CN114172425B - Permanent magnet synchronous motor prediction control method based on extended state observer - Google Patents
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Classifications
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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
- H02P21/0017—Model reference adaptation, e.g. MRAS or MRAC, useful for control or parameter estimation
-
- 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
-
- 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
- H02P21/22—Current control, e.g. using a current control loop
-
- 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
-
- 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
- H02P2205/00—Indexing scheme relating to controlling arrangements characterised by the control loops
- H02P2205/07—Speed loop, i.e. comparison of the motor speed with a speed reference
-
- 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
Abstract
The invention discloses a permanent magnet synchronous motor prediction control method based on an extended state observer, and belongs to the field of permanent magnet synchronous motor control. The method mainly comprises the following steps: 1. designing an extended state observer based on a supercoiled algorithm; 2. designing a parameter self-adaptive law aiming at the observer in the step 1; 3. and designing a parameter updating mechanism, and improving the capability of resisting parameter mismatch. The invention has the advantages that: firstly, prediction errors caused by parameter mismatch are avoided; secondly, time compensation is carried out by using the observed value, a more concise predictive control equation is constructed, and no additional calculation load is added; thirdly, the observer algorithm parameter setting is simpler, and can be converged in a limited time.
Description
Technical Field
The invention relates to the technical field of motor speed regulation control, in particular to a permanent magnet synchronous motor predictive control method based on an extended state observer.
Background
Currently, with the development of rare earth permanent magnet materials and electric power devices, permanent magnet synchronous motors are widely focused on high power density, high moment of inertia ratio and high efficiency. The current control method of the traditional PMSM widely used at present mainly adopts a vector control method and a direct torque control method. The FOC control has the problems of system constraint conditions and the like, and the DTC control has high response speed but larger torque and flux linkage pulsation. Currently, control system dynamic response is required to be fast, operation efficiency is high, noise vibration is small, and the like for PMSM control performance.
In response to the above problems, researchers have proposed dead-beat predictive control that incorporates SVPWM to enable fast dynamic response and smaller current harmonic control at constant switching frequencies. Deadbeat predictive current control is typically based on a discrete mathematical model under the d-q framework, typically in two steps: (1) by passing throughOne-step current prediction can complete time compensation; (2) and calculating the stator voltage by using the predicted current and an accurate model, so as to realize dead-beat control. It is not difficult to find that the control performance of dead beat control depends on the accuracy of a model, and the parameter ψ is caused by the influence of the running state, temperature and the like during the running of the motor f And L changes, resulting in a decrease in control performance. Therefore, this problem has yet to be solved.
Disclosure of Invention
In order to solve the problem of parameter mismatch, the invention provides a permanent magnet synchronous motor prediction control method based on an extended state observer, which comprises the following steps:
step 1, adopting i d Control strategy =0, i q * Obtained by a rotating speed ring PI regulator;
step 2, designing an extended state observer based on a supercoiled algorithm;
step 3, constructing a discrete mathematical model of the supercoiled extended state observer;
step 4, designing a self-adaptive expansion state observer with limited time convergence;
step 5, constructing a predictive control equation based on the self-adaptive supercoiled expansion state observer;
step 6, designing a parameter updating mechanism to realize model-free predictive control of the permanent magnet synchronous motor;
step 7, constructing a concise predictive control equation according to the estimated value of the observer;
step 8, outputting command voltage according to the predictive control equation
Step 9, willIs limited within the hexagonal voltage vector boundary according to the new command voltage +.>Using SVPWM generates an inverter drive signal.
Further, the design method of the extended state observer based on the supercoiled algorithm in the step 2 is as follows:
the system state equation is
In the method, in the process of the invention,u＝[u d u q ] T ，X＝[Δi d Δi q ] T ，/>
wherein i is d 、i q For d, q axis current measurements, ω e For electric rotation speeds R, L and ψ f Respectively a motor stator resistance, a stator inductance and a rotor permanent magnet flux linkage, wherein X is the external disturbance of the system, h (t) is the derivative of the total disturbance, and u is the system input;
combining the above system state equation, F (S 1 ，S 2 ) Considered as motor internal disturbance, X considered as external disturbance, the lumped disturbance expands into a new state variable Z 2 The supercoiled state observer is constructed as follows:
Z 2 ＝F(S 1 ，S 2 )+X
wherein Z is 1 For d and q axis current observations, Z 2 S is the d and q axis current measured value for the system lumped disturbance observed value;
further, the discrete mathematical model of the supercoiled extended state observer in the step 3 is as follows:
wherein beta is 1 And beta 2 For the gain factor of the observer, T s Is the sampling time.
