Permanent magnet synchronous motor model-free predictive current control method and system based on least square method

Abstract

The invention relates to a model-free predictive current control method and system for a permanent magnet synchronous motor based on a least square method, and the method comprises the steps: building a first-order hyper-local model of the permanent magnet synchronous motor through a hyper-local model principle, and estimating an unknown part and a disturbance part in the hyper-local model through employing the least square method; and finally, generating a switching signal of an inverter through space vector modulation (SVPWM) so as to control the motor. Compared with traditional model prediction control, the control effect of the method does not depend on motor parameters, phase current THD can be reduced, dq-axis current ripples are reduced, and high robustness and anti-interference performance are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of permanent magnet synchronous motor control, and in particular to a method and system for model-free predictive current control of a permanent magnet synchronous motor based on a least squares method. Background Art

[0002] Permanent magnet synchronous motor (PMSM) is widely used in aerospace, electric vehicles and other fields due to its simple structure, small size and high power density. In the 1990s, German scholar Holtz applied model predictive control to the field of power electronic transmission. In recent years, model predictive control has become an important control method in the field of power electronic transmission. It has a simple structure, is easy to implement and has a fast response speed; however, model predictive control is highly dependent on the accuracy of the model and parameters of the controlled object. Under the influence of external environment and working conditions, the parameters of the permanent magnet synchronous motor will change, affecting the performance of model predictive control.

[0003] Therefore, Michel Flies proposed a model-free predictive control method that only uses the input and output of the system without considering other parameters, and treats the disturbance and the unknown quantities in the model as the total disturbance, thus avoiding the impact of parameter changes on control performance. However, model-free predictive control does not work well at low sampling frequencies and requires a high-performance controller. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a model-free predictive current control method and system for a permanent magnet synchronous motor based on the least squares method, which can effectively avoid the adverse effects caused by changes in motor parameters and external disturbances, while achieving rapid dynamic response, reducing speed pulsation, and having strong robustness and anti-interference.

[0005] To achieve the above object, the present invention adopts the following technical solution: a model-free predictive current control method for a permanent magnet synchronous motor based on the least squares method, comprising the following steps:

[0006] Step S1: Collect the rotor position information θ(k) and the speed ω of the permanent magnet synchronous motor through the photoelectric encoder 1 (k); Measure the three-phase current i of the permanent magnet synchronous motor stator a (k), i b (k), i c (k); k represents the value of the parameter at time k;

[0007] Step S2: The three-phase current i of the permanent magnet synchronous motor stator obtained in step S1 a (k), i b (k), i c (k), after Park transformation, the direct axis current i of the motor at time k is obtained d(k) and the quadrature axis current i q (k);

[0008] Step S3: Calculate the speed ω of the permanent magnet synchronous motor 1 (k) and permanent magnet synchronous motor speed reference value The difference between

[0009] Step S4: Calculate the reference value of the quadrature-axis current of the permanent magnet synchronous motor through the PI controller according to the difference obtained in step S3

[0010] Step S5: Using i d =0 control strategy, take in is the reference value of the direct-axis current of the permanent magnet synchronous motor;

[0011] Step S6: Establish a super-local model of the permanent magnet synchronous motor, and observe the unknown part and the disturbance part of the system online through the least squares method;

[0012] Step S7: According to the permanent magnet synchronous motor super-local model obtained in step S6, the reference voltage vector at time k+1 is predicted and

[0013] Step S8: Based on the voltage reference vector obtained in step S7 and After coordinate transformation, we get and The switching signal of the inverter is then obtained through SVPWM modulation to realize the control of the permanent magnet synchronous motor.

[0014] In a preferred embodiment, the model-free controller is implemented using a permanent magnet synchronous motor super-local model and a least squares method.

[0015] In a preferred embodiment, the permanent magnet synchronous motor super-local model in step S6 is:

[0016]

[0017] Where: is the system output, u is the system input, G is the sum of the unknown parts and disturbances of the system, and α is the non-physical proportional factor in the model structure.

[0018] In a preferred embodiment, according to formula (1), the permanent magnet synchronous motor super-local model is established as follows:

[0019]

[0020] In the formula, u d and u qare the direct-axis and quadrature-axis voltages of the permanent magnet synchronous motor respectively; G d and G q are the direct axis component and quadrature axis component of the unknown part of the hyperlocal model respectively; α d and α q They are the direct and quadrature components of the non-physical scale factor of the hyperlocal model, respectively.

