A method and system for optimizing dual subspace voltage vector duty cycle allocation for a multiphase motor

Abstract

The application belongs to the field of multiphase motor drive control, and discloses a kind of multiphase motor double subspace voltage vector duty ratio distribution optimization method and system, this method designs a new cost function, according to the control weight of fundamental subspace and harmonic subspace, the voltage vector duty ratio distribution proportion acting on the two subspaces is obtained by optimization, so as to realize the minimum control error under corresponding weight. Compared with the prior art, the method can provide an option considering double subspace current control under the condition of limited frequency converter capacity or insufficient DC bus voltage, and the optimal solution of duty ratio corresponding to the minimum current control error is calculated in real time according to the double subspace control weight set by human. The method disclosed by the application has small calculation amount, the design principle is ingenious, the application environment is friendly, and the market prospect is broad.

Description

Technical Field

[0001] This invention belongs to the field of multiphase motor drive control, specifically relating to an optimization method and system for the duty cycle allocation of the bi-space voltage vector in a multiphase motor. Background Technology

[0002] Electric motors are indispensable devices in industrial production and technological development. Advances in technology and the needs of industrial production have further expanded the application areas of motor systems. With the increasing demand for low-voltage, high-power applications, traditional three-phase motors are finding it difficult to meet the needs of actual production and innovative engineering. Multiphase permanent magnet synchronous motors, with their advantages of low power per phase, low torque ripple, and high redundancy, have been widely used in high-power, high-reliability industrial, aerospace, and military fields.

[0003] Model predictive control (MRC) is an advanced control method capable of handling multiple control variables and various nonlinear constraints simultaneously. Finite control set MRC based on discrete models, as a type of MRC, has been introduced into multiphase motors and their drive systems in recent years. Compared to vector control, MRC exhibits better dynamic response and can eliminate the need for a current regulator. Compared to direct torque control, MRC demonstrates better steady-state performance due to online optimization of the cost function. However, traditional finite control set MRC uses a discrete single voltage vector or a virtual voltage vector as the inverter output, whose vector amplitude is not adjustable, leading to increased current ripple and torque fluctuation. To address this, existing technologies incorporate a zero vector to adjust the duty cycle of the output voltage vector, thereby reducing control errors and improving system steady-state performance.

[0004] Compared to three-phase motors, multi-phase motors, after decoupling, have multiple mutually orthogonal subspaces in their mathematical models. For example, five-phase motors and dual three-phase motors have two subspaces: fundamental and harmonic. Nine-phase motors have four subspaces: fundamental, third harmonic, fifth harmonic, and seventh harmonic. When using finite set model predictive control to decouple the fundamental and harmonic subspaces, a duty cycle allocation problem arises. Taking a 100μs inverter switching cycle as an example, the voltage vector in the fundamental subspace, calculated by the controller, requires 80μs (duty cycle 0.8) to achieve tracking control of the fundamental current at the current moment, while the voltage vector in the harmonic subspace requires 40μs (duty cycle 0.4) to achieve tracking control of the harmonic current. Obviously, the desired voltage vector in both subspaces cannot be output within a 100μs switching cycle, and the sum of their duty cycles is greater than 1, thus affecting the control effect of the two subspaces. Therefore, it is necessary to redistribute the duty cycle according to the control weights of the fundamental and harmonic subspaces, and to obtain the optimal duty cycle corresponding to the minimum control error under different weights. Summary of the Invention

[0005] To address the problem of insufficient duty cycle margin in the decoupling control of multiphase motors in two subspaces, this invention provides an optimization method and system for the duty cycle allocation of voltage vectors in the two subspaces of multiphase motors. This method can provide an option that takes into account the current control of the two subspaces when the inverter capacity is limited or the DC bus voltage is insufficient. The optimal duty cycle allocation method is calculated based on the control weights assigned to each subspace.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] An optimization method for the duty cycle allocation of the twin-space voltage vector in a multiphase motor, the method comprising:

[0008] In finite set model predictive control, the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector are calculated based on the deadbeat current control principle.

[0009] Based on the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector, the optimal voltage vector is obtained by optimization in the fundamental subspace control set and the harmonic subspace control set, respectively. V 1 and V h ;

[0010] Assuming the control period of the algorithm in the digital processor is 1, calculate the fundamental subspace. V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h ;

[0011] Based on fundamental subspace V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h Construct the cost function;

[0012] Based on the cost function, the final output voltage vector of the inverter is obtained, realizing the dual-subspace under the weight factor. and The control error is minimized at that time.

