A method for optimizing the robustness of permanent magnet synchronous motor parameters and related equipment

Abstract

The present application provides a method for optimizing the parameter robustness of a permanent magnet synchronous motor and related equipment. The method for optimizing the parameter robustness of a permanent magnet synchronous motor first represents the initial flux parameters in an initial prediction model with target flux parameters, wherein the target flux parameters are represented by inductance parameters, so that the only parameters in the prediction model that can affect the permanent magnet synchronous motor are resistance parameters and inductance parameters; at the same time, a sliding mode observer is established to obtain an inductance disturbance estimate, and then the inductance disturbance estimate is added as a parameter to the initial prediction model, which can reduce the influence of the inductance parameter disturbance on the permanent magnet synchronous motor; the prediction model is used to select an optimal voltage vector, and then the optimal voltage vector is applied to the inverter of the permanent magnet synchronous motor, thereby optimizing the parameter robustness of the permanent magnet synchronous motor.

Description

Technical Field

[0001] The present application relates to the field of motor control technology, and in particular to a method for optimizing parameter robustness of a permanent magnet synchronous motor and related equipment. Background Art

[0002] Permanent-magnet synchronous motors (PMSMs) have advantages such as simple structure, high operating efficiency and power factor, low losses, and good controllability, making them widely used in industrial production. Model predictive control (MPC) is widely used for high-performance motor control due to its simple algorithm and structure, as well as good dynamic control performance. Model predictive current control (MPCC) is the most widely used. In traditional MPCC methods, the only controlled variable is the stator current. The future state of the current is predicted based on the inherent discrete characteristics of the motor inverter. The optimal voltage vector for the next control cycle is then determined by a cost function composed of the predicted current error. However, MPCC methods are dependent on model parameters. If the motor is subject to internal and external interference during operation, the motor parameters will become mismatched. Therefore, improving the parameter robustness of MPCC methods has become an issue that must be considered in practical applications. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a method for optimizing the robustness of permanent magnet synchronous motor parameters and related equipment.

[0004] Based on the above-mentioned purpose, the present application provides a method for optimizing the parameter robustness of a permanent magnet synchronous motor, including: obtaining an initial prediction model, the initial prediction model including an initial flux parameter; summing and averaging multiple fluxes of the motor at different times to obtain a target flux value, expressing the target flux value with an inductance parameter to obtain a target flux parameter; using a pre-established sliding mode observer to calculate and obtain an inductance disturbance estimate; adding the inductance disturbance estimate as a model parameter to the initial prediction model, and replacing the initial flux parameter in the initial prediction model with the target flux parameter to obtain a prediction model; inputting multiple basic voltage vectors of the inverter of the motor into the prediction model to obtain an optimal voltage vector, and applying the optimal voltage vector to the inverter.

[0005] Optionally, the initial prediction model expression includes:

[0006]

[0007] Among them, k is the current time, ψf is the initial magnetic flux parameter, i d (k+1), i q (k+1) are the predicted current values ​​at time k+1 on the d-axis and q-axis respectively, T is the period, R is the resistance parameter, L is the inductance parameter, i d (k), i q (k) are the current sampling values ​​at time k on the d-axis and q-axis respectively, ω e is the electrical angular velocity of the motor, u d (k),u q (k) are the voltages of the optimal voltage vector at time k on the d-axis and q-axis respectively.

[0008] Optionally, the motor includes a d-axis and a q-axis, and the target flux parameter is:

[0009]

[0010] in:

[0011]

[0012] N=L(k)×i d (k)+L(k-1)×i d (k-1)+L(k-2)×i d (k-2)

[0013]

[0014] S(n)=L(n)·[i q (n)-i q (n-1)]

[0015] Among them, k is the current moment, u q (k),u q (k-1),u q (k-2) are the q-axis voltages of the optimal voltage vector at moments k, k-1, and k-2, respectively; R is the resistance parameter; i q (k), i q (n) are the currents actually measured at time k and n on the q axis, i d (k) is the current actually measured at time k on the d-axis, i q (n-1), i q (k-1), i q (k-2) are the currents stored at moments n-1, k-1, and k-2 on the q axis, i d (k-1), i d(k-2) are the currents stored on the d-axis at time k-1 and k-2, L(n), L(k), L(k-1), and L(k-2) are the inductances on one of the q-axis and d-axis at time n, k, k-1, and k-2, respectively. T is the period, ω e (k) is the electrical angular velocity of the motor at time k.

