A model predictive control method and system for permanent magnet synchronous motor systems

Abstract

This invention relates to a model predictive control method and system for permanent magnet synchronous motor systems, belonging to the field of power electronics technology. It solves the problems of existing model predictive control methods, such as the difficulty in avoiding the influence of subjective factors, low accuracy, and complex design. This includes acquiring kT based on selected control variables. s The system information of the permanent magnet synchronous motor system at any given time is obtained as (k+2)T s The values ​​of each control variable in different switching states of the inverter stage at different times are used to obtain (k+2)T. s The set of error terms for each control variable at time step 1; selecting a preset number of error terms with smaller error terms for each control variable to form the first set of error terms for each control variable, and forming the corresponding first set of switching states for their respective switching states; based on the importance of the control variables and the first set of error terms and first set of switching states, the optimal switching state is obtained and used as the inverter stage of the indirect matrix converter at (k+1)T. s The bridge arm switching status at any time enables predictive control of the permanent magnet synchronous motor.

Description

Technical Field

[0001] This invention relates to the field of power electronics technology, and in particular to a model predictive control method and system for permanent magnet synchronous motor systems. Background Technology

[0002] Indirect matrix converters, lacking intermediate DC energy storage, enable bidirectional energy flow and offer advantages such as compact structure and high integration. They possess significant advantages in industrial applications with stringent size requirements, such as paper and textile manufacturing, oil drilling and extraction, metal rolling, and aerospace. Meanwhile, with the development of microprocessors, model predictive current control, with its simple structure, ease of handling constrained optimization and multivariable nonlinear control problems, and excellent dynamic response speed and steady-state control accuracy, is gradually becoming an emerging control method for power electronic converters and motor drives.

[0003] Model predictive current control (MMDC) selects the optimal switching combination state of the system through variable prediction and value function evaluation. To unify the dimensional differences in the errors of different control variables in the value function, MMDC requires the design of coefficients to adjust the weights of different control variables to achieve the desired control performance. Currently, weight coefficients are mainly selected using empirical tuning methods. While empirically tuned weight coefficients can improve the system's control performance to some extent, this method relies heavily on subjective experience and requires extensive comparative experiments, increasing design complexity. Furthermore, it struggles to adapt to complex and variable operating conditions and cannot achieve the optimal performance of permanent magnet synchronous motor systems driven by indirect matrix converters.

[0004] Therefore, existing model-predictive current control methods are difficult to avoid the influence of subjective factors, have low accuracy, are complex to design, and cannot obtain the optimal control mode for permanent magnet synchronous motors. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a model predictive control method and system for permanent magnet synchronous motor systems, in order to solve the problems of existing model predictive control, which is difficult to avoid the influence of subjective factors, has low accuracy, and is complex to design.

[0006] On one hand, embodiments of the present invention provide a model predictive control method for a permanent magnet synchronous motor system, comprising the following steps:

[0007] kT is collected based on the selected control variable. s The system information of the permanent magnet synchronous motor system at any time is obtained, and (k+2)T is obtained. s The values ​​of each control variable in different switching states of the inverter stage at different times are used to obtain (k+2)T. s The set of error terms for each control variable at any given time;

[0008] In (k+2)T s Constrain the error terms of each control variable under different switching states at different times;

[0009] After sorting the error terms of each control variable by size, select a preset number of error terms with smaller error terms to form the first set of error terms for each control variable, and obtain their corresponding switching states to form the first set of switching states for each control variable.

[0010] Based on the importance of the control variables and the first error term set and first switching state set of each control variable, the optimal switching state is obtained and used as the inverter stage of the indirect matrix converter in (k+1)T. s The bridge arm switching status at any time is used to achieve predictive control of the permanent magnet synchronous motor;

[0011] Where k and T s These represent the control cycle number and control cycle of the permanent magnet synchronous motor system, respectively.

[0012] Furthermore, the optimal switching state is obtained by performing the following steps:

[0013] Obtain the control variable with the highest importance and obtain the first set of error terms corresponding to that control variable;

[0014] Obtain the switch states that have the same switch state from the first set of switch states for each control variable;

[0015] Using the magnitude of the error terms in the first set of error terms corresponding to the most important control variable as the criterion, the switch state with the smallest error term of the control variable is selected from the switch states with the same switch state as the optimal switch state.

[0016] Furthermore, it also includes selecting the remaining error terms of the control variables with the second most important importance among each control variable, forming a second set of error terms for that control variable, and obtaining their corresponding switching states, forming a second set of switching states for that control variable.

[0017] When there are no identical switching states in the first set of switching states for each control variable.

[0018] Obtain the second most important control variable among all control variables, and obtain the set of second error terms corresponding to that control variable;

[0019] Obtain the switch states that have the same switch state from the first switch state set corresponding to the most important control variable and the second switch state set corresponding to the second most important control variable among all control variables;

[0020] Based on the magnitude of the error terms in the second set of error terms corresponding to the less important control variable, the switch state with the smallest error term for that control variable is selected from the switch states with the same switch state as the optimal switch state.