Further, the adaptive parameters of the adaptive extended state observer in the step 4 are designed as follows:
wherein, delta determines the accuracy of the observation error, alpha is the parameter gain, beta 1 (k)、β 2 (k) Is a time-varying parameter.
Further, in the step 5, a predictive control equation based on the adaptive supercoiled state observer is constructed:
based on Z 1 (k+1) and Z 2 (k+1) performing a second-step prediction to construct a current prediction equation:
according to the reference currentConstructing a dead beat prediction voltage equation:
wherein,d-axis and q-axis current observations, respectively,>i is respectively d And i q Is used for the integrated perturbation of (1),and->Is the command voltage vector output by the controller.
Further, in the step 6, the update of the motor parameter L is designed as follows:
from (1) - (2), the product is prepared by:
wherein,is the d-axis current observation,/->Is i d Is a lumped disturbance of beta 1 Is a system time-varying parameter, E id Is i d Error of observed value and measured value of (2), and if |u d (k-1)-u d (k-2) | > Δ (Δ is an adjustable parameter), μ (k) can be updated by the above equation, if not, μ (k) =μ (k-1).
Further, the model-free predictive control equation in the step 7 is as follows:
wherein,and->Is a current reference value, ">Respectively d, q-axis current observations,/->I is respectively d 、i q Is (are) lumped disturbance->Is the command voltage vector output by the controller.
Further, according to the rotor position information θ e The output command voltage in the step 8 is
Further, in the step 9Limiting to hexagonal voltage vector boundaries, to maximize voltage utilization, hexagonal voltage boundaries are employed instead of inscribed circular voltage vector boundaries. When->Outside the boundary, its phase angle remains unchanged and its amplitude shortens to a hexagonal boundary.
The invention has the beneficial effects that:
the design control method of the invention utilizes the observed value to carry out time compensation, and constructs a more concise predictive control equation under the condition of not adding extra calculated amount. The adaptive supercoiled expansion state observer and the parameter updating mode are designed, so that the system can be converged in a limited time, and the anti-interference performance of the whole system is improved.
Description of the drawings and tables
Table i is the parameters of the permanent magnet synchronous motor;
FIG. 1 is a schematic block diagram of a permanent magnet synchronous motor control;
FIG. 2 is a schematic block diagram of an observer;
FIG. 3 is a parameter update algorithm;
FIG. 4 is a d-q axis current diagram of a conventional dead beat predicted current control with motor parameters matched;
FIG. 5 is a d-q axis current diagram of the control method of the present invention with motor parameters matched;
FIG. 6 is a d-q axis current diagram of a conventional dead beat predicted current control with motor parameter mismatch;
FIG. 7 is a d-q axis current diagram of the control method of the present invention under mismatch of motor parameters.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The following description of the embodiments of the invention is provided with reference to specific embodiments, but is intended to be exemplary of the invention.
The control system block diagram of the invention is shown in fig. 1, and is a proposed control system. The motor parameters in the simulation are shown in table 1.
Table 1 parameters of permanent magnet synchronous motor for simulation
| Rated power/(KW) | 0.43 |
| Rated torque/(N.m) | 2.4 |
| Rated current/A | 4.2 |
| Rated voltage/V | 200 |
| Rated speed/(r/min) | 3000 |
| Torque constant/(n.m/A) | 0.18 |
| Moment of inertia/(kg/m 2) | 0.000706 |
| Stator resistor/omega | 0.72 |
| Stator inductance/mH | 0.4 |
| Permanent magnet flux linkage Wb | 0.0455 |
| Polar logarithm | 4 |
| Sampling frequency/kHz | 25 |
A permanent magnet synchronous motor prediction control method based on an extended state observer comprises the following implementation processes:
1、obtaining q-axis command current of the controller according to the rotating speed outer ring PI regulatord-axis current command->But are merely intended to practice the invention and are not limited to the present method.