[0013] Preferably, the method for calculating the fundamental subspace reference voltage vector is as follows:

[0014] ;

[0015] in, , For the fundamental wave subspace α 1- β Voltage reference value in coordinate system 1j The imaginary unit;

[0016] The method for calculating the harmonic subspace reference voltage vector is as follows:

[0017] ;

[0018] in, for α The harmonic voltage reference value of the shaft, for β Reference value for harmonic voltage of the shaft.

[0019] Preferably, based on the fundamental subspace V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h The method for constructing the cost function is as follows:

[0020] ;

[0021] in, W 1 represents the fundamental subspace weighting factor. W h For harmonic subspace weighting factors, k 1 is d A discount factor of 1 k h for d h The discount factor.

[0022] Preferred methods for obtaining the final output voltage vector of the inverter based on the cost function include:

[0023] Preset parameters m Find the value corresponding to the minimum substitution function. m value;

[0024] Based on fundamental subspace V 1. In the harmonic subspace V h and m The value is used to obtain the final output voltage vector of the inverter, realizing the two-subspace under the weighting factor. and The control error is minimized at that time.

[0025] Preferred, preset parameters m The methods include:

[0026] set up , m The domain is ,set up ;

[0027] Find the value corresponding to the minimum substitution function. m The methods for determining values ​​include:

[0028] ;

[0029] in, , It refers to the calculated original Value, then need to be based on The size is limited to within 0 to 1.

[0030] The present invention also provides an optimization system for the duty cycle allocation of the biphase motor voltage vector in the twin space. The system is used to implement the aforementioned method and includes: a first calculation module, an optimization module, a second calculation module, a construction module, and a third calculation module.

[0031] The first calculation module is used to calculate the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector in finite set model predictive control based on the deadbeat current control principle.

[0032] The optimization module is used to find the optimal voltage vector in the fundamental subspace control set and the harmonic subspace control set, respectively, based on the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector. V 1 and V h ;

[0033] The second calculation module is used to set the control period of the algorithm in the digital processor to 1, and to calculate the fundamental subspace. V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h ;

[0034] The construction module is used to construct based on the fundamental subspace. V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h Construct the cost function;

[0035] The third calculation module is used to obtain the voltage vector of the final output of the inverter based on the cost function, and to realize the weighting factor in the two subspace. and The control error is minimized at that time.

[0036] Preferably, the process of calculating the fundamental subspace reference voltage vector is as follows:

[0037] ;

[0038] in, , For the fundamental wave subspace α 1- β Voltage reference value in coordinate system 1 j The imaginary unit;

[0039] The process of calculating the harmonic subspace reference voltage vector is as follows:

[0040] ;

[0041] in, for α The harmonic voltage reference value of the shaft, for β Reference value for harmonic voltage of the shaft.

[0042] Preferably, based on the fundamental subspace V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h The process of constructing the cost function is as follows:

[0043] ;

[0044] in, W 1 represents the fundamental subspace weighting factor. W h For harmonic subspace weighting factors, k 1 is d A discount factor of 1 k h for d h The discount factor.

[0045] Preferably, the third calculation module includes: m Value calculation unit and voltage vector calculation unit;

[0046] The m-value calculation unit is used to preset parameters. m Find the value corresponding to the minimum substitution function. m value;

[0047] The voltage vector calculation unit is used to calculate the voltage vector based on the fundamental subspace. V 1. In the harmonic subspace V h and mThe value is used to obtain the final output voltage vector of the inverter, realizing the two-subspace under the weighting factor. and The control error is minimized at that time.

[0048] Preferred, preset parameters m The process includes:

[0049] set up , m The domain is ,set up ;

[0050] Find the value corresponding to the minimum substitution function. m The process of valuing includes:

[0051] ;

[0052] in, , It refers to the calculated original Value, then need to be based on The size is limited to within 0 to 1.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] Existing technologies directly discard a portion of the duty cycle of the harmonic subspace when the duty cycle of the two subspaces is saturated (for example, the expected duty cycle of the fundamental wave is 0.8, and the expected duty cycle of the harmonic wave is 0.4, at which point the harmonic wave is output at 0.2), resulting in a significant deterioration in the harmonic control effect.