[0021] Furthermore, the three error terms with smaller error terms are selected to form the first set of error terms for each control variable.

[0022] Furthermore, the control variables are selected as d-axis and q-axis current tracking errors, with the q-axis current tracking error being more important than the d-axis current tracking error; the acquired system information includes: acquiring kT s The rotor mechanical angular velocity, rotor position angle, and three-phase current of the permanent magnet synchronous motor at all times, as well as the three-phase input voltage of the indirect matrix converter and the switching state of the inverter stage bridge arm.

[0023] Furthermore, (k+2)T is obtained in the following way. s The values ​​of each control variable in different switching states of the inverter stage at different times are used to obtain (k+2)T. s The set of error terms for each control variable at each time step:

[0024] Based on the collected kT s The rotor mechanical angular velocity of the permanent magnet synchronous motor at any given time and the set rotor mechanical angular velocity reference value are used to obtain kT. s The given values ​​of the d-axis and q-axis currents at time t;

[0025] Based on the collected kT s Given the rotor position angle and three-phase current of the permanent magnet synchronous motor at any given time, the three-phase input voltage of the indirect matrix converter, and the switching states of the inverter stage bridge arms, we can obtain (k+2)T. s Predicted d-axis and q-axis currents of the inverter stage bridge arm under different switching states at any time;

[0026] Based on kT s The current setpoints for the d-axis and q-axis at time (k+2)T s Predicted d-axis and q-axis currents of the inverter stage bridge arm at different switching states at (k+2)T time. s The set of error terms for d-axis and q-axis current tracking errors of the inverter stage bridge arm at different switching states at any given time.

[0027] Furthermore, the statement in (k+2)T s The error terms for the d-axis and q-axis current tracking errors of the inverter stage bridge arm at different switching states are expressed as follows:

[0028]

[0029]

[0030] In the formula, gdj and g qj These represent the error terms for the d-axis and q-axis current tracking errors under the j-th switching state, respectively; i d * and i q * represents the given values ​​of the current along the d-axis and q-axis of the permanent magnet synchronous motor, respectively; j = 1, 2…7, 8, representing the eight different switching states of the inverter stage of the indirect matrix converter; i dj (k+2) and i qj (k+2) represents (k+2)T respectively. s Predicted d-axis and q-axis currents of the inverter stage bridge arm under the j-th switching state at any given moment; I j (k+2) represents (k+2)T s The constraint term of the error term for current tracking error under the j-th switching state of the inverter stage bridge arm at any given time.

[0031] Furthermore, the (k+2)T s Constraint term I of the error term for current tracking error under the j-th switching state of the inverter stage bridge arm at any given moment. j (k+2) is represented as:

[0032]

[0033] In the formula, i smax This is the rated current of the motor.

[0034] On the other hand, embodiments of the present invention provide a model predictive control system for a permanent magnet synchronous motor system, comprising:

[0035] The control variable error term set acquisition module is used to collect kT based on the selected control variables. s The system information of the permanent magnet synchronous motor system at any time is obtained, and (k+2)T is obtained. s The values ​​of each control variable in different switching states of the inverter stage at different times are used to obtain (k+2)T. s The set of error terms for each control variable at time t; where k and T s These represent the control cycle number and control cycle of the permanent magnet synchronous motor system, respectively.

[0036] Error term constraint module, used in (k+2)T s Constrain the error terms of each control variable under different switching states at different times;

[0037] The error term set and switch state set acquisition module is used to sort the error terms of each control variable by size, select a preset number of error terms with smaller error terms to form the first error term set of each control variable, and obtain their corresponding switch states to form the first switch state set of each control variable.

[0038] The optimal switching state acquisition module is used to obtain the optimal switching state based on the importance of the control variables and the first error term set and first switching state set of each control variable, and use it as the inverter stage of the indirect matrix converter in (k+1)T s The bridge arm switching status at any given time is used to achieve predictive control of the permanent magnet synchronous motor.

[0039] Furthermore, the optimal switching state is obtained by performing the following steps:

[0040] Obtain the control variable with the highest importance and obtain the first set of error terms corresponding to that control variable;

[0041] Obtain the switch states that have the same switch state from the first set of switch states for each control variable;

[0042] Using the magnitude of the error terms in the first set of error terms corresponding to the most important control variable as the criterion, the switch state with the smallest error term of the control variable is selected from the switch states with the same switch state as the optimal switch state.

[0043] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0044] This invention provides a model predictive control method and system for permanent magnet synchronous motor (PMSM) systems. By collecting information from the current control cycle of the PMSM system, the error terms of each control variable for the next control cycle are obtained. Based on the importance of each control variable and the ranking of the error terms, the optimal switching state is selected, resulting in the bridge arm switching state of the inverter stage of the indirect matrix converter in the next control cycle. This achieves control of the PMSM, is simple in design, can comprehensively consider multiple control variables, and obtains the optimal control mode for the PMSM, resulting in more stable torque output and better current tracking performance. Furthermore, by replacing the original value function with error terms of different control variables, the adverse effects of poor adaptability of the weighting coefficients under different operating conditions are eliminated, avoiding the difficulties of weighting coefficient tuning and the complexity of the value function form, while ensuring good current tracking performance of the system.

