Permanent magnet synchronous motor multi-objective model predictive control method based on parallel optimization

Abstract

The application discloses a kind of permanent magnet synchronous motor multi-objective model prediction control methods based on parallel optimization, it relates to motor control field, successively include the following steps: establishing permanent magnet synchronous motor mathematical model, model prediction torque control based on Euler discretization and optimal vector based on integrated optimization mechanism selection. Compared with prior art, the optimal vector based on integrated optimization mechanism selection introduced in the application makes the control system self-adaptive switching priority of multi-objective under high-speed and low-speed working conditions, to realize global optimization and fast dynamic coordination of multi-dimensional variable, and makes the performance requirement under the performance requirement of meeting preliminary constraint condition, reduces the ripple of torque and flux linkage, overcomes motor operation instability and additional noise interference, and improves the control precision and steady-state performance of control system.

Description

Technical Field

[0001] This invention relates to the field of motor control, and more specifically to a multi-objective model predictive control method for permanent magnet synchronous motors based on parallel optimization. Background Technology

[0002] As the core execution link for stable operation and precise control of an aircraft, the attitude control electric drive system directly affects the overall quality and mission completion capability of the aircraft. Currently, the traditional proportional-integral (PI) control method is widely used. However, when faced with the complex nonlinear dynamic characteristics of aircraft and the changing working environment, PI control has gradually revealed its inherent limitations, such as limited control accuracy and slow dynamic response speed, making it difficult to meet the urgent needs of modern aircraft for high-precision and highly agile attitude control.

[0003] To improve control performance, advanced control methods such as model predictive control have been introduced into research. While these methods improve dynamic performance to some extent, when dealing with the typical multi-objective optimization problem of aircraft attitude control, the performance of the controller heavily relies on the fine and tedious tuning of the weight coefficients of multiple control objectives (such as tracking accuracy, response speed, and control energy consumption). This weight parameter design process is not only labor-intensive and computationally complex, but also has poor overall adaptability.

[0004] Crucially, aircraft attitude control systems require simultaneous coordination of global optimization across multiple target dimensions and attitude control variables. This means the control system must be able to collaboratively optimize control objectives across multiple degrees of freedom, such as roll, pitch, and yaw, and globally coordinate multiple attitude control variables. However, traditional control methods often fall short in handling such multi-dimensional global optimization and rapid dynamic coordination problems, struggling to achieve optimal trade-offs and rapid responses among multiple objectives while ensuring system stability.

[0005] In view of this, this application has conducted in-depth research on this basis, resulting in this case. Summary of the Invention

[0006] The purpose of this invention is to provide a high-precision and highly adaptable multi-objective model predictive control method for permanent magnet synchronous motors based on parallel optimization. This method can solve the problems of complex weight coefficient design and poor adaptability in traditional multi-objective predictive control, while achieving global optimization of multi-dimensional variables and rapid dynamic coordination.

[0007] To achieve the above objectives, the solution of this invention is: a multi-objective model predictive control method for permanent magnet synchronous motors based on parallel optimization. The control system of the permanent magnet synchronous motor includes an inverter, a speed loop PI controller, and a model predictive controller. The inverter has eight input vectors. The method includes the following steps:

[0008] Step 1: Establish a mathematical model for the permanent magnet synchronous motor;

[0009] Step 2: Based on the mathematical model of the permanent magnet synchronous motor, obtain the stator flux prediction calculation formula and the electromagnetic torque prediction calculation formula, and establish the cost function of model predictive control. The cost function is as follows: In the formula, and They represent in Predicted values ​​of electromagnetic torque and stator flux linkage at time +1 This represents the output value of the speed loop PI controller. Indicates the stator flux linkage reference value. Indicates the weighting parameter;

[0010] Step 3: Select the optimal vector based on the comprehensive optimization mechanism. Decompose the cost function to obtain the torque target term and the flux linkage target term. Then, traverse the eight input vectors and determine the number of intersections that satisfy the constraints based on the torque and flux linkage constraints. Select the input vector with the largest number of intersections as the shortlisted vector and use it as the optimal vector. If there are at least two shortlisted vectors, determine the torque error estimate and flux linkage error estimate of each shortlisted vector under the torque target term and the flux linkage target term, respectively. Then, sort the torque error estimate and the flux linkage error estimate in ascending order and set the sorting value. Select the shortlisted vector with the smallest cumulative sorting value as the pre-selected vector and use it as the optimal vector. If there are at least two pre-selected vectors, evaluate the priority of the torque target term and the flux linkage target term, and select the corresponding pre-selected vector as the optimal vector based on the priority.