[0016] Optionally, the pre-established sliding mode observer expression includes:

[0017]

[0018] Wherein, the sliding mode observer is established on the d-axis of the motor, ud is the voltage on the d-axis, L is the inductance parameter, is the estimated value of the inductance disturbance, is the estimated value of d-axis current, t is time, R s is the resistance parameter, ω is the electrical angular velocity, i q is the current on the q axis, U dsmo is the sliding mode control function, g d is a sliding mode parameter; the pre-establishing of the sliding mode observer includes pre-establishing the sliding mode control function, and establishing the sliding mode control function includes: constructing the sliding mode control function according to the constant velocity convergence law, and the sliding mode control function expression is:

[0019]

[0020] Among them, L is the inductance parameter, k is the sliding mode coefficient, sign() is the sign function, is the estimated value of d-axis current, i d is the current on the d-axis.

[0021] Optionally, the expression of the inductance disturbance estimation value is:

[0022]

[0023] in, is the estimated value of the inductance disturbance, t is the time, k is the sliding mode coefficient, g d is the sliding mode parameter, and L0 is the inductance value in the prediction model.

[0024] Optionally, the inductive disturbance estimation value is added to the initial prediction model, and the target flux parameter is used to replace the initial flux parameter in the initial prediction model. The obtained prediction model expression includes:

[0025]

[0026] Among them, k is the current time, is the target flux parameter, is the estimated value of the inductance disturbance, i d (k+1), i q (k+1) are the predicted current values ​​at time k+1 on the d-axis and q-axis respectively, T is the period, R is the resistance parameter, L is the inductance parameter, i d (k), i q (k) are the current sampling values ​​at time k on the d-axis and q-axis respectively, ω e is the electrical angular velocity of the motor, u d (k),u q (k) are the voltages of the optimal voltage vector at time k on the d-axis and q-axis respectively.

[0027] Optionally, inputting multiple basic voltage vectors of the motor inverter into the prediction model to obtain the optimal voltage vector includes: inputting the multiple basic voltage vectors into the prediction model to obtain multiple current prediction values ​​corresponding one-to-one to the multiple basic voltage vectors; substituting the multiple current prediction values ​​into the value function to obtain multiple value function calculation values ​​corresponding one-to-one to the multiple current prediction values; and selecting the basic voltage vector corresponding to the smallest value function calculation value as the optimal voltage vector.

[0028] Based on the above-mentioned purpose, the present application also provides a device for optimizing the robustness of permanent magnet synchronous motor parameters, characterized in that it includes: an initial prediction model acquisition module, configured to obtain an initial prediction model, the initial prediction model including an initial flux parameter; a first calculation module, configured to sum and average multiple fluxes of the motor at different times to obtain a target flux parameter, wherein the target flux parameter is expressed by an inductance parameter; a second calculation module, configured to use a pre-established sliding mode observer to calculate and obtain an inductance disturbance estimate; a model calculation module, configured to add the inductance disturbance estimate as a model parameter to the initial prediction model, and replace the initial flux parameter in the initial prediction model with the target flux parameter to obtain a prediction model; a selection module, configured to input multiple basic voltage vectors of the inverter of the motor into the prediction model, obtain an optimal voltage vector, and apply the optimal voltage vector to the inverter.

[0029] Based on the above purpose, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, any one of the methods for optimizing the robustness of permanent magnet synchronous motor parameters is implemented.

[0030] Based on the above objectives, the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute any of the methods for optimizing the robustness of permanent magnet synchronous motor parameters.

[0031] From the above, it can be seen that the method and related equipment for optimizing the parameter robustness of a permanent magnet synchronous motor provided in the present application represent the initial flux parameters in the initial prediction model with target flux parameters, wherein the target flux parameters are represented by inductance parameters, so that the only parameters in the prediction model that can affect the permanent magnet synchronous motor are resistance parameters and inductance parameters; at the same time, a sliding mode observer is established to obtain an inductance disturbance estimate, and then the inductance disturbance estimate is added as a parameter to the initial prediction model, which can reduce the influence of the inductance parameter disturbance on the permanent magnet synchronous motor; the prediction model is used to select the optimal voltage vector, and then the optimal voltage vector is applied to the inverter of the permanent magnet synchronous motor, thereby optimizing the parameter robustness of the permanent magnet synchronous motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 This is a flowchart of a method for optimizing parameter robustness of a permanent magnet synchronous motor according to an embodiment of the present application;

[0034] Figure 2 A schematic diagram of the distribution of basic voltage vectors according to an embodiment of the present application;