[0045] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0046] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0047] Figure 1 This is a schematic flowchart of the model predictive control method for a permanent magnet synchronous motor system provided in Embodiment 1 of the present invention;

[0048] Figure 2 This is a schematic diagram of the topology of the permanent magnet synchronous motor system in Embodiment 2 of the present invention;

[0049] Figure 3 This is a control block diagram of the permanent magnet synchronous motor system in Embodiment 2 of the present invention;

[0050] Figure 4 This is a schematic diagram of the model predictive control method for permanent magnet synchronous motor system in Embodiment 2 of the present invention. Detailed Implementation

[0051] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0052] Example 1

[0053] A specific embodiment of the present invention discloses a model predictive control method for a permanent magnet synchronous motor system, such as... Figure 1 As shown, it includes the following steps:

[0054] S1. Collect kT data based on the selected control variable. s The system information of the permanent magnet synchronous motor system at any time is obtained, and (k+2)T is obtained. s The values ​​of each control variable in different switching states of the inverter stage at different times are used to obtain (k+2)T. s The set of error terms for each control variable at time t; where each error term corresponds to a switching state; where k and T s These represent the control cycle number and control cycle of the permanent magnet synchronous motor system, respectively.

[0055] Preferably, the selected control variable can be one or more of the following: d-axis current tracking error, q-axis current tracking error, motor torque, motor flux linkage, and common-mode voltage; if d-axis current tracking error, q-axis current tracking error, or motor torque is selected, the system information includes kT. s The system information includes the rotor mechanical angular velocity, rotor position angle, and three-phase current of the permanent magnet synchronous motor, as well as the three-phase input voltage of the indirect matrix converter and the switching status of the inverter stage bridge arm. If the motor flux linkage is selected, the system information includes the stator voltage and the motor three-phase current. If the common-mode voltage is selected, the system information includes the common-mode voltage.

[0056] S2, at (k+2)T s Constrain the error terms of each control variable under different switching states at different times;

[0057] S3. After sorting the error terms of each control variable by size, select a preset number of error terms with smaller error terms to form the first set of error terms for each control variable, and obtain their corresponding switch states to form the first set of switch states for each control variable. It can be understood that a preset number of error terms from the smallest to the largest are taken out from all the error terms of each control variable to form the corresponding first set of error terms.

[0058] S4. Based on the importance of the control variables and the first error term set and first switching state set of each control variable, the optimal switching state is obtained and used as the inverter stage of the indirect matrix converter in (k+1)T. s The bridge arm switching status at any time is used to achieve predictive control of the permanent magnet synchronous motor;

[0059] During implementation, in step S5, the optimal switching state is obtained by performing the following steps:

[0060] Obtain the control variable with the highest importance and obtain the first set of error terms corresponding to that control variable;

[0061] Obtain the switch states that have the same switch state from the first set of switch states for each control variable;

[0062] Using the magnitude of the error terms in the first set of error terms corresponding to the most important control variable as the criterion, the switch state with the smallest error term of the control variable is selected from the switch states with the same switch state as the optimal switch state.

[0063] In practice, the control method also includes selecting the remaining error terms of the control variables with the second most important importance among each control variable, forming a second set of error terms for that control variable, and obtaining its corresponding switching state, forming a second set of switching states for that control variable.

[0064] When there are no identical switching states in the first set of switching states for each control variable.

[0065] Obtain the second most important control variable among all control variables, and obtain the set of second error terms corresponding to that control variable;

[0066] Obtain the switch states that have the same switch state from the first set of switch states corresponding to the most important control variable and the second set of switch states corresponding to the second most important control variable.

[0067] Based on the magnitude of the error terms in the second set of error terms corresponding to the less important control variable, the switch state with the smallest error term for that control variable is selected from the switch states with the same switch state as the optimal switch state.

[0068] In practice, the three error terms with the smaller error terms are selected to form the first set of error terms for each control variable.

[0069] Compared with existing technologies, this embodiment provides a model predictive control method for permanent magnet synchronous motor (PMSM) systems. By collecting information from the current control cycle of the PMSM system, the error terms of each control variable in the next control cycle are obtained. Based on the importance of each control variable and the ranking of the error terms, the optimal switching state is selected, resulting in the bridge arm switching state of the inverter stage of the indirect matrix converter in the next control cycle. This achieves control of the PMSM, is simple in design, can balance multiple control variables, and obtains the optimal control mode for the PMSM, resulting in more stable torque output and better current tracking performance. Furthermore, by replacing the original value function with error terms of different control variables, the adverse effects of poor adaptability of the weighting coefficients under different operating conditions are eliminated, avoiding the difficulties of weighting coefficient tuning and complex value function forms, while ensuring good current tracking performance of the system.

[0070] Example 2

[0071] In a specific embodiment 2 of the present invention, based on embodiment 1, the control variables are selected as d-axis and q-axis current tracking errors, and the system model predictive current control of the permanent magnet synchronous motor is performed.

