Permanent magnet synchronous motor current prediction control method based on prediction current error compensation

Abstract

The invention discloses a permanent magnet synchronous motor current prediction control method based on prediction current error compensation, and relates to the field of permanent magnet synchronous motor control. According to the method, error expressions of dq-axis predicted current and actual current are listed according to the fact that the predicted current and the actual current have errors when motor parameters in a controller are not matched with actual motor parameters; constructing composite parameters related to the resistance and the flux linkage, and designing a simplified dq-axis prediction current model according to the constructed composite parameters; constructing a target function based on the simplified dq-axis prediction current error expression; solving a gradient for the target function; and calculating the error between the dq-axis predicted current value and the actual current value at the moment k, which is called a predicted current error value, substituting the predicted current error value into the simplified gradient expression, and calculating the predicted current and the control voltage at the next moment by using the obtained inductance and resistance flux linkage composite parameters. According to the method, the influence of motor parameter mismatch can be reduced, and the robustness of dead-beat prediction control is improved.

Description

Technical Field

[0001] The invention relates to the field of permanent magnet synchronous motor control, and in particular to a permanent magnet synchronous motor current prediction control method based on prediction current error compensation. Background Art

[0002] In the context of low-carbon, green and sustainable development, permanent magnet synchronous motors are widely used in aerospace, transportation, industrial machine tools and other fields due to their high power density and high efficiency. As the core of the permanent magnet motor control system, current closed-loop control plays a key role in speed, position control and other output characteristics. Among them, the method based on deadbeat current predictive control (DPCC) has become a hot topic of current research due to its advantages such as simple structure, fast dynamic response and low current harmonics. However, the DPCC algorithm relies on an accurate motor model. During the actual operation of the motor, the motor parameters will change due to the influence of temperature rise and saturation effects. When the motor parameters are mismatched, the control effect will be significantly reduced, and even the control system will lose stability.

[0003] Therefore, it is of great significance to improve the performance of zero-beat predictive current control of permanent magnet synchronous motor and ensure the stability of the control system when the motor parameters and controller parameters do not match. Summary of the invention

[0004] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to improve the stability of the control system of the permanent magnet synchronous motor zero-beat predictive current control when the parameters are mismatched and reduce its sensitivity to the motor parameters.

[0005] To achieve the above object, the present invention provides a permanent magnet synchronous motor current prediction control method based on prediction current error compensation, which specifically includes the following steps:

[0006] S1: According to the permanent magnet synchronous motor predicted current model, the relationship between the dq axis predicted current at time k+1 and the dq axis current, voltage, speed and motor parameters at time k is obtained.

[0007] S2: Write the dq-axis predicted current error expression according to the expressions of the dq-axis predicted current and the actual current when the parameters are mismatched, and design the resistance-flux composite parameters, simplify the dq-axis predicted current error expression, and design a simplified dq-axis predicted current model and a control voltage calculation model according to the designed resistance-flux composite parameters; construct the objective function based on the simplified predicted current error expression, calculate the gradient of the objective function, and simplify the gradient expression.

[0008] S3: Calculate the error between the predicted current value and the actual current value at time k, substitute it into the simplified gradient expression, and calculate the composite parameters of inductance and resistance flux linkage according to the finite-time gradient method (FGD). The obtained composite parameters of inductance and resistance flux linkage are used to predict the current and control voltage calculation at the next moment.

[0009] Furthermore, according to the mathematical model of permanent magnet synchronous motor, when the controller parameters are accurate, the dq axis predicted current expression is:

[0010]

[0011] In the formula, R c , L c , fc Represent the actual resistance, actual inductance and actual flux parameters of the motor respectively, T is the control period, ω e is the motor electrical angular velocity, u d (k) and u q (k) are the d-axis and q-axis components of the stator voltage at time k, respectively.