[0035] Figure 3 Schematic diagram of a surface-mounted permanent magnet synchronous motor drive system with a two-level inverter according to an embodiment of the present application;

[0036] Figure 4 Schematic diagram of the predicted current control of the traditional single vector model;

[0037] Figure 5 Schematic diagram of the influence of resistance, inductance and flux linkage parameters on current prediction error and the corresponding influence degree in the embodiment of the present application;

[0038] Figure 6 This is a schematic block diagram of single vector model predictive current control according to an embodiment of the present application;

[0039] Figure 7Schematic diagram of the voltage simulation results on the d-axis before and after the inductance and resistance parameters are increased by 3 times simultaneously using the traditional MPCC method;

[0040] Figure 8 Schematic diagram of the voltage simulation results on the q-axis before and after the inductance and resistance parameters are increased by 3 times simultaneously using the traditional MPCC method;

[0041] Figure 9 Schematic diagram of voltage simulation results on the d-axis before and after the inductance and resistance parameters are simultaneously increased by 3 times according to the method of an embodiment of the present application;

[0042] Figure 10 Schematic diagram of voltage simulation results on the q-axis before and after the inductance and resistance parameters are simultaneously increased by 3 times according to the method of an embodiment of the present application;

[0043] Figure 11 A block diagram of a device for optimizing parameter robustness of a permanent magnet synchronous motor according to an embodiment of the present application;

[0044] Figure 12 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0046] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0047] In the related art, the initial prediction model is the current prediction model in the traditional single vector model prediction current control (MPCC). The block diagram of the traditional single vector model prediction current control (MPCC) is as follows: Figure 4 As shown, the traditional single-vector MPCC method mainly includes two parts: prediction and optimization, which are represented by the control system block diagram as: current prediction model and value function. The initial prediction model expression is:

[0048]

[0049] Among them, k is the current time, ψ f is the initial magnetic flux parameter, i d (k+1), i q (k+1) are the predicted current values ​​at time k+1 on the d-axis and q-axis respectively, T is the period, R is the resistance parameter, L is the inductance parameter, i d (k), i q (k) are the current sampling values ​​at time k on the d-axis and q-axis respectively, ω e is the electrical angular velocity of the motor, u d (k1),u q (k) are the voltages of the optimal voltage vector at time k on the d-axis and q-axis respectively.

[0050] It can be seen from formula (1) that the prediction model in the traditional MPCC method includes resistance parameters, inductance parameters and flux parameters. Among them, the disturbance of the resistance parameter has little effect on the permanent magnet synchronous motor, but the disturbance of the inductance parameter and the flux parameter will have a greater impact on the operation of the permanent magnet synchronous motor. In addition, in the related technology, there is no direct observation of the disturbance values ​​of the inductance parameter and the flux parameter, resulting in poor parameter robustness of the permanent magnet synchronous motor.

[0051] In view of this, an embodiment of the present application provides a method for optimizing the robustness of permanent magnet synchronous motor parameters, such as Figure 1 Shown, including:

[0052] S101, obtaining an initial prediction model, wherein the initial prediction model includes initial magnetic flux parameters;

[0053] S102, summing and averaging multiple flux linkages of the motor at different times to obtain a target flux linkage parameter, wherein the target flux linkage parameter is expressed by an inductance parameter, that is, the target flux linkage parameter is calculated from the inductance parameter;

[0054] S103, using a pre-established sliding mode observer to calculate and obtain an estimated value of the inductive disturbance;

[0055] S104, adding the inductive disturbance estimation value as a model parameter to the initial prediction model, and replacing the initial flux parameter in the initial prediction model with the target flux parameter to obtain a prediction model;

[0056] S105, inputting multiple basic voltage vectors of the motor inverter into the prediction model to obtain an optimal voltage vector, and applying the optimal voltage vector to the inverter. The distribution of the multiple basic voltage vectors is as follows: Figure 2shown.

[0057] The method for optimizing the parameter robustness of a permanent magnet synchronous motor provided in this embodiment represents the initial flux parameters in an initial prediction model with target flux parameters, wherein the target flux parameters are represented by inductance parameters, so that the only parameters in the prediction model that can affect the permanent magnet synchronous motor are resistance parameters and inductance parameters; simultaneously, a sliding mode observer is established to obtain an inductance disturbance estimate, which is then added as a parameter to the initial prediction model, thereby reducing the impact of the inductance parameter disturbance on the permanent magnet synchronous motor; and the prediction model is used to select an optimal voltage vector, which is then applied to the inverter of the permanent magnet synchronous motor, thereby optimizing the parameter robustness of the permanent magnet synchronous motor.