[0072] During implementation, the collected system information includes: collecting kT s The rotor mechanical angular velocity, rotor position angle, and three-phase current of the permanent magnet synchronous motor at all times, as well as the three-phase input voltage of the indirect matrix converter and the switching state of the inverter stage bridge arm.

[0073] It should be noted that in the model predictive current control of the indirect matrix converter-permanent magnet synchronous motor system, the motor torque is calculated from the q-axis current. Therefore, when selecting the optimal switching state, the q-axis current tracking error is more important than the d-axis current tracking error.

[0074] During implementation, in step S1, (k+2)T is obtained in the following way. s The values ​​of each control variable in different switching states of the inverter stage at different times are used to obtain (k+2)T. s The set of error terms for each control variable at each time step:

[0075] Based on the collected kT s The rotor mechanical angular velocity of the permanent magnet synchronous motor at any given time and the set rotor mechanical angular velocity reference value are used to obtain kT. s The given values ​​of the d-axis and q-axis currents at time t;

[0076] Based on the collected kT s Given the rotor position angle and three-phase current of the permanent magnet synchronous motor at any given time, the three-phase input voltage of the indirect matrix converter, and the switching states of the inverter stage bridge arms, we can obtain (k+2)T. s Predicted d-axis and q-axis currents of the inverter stage bridge arm under different switching states at any time;

[0077] Based on kT s The current setpoints for the d-axis and q-axis at time (k+2)T s Predicted d-axis and q-axis currents of the inverter stage bridge arm at different switching states at (k+2)T time. s The set of error terms for d-axis and q-axis current tracking errors of the inverter stage bridge arm at different switching states at any given time.

[0078] During implementation, the step S2 described in (k+2)T s The error terms for the d-axis and q-axis current tracking errors of the inverter stage bridge arm at different switching states are expressed as follows:

[0079]

[0080]

[0081] In the formula, g dj and g qj These represent the error terms for the d-axis and q-axis current tracking errors under the j-th switching state, respectively; i d * and i q * represents the given values ​​of the current along the d-axis and q-axis of the permanent magnet synchronous motor, respectively; j = 1, 2…7, 8, representing the eight different switching states of the inverter stage of the indirect matrix converter; i dj (k+2) and iqj (k+2) represents (k+2)T respectively. s Predicted d-axis and q-axis currents of the inverter stage bridge arm under the j-th switching state at any given moment; I j (k+2) represents (k+2)T s The constraint term of the error term for current tracking error under the j-th switching state of the inverter stage bridge arm at any given time.

[0082] Specifically, the (k+2)T s Constraint term I of the error term for current tracking error under the j-th switching state of the inverter stage bridge arm at any given moment. j (k+2) is represented as:

[0083]

[0084] In the formula, i smax This is the rated current of the motor.

[0085] This embodiment uses the d-axis and q-axis currents of the permanent magnet synchronous motor as state variables and considers delay compensation to establish a unified prediction model for the system. Based on the unified prediction model, the predicted d-axis and q-axis currents for the next control cycle are obtained, and then the error terms of the d-axis and q-axis current tracking error under different switching states of the inverter stage bridge arm are obtained, as follows:

[0086] The indirect matrix converter in the permanent magnet synchronous motor system adopts an AC-DC-AC two-stage converter structure, including a three-phase AC input power supply, an input filter, a rectifier stage circuit, an inverter stage circuit, and a permanent magnet synchronous motor. The system topology is as follows: Figure 2 As shown, a surface-mount permanent magnet synchronous motor is selected as the permanent magnet synchronous motor. The rectifier stage circuit consists of six bidirectional switches S. mn (m = a, b, c represent the three-phase bridge arms respectively; n = u, l represent the upper and lower bridge arms respectively) The inverter stage circuit is the same as that of a traditional voltage-source inverter, using a unidirectional switch, where switch S... A1 S A2 S A3 This represents the upper arm of the three-phase bridge arm, switch S′. A1 S′ B1 S′ C1 This indicates the lower arm of the three-phase bridge arm.

[0087] The control block diagram of a permanent magnet synchronous motor system driven by an indirect matrix converter is shown below. Figure 3 As shown, the control system includes an indirect matrix converter, a permanent magnet synchronous motor, a rectifier stage controller, an inverter stage controller, and a speed controller.

[0088] In the speed controller, the motor speed error is used to generate the q-axis current setpoint after passing through the PI regulator. The inner loop current control uses the current setpoint d-axis Vector control yields:

[0089]

[0090] In the formula, ω * ω represents the set rotor mechanical angular velocity, which can be set according to the actual situation; ω represents the measured rotor mechanical angular velocity.