[0055] Applying this invention to the model predictive control strategy of multiphase motors can avoid the runaway problem of ground harmonic current caused by the saturation of the duty cycle of the two subspaces. By assigning higher control weights to the harmonic subspaces, a lower current distortion rate is obtained than existing methods when harmonic current suppression is required; and a higher harmonic current injection rate is obtained when harmonic current injection is required. Simultaneously, the control errors of the fundamental and harmonic frequencies are minimized under the current weights.

[0056] The designed optimization algorithm is highly versatile and can be used in five-phase, dual three-phase, and nine-phase motors. It optimizes the duty cycle of the bi-space voltage vector and is independent of inverter topology and motor parameters. Attached Figure Description

[0057] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the fundamental subspace voltage vector according to an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of the harmonic subspace voltage vector according to an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of the bi-subspace decoupling control according to an embodiment of the present invention;

[0061] Figure 4 This is a schematic diagram comparing the experimental results of q-axis current before and after the duty cycle allocation of the bi-space voltage vector, taking harmonic injection as an example, in an embodiment of the present invention. (a) is a schematic diagram before the optimal duty cycle allocation; (b) is a schematic diagram after the optimal duty cycle allocation.

[0062] Figure 5 This is a schematic diagram comparing the experimental results of phase current before and after the distribution of the bi-space voltage vector duty cycle in an embodiment of the present invention, taking harmonic injection as an example. (a) is a schematic diagram before the optimal duty cycle distribution; (b) is a schematic diagram after the optimal duty cycle distribution.

[0063] Figure 6 This is a schematic diagram comparing the phase current THD experimental results before and after the bi-space voltage vector duty cycle allocation in an embodiment of the present invention, taking harmonic injection as an example. (a) is a schematic diagram before the optimal duty cycle allocation; (b) is a schematic diagram after the optimal duty cycle allocation.

[0064] Figure 7 For different bispace control weights in embodiments of the present invention m A diagram illustrating the correspondence between the cost function values ​​and the cost function values;

[0065] Figure 8 This is a schematic diagram of a method for optimizing the duty cycle allocation of the bi-space voltage vector of a multiphase motor according to an embodiment of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] Example 1

[0069] like Figure 8 As shown, this invention provides an optimization method for the duty cycle allocation of voltage vectors in the dual-subspace of a multiphase motor, applicable to closed-loop control of harmonic current in multiphase motors. This method designs a new cost function, optimizing the duty cycle allocation ratio of the voltage vectors acting on these two subspaces based on the control weights of the fundamental and harmonic subspaces, thereby achieving the minimum control error under the corresponding weights. Compared with existing technologies, this method provides an option that considers dual-subspace current control when inverter capacity is limited or DC bus voltage is insufficient. Based on the set dual-subspace control weights, the optimal duty cycle solution corresponding to the minimum current control error is calculated in real time. The method disclosed in this invention has low computational complexity, ingenious design principles, a user-friendly application environment, and broad market prospects. The specific implementation steps are as follows:

[0070] Step S1: Select the optimal voltage vector to track the reference value of the fundamental current based on the reference voltage vector in the fundamental subspace;

[0071] The voltage equation for a permanent magnet motor in the fundamental subspace is as follows:

[0072] ;

[0073] in, It is the stator resistance. It is a permanent magnet flux chain. It is the rotor's electrical angular velocity. and These are the quadrature-axis and direct-axis current components in the fundamental subspace, respectively. and These are the quadrature-axis and direct-axis inductance components of the fundamental subspace. and These are the quadrature-axis and direct-axis voltage components of the fundamental subspace, respectively.

[0074] According to the forward Euler formula:

[0075] ;

[0076] in, t For time, i For current, i k refer to k The instantaneous value of the current at time t. i k+1 Then it is k The instantaneous current value at time +1. k Time and k +1 time interval T s Time, This represents the sampling time interval.