[0058] In one embodiment provided in the present application, the permanent magnet synchronous motor adopts a surface mounted permanent magnet synchronous motor (SPMSM) drive system with a two-level inverter, including a d-axis and a q-axis, such as Figure 3 As shown, S1 to S6 are transistors, and D1 to D6 are diodes. Since the d-axis and q-axis inductances of the surface-mounted permanent magnet synchronous motor are equal, that is, Ld=Lq=L, the voltage equation of the permanent magnet synchronous motor in the embodiment of the present application can be expressed as:

[0059]

[0060] Among them, u d ,u q is the d, q axis stator voltage component, R is the resistance parameter, i d 、i q are the currents on the d-axis and q-axis respectively, L is the inductance parameter, t is the time, ω e is the electrical angular velocity of the motor, ψ f is the initial magnetic flux parameter.

[0061] The error of the resistance parameter in the prediction model in the method of this embodiment has little effect on the permanent magnet synchronous motor. A simulation experiment and data results are given below to address this point.

[0062] In the traditional MPC method, during the operation of the motor, the current prediction model constructed based on the actual motor parameters containing disturbances is:

[0063]

[0064] Among them, R0, ψ f0 , L0 represents the actual parameters of the running motor (other parameter definitions refer to the parameters in the above initial prediction model expression), and its specific expression is:

[0065]

[0066] Among them, L, R, ψ f Represents the parameters of the initial prediction model, ΔL, ΔR, Δψ f The disturbance of the parameters in the actual operation process of the motor is represented by the initial prediction model containing the actual disturbance minus the initial prediction model of the model parameters, that is, subtracting formula (1) from formula (3). The initial prediction model containing only the disturbance can be obtained, and its expression is:

[0067]

[0068] Among them, E d and E q They represent the current prediction errors of the d-axis and q-axis respectively, and u d (k),u q (k) are the voltages of the optimal voltage vector at time k-1 on the d-axis and q-axis respectively (refer to the parameters in the above expressions for the definitions of other parameters).

[0069] The embodiment of the present application uses the initial prediction model containing only the disturbance amount in the above formula (5) to simulate the influence of each parameter on the motor control system. The resistance, inductance and flux linkage of the motor are mutated at appropriate times. The influence of each parameter on the current prediction error and the corresponding influence degree are shown as follows: Figure 5 shown. Figure 5 It represents the d-axis and q-axis current prediction errors when different parameters are disturbed. Figure 5 It is obvious that when the resistance changes suddenly, the current prediction error is less sensitive to it. The second is the flux, which mainly affects the q-axis current prediction error, and finally the inductance, which has the greatest disturbance on the motor system. Figure 5 It can be concluded that when the inductance parameter suddenly changes by a factor of 2, the ripple of the current error has the greatest impact compared to the resistance and flux linkage. Therefore, from the above simulation results, it can be seen that during system operation, the resistance has a relatively small impact on the system and can be ignored when analyzing and solving the motor parameter robustness problem.

[0070] From the voltage equation of the permanent magnet synchronous motor of the present application, it can be seen that the flux linkage parameter in the motor can be expressed by the following formula:

[0071]

[0072] Among them, k is the current time, ψ f (k) is the magnetic flux at time k, and L(k) is the inductance at time k (refer to the parameters in the above expressions for the definitions of other parameters).

[0073] By analogy, the magnetic flux expressions at time k-1 and k-2 are as follows (the definitions of each parameter refer to the parameters in the above expressions):

[0074]

[0075] Since the motor is running in steady state, the electrical angular velocity at different times can be regarded as the same value, that is, ω e =ω e (k)=ω e (k-1)=ω e (k-2). The flux linkage at different control moments is summed and averaged to obtain formula (8):

[0076]

[0077] Substitute the expression of flux at different times into the above formula, that is, substitute formula (7) into formula (8), and obtain the target flux parameter. The target flux parameter expression is:

[0078]

[0079] in:

[0080]

[0081] N=L(k)×i d (k)+L(k-1)×i d (k-1)+L(k-2)×i d (k-2) (11)

[0082]

[0083] S(n)=L(n)·[i q (n)-i q (n-1)] (13)

[0084] Among them, k is the current moment, u q (k),u q (k-1),u q (k-2) are the q-axis voltages of the optimal voltage vector at moments k, k-1, and k-2, respectively; R is the resistance parameter; i q (k), i q (n) are the currents actually measured at time k and n on the q axis, i d (k) is the current actually measured at time k on the d-axis, i q (n-1), i q (k-1), i q (k-2) are the currents stored at moments n-1, k-1, and k-2 on the q axis, id (k-1), i d (k-2) are the currents stored on the d-axis at time k-1 and k-2, L(n), L(k), L(k-1), and L(k-2) are the inductances on one of the q-axis and d-axis at time n, k, k-1, and k-2, respectively. T is the period, ω e (k) is the electrical angular velocity of the motor at time k. As can be seen from equations (9) to (13), the method provided in this embodiment represents the target flux parameters using inductance parameters and other parameters, eliminating the influence of the flux parameters on the current prediction model.

