is S Is a stator resistor; l is a radical of an alcohol S Is a stator inductance;
a predicted value of two-phase stator current, t representing time; a is a normal number, K S Is the optimized function gain.
3. The sensorless control method of the surface-mounted permanent magnet synchronous motor according to claim 1, wherein a slip film current observer is constructed based on a hyperbolic tangent function tanh, and a control equation of the slip film current observer is as follows:
in the formula: i all right angle α 、i β As measured values of two-phase stator currents, e α 、e β Two-phase back electromotive force which changes along with time; r is S Is a stator resistor; l is S Is a stator inductance;
for a predicted value of the two-phase stator current, t representing time, K S Is the optimized function gain.
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