[0011] Step 4: The optimal vector output is sent to the inverter to control the on / off state of each switch in the inverter, and then the inverter outputs three-phase current to the permanent magnet synchronous motor.

[0012] In step 1, the mathematical model of the permanent magnet synchronous motor is:

[0013] ,

[0014] ,

[0015] ,

[0016] ,

[0017] In the formula, u sd , u sqThey represent dq Stator voltage in axial coordinate system i sd , i sq They represent dq Stator current in axial coordinate system This represents the stator resistance of a permanent magnet synchronous motor. , These represent the stator flux linkages. dq Axial components, Indicates permanent magnet flux linkage. Indicates electromagnetic torque. , These represent the stator inductance. dq Axial components, This represents the rotor electrical angular velocity of a permanent magnet synchronous motor. This represents the number of pole pairs in a permanent magnet synchronous motor.

[0018] The inverter has three-phase bridge arms, which are respectively phase a, phase b and phase c, and each phase is provided with two switching transistors. The two switching transistors in each phase bridge arm are respectively located in the upper bridge arm and the lower bridge arm.

[0019] The formula for calculating the output voltage of the inverter is as follows:

[0020] In the formula, Indicates the output voltage. Indicates DC voltage. , and These respectively represent the inverter in Mutually, Harmony The switching state of the upper bridge arm switch transistor. Represents the imaginary unit. Represents the base of the natural logarithm in complex form; =0、 =0 and =0 respectively represent the inverter in Mutually, Harmony The upper bridge arm switch of the phase is turned off. =1、 =1 and =1 respectively represent the inverter in Mutually, Harmony The upper bridge arm switch of the phase is turned on;

[0021] Specifically, a three-bit binary number is obtained based on the switching states of the upper bridge arm switches of the three phases in the inverter. Using control input As input vector, This indicates the current state of the switch.

[0022] The three-phase voltage is obtained based on the relationship between the switch state and the three-phase voltage of the permanent magnet synchronous motor, and then... Clarke Transformation and Park Transform the three-phase voltage from abc Transformation to three-phase natural coordinate system dq In a two-phase synchronous rotating coordinate system, the relationship is: , , ;

[0023] in, Clarke Transform into , Park Transform into In the formula, , , They represent Phase voltage, Phase voltage, Phase voltage, , They represent Stator voltage in axial coordinate system , They represent dq Stator voltage in axial coordinate system θ This refers to the rotor mechanical angle of the permanent magnet synchronous motor.

[0024] In step 2, the stator voltage equations in the mathematical model of the permanent magnet synchronous motor are discretized using forward Euler discretization to obtain the values ​​of the permanent magnet synchronous motor in the model. dq The formula for predicting current in the axial coordinate system is:

[0025] ,

[0026] ,

[0027] In the formula, Indicates the first In each sampling period d , q Predicted shaft current value Indicates the first One cycle d , q Shaft current sampling value, The time interval of the sampling period;

[0028] The model predictive controller uses the current prediction formula and the permanent magnet synchronous motor mathematical model to derive the stator flux prediction calculation formula and the electromagnetic torque prediction calculation formula, which correspond to the following respectively:

[0029] ,

[0030] ,

[0031] ,

[0032] ,

[0033] In the formula, and They represent The predicted electromagnetic torque at time +1 and Stator flux linkage prediction at time +1 express +1 time d Shaft stator flux linkage prediction value express +1 time q Predicted values ​​of stator flux linkage.

[0034] In step 2, the formula for calculating the stator flux linkage reference value is as follows:

[0035] In the formula, express dq In the axial coordinate system q The stator current reference value of the shaft, where =0, express dq In the axial coordinate system Reference value for stator current of shaft.

[0036] Step 3 includes the following steps;

[0037] Step 3-1, Preliminary Screening; First, the cost function is decomposed into a problem, and the error terms for the torque target and flux linkage target are obtained as follows: , In the formula, The error term representing the torque target, The error term representing the magnetic flux target, This indicates the reference value for electromagnetic torque. Indicates the stator flux linkage reference value;

[0038] Then, calculate the number of intersections where each input vector satisfies the torque constraint and flux linkage constraint respectively. If either the torque constraint or the flux linkage constraint is satisfied, the number of intersections is 1. If both the torque constraint and the flux linkage constraint are satisfied, the number of intersections is 2.