[0085] Sliding Mode Control (SMC) is the primary optimization method for improving motor parameter robustness. This method offers the advantage of improved dynamic performance in the presence of parameter mismatches and disturbances. Furthermore, SMC strategies do not require a high-precision system model and are insensitive to internal parameters and external disturbances.

[0086] The expression of the d-axis voltage can be obtained from the voltage equation of the permanent magnet synchronous motor of the present application (refer to the parameters in the above expressions for the definition of each parameter):

[0087]

[0088] Since the d-axis does not contain flux parameters, in order to make the estimated value of the observed inductive disturbance more accurate, the inductive disturbance on the d-axis is selected for observation. According to formula (14), a sliding mode observer is pre-established on the d-axis. The sliding mode observer expression is:

[0089]

[0090] Where, ud is the voltage on the d-axis, L is the inductance parameter, is the estimated value of the inductance disturbance, is the estimated value of d-axis current, t is time, R s is the resistance parameter, ω is the electrical angular velocity, i q is the current on the q axis, U dsmo is the sliding mode control function, g d is the sliding mode parameter;

[0091] The pre-established sliding mode observer includes designing a sliding mode surface and establishing the sliding mode control function.

[0092] The expression of the sliding surface is:

[0093]

[0094] Among them, s d represents the sliding surface, is the estimated value of d-axis current, i d is the current on the d-axis.

[0095] Establishing the sliding mode control function includes:

[0096] The sliding mode control function is constructed according to the constant velocity convergence law. The sliding mode control function expression is:

[0097]

[0098]

[0099] Among them, L is the inductance parameter, k is the sliding mode coefficient, sign() is the sign function, is the estimated value of d-axis current, i d is the current on the d-axis.

[0100] Combining the above sliding mode control function and sliding mode observer expression, we can get (the definition of each parameter refers to the parameters in the above expressions):

[0101]

[0102] The expression of the inductance disturbance estimation value calculated by equations (15) to (19) is:

[0103]

[0104] in, is the estimated value of the inductance disturbance, t is the time, k is the sliding mode coefficient, g d is the sliding mode parameter, and L0 is the inductance value in the prediction model.

[0105] In some embodiments, the inductive disturbance estimation value is added as a parameter to the initial prediction model, and the target flux parameter is used to replace the initial flux parameter in the initial prediction model. The prediction model expression obtained is:

[0106]

[0107] Among them, k is the current time, is the target flux parameter, is the estimated value of the inductance disturbance, i d (k+1), i q (k+1) are the predicted current values ​​at time k+1 on the d-axis and q-axis respectively, T is the period, R is the resistance parameter, L is the inductance parameter, i d (k), i q (k) are the current sampling values ​​at time k on the d-axis and q-axis respectively, ω e is the electrical angular velocity of the motor, ud (k),u q (k) are the voltages of the optimal voltage vector at time k on the d-axis and q-axis respectively.

[0108] The block diagram of the single vector model predictive current control (MPCC) using the method of the embodiment of the present application is as follows: Figure 6 As shown, compared with the traditional single vector MPCC ( Figure 4 ), the embodiment of the present application adds a target inductance calculation module and a sliding mode observer.

[0109] In some embodiments, inputting a plurality of basic voltage vectors of the motor inverter into the prediction model to obtain an optimal voltage vector includes:

[0110] Inputting the multiple basic voltage vectors into the prediction model to obtain multiple current prediction values ​​corresponding to the multiple basic voltage vectors;

[0111] Substituting the multiple current prediction values ​​into the cost function to obtain multiple cost function calculation values ​​corresponding to the multiple current prediction values;

[0112] The basic voltage vector corresponding to the minimum calculated value of the cost function is selected as the optimal voltage vector.

[0113] In specific implementation, the value function is expressed as:

[0114]

[0115] in, are the reference values ​​of the current on the d-axis and q-axis, i d (k+1), i q (k+1) are the current prediction values ​​at time k+1 on the d-axis and q-axis respectively, and g is the calculated value of the cost function.