[0077] Substituting the forward Euler formula into the voltage equation yields the current prediction model:

[0078] ;

[0079] in, for k time d Instantaneous value of the fundamental current of the direct axis (or straight axis). for k time q Instantaneous value of the quadrature axis (or cross-axis) fundamental current. for k Inverter output at all times d The fundamental voltage component of the axial (or direct-axis) wave. for k Inverter output at all times q Cross-axis (or quadrature-axis) fundamental voltage component, for k The electric angular velocity of the rotor at any given moment.

[0080] Considering the one-step delay compensation of the digital processor, the current prediction model is corrected to a two-step prediction:

[0081] ;

[0082] According to the principle of no-difference beats, let:

[0083] ;

[0084] in, and These are the reference values ​​for the quadrature-axis and direct-axis current components in the fundamental subspace, respectively.

[0085] Substituting this into the current prediction model, we get:

[0086] ;

[0087] This yields the reference voltage vector;

[0088] The significance of this reference voltage vector is that when the inverter outputs this vector and it acts on the motor, at the next sampling moment, the quadrature and direct axis current components of the fundamental subspace will just reach the reference value.

[0089] Transform the reference voltage vector to α 1- β In coordinate system 1:

[0090] ;

[0091] in, The rotor electrical angle; , For the fundamental wave subspace d 1- q Voltage reference value in coordinate system 1; , For the fundamental wave subspace α 1- β Voltage reference value in coordinate system 1.

[0092] The fundamental subspace reference voltage vector can be expressed as: ,in, j It is the imaginary unit (which is a complex number).

[0093] Step S2: Select the optimal voltage vector to track the harmonic current reference value based on the reference voltage vector in the harmonic subspace;

[0094] The voltage equation for a permanent magnet motor in harmonic subspace is as follows:

[0095] ;

[0096] in, h It is the harmonic order. It is a permanent magnet flux. h Second harmonic components and They are h Sub-harmonic subspace quadrature-axis and direct-axis current components. and They are h Sub-harmonic subspace quadrature-axis and direct-axis inductance components and They are h Sub-harmonic subspace quadrature-axis and direct-axis voltage components;

[0097] Similarly, the reference voltage vector is transformed to α h - βh In the coordinate system:

[0098] ;

[0099] in, for α The harmonic voltage reference value of the shaft, for β The harmonic voltage reference value of the shaft, for d The harmonic voltage reference value of the shaft, for q Reference value for harmonic voltage of the shaft.

[0100] h The subspace reference voltage vector of the subharmonic can be expressed as: .

[0101] Step S3: Based on the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector, the optimal voltage vector is obtained by optimizing the control set in their respective subspaces. V 1 and V h ;

[0102] The specific optimization process is as follows: through the cost function:

[0103] ;

[0104] ;

[0105] in, V 1-i For any voltage vector in the fundamental control set, V h-i For any voltage vector in the harmonic control set, find the fundamental voltage vector that minimizes the cost function. V 1 and harmonic voltage vector V h ,like Figure 1 and Figure 2 As shown;

[0106] The fundamental subspace control set and the harmonic subspace control set can be virtual voltage vectors obtained by a single vector or a combination of multiple vectors. The method disclosed in this invention does not have any special requirements for this.

[0107] Step S4: Assume the control period of the algorithm in the digital processor (DSP28335) is 1, and calculate the fundamental subspace. V Duty cycle of 1 d 1. Its domain is Calculate the harmonic subspace V h duty cycle d h Its domain is ;

[0108] like Then no further optimization is needed; the voltage vectors used for bispace control can be output according to the desired duty cycle. The control cycle outputs a zero vector;

[0109] like Then the parameters need to be solved according to the subsequent steps. m The value can be determined from subsequent steps. , This means discounting the duty cycle of the voltage vector used for bispace control.

[0110] Step S5: Construct the cost function ,definition W 1 represents the fundamental subspace weighting factor, and its domain is... , W h The harmonic subspace weighting factor has the following domain: , k 1 is d The discount factor of 1 has a domain of 1. , k h for d h The discount factor, whose domain is .

[0111] Step S6: Set , m The domain is Since the sum of the duty cycles of the voltage vectors used to control the bispace cannot exceed 1 in each control cycle, therefore let The cost function is then expressed as Except m All external quantities are known quantities.

[0112] Step S7: Find the value corresponding to the minimum substitution function. m Value, let the first derivative Solve m The value of , i.e., the extreme point, can be derived to be at this point. m The expression is:

[0113] .