[0039] Next, the input vector corresponding to the maximum value of the number of intersections is taken as the inbound vector. When there is only one inbound vector, the inbound vector is output as the optimal vector. If there are at least two inbound vectors, then step 3-2 is executed.

[0040] Step 3-2: Determine the cumulative sorting value of the shortlisted vectors;

[0041] First, obtain the torque error estimate of the torque target under each of the included vectors and the flux linkage estimate of the flux linkage target under each of the included vectors. Then, sort the torque error estimate and the flux linkage error estimate in ascending order and obtain the ranking of each included vector under the torque target item and the flux linkage target item, so as to obtain the sorting value of each included vector under the two target items respectively.

[0042] Next, the sum of the ranking values ​​of each shortlisted vector under the torque target item and the flux linkage target item is calculated to obtain the cumulative ranking value. The shortlisted vector with the smallest cumulative ranking value is selected as the pre-selected vector. If there is only one pre-selected vector, the pre-selected vector is output as the optimal vector. If there are at least two pre-selected vectors, each pre-selected vector is selected and step 3-3 is executed.

[0043] Step 3-3: Priority assessment; determine the priority of the torque target and the flux linkage target, and select the pre-selected vector with the smallest ranking value as the optimal vector among the targets with high priority.

[0044] In step 3-2, the torque constraint condition is: <20% The flux linkage constraint condition is as follows: < (10%-20%) .

[0045] By adopting the above structure, the present invention has the following beneficial effects: It decomposes the cost function in model predictive control into single objective error terms for torque and flux linkage (i.e., torque objective term and flux linkage objective term), eliminating the cumbersome weight coefficient design of the cost function in traditional methods. This avoids the problem of control performance degradation caused by the inability of fixed weight coefficients to adapt to varying operating conditions, and solves the problem of cumbersome weight coefficient design in traditional model predictive control. Furthermore, it introduces a comprehensive optimization mechanism to select the optimal vector, enabling the control system to adaptively switch the priority of multiple objectives under high-speed and low-speed conditions. This achieves global optimization of multi-dimensional variables and rapid dynamic coordination, reducing torque and flux linkage ripple while meeting the performance requirements of the initial constraints, overcoming motor instability and additional noise interference, and improving the control accuracy and steady-state performance of the control system. Attached Figure Description

[0046] Figure 1 This is a circuit topology diagram of the permanent magnet synchronous motor and inverter in this invention.

[0047] Figure 2 This is a block diagram illustrating the control principle of the multi-objective model predictive control method of the present invention.

[0048] Figure 3 This is a flowchart of the comprehensive optimization mechanism in this invention. Detailed Implementation

[0049] To further explain the technical solution of the present invention, the present invention will be described in detail below through specific embodiments.

[0050] A multi-objective model predictive control method for permanent magnet synchronous motors (PMSMs) based on parallel optimization is proposed. This method is based on common control systems for PMSMs, and the PMSM can be an embedded type. Figures 1-2 As shown, the inverter is a conventional two-level three-phase inverter. The inverter includes three phase arms, which correspond to the following... Mutually, Harmony Each phase has two switching transistors, and the two switching transistors in each phase arm are located in the upper and lower arms respectively. For example: upper arm and lower bridge arm The on and off states of the switching transistors are represented by binary numbers "1" and "0" respectively. During operation, in order to prevent short circuits from burning out the inverter, the two switching transistors in each phase arm cannot be turned on at the same time.

[0051] Furthermore, the aforementioned permanent magnet synchronous motor serves as the output load of the inverter. The formula for calculating the input vector (i.e., the input voltage vector) in this inverter is as follows: In the formula, Indicates the output voltage. Indicates DC voltage. , and These represent the inverters in... Mutually, Harmony The switching state of the upper bridge arm switch transistor. Represents the imaginary unit. Represents the base of the natural logarithm in complex form; =0、 =0 and =0 respectively represent the inverter in Mutually, Harmony The upper bridge arm switch is turned off, and the lower bridge arm switch is turned on. =1、 =1 and =1 respectively represent the inverter in Mutually, Harmony The upper bridge arm switch is turned on, and the lower bridge arm switch is turned off.