[0091] In order to ensure that the DC-side output voltage of the rectifier stage is positive and to achieve maximum voltage utilization, while keeping the input power factor fixed at 1 (i.e., the input current vector always follows the input voltage vector in a sinusoidal manner), the rectifier stage controller of the indirect matrix converter typically adopts a zero-vector-free SVPWM modulation strategy to provide a stable and reliable virtual intermediate DC voltage for the subsequent inverter stage and permanent magnet synchronous motor. Therefore, the input voltage is divided into equal intervals based on the zero-crossing point of the input phase voltage, with each interval occupying π / 3 of the angle. Each interval is called a sector, for a total of six sectors. The switching states and duty cycles of the six sectors of the rectifier stage are shown in Table 1, where u... a u b u c This represents the three-phase input voltage of the indirect matrix converter.

[0092] Table 1 Switching states and duty cycles of the six-sector rectifier stage

[0093]

[0094] The rectifier stage controller of an indirect matrix converter typically employs a zero-vector-less SVPWM modulation strategy to provide a stable and reliable virtual intermediate DC voltage for the subsequent inverter stage and permanent magnet synchronous motor. Specifically:

[0095] Within a unit switching cycle, the rectifier stage DC side outputs two relatively large line voltages with positive polarity to the virtual intermediate DC bus according to a certain duty cycle. The average value of this virtual intermediate DC voltage is u. dc_av The expression is:

[0096]

[0097] In the formula, cosθ in =max{|cosθ a |,|cosθ b |,|cosθ c |};θ a θ b θ c These are the phase angles of the input phase voltages of the indirect matrix converter, u and u'. im phase angle θ inThe magnitude of the input phase voltage of the corresponding indirect matrix converter.

[0098] Understandably, the rectifier stage employs a simple space vector modulation strategy without zero vectors, while the inverter stage and motor use model predictive current control methods, thereby achieving independent control of each stage of the indirect matrix converter.

[0099] The inverter stage controller considers a model-predictive current control structure, such as... Figure 4 As shown, kT is obtained through sampling. s The current at time t is used to obtain the (k+2)T current of the permanent magnet synchronous motor under each switching state combination using the established unified prediction model. s The predicted current at each moment is used to obtain the error terms of the d-axis and q-axis current tracking errors under different switching states of the inverter stage bridge arm. Finally, the optimal switching state is comprehensively optimized and selected. Specifically:

[0100] The structure of the inverter stage is as follows Figure 2 As shown in Table 2, the switching states of the inverter stage bridge arms are as follows, with a total of 8 switching states. Among them, S... A1 S B1 S C1 These represent the switching states of the upper bridge arms of the inverter stage A, B, and C, respectively. "1" indicates on and "0" indicates off. The switching states of the upper and lower bridge arms are complementary. The switching states of the upper bridge arms of the inverter stage A, B, and C represent the switching states of the inverter stage bridge arms.

[0101] Table 2 Switching Status of Inverter Stage Arms

[0102]

[0103] In the dq-axis synchronous rotating coordinate system, the voltage equation of the surface-mounted permanent magnet synchronous motor is as follows:

[0104]

[0105] In the formula, L d L q These are the direct-axis and quadrature-axis inductors of the stator, respectively, and L... d =L q =L, where L is the motor inductance; u d u q These are the d-axis and q-axis voltages of the motor, respectively; i d i q These are the d-axis and q-axis currents of the motor, respectively; R s ψ f and ω r These are the stator resistance, permanent magnet flux linkage, and rotor angular velocity, respectively, and ω r =pω; ω is the rotor mechanical angular velocity, and p is the number of pole pairs of the motor.

[0106] Euclideanizing equation (3) yields (k+1)T s Current state values ​​i of the motor's d-axis and q-axis at any given time d (k+1),i q (k+1), represented as:

[0107]

[0108] In the formula, A = 1 - T s R s / L,B(k)=T s ω r (k), C = T s / L, D(k)=T s ω r (k)ψ f / L, ω r (k) = pω(k); k represents the ordinal number of the control cycle, T s For the system control period, ω r (k) represents kT s The rotor angular velocity at time t, ω(k) represents kT s The rotor mechanical angular velocity at time i; d (k), i q (k) represents the motor at kT respectively. s Currents along the d-axis and q-axis at time u; d (k), u q (k) represents the motor at kT respectively. s The d-axis and q-axis voltages at time t.

[0109] It should be noted that i d (k), i q (k) The three-phase current and rotor position angle θ of the motor can be obtained by coordinate transformation; based on equation (2), the motor position angle at kT is obtained. s Voltages u on the d-axis and q-axis at time t d (k), u q (k), represented as:

[0110]

[0111] in,

[0112] S VSI (k)=[S A (k) S B (k) S C (k)] T ,

[0113]

[0114] In the formula, S VSI (k) represents kT s The switching state matrix of the inverter stage bridge arm at any given time, S A (k), S B (k), S C (k) represents the values ​​at kT respectively. s The switching states of the A, B, and C phase upper bridge arms of the inverter stage at time kT; E(k) represents the switching state of the upper bridge arms of the inverter stage at time kT. s The transformation matrix from the ABC three-phase stationary coordinate system to the dq rotating coordinate system at any time.