[0012] Assume ΔR, ΔL, Δψ f Respectively represent the error between the resistance, inductance, flux linkage parameters in the controller and the actual parameters of the motor. Then the dq axis predicted current expression when the parameters are mismatched is:

[0013]

[0014] The error between the predicted current and the actual current when the parameters do not match is expressed as:

[0015]

[0016] In the formula, E d0 、E q0 Represent the error between the d and q axis predicted current and the actual current respectively; for the d axis predicted current error, since TR c / L c <<1, so the predicted current error caused by the resistance part can be ignored; for the q-axis predicted current error, R c i q (k) compared to ω e ψ fc It cannot be ignored, so the resistance part is retained, and the dq axis predicted current error can be simplified to:

[0017]

[0018] In the formula, R0, L0, ψ f0 Respectively represent the resistance, inductance, and flux linkage parameters in the controller, satisfying R0=Rc +ΔR,L0=L c +ΔL,ψ f0 =ψ fc +Δψ f .

[0019] When the controller inductance converges to the true value, the q-axis predicted current error is only related to the resistance and flux parameters. Therefore, the composite parameters N0 and N related to the resistance and flux are constructed. c ,satisfy Then the final simplified dq axis predicted current error becomes:

[0020]

[0021] Furthermore, the present invention constructs an objective function based on the predicted current error expression:

[0022] The objective function designed based on the d-axis predicted current error is:

[0023]

[0024] If the controller inductance converges to the true value, the objective function designed based on the q-axis predicted current error is:

[0025]

[0026] Furthermore, the present invention calculates the gradient of the objective function and simplifies the gradient expression as follows:

[0027] The objective function gradient designed based on the d-axis predicted current error is:

[0028]

[0029] Since the motor is actually running d The change is large, directly using u in the gradient formula d This will cause the gradient to fluctuate, so according to the d-axis prediction current model, -u d Designed as the proportional gain of L0 (-u d ≈p L L0), the gradient changes to:

[0030]

[0031] In the formula, p L is the ratio coefficient, and its value determines the identification speed of the inductance parameters; according to the motor voltage equation, when i q When greater than 0, -u d If p is greater than 0, L Take a positive number, otherwise, p L Take a negative number.

[0032] The objective function gradient designed based on the q-axis predicted current error is:

[0033]

[0034] Similarly, according to the q-axis prediction current model, Designed for Proportional gain

[0035]

[0036] In the formula, p n is the ratio coefficient, and its value determines the identification speed of the resistance-flux linkage composite parameters.

[0037] Furthermore, the present invention designs an identification model of inductance parameters and resistance flux composite parameters according to FGD:

[0038] The FGD algorithm is expressed as:

[0039]

[0040] Where f(x) is the objective function with extreme points, θ is the search step size, and λ and ρ are two coefficients used to adjust the adaptive iteration step size.

[0041] The convergence condition and bounded time are expressed as:

[0042]

[0043] Then the inductance identification model is expressed as:

[0044]

[0045] Where L0(k) is the inductance parameter identified at time k.

[0046] The resistance flux complex parameter identification model is expressed as:

[0047]

[0048] Where N0(k) is the resistance flux complex parameter identified at time k.

[0049] Furthermore, the present invention simplifies the prediction current model and the control voltage calculation model according to the constructed resistance flux complex parameters.

[0050] The simplified dq axis prediction current model is expressed as:

[0051]

[0052] The simplified control voltage calculation model is expressed as:

[0053]

[0054] In the formula, is the given dq axis current.

[0055] The beneficial effects of the present invention are:

[0056] 1. The present invention uses DPCC control to predict the error between the current and the actual current when the motor parameters are mismatched. The objective function is constructed by predicting the current error expression, and the gradient of the objective function is calculated. The motor parameters are identified by the FGD algorithm. When the predicted current error is zero, it proves that the motor parameters in the controller are consistent with the actual values, thereby reducing the impact caused by the motor parameter mismatch. The present invention has a faster identification speed and higher identification accuracy, and does not inject other signals, ensuring good control performance.