[0052] Therefore, based on the switching states of the upper arm switches of the three phases in the inverter, a three-bit binary number is obtained. ,For example , In this embodiment, there are eight input vectors, numbered... =1~8 indicates that control input is used. As input vector, This represents the current state of the switch; for example, when... When, it indicates the upper bridge arm Switch cut off, upper bridge arm and All switching transistors are turned on; simultaneously, the switching transistors of each lower bridge arm are in the opposite state, i.e., the lower bridge arm... The switch is turned on, and the lower bridge arm is activated. and The switching transistor is off.

[0053] In addition, the above control system also includes a torque and flux estimation module, a speed loop PI controller and a model predictive controller. The input quantities of each of these can be obtained by conventional means in the art or by the methods described below, so they will not be described in detail here.

[0054] In this embodiment, the multi-objective model predictive control method includes the following steps.

[0055] Step 1: Establish the mathematical model of the permanent magnet synchronous motor: This mathematical model includes the stator voltage equation, the stator flux linkage equation, and the electromagnetic torque state-space equation.

[0056] To elaborate, the established mathematical model of the permanent magnet synchronous motor is as follows:

[0057] (1),

[0058] , (2),

[0059] (3);

[0060] In the formula, u sd , u sq They represent dq Stator voltage in axial coordinate system i sd , i sq They represent dq Stator current in axial coordinate system This represents the stator resistance of a permanent magnet synchronous motor. , These represent the stator flux linkages. dq Axial components, Indicates permanent magnet flux linkage. Indicates electromagnetic torque. , These represent the stator inductance. dq Axial components, This represents the rotor electrical angular velocity of a permanent magnet synchronous motor. This represents the number of pole pairs in a permanent magnet synchronous motor.

[0061] Step 2, Model Predictive Torque Control Based on Euler Discretization: The stator flux prediction calculation formula and electromagnetic torque prediction calculation formula are obtained through the permanent magnet synchronous motor mathematical model in Step 1, and the cost function of model predictive control is established.

[0062] To elaborate, such as Figure 2 As shown, step 2 includes the following steps.

[0063] Step 2-1, Obtain the current prediction formula: Perform conventional forward Euler discretization on the differential term of formula (1) in the mathematical model of the permanent magnet synchronous motor, i.e. (4), and then simplified to obtain the permanent magnet synchronous motor in dq The formula for current prediction in the axial coordinate system is as follows.

[0064] (5),

[0065] (6);

[0066] In the formula, Indicates the first In each sampling period d , q Predicted shaft current value Indicates the first One cycle d , q Shaft current sampling value, This is the time interval of the sampling period.

[0067] Furthermore, in the current prediction formula, , The calculation process is as follows: Based on the relationship between the inverter's switching state and the three-phase voltage of the permanent magnet synchronous motor, the three-phase voltage is obtained, and then... Clarke Transformation and Park Transformation from three-phase voltage abc Transformation to three-phase natural coordinate system dq In a two-phase synchronous rotating coordinate system.

[0068] To elaborate, the relationship between the switching state of the inverter and the three-phase voltage is as follows:

[0069] , , In the formula, , , They represent Phase voltage, Phase voltage, Phase voltage.

[0070] in, Clarke Transformed into: In the formula, , They represent Stator voltage in axial coordinate system.

[0071] Park Transformed into: In the formula, , They represent dq Stator voltage in axial coordinate system θ This refers to the rotor mechanical angle of the permanent magnet synchronous motor.

[0072] Step 2-2: Obtain the stator flux linkage and electromagnetic torque prediction calculation formulas: Based on the current prediction formula in Step 2-1 and the permanent magnet synchronous motor mathematical model in Step 1, the above model prediction controller can derive the stator flux linkage and electromagnetic torque prediction calculation formulas. That is, by substituting formulas (5) and (6) into formulas (2) and (3), the formulas can be obtained.

[0073] The formulas for predicting and calculating stator flux linkage and electromagnetic torque are as follows:

[0074] (7),

[0075] , (8),

[0076] (9);

[0077] In the formula, and They represent The predicted electromagnetic torque at time +1 and Stator flux linkage prediction at time +1 express +1 time d Shaft stator flux linkage prediction value express +1 time q Predicted values ​​of stator flux linkage.

[0078] It is worth mentioning that all of the above are... The predicted electromagnetic torque at time +1 and The predicted value of the stator flux linkage at time +1 depends on the traversed pre-selected voltage vector (i.e., the included vector).

