CN111371356B - PMSM rotor observation method based on variable parameter PI control - Google Patents
PMSM rotor observation method based on variable parameter PI control 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
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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
- H02P21/0014—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control using neural networks
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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
- 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
- H02P2203/00—Indexing scheme relating to controlling arrangements characterised by the means for detecting the position of the rotor
- H02P2203/03—Determination of the rotor position, e.g. initial rotor position, during standstill or low speed operation
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- Feedback Control In General (AREA)
- Control Of Electric Motors In General (AREA)
Abstract
The invention relates to a PMSM rotor observation method based on variable parameter PI control, which obtains the extended back electromotive force of PMSM through an observer, and obtains the rotor position and the rotating speed through the calculation of a phase-locked loop according to the extended back electromotive force, and the proportional parameters of the observer and a PI controller in the phase-locked loop are adjusted in real time according to one or more parameters of the PMSM rotor position, the PMSM rotor rotating speed and the controller input deviation. Compared with the prior art, the invention has the advantages of wide application range and the like.
Description
Technical Field
The invention relates to the technical field of motor control, in particular to a PMSM rotor observation method based on variable parameter PI control.
Background
For the PMSM magnetic field directional control FOC, the rotating position of a rotor is required to be detected in real time. Usually, a rotary encoder is used to detect the position of the rotor, but the external encoder increases the cost, reduces the reliability, and has limited application occasions, so that a position sensor-free method is required in some applications. At present, the common position-sensorless method based on a motor mathematical model in the high-speed stage of the motor comprises the following steps: SMO (sliding mode observer), EKF (extended Kalman Filter), MRAS (reference model adaptive method), and the like. These methods have various advantages and disadvantages: the SMO method is simple in calculation and good in robustness, but buffeting exists, and various methods adopted by the academic world can only reduce influence but cannot eliminate the buffeting; the EKF method has excellent anti-interference performance, but has large calculation amount, high requirement on the calculation capability of a microcontroller and difficult realization; the MRAS has a simple structure, but the position is obtained by velocity integration, so that the problem of error accumulation exists; meanwhile, because the motor and the model are actually nonlinear, the fixed observer control law parameters can keep better control performance only when the rotating speed of the rotor is within a certain range, the control performance comprises steady-state error, dynamic performance and robustness, and the applicable rotating speed range is narrow.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a PMSM rotor observation method based on variable parameter PI control, and the PMSM rotor observation method is wide in application range.
The purpose of the invention can be realized by the following technical scheme:
a PMSM rotor observation method based on variable parameter PI control obtains the extended back electromotive force of PMSM through an observer, and obtains the rotor position and rotation speed through the calculation of a phase-locked loop according to the extended back electromotive force, the proportional parameters of the observer and a PI controller in the phase-locked loop are adjusted in real time according to one or more of the PMSM rotor rotation speed and the controller input deviation, or a variable parameter proportional link is connected in series at the input end or the output end of the PI controller under the condition of not changing the proportional parameter of the PI controller, and the control effect same as that of adjusting the proportional parameter of the PI controller is achieved.
The proportional parameter of the PI controller is adjusted by connecting a variable parameter proportional link in series at the input end or the output end of the PI controller.
The observer comprises a PMSM model and a PI controller, and is used for measuring two-phase voltage [ u ] under a two-phase static coordinate system a u β ] T Inputting actual PMSM and PMSM model, and correspondingly obtaining [ i a i β ] T And
PI controller in observer with [ i ] a i β ] T And
as an input, outputs an extended back-emf
The expanded back electromotive force is fed back to the PMSM model to generate a control action so that the output deviation of the actual PMSM and the actual PMSM model is zero;
the phase-locked loop comprises a comparison link, a PI controller, a low-pass filter, an integration link and a feedback link which are sequentially connected to form a loop, wherein the comparison link takes an expanded counter potential as input and outputs a phase difference, the phase difference is input into the PI controller in the phase-locked loop and outputs a PMSM rotor rotating speed omega r Said low pass filter pair ω r Filtering to suppress omega r The integrating element is filtered omega r And the PMSM rotor position theta is input and output, and the sine and cosine of the theta are fed back to the comparison link by the feedback link.