[0114] Step S8: Because It is a quadratic function (the coefficient of the quadratic term is positive, and the opening is upward), and its extreme point is a unique minimum point, combined with its domain. Cost function can be obtained g Minimum value m The value is:

[0115] ;

[0116] in, ,here It refers to the calculated original Value, then need to be based on The size is limited to within 0 to 1.

[0117] Step S9: The final output voltage vector of the inverter is determined by... This ensures that the bispace is within the weight factor. W 1 and Wh The control error is minimized at that time.

[0118] Overall control block diagram as follows Figure 3 As shown.

[0119] Taking harmonic current injection control as an example, without using this method for duty cycle allocation optimization, the total duty cycle upper limit is 1, resulting in small harmonic current injection, large harmonic current ripple, and large phase current peak value. After adopting this method, the harmonic current injection increases, the harmonic current ripple decreases, and the phase current exhibits a saddle-shaped curve. At this point, the weighting factor of the harmonic subspace is larger. Although some fundamental current control performance is sacrificed, a new scheme is provided to meet harmonic current control requirements. Experimental comparison results are as follows: Figure 4-6 As shown.

[0120] Figure 7 When different weighting factors are displayed, m The correspondence between the cost function value and the control weights in different subspaces is used to prove that the cost function reaches its minimum value when the control weights are changed. m The value represents the optimal allocation ratio, which can be calculated in real time according to step S8. m value.

[0121] Example 2

[0122] This invention provides an optimization system for the duty cycle allocation of the bi-space voltage vector of a multiphase motor. The system is used to implement the method described in Embodiment 1. The system includes: a first calculation module, an optimization module, a second calculation module, a construction module, and a third calculation module.

[0123] The first calculation module is used to calculate the fundamental subspace reference voltage vector in finite set model predictive control based on the deadbeat current control principle. V ref-1 With harmonic subspace reference voltage vector V ref-h ,in h The order of the desired control harmonic subspace;

[0124] The optimization module is used to find the optimal voltage vector in the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector, respectively, within the fundamental subspace control set and the harmonic subspace control set. V 1 and V h ;

[0125] The second calculation module is used to set the control period of the algorithm in the digital processor to 1, and to calculate the fundamental subspace. V Duty cycle of 1 d 1. In harmonic subspace V h duty cycled h ;

[0126] Modules for building upon fundamental subspace V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h Construct the cost function;

[0127] The third calculation module is used to obtain the voltage vector of the inverter's final output based on the cost function, realizing the weighting factor in the two-subspace. and The control error is minimized at that time.

[0128] In this embodiment, the process of calculating the fundamental subspace reference voltage vector is as follows:

[0129] ;

[0130] in, , For the fundamental wave subspace α 1- β Voltage reference value in coordinate system 1 j The imaginary unit;

[0131] The method for calculating the harmonic subspace reference voltage vector is as follows:

[0132] ;

[0133] in, for α The harmonic voltage reference value of the shaft, for β Reference value for harmonic voltage of the shaft.

[0134] In this embodiment, based on the fundamental subspace V In the harmonic subspace with a duty cycle of 1 V h The process of constructing the cost function based on the duty cycle is as follows:

[0135] ;

[0136] in, W 1 represents the fundamental subspace weighting factor. W h For harmonic subspace weighting factors, k 1 is d A discount factor of 1 k h for d hThe discount factor.

[0137] In this embodiment, the third computing module includes: m Value calculation unit and voltage vector calculation unit;

[0138] The m-value calculation unit is used to preset parameters. m Find the value corresponding to the minimum substitution function. m value;

[0139] Voltage vector calculation unit, used for calculation based on fundamental subspace V 1. In the harmonic subspace V h and m The value is used to obtain the final output voltage vector of the inverter, realizing the two-subspace under the weighting factor. and The control error is minimized at that time.

[0140] In this embodiment, preset parameters m The process includes:

[0141] set up , m The domain is ,set up ;

[0142] Find the value corresponding to the minimum substitution function. m The process of valuing includes:

[0143] ;

[0144] in, ,here It refers to the calculated original Value, then need to be based on The size is limited to within 0 to 1.