2. The design of the extended state observer based on the supercoiled algorithm is as follows:
the system state equation is
In the method, in the process of the invention,＝[u d u q ] T ，X＝[Δi d Δi q ] T ，/>
wherein i is d 、i q For d, q axis current measurements, ω e For electric rotation speeds R, L and ψ f Respectively a motor stator resistance, a stator inductance and a rotor permanent magnet flux linkage, wherein X is the external disturbance of the system, h (t) is the derivative of the total disturbance, and u is the system input;
combining the above system state equation, F (S 1 ，S 2 ) Considered as motor internal disturbance, X considered as external disturbance, the lumped disturbance expands into a new state variable Z 2 The supercoiled state observer is constructed as follows:
Z 2 ＝F(S 1 ，S 2 )+X
wherein Z is 1 For d and q axis current observations, Z 2 S is the d and q axis current measured value for the system lumped disturbance observed value.
3. Obtaining a discrete mathematical model based on the supercoiled algorithm extended state observer according to the Euler formula as
Wherein beta is 1 And beta 2 For the gain factor of the observer, T s Is the sampling time.
4. The adaptive parameters of the adaptive extended state observer are designed as follows:
wherein->
Wherein δ=0.01, α=6.3×10 10 ，β 1 (k)、β 2 (k) Is a time-varying parameter.
5. The method for constructing the predictive control equation based on the adaptive supercoiled state observer comprises the following steps:
based on Z 1 (k+1) and Z 2 (k+1) performing a second-step prediction to construct a current prediction equation
According to the reference currentConstruction of dead beat prediction voltage equation
Wherein,respectively d, q-axis current observations,/->I is respectively d And i q Is (are) lumped disturbance->And->Is the command voltage vector output by the controller.
6. The update mechanism for the motor parameter L is as follows:
from (1) - (2), the product is prepared by:
wherein,is the d-axis current observation,/->Is i d Is a lumped disturbance of beta 1 Is a system time-varying parameter, E id Is i d Is a function of the observed and measured values of (a)Error, and if |u d (k-1)-u d (k-2) | > 90, μ (k) can be updated by the above equation, if not, μ (k) =μ (k-1).
7. Constructing a succinct predictive control equation:
wherein,and->Is a current reference value, ">Respectively d, q-axis current observations,/->I is respectively d 、i q Is (are) lumped disturbance->Is the command voltage vector output by the controller.
8. According to rotor position information theta e The output command voltage in the step 8 is
9. Will beLimiting to hexagonal voltage vector boundaries, to maximize voltage utilization, hexagonal voltage boundaries are employed instead of inscribed circular voltage vector boundaries. When->Outside the boundary, its phase angle remains unchanged and its amplitude shortens to a hexagonal boundary.
Specifically, at t=0.2 s, a load of 2n·m is suddenly applied, and at t=0.7 s, the applied load is suddenly removed. The parameter δ=0.01, α=6.3×10 is chosen 10 ，Δ＝90。
The rotating speed ring in the whole system adopts traditional PI control, K p ＝0.7、K i The desired rotation speed of =10 is 500rpm. As shown in fig. 2 and fig. 3, in the case of matching parameters, the control effect of the conventional DPCC is substantially the same as that of the method proposed by the present invention. As shown in fig. 4 and 5, when the parameters do not match (1 r,2 ψ f 0.5L), after adding the observer, i d 、i q The tracking effect is obviously better than that of the traditional DPCC, and the anti-parameter disturbance algorithm is further designed in the method, so that the anti-interference capability of the DPCC is further improved.
In summary, the invention relates to a permanent magnet synchronous motor prediction control method based on an extended state observer, and belongs to the field of permanent magnet synchronous motor control. Firstly constructing a predictive control discrete mathematical model of a permanent magnet synchronous motor, then designing an extended state observer with limited time convergence, and then designing a parameter updating mechanism to obtain the control method with strong immunity. The controller provided by the invention has the advantages that prediction errors caused by parameter mismatch are avoided; the observation value is utilized for time compensation, the calculation load is not increased, and a more concise predictive control equation is constructed; the observer algorithm parameter setting is simpler and can be converged in a limited time.
The above list of detailed descriptions is only specific to practical embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent embodiments or modifications that do not depart from the spirit of the present invention should be included in the scope of the present invention.