[0057] 2. The present invention constructs a resistance-flux linkage composite parameter, which solves the problem that when the inductance parameter reaches an accurate value, the q-axis predicted current error is affected by both the resistance and flux linkage parameters and a single parameter cannot be identified. The q-axis predicted current error caused by the mismatch between the resistance parameter and the flux linkage parameter is simplified to a q-axis predicted current error caused by the mismatch between the resistance-flux linkage composite parameter, and the composite parameter is identified through the q-axis predicted current error.

[0058] 3. The present invention simplifies the predicted current model and the control voltage calculation model based on the constructed resistance-flux composite parameters, and obtains the predicted current and control voltage by substituting the identified inductance and resistance-flux composite parameters into the simplified model, so as to identify the motor parameters at the next moment and generate a drive signal to drive the motor to operate. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 It is a schematic diagram of the overall structure of a method for parameter adaptive deadbeat current prediction control of a permanent magnet synchronous motor based on prediction current error compensation according to a specific embodiment of the present invention;

[0061] Figure 2 is a flow chart of a specific embodiment of the present invention;

[0062] Figure 3 In a specific embodiment of the present invention, the controller initial inductance L0, initial flux ψf0 , the initial resistance R0 is twice the actual parameters of the motor, and the d-axis current waveform of the motor parameter identification starts at 0.3s;

[0063] Figure 4 In a specific embodiment of the present invention, the controller initial inductance L0, initial flux ψ f0 , the initial resistance R0 is twice the actual parameters of the motor, and the q-axis current waveform of the motor parameter identification starts at 0.3s;

[0064] Figure 5 In a specific embodiment of the present invention, the motor speed waveform;

[0065] Figure 6 In a specific embodiment of the present invention, the controller initial inductance L0, initial flux ψ f0 , the initial resistance R0 is twice the actual motor parameter, and the inductance identification waveform of the motor parameter identification starts at 0.3s;

[0066] Figure 7 In a specific embodiment of the present invention, the controller initial inductance L0, initial flux ψ f0 , the initial resistance R0 is twice the actual parameters of the motor, and the resistance-flux composite parameter identification waveform of the motor parameter identification starts at 0.3s. DETAILED DESCRIPTION

[0067] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0068] While some exemplary embodiments of the present invention have been described for the purpose of illustration, it should be understood that the present invention may be implemented in other ways not specifically shown in the drawings.

[0069] like Figure 1 As shown, the present invention provides a permanent magnet synchronous motor current prediction control method based on prediction current error compensation, comprising the following steps:

[0070] S1. According to the permanent magnet synchronous motor predicted current model, the relationship between the dq axis predicted current at time k+1 and the dq axis current, voltage, speed and motor parameters at time k is obtained.

[0071] S2. According to the DPCC control, when the motor parameters are mismatched, there is an error between the predicted current and the actual current, and the motor parameters are identified using the FGD algorithm, which specifically includes the following steps:

[0072] S21. According to the mathematical model of permanent magnet synchronous motor, the dq axis predicted current expression is obtained when the controller motor parameters are accurate:

[0073]

[0074] In the formula, R c , L c , fc Represent the actual resistance, actual inductance and actual flux parameters of the motor respectively, T is the control period, ω e is the motor electrical angular velocity, u d (k) and u q (k) are the d-axis and q-axis components of the stator voltage at time k.