Further, the state equation of the PMSM model adopted in the observer is:
wherein i a And i β Is two-phase current in a two-phase static coordinate system, R is phase resistance, L s Is a phase inductance,. Psi r For rotor flux linkage, omega r The rotating speed of the PMSM rotor is shown, and theta is the position of the PMSM rotor;
in the observer, ignore ψ r Said extended back-emf and ω r Proportional relation, therefore, the PI controller in the observer is only within a certain range of omega r To achieve better performance, K p1 Should sum ω r Proportional change, in the phase-locked loop, the closed loop pole will expand with the counter potential or omega r Change in size, K p2 And ω r The inverse proportion is changed; adjusting K in real time according to set rule p1 And K p2 Can achieve the same excellent performance in a wider speed range,wherein omega r The output of the phase-locked loop can be obtained by expanding the square sum of counter potentials to obtain a root, and the rotation position differential can also be obtained.
Further, the calculation formula for adjusting the proportional parameter according to the PMSM rotor speed is as follows:
wherein, K p1 Proportional parameters, x, for PI controllers in the observer 1 、x 2 A and b are constants, ω rpu Is the per unit value, K, of the PMSM rotor speed p11 Is omega rpu K when =1 p1 A value;
wherein, K p2 Proportional parameter, x, for PI controller in phase-locked loop 3 And x 4 Is a constant number, K p21 Is omega rpu K at =1 p2 A value;
because of the omega of the actual PMSM r Is of maximum extent, and K p1 And K p2 Too small a parameter will lose control and too large will cause oscillation, so for K p1 And K p2 The upper and lower boundary limits are made because the phase-locked loop input introduces omega simultaneously r The information of the size and the direction of the motor can cause the reversal position to be reversed, namely the position of a motor rotor is theta, the calculation result is-theta, the motor is not rotated, and therefore K p2 The change law contains both velocity and direction information to correct the phase reversal.
Further, the calculation formula for adjusting the proportional parameter according to the input deviation of the controller is as follows:
wherein, K p1 As proportional parameters of PI controllers in the observer，e pu For the controller, the per unit value of the deviation, x, is input 5 、x 6 、x 7 M and n are constants, K p11 Is e pu K when = (m + n)/2 p1 A value;
wherein, K p2 Proportional parameter, K, for PI controllers in phase-locked loops p21 Is e pu K when = (m + n)/2 p2 The value is obtained.
Further, the proportional parameters are controlled and adjusted through a neural network according to the input deviation of the controller and the rotation speed of the PMSM rotor, the neural network is a single-layer neural network formed by 2 neurons, and the sum of the input deviation of the controller and the rotation speed of the PMSM rotor is calculated as e pu And ω rpu As input, output K p1 And K p2 Calculating according to formula (3) and formula (5) and superposing according to a certain weight to obtain K p1 K is obtained by calculation according to formula (4) and formula (5) and superposition according to certain weight p2 。
Further, the proportional parameter is adjusted by a fuzzy control method according to the input deviation of the controller and the rotation speed of the PMSM rotor, and the fuzzy control method specifically comprises the following steps:
inputting the per unit value e of the deviation into the controller pu And per unit value omega of PMSM rotor speed rpu Fuzzification, respectively constructing proportional parameter K of PI controller in observer p1 Proportional parameter K of PI controller in phase-locked loop p2 With respect to e pu And omega rpu Obtaining K from the fuzzy rule table p1 And K p2 Establishing a de-fuzzy rule table, and obtaining K according to the fuzzy grade and the de-fuzzy rule table p1 And K p2 。
Further, K is adjusted by a fuzzy control method according to the input deviation and the PMSM rotor speed p1 And K p2 The method specifically comprises the following steps:
e is to be pu And ω rpu Fuzzification of each of the constructs K p1 And K p2 With respect to e pu And ω rpu Of the fuzzy rule table, obtainingK p1 And K p2 Establishing a de-fuzzy rule table, and obtaining K according to the fuzzy grade and the de-fuzzy rule table p1 And K p2 。