[0115] Using the d-axis and q-axis currents of the surface-mounted permanent magnet synchronous motor as state variables, and considering time delay compensation, a unified prediction model for the system is established, specifically as follows:

[0116] X(k+2)=G(k+1)·X(k+1)+F·U(k+1)+K·D(k+1) (6)

[0117] in,

[0118] X(k+1)=[i d (k+1)·i q (k+1)] T X(k+2)=[i d (k+2)·i q (k+2)] T ,

[0119] U(k+1)=[u d (k+1)·u q (k+1)] T ,

[0120]

[0121] B(k+1)=T s ω r (k+1), D(k+1)=T s ω r (k+1)ψ f / L;

[0122] In the formula, X(k+2) represents the (k+2)Tth ... s The state vector at time (k+1)T, X(k+1) represents the state vector at time (k+1)T. s The state vector at time (k+1) represents the state vector at time (k+1)T. s The input vector at time (k+1); D(k+1) represents the input vector at time (k+1)T. s The transmission coefficient at time (k+1); G(k+1) represents the (k+1)Tth time. s The system matrix at time (k+1)T; F represents the system matrix at time (k+1)T. sThe input matrix at time (k+1)T; K represents the input matrix at time (k+1)T. s The transfer matrix at time step; i d (k+1),i q (k+1) represents the motor at (k+1)T respectively. s The d-axis and q-axis currents at time i; d (k+2), i q (k+2) represents the motor at (k+2)T. s The d-axis and q-axis currents at time u; d (k+1), u q (k+1) represents the motor at (k+1)T. s The d-axis and q-axis voltages at time ω; r (k+1) represents the (k+1)Tth digit. s The rotor angular velocity of the motor at any given time.

[0123] According to equation (5), we can obtain the result at (k+1)T s d-axis and q-axis voltages u at time t d (k+1), u q (k+1) is represented as:

[0124]

[0125] Where, E(k+1)=E(k), S′ VSI (k+1) represents the time interval (k+1)T. s The switching state matrix of the inverter stage bridge arm at any given time.

[0126] The switching state matrix S′ in equation (7) is set according to the different switching states of the inverter stage bridge arm. VSI (k+1) can be used to obtain the result at (k+1)T s The d-axis and q-axis voltages u of the inverter stage under different switching states at any time dj (k+1), u qj (k+1), where j represents the j-th switching state of the inverter stage bridge arm, j = 1, ..., 8.

[0127] Substituting equation (4) into equation (6), and obtaining the result in (k+1)T s Substituting the d-axis and q-axis voltage components of the inverter stage under different switching states into equation (6), we obtain (k+2)T. s Predicted d-axis and q-axis currents i of the inverter stage bridge arm under different switching states at any time dj (k+2), i qj (k+2).

[0128] The weighted summation value function of a permanent magnet synchronous motor system based on an indirect matrix converter is:

[0129] g = λ d g d +λ q g q (8)

[0130] In the formula, λ d and λ q These are the weighting coefficients for the motor's d-axis and q-axis current tracking errors, respectively; g d and g q They represent (k+2)T respectively s The tracking errors of the motor's d-axis and q-axis currents at any given time are expressed as follows:

[0131]

[0132] Equation (1) and the obtained (k+2)T s Predicted d-axis and q-axis currents i of the inverter stage bridge arm under different switching states at any time dj (k+2), i qj Substituting (k+2) into equation (9), we get the result at (k+2)T. s Error term g of d-axis and q-axis current tracking error when the inverter bridge arm is in the j-th switching state. dj g qj , is represented as:

[0133]

[0134] To achieve current limiting, at (k+2)T s Error term g for tracking d-axis and q-axis current under different switching states at different times dj g qj By applying constraints, we obtain

[0135]

[0136]

[0137] In the formula, g dj and g qj These represent the error terms for the d-axis and q-axis current tracking errors under the j-th switching state, respectively; i d * and i q * represents the given values ​​of the current along the d-axis and q-axis of the permanent magnet synchronous motor, respectively; j = 1, 2…7, 8, representing the eight different switching states of the inverter stage of the indirect matrix converter; i dj (k+2) and i qj (k+2) represents (k+2)T respectively. sPredicted d-axis and q-axis currents of the inverter stage bridge arm under the j-th switching state at any given moment; I j (k+2) represents (k+2)T s The constraint term of the error term for current tracking error under the j-th switching state of the inverter stage bridge arm at any given time.

[0138] Specifically, the (k+2)T s Constraint term I of the error term for current tracking error under the j-th switching state of the inverter stage bridge arm at any given moment. j (k+2) is represented as:

[0139]

[0140] In the formula, i smax This is the rated current of the motor.

[0141] After sorting the error terms of the d-axis and q-axis current tracking errors, the three error terms with the smallest values ​​are selected to form the first set of error terms for the d-axis and q-axis current tracking errors. Their corresponding switching states are then obtained, forming the first set of switching states Y for the d-axis and q-axis current tracking errors. d and Y q .

[0142] Based on the importance of the d-axis and q-axis current tracking errors, the q-axis current tracking error with the highest importance and the d-axis current tracking error with the second highest importance are obtained.