[0075] S22, let ΔR, ΔL, Δψ f Respectively represent the error between the resistance, inductance, flux linkage parameters in the controller and the actual parameters of the motor. Then the dq axis predicted current expression when the parameters are mismatched is:

[0076]

[0077] S23, write the dq axis predicted current error expression according to the expressions of the dq axis predicted current and the actual current when the parameters are mismatched, and design the resistance flux composite parameters to simplify the dq axis predicted current error expression;

[0078] The dq axis predicted current error expression is expressed as:

[0079]

[0080] In the formula, E d0 、E q0 Represent the error between the d and q axis predicted current and the actual current respectively; for the d axis predicted current error, since TR c / L c <<1, so the predicted current error caused by the resistance part can be ignored; for the q-axis predicted current error, R c i q (k) compared to ω e ψ fc It cannot be ignored, so the resistance part is retained, and the dq axis predicted current error can be simplified to:

[0081]

[0082] In the formula, R0, L0, ψ f0Respectively represent the resistance, inductance, and flux linkage parameters in the controller, satisfying R0=R c +ΔR,L0=L c +ΔL,ψ f0 =ψ fc +Δψ f .

[0083] When the controller inductance converges to the true value, the q-axis predicted current error is only related to the resistance and flux parameters. Therefore, the composite parameters N0 and N related to the resistance and flux are constructed. c ,satisfy Then the final simplified dq axis predicted current error becomes:

[0084]

[0085] S24, constructing an objective function according to the simplified predicted current error;

[0086] The objective function designed based on the d-axis predicted current error is:

[0087]

[0088] If the controller inductance converges to the true value, the objective function designed based on the q-axis predicted current error is:

[0089]

[0090] S25, finding the gradient of the objective function and simplifying the gradient expression;

[0091] The objective function gradient designed based on the d-axis predicted current error is:

[0092]

[0093] Since the motor is actually running d The change is large, directly using u in the gradient formula d This will cause the gradient to fluctuate, so according to the d-axis prediction current model, -u d Designed as the proportional gain of L0 (-u d ≈p L L0), the gradient changes to:

[0094]

[0095] In the formula, p L is the ratio coefficient, and its value determines the identification speed of the inductance parameters; according to the motor voltage equation, when i q When greater than 0, -u d If p is greater than 0, L Take a positive number, otherwise, p LTake a negative number.

[0096] The objective function gradient designed based on the q-axis predicted current error is:

[0097]

[0098] Similarly, according to the q-axis prediction current model, Designed for Proportional gain

[0099]

[0100] In the formula, p n is the ratio coefficient, and its value determines the identification speed of the resistance-flux linkage composite parameters.

[0101] S26, designing an inductance and resistance flux composite parameter identification model according to FGD, calculating the error between the predicted current value and the actual current value, substituting the error into the gradient expression, and calculating the inductance parameter and the resistance flux composite parameter according to the identification model;

[0102] The FGD algorithm is expressed as:

[0103]

[0104] Where f(x) is the objective function with extreme points, θ is the search step size, and λ and ρ are two coefficients used to adjust the adaptive iteration step size.

[0105] The convergence condition and bounded time are expressed as:

[0106]

[0107] Then the inductance identification model is expressed as:

[0108]

[0109] Where L0(k) is the inductance parameter identified at time k.

[0110] The resistance flux complex parameter identification model is expressed as:

[0111]

[0112] Where N0(k) is the resistance flux complex parameter identified at time k.

[0113] The error between the predicted current value and the actual current value of the dq axis at time k is calculated, where the actual current value of the dq axis is obtained by collecting the three-phase current through the current sensor and extracting it through the Clark-Park module, and the predicted current value is obtained by inputting the actual current value of the dq axis, the dq axis voltage, the speed and the motor parameters in the controller of the motor at the previous moment into the dq axis predicted current model; the predicted current error is substituted into the gradient expression to obtain the gradient value, and then the obtained gradient value is substituted into the inductance identification model and the resistance flux composite parameter identification model to obtain the motor parameter identification value at the next moment.

[0114] S3. Simplify the predicted current model and the control voltage calculation model according to the constructed resistance flux composite parameters, and substitute the identified motor parameter values ​​into the simplified model to calculate the predicted current and control voltage.