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention arranges a first PI controller in an observer, inputs the output deviation of actual PMSM and PMSM models into the first PI controller, outputs extended back electromotive force, inputs the extended back electromotive force into a phase-locked loop to obtain the position and the rotating speed of a PMSM rotor, wherein a second PI controller is added into the phase-locked loop, and a proportionality coefficient K of the first PI controller p1 And the proportionality coefficient K of the second PI controller p2 One or more parameters of PMSM rotor speed and input deviation are adjusted in real time, and the expanded counter potential is in direct proportion to the rotor speed, so that the application range of the rotor speed is expanded, and the same excellent steady-state error, dynamic performance and robustness can be achieved in a wider range of the rotor speed;
(2) The invention adjusts K in real time through the parameter adjusting module p1 And K p2 While at the same time limiting K p1 And K p2 Upper and lower boundaries of, avoiding K p1 And K p2 Too large leads to unstable rotating speed of a motor rotor, or too small leads to loss of control of a PI controller, the motor cannot rotate, and simultaneously, because the rotating speed and the direction information are introduced into the phase-locked loop input at the same time, the reverse position is reversed, and K is adjusted p2 The rotating speed and direction information of the rotor are referred, the reverse position phase reversal can be corrected, and the normal operation of the motor is ensured;
(3) The invention adopts a single-layer neural network consisting of two neurons to optimize K p1 And K p2 The stability is good in the adjusting process of (2);
(4) The invention integrates the rotor speed and the deviation input into the PI controller, adopts a fuzzy control method and has good robustness.
Drawings
FIG. 1 is a schematic view of the structure of an observer;
FIG. 2 is a diagram illustrating a phase-locked loop;
FIG. 3 is a schematic diagram of a PI controller;
FIG. 4 is e pu Fuzzification schematic diagram;
FIG. 5 is ω rpu Fuzzification schematic diagram;
FIG. 6 is a schematic diagram of a neural network architecture;
FIG. 7 is a schematic diagram of a series connection of variable parameter ratio links at the output of a PI controller;
fig. 8 is a schematic structural diagram of a series connection of variable parameter ratio links at the input end of the PI controller.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
A PMSM rotor observation method based on variable parameter PI control obtains an extended back electromotive force of a PMSM through an observer, obtains a rotor position and a rotating speed through a phase-locked loop according to the extended back electromotive force, and the proportional parameters of a PI controller in the observer and the phase-locked loop are adjusted in real time according to one or more parameters of the rotating speed of the PMSM rotor and input deviation of the controller.
As shown in FIG. 1, the observer comprises a PMSM model and a PI controller, and converts two-phase voltage [ u ] in a two-phase static coordinate system a u β ] T Inputting actual PMSM and PMSM model, and correspondingly obtaining [ i a i β ] T And
PI controller in observer with [ i ] a i β ] T And
as an input, outputs an extended back-emf
And feeding back the extended back-emf to the PMSM model to generate a control action to enable the actual PMSM and PMSM model to beThe output deviation is zero;
as shown in FIG. 2, the phase-locked loop comprises a comparison link, a PI controller, a low-pass filter, an integration link and a feedback link which are connected in sequence to form a loop, wherein the comparison link takes an expanded counter potential as input, outputs a phase difference, the phase difference is input into the PI controller in the phase-locked loop, and outputs a PMSM rotor rotation speed omega r Low pass filter pair omega r Filtering to suppress omega r High frequency noise above, integration element with filtered omega r And the PMSM rotor position theta is input and output, and the sine and the cosine of the theta are fed back to the comparison link by the feedback link.
The observer and the controller in the phase locked loop both adopt the structure shown in fig. 3, k p To adjustable proportionality coefficient, T i For the adjustable integral constant, as shown in fig. 7 and 8, a parameter-variable proportional link is connected in series at the input end or the output end of the PI controller under the condition of not changing the proportional parameter of the PI controller, so that the control effect same as that of adjusting the proportional parameter of the PI controller is achieved.