[0143] Obtain the most important control variable, q-axis current tracking error, and obtain the first set of error terms corresponding to this control variable;

[0144] Obtain the first set of switching states Y for the d-axis and q-axis current tracking errors. d and Y q Switch states that are the same as the switch states in the middle;

[0145] If there are identical switch states, then

[0146] Using the magnitude of the error terms in the first set of error terms corresponding to the most important control variable, q-axis current tracking error, as the criterion, the switching state with the smallest error term of q-axis current tracking error is selected from the switching states with the same switching state as the optimal switching state.

[0147] If there are no identical switch states, then

[0148] The remaining error terms of the second most important control variable, d-axis current tracking error, are selected to form the second set of error terms for obtaining the d-axis current tracking error. The corresponding switching states are then obtained, forming the second set of switching states Y' for the d-axis current tracking error. d ;

[0149] Obtain the first set of switching states Y corresponding to the most important control variable, q-axis current tracking error. q The second set of switching states Y' corresponding to the secondary control variable d-axis current tracking error d Switch states that are the same as the switch states in the middle;

[0150] Based on the magnitude of the error terms in the second set of error terms corresponding to the second most important control variable, d-axis current tracking error, the switch state with the smallest error term of d-axis current tracking error is selected from the switch states with the same switching state as the optimal switch state.

[0151] The obtained optimal switching state is used as the output of the inverter stage of the indirect matrix converter at the next moment, thus completing the predictive control of the permanent magnet synchronous motor.

[0152] Example 3

[0153] A specific embodiment 3 of the present invention provides a system model predictive control system for permanent magnet synchronous motors, comprising:

[0154] The control variable error term set acquisition module is used to collect kT based on the selected control variables. s The system information of the permanent magnet synchronous motor system at any time is obtained, and (k+2)T is obtained. s The values ​​of each control variable in different switching states of the inverter stage at different times are used to obtain (k+2)T. s The set of error terms for each control variable at time t; where k and T s These represent the control cycle number and control cycle of the permanent magnet synchronous motor system, respectively.

[0155] Error term constraint module, used in (k+2)T s Constrain the error terms of each control variable under different switching states at different times;

[0156] The error term set and switch state set acquisition module is used to sort the error terms of each control variable by size, select a preset number of error terms with smaller error terms to form the first error term set of each control variable, and obtain their corresponding switch states to form the first switch state set of each control variable.

[0157] The optimal switching state acquisition module is used to obtain the optimal switching state based on the importance of the control variables and the first error term set and first switching state set of each control variable, and use it as the inverter stage of the indirect matrix converter in (k+1)T s The bridge arm switching status at any given time is used to achieve predictive control of the permanent magnet synchronous motor.

[0158] During implementation, the optimal switch state acquisition module obtains the optimal switch state by performing the following steps:

[0159] Obtain the control variable with the highest importance and obtain the first set of error terms corresponding to that control variable;

[0160] Obtain the switch states that have the same switch state from the first set of switch states for each control variable;

[0161] Using the magnitude of the error terms in the first set of error terms corresponding to the most important control variable as the criterion, the switch state with the smallest error term of the control variable is selected from the switch states with the same switch state as the optimal switch state.

[0162] The specific implementation process of this invention can be found in the above method embodiments, and will not be repeated here.

[0163] Since this embodiment is based on the same principle as the above method embodiments, this system also has the corresponding technical effects of the above method embodiments.

[0164] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0165] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A model predictive control method for a permanent magnet synchronous motor system, characterized in that, Includes the following steps: According to the selected control variable, the system information of the permanent magnet synchronous motor system at the time t is acquired The system information of the permanent magnet synchronous motor system at the time t is acquired The control variable value of the inverter stage under different switch states at the time t is acquired, and then the control variable value of the inverter stage under different switch states at the time t is acquired The error item set of each control variable at the time t is acquired exist Constrain the error terms of each control variable under different switching states at different times; After sorting the error terms of each control variable by size, select a preset number of error terms with smaller error terms to form the first set of error terms for each control variable, and obtain their corresponding switching states to form the first set of switching states for each control variable. Based on the importance of the control variables and the first error term set and first switching state set of each control variable, the optimal switching state is obtained and used as the inverter stage of the indirect matrix converter. The bridge arm switching status at any time is used to achieve predictive control of the permanent magnet synchronous motor; in, and These represent the control cycle number and control cycle of the permanent magnet synchronous motor system, respectively. The optimal switching state is obtained by performing the following steps: Obtain the control variable with the highest importance and obtain the first set of error terms corresponding to that control variable; Obtain the switch states that have the same switch state from the first set of switch states for each control variable; Using the magnitude of the error terms in the first set of error terms corresponding to the most important control variable as the criterion, the switch state with the smallest error term of the control variable is selected from the switch states with the same switch state as the optimal switch state. It also includes selecting the remaining error terms of the control variables with the second most important importance among each control variable, forming a second set of error terms for that control variable, and obtaining their corresponding switching states to form a second set of switching states for that control variable. When there are no identical switching states in the first set of switching states for each control variable. Obtain the second most important control variable among all control variables, and obtain the set of second error terms corresponding to that control variable; Obtain the switch states that have the same switch state from the first switch state set corresponding to the most important control variable and the second switch state set corresponding to the second most important control variable among all control variables; Based on the magnitude of the error terms in the second set of error terms corresponding to the less important control variable, the switch state with the smallest error term for that control variable is selected from the switch states with the same switch state as the optimal switch state.