[0145] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An optimization method for the duty cycle allocation of the twin-space voltage vector in a multiphase motor, characterized in that, The method includes: In finite set model predictive control, the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector are calculated based on the deadbeat current control principle. Based on the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector, the optimal voltage vector is obtained by optimization in the fundamental subspace control set and the harmonic subspace control set, respectively. V 1 and V h ; Assuming the control period of the algorithm in the digital processor is 1, calculate the fundamental subspace. V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h ; Based on fundamental subspace V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h Construct the cost function; Based on the cost function, the final output voltage vector of the inverter is obtained, realizing the dual-subspace under the weight factor. and The control error is minimized at this time; Based on fundamental subspace V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h The method for constructing the cost function is as follows: ； in, W 1 represents the fundamental subspace weighting factor. W h For harmonic subspace weighting factors, k 1 is d A discount factor of 1 k h for d h Discount factor, The fundamental subspace reference voltage vector, This is the harmonic subspace reference voltage vector.

2. The method according to claim 1, characterized in that, The method for calculating the fundamental subspace reference voltage vector is as follows: ； in, , For the fundamental wave subspace α 1- β Voltage reference value in coordinate system 1 j The imaginary unit; The method for calculating the harmonic subspace reference voltage vector is as follows: ； in, for α The harmonic voltage reference value of the shaft, for β Reference value for harmonic voltage of the shaft.

3. The method according to claim 1, characterized in that, Based on the cost function, methods for obtaining the final output voltage vector of the inverter include: Preset parameters m Find the value corresponding to the minimum substitution function. m value; Based on fundamental subspace V 1. In harmonic subspace V h and m The value is used to obtain the final output voltage vector of the inverter, realizing the two-subspace under the weighting factor. and The control error is minimized at that time.

4. The method according to claim 3, characterized in that, Preset parameters m The methods include: set up , m The domain is ,set up ; Find the value corresponding to the minimum substitution function. m The methods for determining values ​​include: ； in, , It refers to the calculated original Value, then need to be based on The size is limited to within 0 to 1.

5. An optimization system for the duty cycle allocation of the biphase motor voltage vector, the system being used to implement the method described in any one of claims 1-4, characterized in that, The system includes: a first calculation module, an optimization module, a second calculation module, a construction module, and a third calculation module; The first calculation module is used to calculate the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector in finite set model predictive control based on the deadbeat current control principle. The optimization module is used to find the optimal voltage vector in the fundamental subspace control set and the harmonic subspace control set, respectively, based on the fundamental subspace reference voltage vector and the harmonic subspace reference voltage vector. V 1 and V h ; The second calculation module is used to set the control period of the algorithm in the digital processor to 1, and to calculate the fundamental subspace. V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h ; The construction module is used to construct based on the fundamental subspace. V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h Construct the cost function; The third calculation module is used to obtain the voltage vector of the final output of the inverter based on the cost function, and to realize the weighting factor in the two subspace. and The control error is minimized at that time.

6. The system according to claim 5, characterized in that, The process of calculating the fundamental subspace reference voltage vector is as follows: ； in, , For the fundamental wave subspace α 1- β Voltage reference value in coordinate system 1 j The imaginary unit; The process of calculating the harmonic subspace reference voltage vector is as follows: ； in, for α The harmonic voltage reference value of the shaft, for β Reference value for harmonic voltage of the shaft.

7. The system according to claim 6, characterized in that, Based on fundamental subspace V Duty cycle of 1 d 1. In harmonic subspace V h duty cycle d h The process of constructing the cost function is as follows: ； in, W 1 represents the fundamental subspace weighting factor. W h For harmonic subspace weighting factors, k 1 is d A discount factor of 1 k h for d h Discount factor.

8. The system according to claim 7, characterized in that, The third calculation module includes: m Value calculation unit and voltage vector calculation unit; The m-value calculation unit is used to preset parameters. m Find the value corresponding to the minimum substitution function. m value; The voltage vector calculation unit is used to calculate the voltage vector based on the fundamental subspace. V 1. In harmonic subspace V h and m The value is used to obtain the final output voltage vector of the inverter, realizing the two-subspace under the weighting factor. and The control error is minimized at that time.

9. The system according to claim 8, characterized in that, Preset parameters m The process includes: set up , m The domain is ,set up ; Find the value corresponding to the minimum substitution function. m The process of valuing includes: ； in, , It refers to the calculated original Value, then need to be based on The size is limited to within 0 to 1.

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