[0115] The simplified dq axis prediction current model is expressed as:

[0116]

[0117] The simplified control voltage calculation model is expressed as:

[0118]

[0119] Where L0(k) and N0(k) are the inductance parameter and resistance flux composite parameter identified at time k, respectively, and R0 represents the resistance parameter in the controller. is the given value of the dq axis current. The control voltage obtained at this moment will generate a drive waveform to act on the motor at the next moment through the SVPWM module, realizing the one-beat delay compensation of the predictive control.

[0120] In order to further illustrate the effects of the present invention, the following comparative examples and embodiments are provided.

[0121] In a specific embodiment, a permanent magnet synchronous motor is used to verify the above embodiment. The parameters of the permanent magnet synchronous motor are shown in Table 1.

[0122] Table 1

[0123]

[0124] The initial speed of the motor is set to 1200r / min, and the speed setting changes to 1000r / min at 0.9s. The load torque is set to 4N·m. The initial inductance, resistance, and flux parameters of the controller are set to twice the actual parameters of the motor. The control period T is set to 0.0001S, and λ in the FGD algorithm is set to 1e 4 , ρ is 2e 8 , p L Take ±0.01, p nTake 0.01, start parameter identification at 0.3s, and adopt the control strategy of d-axis current being 0.

[0125] Figure 3 The d-axis current waveform of the present invention under the above control conditions can be seen. Due to the influence of parameter mismatch, the d-axis current fluctuates greatly before 0.3s, about ±2.5A, and the fluctuation value drops rapidly after 0.3s, about ±0.1A.

[0126] Figure 4 The q-axis current waveform of the present invention under the above control conditions is shown. It can be seen that due to the influence of parameter mismatch, the q-axis current fluctuates greatly before 0.3s, about ±2.5A, and the fluctuation value drops rapidly after 0.3s, about ±0.1A.

[0127] Figure 5 This is the speed waveform of the present invention under the above control conditions. It can be seen that the motor speed can quickly reach a given value and has good dynamic performance.

[0128] Figure 6 This is the inductance identification waveform of the present invention under the above control conditions. It can be seen that after the identification is started, the inductance identification value of the controller quickly converges to the actual value of the motor.

[0129] Figure 7 This is the resistance-flux composite parameter identification waveform of the present invention under the above control conditions. It can be seen that in the interval of 0.34s to 0.44s, the resistance-flux composite parameter identification value is about 0.107, while the q-axis current value is about 9A, and the electrical angular velocity is about 502.6rad / s. According to the actual motor resistance of 1.5Ω and the actual flux of 0.08wb, the calculated true value of the resistance-flux composite parameter is about 0.1069, which is very close to the identification value; in the interval of 1.35s to 1.45s, the resistance-flux composite parameter identification value is about 0.112, while the q-axis current value is about 8.9A, and the electrical angular velocity is about 418.8rad / s. The calculated true value of the resistance-flux composite parameter is about 0.1119, which is also close to the identification value; therefore, it is proved that the method can effectively identify the resistance-flux composite parameters of the structure and reduce the influence of parameter mismatch on the motor control performance.

[0130] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A current prediction control method for a permanent magnet synchronous motor based on prediction current error compensation, characterized in that: The steps include: S1: According to the permanent magnet synchronous motor predicted current model, the relationship between the dq axis predicted current at time k+1 and the dq axis current, voltage, speed and motor parameters at time k is obtained; S2: Write the dq axis predicted current error expression according to the expressions of the dq axis predicted current and the actual current when the parameters are mismatched, and design the resistance flux composite parameters, simplify the dq axis predicted current error expression, and design a simplified dq axis predicted current model and control voltage calculation model according to the designed resistance flux composite parameters; construct the objective function based on the simplified predicted current error expression, calculate the gradient of the objective function, and simplify the gradient expression; S3: Calculate the error between the predicted current value and the actual current value at time k, substitute it into the simplified gradient expression, calculate the composite parameters of inductance and resistance flux according to FGD, and the obtained composite parameters of inductance and resistance flux are used to predict the current and control voltage calculation at the next moment.