[0079] Step 2-3: Establish the cost function of model predictive control: To eliminate errors caused by system computation delays, after considering delay compensation, the established cost function of model predictive control is as follows:

[0080] (10), where, where, This represents a weighting parameter to balance the magnitude difference between torque and flux linkage; This indicates the torque output value of the speed loop PI controller. This indicates the stator flux linkage reference value.

[0081] Furthermore, the electromagnetic torque equation in formula (3) gives the electromagnetic torque and dq The relationship between stator currents in the axial coordinate system, therefore based on Control strategy and torque reference value It can be calculated dq In the axial coordinate system q The stator current reference value of the shaft, i.e. ,in, express dq In the axial coordinate system The reference value of the stator current on the shaft allows for the further calculation of the reference value of the stator flux linkage. That is, the formula for calculating the stator flux linkage reference value is: .

[0082] Step 3: Select the optimal vector based on the comprehensive optimization mechanism: Decompose the cost function in Steps 2-3 to obtain the torque target term and the flux linkage target term. Then, traverse the eight input vectors and determine the number of intersections that satisfy the constraints based on the torque and flux linkage constraints. The input vector with the largest number of intersections is selected as the shortlisted vector and becomes the optimal vector. If there are multiple shortlisted vectors, determine the torque error estimate and flux linkage error estimate of each shortlisted vector under the torque and flux linkage target terms. Then, sort the torque error estimate and flux linkage error estimate by ascending value and set the sorting value. Select the shortlisted vector with the smallest cumulative sorting value as the optimal vector. If at least two cumulative sorting values ​​are the same, evaluate the priority of the torque target term and the flux linkage target term, and select the shortlisted vector with the smallest cumulative sorting value as the optimal vector based on the priority.

[0083] To elaborate, such as Figure 3 As shown, step 3 includes the following steps.

[0084] Step 3-1, Preliminary Screening: First, decompose the cost function from Step 2-3 to obtain the error terms for each single objective, namely the error terms for the torque objective and the flux linkage objective, thereby eliminating the weighting coefficients in the cost function; then calculate the number of intersections where each input vector satisfies the torque constraint and the flux linkage constraint, denoted as . q For each input vector, if one of the torque constraint or flux linkage constraint is satisfied, then... q= 1. If both torque constraint and flux linkage constraint are satisfied, then q= 2.

[0085] To elaborate, the error terms for the torque target term and the flux linkage target term are as follows:

[0086] , (11);

[0087] In the formula, The error term representing the torque target, The error term representing the magnetic flux target, This indicates the torque output value of the speed loop PI controller. This indicates the stator flux linkage reference value.

[0088] To elaborate further, the torque constraint conditions described above are: <20% The flux linkage constraint is: < (10%-20%) .

[0089] It should be noted that the error terms of the torque target and the flux linkage target must be within a certain range. The range is designed according to the actual working conditions. For example, in scenarios with high requirements for torque stability, a reference torque with an error term of <20% of the torque target needs to be designed.

[0090] For example, , There are two input vectors. If both the torque constraint and the flux linkage constraint are satisfied, then for Its corresponding q =2, and It satisfies one of the torque constraint condition and the flux linkage constraint condition, therefore q =1.

[0091] Then, with q The input vector corresponding to the maximum value is used as the in bounding vector, and it is determined whether there are at least two. q If the values ​​are the same, and there is no value, it means that there is only one shortlisted vector, so the shortlisted vector is output as the optimal vector; if there is a value, it means that there are at least two shortlisted vectors, so step 3-2 is executed.

[0092] For example, if , There are two input vectors. If both the torque constraint and the flux linkage constraint are satisfied, then for Its corresponding q =2, and It satisfies one of the torque constraint condition and the flux linkage constraint condition, therefore q =1, thus making As the optimal vector output.

[0093] For example, there are multiple input vectors. , and of q If the values ​​are the same, that is, there are 3 shortlisted vectors, then proceed to step 3-2.

[0094] For example, if , and There are three input vectors. If both the torque constraint and the flux linkage constraint are satisfied, then for Its corresponding q =2, and It satisfies one of the torque constraint condition and the flux linkage constraint condition, therefore q =1; If one of the torque constraint and flux linkage constraint conditions is satisfied, then for Its corresponding q =1, that is, and of q The values ​​are the same, but corresponding q The value is the largest, therefore the included vector is ,but As the optimal vector output.