[0116] The simulation results of the method provided by the embodiment of the present application are as follows: Figures 7 to 10 As shown, Figure 7 and Figure 8 The voltage simulation results on the d-axis and q-axis of the traditional MPCC method are shown before and after the inductance and resistance parameters are increased by 3 times. Figure 9 and Figure 10The voltage simulation results on the d-axis and q-axis are respectively obtained for the method provided in the embodiment of the present application before and after the inductance and resistance parameters are increased by 3 times at the same time. It can be seen from the simulation results that the two methods simultaneously perform parameter mutation at the time of 0.2s. In the traditional MPCC method, the ripple changes of the d-axis and q-axis current waveforms are large after the parameter mutation, and the sudden increase in the inductance and resistance parameters has a more obvious impact on the control system. Therefore, the parameter robustness of the traditional MPCC is weak. However, for the method provided in the embodiment of the present application, the changes in the d-axis and q-axis current waveforms are not obvious before and after the parameters are mutated, so it can be verified that the method proposed in the present application can reduce the impact of the error of the inductance parameters on the permanent magnet synchronous motor, and optimize the parameter robustness of the permanent magnet synchronous motor.

[0117] A semi-physical experiment simulation was also carried out using the method provided in the embodiment of the present application. The parameters of the experimental platform are shown in Table 1. The experimental conditions are that the motor speed is 1000 r / min, the load torque is 6 N·m, and the control frequency set in the experiment is 12 kHz.

[0118] Table 1: Experimental platform parameters

[0119] parameter Parameter Definition Numerical <![CDATA[U dc (V)]]> DC bus voltage 310 <![CDATA[n N (rpm)]]> Rated speed 2000 P Pole pairs 3 R(Ω) stator resistance 3.0 L(mH) stator inductance 11 <![CDATA[ψ f (Wb)]]> rotor flux 0.24 <![CDATA[J(kg.m 2 )]]> moment of inertia 0.00129 <![CDATA[T e (N·m)]]> Rated torque 6

[0120] The experimental results are shown in Table 2.

[0121] Table 2: Comparison of experimental data between the traditional MPCC method and the method provided in the examples of this application

[0122]

[0123]

[0124] It is obvious that when the resistance parameters suddenly change, the ripples of the d-axis and q-axis currents are basically the same for both the traditional MPCC and the method proposed in this application before and after the parameter mutation. However, when the inductance parameters suddenly change, the current ripple of the traditional method changes significantly before and after the parameter mutation, with the d-axis current ripple changing to 0.4A and the q-axis current ripple changing to 1.02A. The current ripple of the method proposed in this application changes relatively slightly before and after the inductance parameter mutation, with the d-axis current ripple changing to 0.01A and the q-axis changing to 0.16A. Similarly, when the resistance and inductance suddenly change at the same time, the d-axis changes by 0.02A and the q-axis changes by 0.14A. It can be seen from the collated experimental data that the method proposed in this application can reduce the impact of the error of the inductance parameters on the permanent magnet synchronous motor and optimize the parameter robustness of the permanent magnet synchronous motor.

[0125] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0126] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] Based on the same inventive concept, corresponding to any of the above embodiments and methods, the present application also provides a device for optimizing the robustness of permanent magnet synchronous motor parameters, such as Figure 11 Shown, including:

[0128] The initial prediction model acquisition module 10 is configured to acquire an initial prediction model, wherein the initial prediction model includes initial magnetic flux parameters;

[0129] The first calculation module 20 is configured to sum and average multiple flux linkages of the motor at different times to obtain a target flux linkage value, and then express the target flux linkage value with an inductance parameter to obtain a target flux linkage parameter;

[0130] A second calculation module 30 is configured to calculate and obtain an estimated value of the inductive disturbance using a pre-established sliding mode observer;

[0131] The model calculation module 40 is configured to add the inductive disturbance estimation value as a model parameter to the initial prediction model, and replace the initial flux parameter in the initial prediction model with the target flux parameter to obtain a prediction model;

[0132] The selection module 50 is configured to input a plurality of basic voltage vectors of the inverter of the motor into the prediction model to obtain an optimal voltage vector, and apply the optimal voltage vector to the inverter.