[0021] The forward Euler method is used to discretize equation (2):

[0022]

[0023] Among them, T s is the sampling period.

[0024] In a preferred embodiment, the least square method in step S6 is specifically:

[0025] y dq =A dq G dq (4)

[0026] Where:

[0027]

[0028]

[0029] in, is the AC and DC axis current at time k, is the AC and DC axis voltage at time k; represents the current value at time k-1, represents the current value at time k-2, represents the voltage value at time k-1, represents the voltage value at time k-2; T s is the sampling period; δ is the forgetting factor, which ranges from [0,1] and is used to adjust the convergence speed of the algorithm; when δ approaches 0, the convergence speed of the algorithm is faster, but it is sensitive to noise; when δ approaches 1, the convergence speed of the algorithm is slower.

[0030] In a preferred embodiment, the unknown part and the disturbance part in step S6 are observed online by the least square method, which is expressed as:

[0031]

[0032] In the formula, the superscript T represents the transpose of the matrix; after calculation and simplification, G dq The observation results are expressed as:

[0033]

[0034] Where N k is the sum of the AC and DC axis voltage and current information at time k, including the forgetting factor; D k is the input coefficient at time k, which also includes the forgetting factor.

[0035]

[0036] Where N k-1 is the sum of the AC and DC axis voltage and current information at time k-1; D k-1 is the input coefficient at time k-1.

[0037] In a preferred embodiment, the current prediction at time k+1 is performed based on the observation results of the permanent magnet synchronous motor super-local model and the least squares method, and the prediction result is:

[0038]

[0039] In the formula, and They represent the predicted values ​​of direct-axis and quadrature-axis currents at time k+1 respectively; and Respectively represent the AC and DC axis currents at time k; T s To control the cycle; and The AC and DC axis voltages at time k are shown respectively; and The observation results of the unknown parts in the hyperlocal model are shown respectively.

[0040] In a preferred embodiment, deadbeat current control is adopted to make the stator current of the motor's DC axis equal to the reference current at time k+1, that is, Get the reference voltage of the quadrature and direct axes at time k+1

[0041] Where: in, and They are the reference voltages of the motor direct axis and quadrature axis at time k+1 respectively.

[0042] In a preferred embodiment, the angles used for the coordinate transformation in steps S2 and S8 are both the rotor position θ(k) at that moment measured in step S1.

[0043] The present invention also provides a permanent magnet synchronous motor model-free predictive current control system based on the least squares method, and operates as the above-mentioned permanent magnet synchronous motor model-free predictive current control method based on the least squares method.

[0044] Compared with the prior art, the present invention has the following beneficial effects: based on the hyperlocal modeling theory, the present invention establishes a hyperlocal model of a permanent magnet synchronous motor that only considers input and output, and uses the least squares method to observe motor parameters and external disturbances that are not considered in the hyperlocal model, thereby avoiding the adverse effects of parameter changes and external disturbances on motor operation, and at the same time reducing the phase current THD and dq axis current ripple, and having strong robustness and anti-interference performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a control block diagram of a method in an embodiment of the present invention;

[0046] Figure 2 is a flow chart of a method in an embodiment of the present invention;

[0047] Figure 3 1 is an experimental waveform diagram of the embodiment of the present invention and the traditional model-free predictive control under rated parameter conditions, wherein (a) is the traditional model-free predictive control and (b) is the embodiment of the present invention;

[0048] Figure 4 1 is an experimental waveform diagram of the embodiment of the present invention and the traditional model-free predictive control under parameter mismatch conditions, wherein (a) is the traditional model-free predictive control and (b) is the embodiment of the present invention;

[0049] Figure 5 The current pulsation comparison results of the embodiment of the present invention and the traditional model-free predictive control under different parameter mismatch conditions, where (a) is the operating condition: 0.5L dq , 1000rpm, 6Nm, (b) is the operating condition: 1.5L dq , 1000rpm, 6Nm. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0051] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0052] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0053] Please refer to Figure 1-5 The present invention provides a model-free predictive current control method for a permanent magnet synchronous motor based on a least squares method, comprising the following steps:

[0054] Step S1: Collect θ(k), ω through the photoelectric encoder 1 (k), measurement i a (k), i b (k), i c (k); k represents the value of the parameter at time k; where: θ(k) is the rotor position information, ω 1 (k) is the speed of the permanent magnet synchronous motor, i a (k), i b (k), i c (k) is the three-phase current of the permanent magnet synchronous motor stator;

[0055] Step S2: Based on the i obtained in step S1 a (k), i b (k), i c (k), after Park transformation, the direct-axis and quadrature-axis currents i of the motor at time k are obtained d (k), i q (k);

[0056] Step S3: Calculate ω 1 (k) The difference between is the speed reference value of the permanent magnet synchronous motor;

[0057] Step S4: Calculate the q-axis current reference value of the permanent magnet synchronous motor through a PI controller according to the difference obtained in step S3;

[0058]

[0059] Where: k p is the proportional gain coefficient of the PI controller, k i is the integral gain coefficient of the pi controller.

[0060] Step S5: Use i d =0 control strategy, take in is the reference value of the d-axis current of the permanent magnet synchronous motor;

[0061] Step S6: Performing hyperlocal modeling on the permanent magnet synchronous motor:

[0062] i dq (k+1)=i dq (k)+(α dq (k)u dq (k)+Gdq (k))T s

[0063] Use the least squares method to observe G in the above formula dq , specifically:

[0064] y dq =A dq G dq

[0065] Where:

[0066]

[0067]

[0068] in, is the dq axis current at time k, is the dq axis voltage at time k; k-1, k-2, and k-3 represent the current or voltage value at that time respectively; T s is the sampling period; δ is the forgetting factor, which ranges from [0,1] and is used to adjust the convergence speed of the algorithm. When δ approaches 0, the convergence speed of the algorithm is faster, but it is sensitive to noise; when δ approaches 1, the convergence speed of the algorithm is slower, but the estimation result is more robust to noise. In this paper, δ=0.999 is taken.

[0069] Step S7: Based on the current reference vector obtained in step S4 and step S5 and The deadbeat current control is adopted to make the stator current of the motor DC axis equal to the reference current at time (k+1), that is, The quadrature and direct axis reference voltages at time (k+1) are obtained as follows:

[0070]

[0071] Step S8: Based on the voltage reference vector obtained in step S7 and After coordinate transformation, we get and The switching signal of the inverter is then obtained through SVPWM modulation to realize the control of the permanent magnet synchronous motor.

[0072] In this embodiment, the model-free controller is implemented by a hyperlocal model and a least squares method.

[0073] In this embodiment, the hyperlocal model directly considers the input and output of the motor model without relying on other parameters of the motor, and the least squares method is used to observe the unknown parts in the hyperlocal model and the influence of external disturbances on the current when the motor is running. The hyperlocal model does not rely on the motor parameters, so the parameter changes caused by force majeure during the operation of the motor will not reduce the accuracy of the hyperlocal model. The least squares method also observes external disturbances at the same time, so when the operating conditions of the motor change, the permanent magnet synchronous motor model-free predictive current control method based on the least squares method can quickly control the motor, reduce the phase current THD, reduce the dq axis current ripple, and has strong robustness and anti-interference.

[0074] The present invention is experimentally verified on a permanent magnet synchronous motor-induction motor pair test platform, and the test platform parameters are shown in Table 1.

[0075] Table 1

[0076]

[0077] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made according to the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A model-free predictive current control method for a permanent magnet synchronous motor based on the least squares method, characterized in that: The following steps are involved: Step S1: Collect the rotor position information θ(k) and the speed ω1(k) of the permanent magnet synchronous motor through the photoelectric encoder; measure the three-phase current i of the permanent magnet synchronous motor stator a (k), i b (k), i c (k); k represents the value of the parameter at time k; Step S2: The three-phase current i of the permanent magnet synchronous motor stator obtained in step S1 a (k), i b (k), i c (k), after Park transformation, the direct axis current i of the motor at time k is obtained d (k) and the quadrature axis current i q (k); Step S3: Calculate the speed ω1(k) of the permanent magnet synchronous motor and the speed reference value of the permanent magnet synchronous motor The difference between Step S4: Calculate the reference value of the quadrature-axis current of the permanent magnet synchronous motor through the PI controller according to the difference obtained in step S3 Step S5: Using i d =0 control strategy, take in is the reference value of the direct-axis current of the permanent magnet synchronous motor; Step S6: Establish a super-local model of the permanent magnet synchronous motor, and observe the unknown part and the disturbance part of the system online through the least squares method; Step S7: According to the permanent magnet synchronous motor super-local model obtained in step S6, the reference voltage vector at time k+1 is predicted and Step S8: Based on the voltage reference vector obtained in step S7 and After coordinate transformation, we get and The switching signal of the inverter is then obtained through SVPWM modulation to realize the control of the permanent magnet synchronous motor.