2. The model predictive control method for permanent magnet synchronous motor systems according to claim 1, characterized in that, The three error terms with the smallest error terms are selected to form the first set of error terms for each control variable.

3. The model predictive control method for permanent magnet synchronous motor systems according to claim 1, characterized in that, The control variables are selected as d-axis and q-axis current tracking errors, with the q-axis current tracking error being more important than the d-axis current tracking error; the collected system information includes: collected... The rotor mechanical angular velocity, rotor position angle, and three-phase current of the permanent magnet synchronous motor at all times, as well as the three-phase input voltage of the indirect matrix converter and the switching state of the inverter stage bridge arm.

4. The model predictive control method for permanent magnet synchronous motor systems according to claim 3, characterized in that, Obtained through the following methods The values ​​of each control variable in different switching states of the inverter stage at any given time are then obtained. The set of error terms for each control variable at each time step: Based on collection The rotor mechanical angular velocity of the permanent magnet synchronous motor at any given time and the set rotor mechanical angular velocity reference value are used to obtain... The given values ​​of the d-axis and q-axis currents at time t; Based on collection The rotor position angle and three-phase current of the permanent magnet synchronous motor at constant time, the three-phase input voltage of the indirect matrix converter, and the switching state of the inverter stage bridge arm are used to obtain... Predicted d-axis and q-axis currents of the inverter stage bridge arm under different switching states at any time; based on The current setpoints for the d-axis and q-axis at time points. Predicted d-axis and q-axis currents of the inverter stage bridge arm at different switching states at any given time, obtaining... The set of error terms for d-axis and q-axis current tracking errors of the inverter stage bridge arm at different switching states at any given time.

5. The model predictive control method for a permanent magnet synchronous motor system according to claim 4, characterized in that, The above The error terms for the d-axis and q-axis current tracking errors of the inverter stage bridge arm at different switching states are expressed as follows: ； ； In the formula, and They represent the first Error terms for d-axis and q-axis current tracking errors under various switching states; and These represent the given values ​​of the current along the d-axis and q-axis of the permanent magnet synchronous motor, respectively. = 1,2…7,8 represent the eight different switching states of the inverter stage of the indirect matrix converter; and They represent Inverter bridge arm Predicted d-axis and q-axis currents under various switching states; express Inverter bridge arm Constraints on the error term of current tracking error under various switching states.

6. The model predictive control method for permanent magnet synchronous motor systems according to claim 5, characterized in that, The Inverter bridge arm Constraints on the error term of current tracking error under various switching states Represented as: ； In the formula, This is the rated current of the motor.

7. A model predictive control system for a permanent magnet synchronous motor system, characterized in that, include: The control variable error term set acquisition module is used to collect data based on the selected control variables. The system information of the permanent magnet synchronous motor system is obtained. The values ​​of each control variable in different switching states of the inverter stage at any given time are then obtained. The set of error terms for each control variable at time t; where, and These represent the control cycle number and control cycle of the permanent magnet synchronous motor system, respectively. Error term constraint module, used in Constrain the error terms of each control variable under different switching states at different times; The error term set and switch state set acquisition module is used to sort the error terms of each control variable by size, select a preset number of error terms with smaller error terms to form the first error term set of each control variable, and obtain their corresponding switch states to form the first switch state set of each control variable. The optimal switching state acquisition module is used to obtain the optimal switching state based on the importance of the control variables and the first error term set and first switching state set of each control variable, and then use it as the inverter stage of the indirect matrix converter. The bridge arm switching status at any time is used to achieve predictive control of the permanent magnet synchronous motor; The optimal switching state is obtained by performing the following steps: Obtain the control variable with the highest importance and obtain the first set of error terms corresponding to that control variable; Obtain the switch states that have the same switch state from the first set of switch states for each control variable; Using the magnitude of the error terms in the first set of error terms corresponding to the most important control variable as the criterion, the switch state with the smallest error term of the control variable is selected from the switch states with the same switch state as the optimal switch state. It also includes selecting the remaining error terms of the control variables with the second most important importance among each control variable, forming a second set of error terms for that control variable, and obtaining their corresponding switching states to form a second set of switching states for that control variable. When there are no identical switching states in the first set of switching states for each control variable. Obtain the second most important control variable among all control variables, and obtain the set of second error terms corresponding to that control variable; Obtain the switch states that have the same switch state from the first switch state set corresponding to the most important control variable and the second switch state set corresponding to the second most important control variable among all control variables; Based on the magnitude of the error terms in the second set of error terms corresponding to the less important control variable, the switch state with the smallest error term for that control variable is selected from the switch states with the same switch state as the optimal switch state.

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