The state equation of the PMSM model adopted in the observer is as follows:
wherein i a And i β Is two-phase current in a two-phase static coordinate system, R is phase resistance, L s Is a phase inductance,. Psi r For rotor flux linkage, ω r The rotating speed of the PMSM rotor is shown, and theta is the position of the PMSM rotor;
in the observer, ignore ψ r Expanding the back emf and ω r Proportional relation, therefore, the PI controller in the observer is only within a certain range of omega r To achieve better performance, K p1 Should sum with ω r Proportional change, in the phase-locked loop, the closed loop pole will expand with the counter potential or omega r Change in size, K p2 And ω r In inverse proportion; adjusting K in real time according to set rules p1 And K p2 The same excellent performance can be achieved over a wider speed range, where ω r The output can be obtained by a phase-locked loop, the square sum root of the expanded counter electromotive force can be obtained, and the rotation position can be differentiated.
The calculation formula for adjusting the proportional parameter according to the PMSM rotor speed is as follows:
wherein, K p1 Proportional parameters, x, of PI controllers in the observer 1 、x 2 A and b are constants, ω rpu Is the per unit value, K, of PMSM rotor speed p11 Is omega rpu K when =1 p1 A value;
wherein, K p2 Proportional parameter, x, for PI controller in phase-locked loop 3 And x 4 Is a constant number, K p21 Is omega rpu K when =1 p2 A value;
because of the omega of the actual PMSM r Is of maximum extent, and K p1 And K p2 Too small a parameter will lose control and too large will cause oscillation, so for K p1 And K p2 The upper and lower boundary limits are made because the phase-locked loop input introduces omega simultaneously r The information of the size and the direction of the motor can cause the reversal position to be reversed, namely the position of a motor rotor is theta, the calculation result is-theta, the motor is not rotated, and therefore K p2 The change law contains both velocity magnitude and direction information to correct the phase reversal.
The calculation formula for adjusting the proportional parameter according to the input deviation of the controller is as follows:
wherein, K p1 Proportional parameters of PI controllers in the observer, e pu For the controller, the per unit value of the deviation, x, is input 5 、x 6 、x 7 M and n are constants, K p11 Is e pu K at =0.005 p1 A value;
wherein, K p2 For proportional parameters of PI controllers in phase-locked loops, K p21 Is e pu K at =0.005 p2 The value is obtained.
The proportional parameter is controlled and adjusted through a neural network according to the input deviation of the controller and the PMSM rotor speed, as shown in figure 6, the neural network is a single-layer neural network formed by 2 neurons, and e is pu And ω rpu As input, output K p1 And K p2 K is obtained by calculation according to the formula (3) and the formula (5) and superposition according to a certain weight p1 K is obtained by calculation according to formula (4) and formula (5) and superposition according to certain weight p2 。
And adjusting the proportional parameter according to the input deviation of the controller and the PMSM rotor speed by a fuzzy control method, wherein the fuzzy control method specifically comprises the following steps:
inputting the per unit value e of the deviation into the controller pu And per unit value omega of PMSM rotor speed rpu Fuzzification, respectively constructing proportional parameters K of PI controllers in the observer p1 Proportional parameter K of PI controller in phase-locked loop p2 With respect to e pu And ω rpu Obtaining K from the fuzzy rule table p1 And K p2 Establishing a de-fuzzy rule table, and obtaining K according to the fuzzy grade and the de-fuzzy rule table p1 And K p2 。
Adjusting K according to input deviation and PMSM rotor speed by fuzzy control method p1 And K p2 The method specifically comprises the following steps:
as shown in FIG. 4, e pu And K p1 Fuzzification, equally dividing intoThe small, medium and large fuzzy sets are 3 fuzzy sets, and the intervals are 0-0.002, 0.001-0.01 and more than 0.008 correspondingly; as shown in fig. 5, let ω be rpu And K p2 Fuzzification, which is divided into 6 fuzzy sets of-large, -medium, -small, small and medium, and the intervals are correspondingly below-1, -1.2 to-0.1, -0.25 to 0.25, 0.1 to 1.2 and more than 1; respectively construct K p1 And K p2 With respect to e pu And ω rpu Fuzzy rule table of, K p1 The fuzzy rule of (1):
TABLE 1K p1 Fuzzy rule table
K p2 The fuzzy rules of (1) are as in table 2:
TABLE 2K p2 Fuzzy rule table
Obtaining K p1 And K p2 Establishing a de-fuzzy rule table, and obtaining K according to the fuzzy grade and the de-fuzzy rule table p1 And K p2 The deblurring rule is shown in the table 3:
TABLE 3 deblurring rule Table
Wherein, K is the K with better effect at medium speed p1 Value sum K p2 The value is obtained.