2. The method for model-free predictive current control of a permanent magnet synchronous motor based on least squares method according to claim 1, characterized in that: The model-free controller is implemented using a permanent magnet synchronous motor hyperlocal model and the least squares method.

3. The method for model-free predictive current control of a permanent magnet synchronous motor based on least squares method according to claim 1, characterized in that: The permanent magnet synchronous motor super-local model in step S6 is: Where: is the system output, u is the system input, G is the sum of the unknown parts and disturbances of the system, and α is the non-physical proportional factor in the model structure.

4. A method for model-free predictive current control of a permanent magnet synchronous motor based on least squares method according to claim 3, characterized in that: According to formula (1), the super-local model of permanent magnet synchronous motor is established as: In the formula, u d and u q are the direct-axis and quadrature-axis voltages of the permanent magnet synchronous motor respectively; G d and G q are the direct axis component and quadrature axis component of the unknown part of the hyperlocal model respectively; α d and α q are the direct axis component and the quadrature axis component of the non-physical scale factor of the hyperlocal model, respectively; The forward Euler method is used to discretize equation (2): Where, T s is the sampling period.

5. The method for model-free predictive current control of a permanent magnet synchronous motor based on least squares method according to claim 1, characterized in that: The least square method in step S6 is specifically: and dq =A dq G dq (4) Where: in, is the AC and DC axis current at time k, is the AC and DC axis voltage at time k; represents the current value at time k-1, represents the current value at time k-2, represents the voltage value at time k-1, represents the voltage value at time k-2; T s is the sampling period; δ is the forgetting factor, which ranges from [0,1] and is used to adjust the convergence speed of the algorithm; when δ approaches 0, the convergence speed of the algorithm is faster, but it is sensitive to noise; when δ approaches 1, the convergence speed of the algorithm is slower.

6. The method for model-free predictive current control of a permanent magnet synchronous motor based on least squares method according to claim 1, characterized in that: The unknown part and the disturbance part in step S6 are observed online by the least square method, which is expressed as: In the formula, the superscript T represents the transpose of the matrix; after calculation and simplification, G dq The observation results are expressed as: Where N k is the sum of the AC and DC axis voltage and current information at time k, including the forgetting factor; D k is the input coefficient at time k, which also includes the forgetting factor; Where N k-1 is the sum of the AC and DC axis voltage and current information at time k-1; D k-1 is the input coefficient at time k-1.

7. The method for model-free predictive current control of a permanent magnet synchronous motor based on least squares method according to claim 1, characterized in that: Based on the observation results of the permanent magnet synchronous motor super-local model and the least squares method, the current prediction at time k+1 is carried out, and the prediction result is: In the formula, and They represent the predicted values ​​of direct-axis and quadrature-axis currents at time k+1 respectively; and Respectively represent the AC and DC axis currents at time k; T s To control the cycle; and The AC and DC axis voltages at time k are shown respectively; and The observation results of the unknown parts in the hyperlocal model are shown respectively.

8. The method for model-free predictive current control of a permanent magnet synchronous motor based on least squares method according to claim 1, characterized in that: The deadbeat current control is adopted to make the stator current of the AC and DC axes of the motor equal to the reference current at time k+1, that is, Get the reference voltage of the quadrature and direct axes at time k+1 Where: in, and They are the reference voltages of the motor direct axis and quadrature axis at time k+1 respectively.

9. The method for model-free predictive current control of a permanent magnet synchronous motor based on least squares method according to claim 1, characterized in that: The angles used for the coordinate transformation in steps S2 and S8 are both the rotor positions θ(k) at that moment measured in step S1.

10. A model-free predictive current control system for a permanent magnet synchronous motor based on the least squares method, characterized in that Run a model-free predictive current control method for a permanent magnet synchronous motor based on the least squares method as described in any one of claims 1 to 9.

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