[0095] Step 3-2: Determine the cumulative sort value of the included vectors:

[0096] Step 3-2-1: Perform parallel optimization on the torque target and flux linkage target respectively, traversing each bounded vector to obtain the torque error estimate of the torque target under each bounded vector and the flux linkage error estimate of the flux linkage target under each bounded vector.

[0097] To elaborate, regarding formula (11) The specific steps for calculating and solving for the estimated value are as follows:

[0098] Based on the relationship in step 2-1, calculate the three-phase voltage corresponding to each input vector (i.e., the input voltage vector). ), after the above Clarke Transformation and Park Transformation dq Stator voltage in axial coordinate system ( , ), combined with the current sensor sampling on the permanent magnet synchronous motor, obtained the first One cycle d , q shaft current sampling value (i.e. Substituting these into formulas (5) and (6), we obtain the first... In each sampling period d , q Predicted shaft current (i.e.) Substitute it into formula (7) and solve for the result. The predicted electromagnetic torque value at time +1 is then subtracted from the current torque reference value to obtain the torque error estimate.

[0099] Similarly, by iterating through the included vectors of the current period, we can obtain the flux linkage error estimate corresponding to each included vector in the flux linkage target.

[0100] Step 3-2-2: Sort the torque error estimates in Step 3-2-1 in ascending order and obtain the ranking of each shortlisted vector under the torque target, so as to obtain the sorting value of each shortlisted vector under the torque target; Correspondingly, sort the flux linkage error estimates in Step 3-2-1 in ascending order and obtain the ranking of each shortlisted vector under the flux linkage target, so as to obtain the sorting value of the next shortlisted vector under the flux linkage target, and then execute Step 3-2-3.

[0101] For example, let the three shortlisted vectors be... , and If the torque error estimates of the three shortlisted vectors are ranked as follows: , and Then the shortlisted vector The ranking value for the torque target is 1, and the included vector is... The ranking value for the torque target is 2, and the included vector is... The ranking value for the torque target is 3.

[0102] Step 3-2-3: Calculate the sum of the ranking values ​​of each shortlisted vector under the torque target item and the flux linkage target item to obtain the cumulative ranking value of each shortlisted vector. Select the shortlisted vector with the smallest cumulative ranking value as the pre-selected vector. Determine whether there are at least two pre-selected vectors. If not, it means that there is only one pre-selected vector, so the pre-selected vector is output as the optimal vector. If there are, it means that there are at least two pre-selected vectors, so execute step 3-3.

[0103] For example, The ranking values ​​for the torque target and the flux linkage target are 1 and 3, respectively. The corresponding sorting values ​​are 4 and 1. The corresponding sorting values ​​are 2 and 2, then , and The cumulative sorted values ​​are 4, 5, and 4 respectively, meaning there are two pre-defined vectors. and ,therefore and Proceed to the next step.

[0104] For example, The ranking values ​​for the torque target and the flux linkage target are 4 and 1, respectively. The corresponding sorting values ​​are 1 and 4. The corresponding sorting values ​​are 2 and 2, then , and The cumulative sorted values ​​are 5, 5, and 4 respectively. Since there is only one pre-selected vector, that is... ,therefore Proceed to the next step. It should be noted that if an included vector has a ranking value of 4 and 1 in the torque target and flux linkage target, then the ranking value of other included vectors in the torque target can no longer be 4. That is, other included vectors can only occupy ranking values ​​other than 4 in the torque target.

[0105] Step 3-3: Priority Assessment: Determine the priority of the torque target item and the flux linkage target item, and select the pre-selected vector with the smallest ranking value from the target items with high priority as the optimal vector.

[0106] For example, with and Proceed to step 3-3. The ranking values ​​for the torque target and the flux linkage target are 1 and 3, respectively. The corresponding sorting values ​​are 2 and 2. Therefore, if the priority of the torque target is higher than the priority of the flux linkage target, then select... As the optimal vector, the converse choice is... As the optimal vector.

[0107] It should be noted that, based on conventional factors such as actual operating conditions and user scenario requirements, the priorities of torque and flux linkage targets are pre-set before the permanent magnet synchronous motor is put into operation. For example, in scenarios requiring stable torque output, the torque target is designed with higher priority; for scenarios with low speed and high requirements for smooth current harmonics, the flux linkage target is generally designed with higher priority. Furthermore, both designs have been verified to be easily adjustable.