The embodiment provides a PMSM rotor observation method based on variable parameter PI control, which is applied to digital control on a micro control chip of a PMSM controller.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations can be devised by those skilled in the art in light of the above teachings. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (5)
1. A PMSM rotor observation method based on variable parameter PI control is characterized in that an observer obtains an extended back electromotive force of a PMSM, a phase-locked loop calculates and obtains a rotor position and a rotating speed according to the extended back electromotive force, and proportional parameters of a PI controller in the observer and the phase-locked loop are adjusted in real time according to one or more parameters of the PMSM rotor rotating speed and input deviation of a controller;
the calculation formula for adjusting the proportional parameter according to the PMSM rotor speed is as follows:
wherein, K p1 Proportional parameters, x, for PI controllers in the observer 1 、x 2 A and b are constants, ω rpu Is the per unit value, K, of the PMSM rotor speed p11 Is omega rpu K when =1 p1 A value;
wherein, K p2 Proportional parameter, x, of PI controller in phase-locked loop 3 And x 4 Is a constant, K p21 Is omega rpu K at =1 p2 A value;
the calculation formula for adjusting the proportional parameters according to the input deviation of the controller is as follows:
wherein, K p1 Proportional parameters of PI controllers in the observer, e pu For the controller input per unit value of the deviation, x 5 、x 6 、x 7 M and n are constants, K p11 Is e pu K when = (m + n)/2 p1 A value;
wherein, K p2 For proportional parameters of PI controllers in phase-locked loops, K p21 Is e pu K when = (m + n)/2 p2 A value;
controlling and adjusting the proportional parameter through a neural network according to the input deviation of the controller and the rotating speed of the PMSM rotor;
and adjusting the proportional parameters by a fuzzy control method according to the input deviation of the controller and the PMSM rotor speed.
2. The PMSM rotor observation method based on variable parameter PI control as claimed in claim 1, wherein the state equation of the PMSM model adopted in the observer is as follows:
wherein i a And i β Two-phase current in two-phase static coordinate system, R is phase resistance, L s Is a phase inductance,. Psi r For rotor flux linkage, omega r The PMSM rotor speed is shown, and theta is the PMSM rotor position.
3. The PMSM rotor observation method based on variable parameter PI control as claimed in claim 1, wherein the fuzzy control method specifically comprises:
inputting the per unit value e of the deviation into the controller pu And per unit value omega of PMSM rotor speed rpu Fuzzification, respectively constructing proportional parameter K of PI controller in observer p1 Proportional parameter K of PI controller in phase-locked loop p2 With respect to e pu And ω rpu Obtaining K from the fuzzy rule table p1 And K p2 Establishing a de-fuzzy rule table, and obtaining K according to the fuzzy grade and the de-fuzzy rule table p1 And K p2 。
4. The PMSM rotor observation method based on variable parameter PI control as claimed in claim 1, further adjusting the proportional parameters of the PI controller by connecting a variable parameter proportional link in series at the input end of the PI controller.
5. The PMSM rotor observation method based on variable parameter PI control as claimed in claim 1, further adjusting a proportional parameter of the PI controller by connecting a variable parameter proportional link in series at an output terminal of the PI controller.
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