Claims (1)
1. The permanent magnet synchronous motor prediction control method based on the extended state observer is characterized by comprising the following steps of:
step 1, designing an extended state observer based on a supercoiled algorithm;
step 2, constructing a discrete mathematical model of the supercoiled extended state observer;
step 3, designing a self-adaptive expansion state observer with limited time convergence;
step 4, constructing a dead-beat prediction control equation based on the self-adaptive supercoiled expansion state observer;
step 5, designing a parameter updating mechanism to further improve the immunity;
step 6, constructing a model-free predictive control equation;
the design method of the extended state observer based on the supercoiled algorithm in the step 1 is as follows:
the system state equation is
In the method, in the process of the invention,u＝[u d u q ] T ，X＝[Δi d Δi q ] T ，/>
wherein i is d 、i q For d, q axis current measurements, ω e For rotor electrical angular velocity R, L and ψ f The motor stator resistance, the stator inductance and the rotor permanent magnet flux linkage are respectively adopted, X is the external disturbance of the system, h (t) is the derivative of the external disturbance, and u is the system input;
combining the above system state equation, F (S 1 ，S 2 ) Considered as motor internal disturbance, X considered as external disturbance, lumped disturbanceExpanded into a new state variable Z 2 The supercoiled state observer is constructed as follows:
Z 2 ＝F(S 1 ，S 2 )+X
wherein Z is 1 For d and q axis current observations, Z 2 S is the d and q axis current measured value for the system lumped disturbance observed value;
constructed supercoiled state observer using non-smooth term-beta 1 |E| 1/2 sign (E) to achieve finite time convergence, the discrete mathematical model in step 2 is as follows:
wherein beta is 1 And beta 2 For the gain factor of the observer, T s Is the sampling time;
the adaptive extended state observer takes the parameter beta into consideration 1 、β 2 It may be difficult to find an optimal value at every moment, and the adaptive extended state observer in the step 3 is designed as follows:
wherein->
Wherein, delta determines the accuracy of the observation error, alpha is the parameter gain, beta 1 (k)、β 2 (k) Is a time-varying parameter;
the predictive control equation based on the adaptive supercoiled state observer in the step 4 is as follows:
based on Z 1 (k+1) and Z 2 (k+1) performing a second-step prediction to construct a current prediction equation
According to the reference currentConstruction of dead beat prediction voltage equation
Wherein,d-axis and q-axis current observations, respectively,>i is respectively d And i q Is (are) lumped disturbance->Andthe controller outputs a command voltage vector;
the parameter updating mechanism, in step 4, the dead-beat prediction control equation is still affected by the parameter L, and in step 5, the updating step for the motor parameter L is designed as follows:
from (1) - (2), the product is prepared by:
wherein,is the d-axis current observation,/->Is i d Is the lumped disturbance of E id Is i d Error of observed value and measured value of (2), and if |u d (k-1)-u d (k-2) | > Δ, Δ being an adjustable parameter, μ (k) being updatable by the above formula, if not, μ (k) =μ (k-1);
the model-free predictive control equation in the step 6 is as follows:
wherein,and->Is a current reference value, ">Respectively d, q-axis current observations,/->I is respectively d 、i q Is (are) lumped disturbance->Is the command voltage vector output by the controller.
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| CN110492804A (en) * | 2019-07-08 | 2019-11-22 | 江苏大学 | A kind of permanent magnet synchronous motor Second Order Sliding Mode Control method based on novel disturbance observer |
| CN111371357A (en) * | 2020-02-20 | 2020-07-03 | 江苏大学 | Permanent magnet synchronous motor speed regulation control method based on self-adaptive supercoiling algorithm |
| CN112671027A (en) * | 2020-03-26 | 2021-04-16 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | Method and system for controlling a grid-connected power converter |
| CN112953287A (en) * | 2021-03-26 | 2021-06-11 | 淮阴工学院 | Inverter self-adaptive control method based on variable perturbation extended observer |
| CN113183950A (en) * | 2021-05-11 | 2021-07-30 | 江苏大学 | Self-adaptive control method for steering of active front wheel of electric automobile |
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| CN110492804A (en) * | 2019-07-08 | 2019-11-22 | 江苏大学 | A kind of permanent magnet synchronous motor Second Order Sliding Mode Control method based on novel disturbance observer |
| CN111371357A (en) * | 2020-02-20 | 2020-07-03 | 江苏大学 | Permanent magnet synchronous motor speed regulation control method based on self-adaptive supercoiling algorithm |
| CN112671027A (en) * | 2020-03-26 | 2021-04-16 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | Method and system for controlling a grid-connected power converter |
| CN112953287A (en) * | 2021-03-26 | 2021-06-11 | 淮阴工学院 | Inverter self-adaptive control method based on variable perturbation extended observer |
| CN113183950A (en) * | 2021-05-11 | 2021-07-30 | 江苏大学 | Self-adaptive control method for steering of active front wheel of electric automobile |
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