[0133] The device for optimizing the parameter robustness of a permanent magnet synchronous motor provided in this embodiment represents the initial flux parameters in an initial prediction model with target flux parameters, wherein the target flux parameters are represented by inductance parameters, so that the only parameters in the prediction model that can affect the permanent magnet synchronous motor are resistance parameters and inductance parameters; simultaneously, a sliding mode observer is established to obtain an inductance disturbance estimate, which is then added as a parameter to the initial prediction model, thereby reducing the impact of the inductance parameter disturbance on the permanent magnet synchronous motor; the prediction model is used to select an optimal voltage vector, which is then applied to the inverter of the permanent magnet synchronous motor, thereby optimizing the parameter robustness of the permanent magnet synchronous motor.

[0134] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0135] The device of the above embodiment is used to implement the corresponding method for optimizing the robustness of permanent magnet synchronous motor parameters in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0136] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the method for optimizing the robustness of the parameters of the permanent magnet synchronous motor described in any of the above embodiments is implemented.

[0137] Figure 12 12 shows a more specific hardware structure diagram of an electronic device provided in this embodiment. The device may include: a processor 1210, a memory 1220, an input / output interface 1230, a communication interface 1240, and a bus 1250. The processor 1210, the memory 1220, the input / output interface 1230, and the communication interface 1240 are connected to each other within the device via the bus 1250.

[0138] The processor 1210 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0139] The memory 1220 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1220 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1220 and is called and executed by the processor 1210.

[0140] The input / output interface 1230 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0141] The communication interface 1240 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0142] The bus 1250 comprises a pathway for transmitting information between the various components of the device (eg, the processor 1210 , the memory 1220 , the input / output interface 1230 , and the communication interface 1240 ).

[0143] It should be noted that although the above device only shows the processor 1210, the memory 1220, the input / output interface 1230, the communication interface 1240, and the bus 1250, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0144] The electronic device of the above embodiment is used to implement the corresponding method for optimizing the robustness of permanent magnet synchronous motor parameters in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0145] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for optimizing the robustness of permanent magnet synchronous motor parameters as described in any of the above embodiments.

[0146] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0147] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for optimizing the robustness of permanent magnet synchronous motor parameters as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0148] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. Within the scope of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0149] In addition, for simplicity of description and discussion, and in order not to make the embodiment of the application difficult to understand, the known power supply / ground connection with integrated circuit (IC) chip and other components may or may not be shown in the accompanying drawings provided. In addition, the device can be shown in the form of a block diagram to avoid making the embodiment of the application difficult to understand, and this also takes into account the following fact, that is, the details of the embodiment of these block diagram devices are highly dependent on the platform to be implemented in the embodiment of the application (that is, these details should be fully within the scope of understanding of those skilled in the art). When specific details (for example, circuit) are set forth to describe exemplary embodiments of the application, it will be apparent to those skilled in the art that the embodiment of the application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0150] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the embodiments discussed.

[0151] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of this application.

Claims

1. A method for optimizing the robustness of permanent magnet synchronous motor parameters, characterized in that: include: Acquiring an initial prediction model, wherein the initial prediction model includes initial magnetic flux parameters; A plurality of flux linkages of the motor at different times are summed and averaged to obtain a target flux linkage parameter, wherein the target flux linkage parameter is a target flux linkage parameter represented by an inductance parameter; Utilizing the pre-established sliding mode observer, the estimated value of the inductive disturbance is calculated and obtained; adding the inductive disturbance estimation value as a model parameter to the initial prediction model, and replacing the initial flux parameter in the initial prediction model with the target flux parameter to obtain a prediction model; Inputting a plurality of basic voltage vectors of the inverter of the motor into the prediction model to obtain an optimal voltage vector, and applying the optimal voltage vector to the inverter; The expression of the estimated value of the inductance disturbance is: in, is the estimated value of the inductance disturbance, t is the time, k is the sliding mode coefficient, g d is the sliding mode parameter, and L0 is the inductance value in the prediction model.

2. The method for optimizing the robustness of permanent magnet synchronous motor parameters according to claim 1, characterized in that: The motor includes a d-axis and a q-axis, and the initial prediction model expression includes: Among them, k is the current time, ψ f is the initial magnetic flux parameter, i d (k+1), i q (k+1) are the predicted current values ​​at time k+1 on the d-axis and q-axis respectively, T is the period, R is the resistance parameter, L is the inductance parameter, i d (k), i q (k) are the current sampling values ​​at time k on the d-axis and q-axis respectively, ω e is the electrical angular velocity of the motor, u d (k),u q (k) are the voltages of the optimal voltage vector at time k on the d-axis and q-axis respectively.