[0108] Step 4: Obtain the optimal vector from Step 3 and output it to the inverter. The inverter operates normally to control the on / off state of each switch in the inverter, and then the inverter outputs three-phase current (i.e., , , (For permanent magnet synchronous motors)

[0109] It should be noted that the operations after the optimal vector is input into the inverter in step 4 are all existing conventional operations, and therefore will not be described in detail. Specifically, in... Figure 2 In the process, the three-phase current is output after Clarke / Park conversion. , ,in, This represents the rotor mechanical angular velocity of a permanent magnet synchronous motor, measured in rad / s. The magnitude of the rotor electrical angular velocity is a fraction of the mechanical angular velocity. times; This represents the design reference value for the current rotor mechanical angular velocity, which is set manually.

[0110] The above description is only a preferred embodiment of this invention. Any equivalent changes and modifications made within the scope of the claims of this invention shall fall within the scope of the claims of this invention.

Claims

1. A parallel optimization-based multi-objective model predictive control method for permanent magnet synchronous motor, the control system of the permanent magnet synchronous motor comprising an inverter, a speed loop PI controller and a model predictive controller, the inverter having eight input vectors; characterized in that, The method comprises the following steps: Step 1, establishing a permanent magnet synchronous motor mathematical model; Step 2, obtaining a stator flux linkage prediction calculation formula and an electromagnetic torque prediction calculation formula according to the permanent magnet synchronous motor mathematical model, and establishing a cost function of model prediction control, the cost function is: , wherein, and respectively represent the electromagnetic torque prediction value and the stator flux linkage prediction value at the time of +1, represents an output value of the speed loop PI controller, represents a stator flux linkage reference value, represents a weight parameter; Then, the cost function is problem decomposed to get error terms of torque target and flux linkage target respectively as follows, , , wherein, represents an error term of torque target, represents an error term of flux linkage target, represents a torque output value of the speed loop PI controller, represents a stator flux linkage reference value; Wherein, the calculation formula of the stator flux linkage reference value is, In the formula, express dq In the axial coordinate system q The stator current reference value of the shaft, where =0, express dq In the axial coordinate system Stator current reference value for the shaft; Indicates permanent magnet flux linkage. Indicating stator inductance q Axial components; Step 3, selecting an optimal vector based on a comprehensive optimization mechanism, problem decomposition of the cost function to obtain torque target items and flux linkage target items, then traversing eight input vectors, judging the number of intersections of the eight input vectors that meet the constraint conditions according to the torque constraint condition and the flux linkage constraint condition, and taking the input vector corresponding to the maximum number of intersections as the shortlisted vector, and taking the shortlisted vector as the optimal vector; if there are at least two shortlisted vectors, then judging the torque error estimation value and the flux linkage error estimation value of each shortlisted vector under the torque target item and the flux linkage target item respectively, and then sorting the torque error estimation values and the flux linkage error estimation values according to the ascending order to set the sorting values, and taking the shortlisted vector with the minimum cumulative sorting value as the preselected vector, and taking the preselected vector as the optimal vector; if there are at least two preselected vectors, then evaluating the priority of the torque target item and the flux linkage target item, and selecting the corresponding preselected vector as the optimal vector according to the priority; Step 4, outputting the optimal vector to the inverter to control the on-off of each switch tube in the inverter, and then outputting three-phase current to the permanent magnet synchronous motor by the inverter.

2. The parallel optimization based multi-objective model predictive control method for permanent magnet synchronous motor according to claim 1, characterized in that: In step 1, the permanent magnet synchronous motor mathematical model is, ， ， ， ， In the formula, u sd , u sq They represent dq Stator voltage in axial coordinate system i sd , i sq They represent dq Stator current in axial coordinate system This represents the stator resistance of a permanent magnet synchronous motor. , These represent the stator flux linkages. dq Axial components, Indicates electromagnetic torque. Indicating stator inductance d Axial components, This represents the rotor electrical angular velocity of a permanent magnet synchronous motor. This represents the number of pole pairs in a permanent magnet synchronous motor.