2. The method for predictive control of permanent magnet synchronous motor current based on predicted current error compensation according to claim 1, characterized in that: According to the mathematical model of permanent magnet synchronous motor, when the controller parameters are accurate, the dq axis predicted current expression is: In the formula, R c , L c , fc Respectively represent the actual resistance, actual inductance and actual flux parameters of the motor, T is the control period, ω e is the motor electrical angular velocity, u d (k) and u q (k) are the d-axis and q-axis components of the stator voltage at time k; Let ΔR, ΔL, Δψ f Respectively represent the errors between the resistance, inductance, flux linkage parameters in the controller and the actual parameters of the motor. The dq axis predicted current expression when the parameters are mismatched is: The error between the predicted current and the actual current when the parameters do not match is expressed as: In the formula, E d0 、E q0 Represent the error between the d and q axis predicted current and the actual current respectively; for the d axis predicted current error, since TR c / L c <<1, so the predicted current error caused by the resistance part can be ignored; for the q-axis predicted current error, R c i q (k) compared to ω e ψ fc It cannot be ignored, so the resistance part is retained, and the dq axis predicted current error can be simplified to: In the formula, R0, L0, ψ f0 Respectively represent the resistance, inductance, and flux linkage parameters in the controller, satisfying R0=R c +ΔR,L0=L c +ΔL,ψ f0 =ψ fc +Δψ f ; When the controller inductance converges to the true value, the q-axis predicted current error is only related to the resistance and flux parameters. Therefore, the composite parameters N0 and N related to the resistance and flux are constructed. c ,satisfy Then the final simplified dq axis predicted current error becomes:

3. The method for predictive control of permanent magnet synchronous motor current based on predicted current error compensation according to claim 1, characterized in that: Construct the objective function: The objective function designed based on the d-axis predicted current error is: If the controller inductance converges to the true value, the objective function designed based on the q-axis predicted current error is:

4. The method for predictive control of permanent magnet synchronous motor current based on predicted current error compensation according to claim 1, characterized in that: Find the gradient of the objective function and simplify the gradient expression: The objective function gradient designed based on the d-axis predicted current error is: Since the motor is actually running d The change is large, directly using u in the gradient formula d This will cause the gradient to fluctuate, so according to the d-axis prediction current model, -u d Designed as the proportional gain of L0 (-u d ≈p L L0), the gradient changes to: In the formula, p L is the ratio coefficient, and its value determines the identification speed of the inductance parameters; according to the motor voltage equation, when i q When greater than 0, -u d If p is greater than 0, L Take a positive number, otherwise, p L Take negative numbers; The objective function gradient designed based on the q-axis predicted current error is: Similarly, according to the q-axis prediction current model, Designed for Proportional gain In the formula, p n is the ratio coefficient, and its value determines the identification speed of the resistance-flux linkage composite parameters.

5. The method for predictive control of permanent magnet synchronous motor current based on predicted current error compensation according to claim 1, characterized in that: Design the identification model of inductance parameters and resistance flux composite parameters based on FGD: The FGD algorithm is expressed as: Where f(x) is the objective function with extreme points, θ is the search step size, and λ and ρ are two coefficients used to adjust the adaptive iteration step size; The convergence condition and bounded time are expressed as: Then the inductor parameter identification model is expressed as: Where L0(k) is the inductance parameter identified at time k; The resistance flux complex parameter identification model is expressed as: Where N0(k) is the resistance flux complex parameter identified at time k.

6. The method for predictive control of permanent magnet synchronous motor current based on predicted current error compensation according to claim 1, characterized in that: The prediction current model and control voltage calculation model are simplified according to the constructed resistance flux complex parameters; The simplified dq axis prediction current model is expressed as: The simplified control voltage calculation model is expressed as: In the formula, is the given dq axis current.

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