3. The method for optimizing the robustness of permanent magnet synchronous motor parameters according to claim 1, characterized in that: The motor includes a d-axis and a q-axis, and the target flux parameters are: in: N=L(k)×i d (k)+L(k-1)×i d (k-1)+L(k-2)×i d (k-2) S(n)=L(n)·[i q (n)-i q (n-1)] Among them, k is the current moment, u q (k),u q (k-1),u q (k-2) are the q-axis voltages of the optimal voltage vector at moments k, k-1, and k-2, respectively; R is the resistance parameter; i q (k), i q (n) are the currents actually measured at time k and n on the q axis, i d (k) is the current actually measured at time k on the d-axis, i q (n-1), i q (k-1), i q (k-2) are the currents stored at the time n-1, k-1, and k-2 on the q axis, i d (k-1), i d (k-2) are the currents stored on the d-axis at time k-1 and k-2, L(n), L(k), L(k-1), and L(k-2) are the inductances on one of the q-axis and d-axis at time n, k, k-1, and k-2, respectively. T is the period, ω e (k) is the electrical angular velocity of the motor at time k.

4. The method for optimizing the robustness of permanent magnet synchronous motor parameters according to claim 1, characterized in that: The motor includes a d-axis and a q-axis, and the pre-established sliding mode observer expression includes: Wherein, the sliding mode observer is established on the d-axis of the motor, u d is the voltage on the d-axis, L is the inductance parameter, is the estimated value of the inductance disturbance, is the estimated value of d-axis current, t is time, R s is the resistance parameter, ω is the electrical angular velocity, i q is the current on the q axis, U dsmo is the sliding mode control function, g d is the sliding mode parameter; The pre-establishing of the sliding mode observer includes pre-establishing the sliding mode control function, and establishing the sliding mode control function includes: The sliding mode control function is constructed according to the constant velocity convergence law. The sliding mode control function expression is: Among them, L is the inductance parameter, k is the sliding mode coefficient, sign() is the sign function, is the estimated value of d-axis current, i d is the current on the d-axis.

5. The method for optimizing the robustness of permanent magnet synchronous motor parameters according to claim 3, characterized in that: The motor includes a d-axis and a q-axis. The inductance disturbance estimation value is added as a model parameter to the initial prediction model, and the target flux parameter is used to replace the initial flux parameter in the initial prediction model. The prediction model expression obtained includes: Among them, k is the current time, is the target flux parameter, is the estimated value of the inductance disturbance, i d (k+1), i q (k+1) are the predicted current values ​​at time k+1 on the d-axis and q-axis respectively, T is the period, R is the resistance parameter, L is the inductance parameter, i d (k), i q (k) are the current sampling values ​​at time k on the d-axis and q-axis respectively, ω e is the electrical angular velocity of the motor, u d (k),u q (k) are the voltages of the optimal voltage vector at time k on the d-axis and q-axis respectively.

6. The method for optimizing the robustness of permanent magnet synchronous motor parameters according to claim 1, characterized in that: Inputting a plurality of basic voltage vectors of the motor inverter into the prediction model to obtain an optimal voltage vector includes: Inputting the multiple basic voltage vectors into the prediction model to obtain multiple current prediction values ​​corresponding to the multiple basic voltage vectors; Substituting the multiple current prediction values ​​into the cost function to obtain multiple cost function calculation values ​​corresponding to the multiple current prediction values; The basic voltage vector corresponding to the minimum calculated value of the cost function is selected as the optimal voltage vector.

7. A device for optimizing the robustness of permanent magnet synchronous motor parameters, characterized in that: include: An initial prediction model acquisition module is configured to acquire an initial prediction model, wherein the initial prediction model includes initial magnetic flux parameters; a first calculation module configured to sum and average multiple flux linkages of the motor at different times to obtain a target flux linkage parameter, wherein the target flux linkage parameter is represented by an inductance parameter; A second calculation module is configured to calculate and obtain an estimated value of the inductive disturbance using a pre-established sliding mode observer; a model calculation module configured to add the inductive disturbance estimation value as a model parameter to the initial prediction model, and replace the initial flux parameter in the initial prediction model with the target flux parameter to obtain a prediction model; a selection module configured to input a plurality of basic voltage vectors of the inverter of the motor into the prediction model to obtain an optimal voltage vector, and apply the optimal voltage vector to the inverter; The expression of the estimated value of the inductance disturbance is: in, is the estimated value of the inductance disturbance, t is the time, k is the sliding mode coefficient, g d is the sliding mode parameter, and L0 is the inductance value in the prediction model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.

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