3. The parallel optimization based multi-objective model predictive control method for permanent magnet synchronous motor according to claim 1, characterized in that: The inverter has three-phase bridge arms, and the three-phase bridge arms correspond to phase a, phase b and phase c respectively, and each phase is provided with two switch tubes, and the two switch tubes in each phase bridge arm correspond to the upper bridge arm and the lower bridge arm respectively; Wherein, the output voltage calculation formula of the inverter is, In the formula, Indicates the output voltage. Indicates DC voltage. , and These respectively represent the inverter in Mutually, Harmony The switching state of the upper bridge arm switch transistor. Represents the imaginary unit. Represents the base of the natural logarithm in complex form; =0、 =0 and =0 respectively represent the inverter in Mutually, Harmony The upper bridge arm switch of the phase is turned off. =1、 =1 and =1 respectively represent the inverter in Mutually, Harmony The upper bridge arm switch of the phase is turned on; Wherein, according to the switching state of the upper bridge arm switching tube of three-phase in the inverter, a three-bit binary number is obtained , the control input is used as the input vector, the existing state of the switching state is represented.

4. The parallel optimization based multi-objective model predictive control method for permanent magnet synchronous motor according to claim 3, characterized in that: According to a relationship between the switch states and three-phase voltages of the permanent magnet synchronous motor, the three-phase voltages are obtained, and then transformed from a three-phase natural coordinate system to a two-phase synchronous rotating coordinate system through Clarke transformations and Park transformations, the relationship being abc , dq , , ;​ wherein Clarke is transformed into , Park is transformed into ; in which , , denote phase voltages, phase voltages, phase voltages, , denote stator voltages in the d-q coordinate system, , denote dq stator voltages in the d-q coordinate system, θ is the rotor mechanical angle of the permanent magnet synchronous machine.

5. The parallel optimization based multi-objective model predictive control method for permanent magnet synchronous motor according to claim 4, characterized in that: In step 2, the stator voltage equation in the permanent magnet synchronous motor mathematical model is forward Euler-discreted to obtain a current prediction formula of the permanent magnet synchronous motor in dq an axis coordinate system as follows: ， ， wherein represents the sampled value of the d , q axis current prediction value in the , sampled value of the d , q axis current in the is the time interval of the sampling period; The model predictive controller is adopted to obtain the stator flux linkage prediction calculation formula and the electromagnetic torque prediction calculation formula according to the current prediction formula and the permanent magnet synchronous motor mathematical model, which correspond to, ， ， ， ， wherein and respectively represent the electromagnetic torque prediction value at time instant the stator flux prediction value at time instant represent the d axis stator flux prediction value at time instant represent the q axis stator flux prediction value at time instant 6. The parallel optimization based multi-objective model predictive control method for permanent magnet synchronous motor according to claim 5, characterized in that: In step 3, the following steps are included. Step 3-1, preliminary screening; first, calculate the number of intersections of each input vector that meets the torque constraint condition and the flux linkage constraint condition, if it meets one of the torque constraint condition and the flux linkage constraint condition, the number of intersections is 1, and if it meets the torque constraint condition and the flux linkage constraint condition at the same time, the number of intersections is 2; Then, taking the input vector corresponding to the maximum value of the number of intersections as the shortlisted vector, and taking the shortlisted vector as the optimal vector when there is only one shortlisted vector, and executing step 3-2 when there are at least two shortlisted vectors; Step 3-2, judging the cumulative sorting value of the shortlisted vector; First, obtain each torque error estimation value of the torque target under each of the shortlisted vectors and each flux error estimation value of the flux target under each of the shortlisted vectors, then arrange each of the torque error estimation values and the flux error estimation values in ascending order respectively, and obtain the ranking of each of the shortlisted vectors under the torque target and the flux target respectively to obtain the ranking value of each of the shortlisted vectors under the two target items respectively; Next, calculate the sum of the ranking values of each of the shortlisted vectors under the torque target and the flux target to obtain a cumulative ranking value, take the shortlisted vector with the smallest cumulative ranking value as a preselected vector, if there is only one preselected vector, take the preselected vector as the optimal vector, if there are at least two preselected vectors, select each of the preselected vectors to perform step 3-3; Step 3-3, evaluate the priority; judge the priority of the torque target and the flux target, and select the preselected vector with the smallest ranking value in the target with higher priority as the optimal vector.

7. The parallel optimization based multi-objective model predictive control method for permanent magnet synchronous motor according to claim 6, characterized in that: In step 3-1, the torque constraint is <20% The flux linkage constraint is: <(10%-20%) .

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