Cascade model predictive control method, system and equipment of dual three-phase motor and medium

Abstract

The invention relates to the technical field of motor control, and provides a cascade model prediction control method, system and device for dual three-phase motors and a medium, and the method comprises the steps: determining a target motor, collecting motor operation parameters, and building an initial operation equation of the target motor through the motor operation parameters; establishing a double-dq coordinate transformation matrix, obtaining an intermediate operation equation through the double-dq coordinate transformation matrix and the initial operation equation, performing decoupling to obtain a motor operation equation, and establishing a state space expression of the target motor through the motor operation equation; determining an operation sector of the target motor, and obtaining a time regulation factor of the target motor in the operation sector; constructing a cost function to obtain an input matrix set of a target motor, thereby constructing a quadratic programming equation; initial parameters are determined, initial input is extracted, a gradient descent method is used for conducting iterative solution on the quadratic programming equation, and vector action time is obtained; and controlling the target motor. According to the invention, efficient control of the target motor is realized.

Description

Technical Field

[0001] The present invention relates to the field of motor control technology, and in particular to a cascade model predictive control method, system, equipment and medium for dual three-phase motors. Background Art

[0002] Compared with traditional three-phase motors, multi-phase motors have the advantages of low voltage and high power output, small torque ripple amplitude, and strong fault tolerance. Dual three-phase motors have received more attention due to their close relationship with traditional three-phase motors. For the inverter in the dual three-phase motor, the three-level voltage source inverter has the advantages of low voltage stress of switching devices, small voltage change rate, and small output voltage harmonic content. It is particularly suitable for medium voltage and high power applications. Its three-level six-phase voltage source inverter outputs three levels per phase, so the entire inverter has three 6 There are 729 state combinations, i.e. 729 pre-selected voltage vectors, which is very complex for designing space vector modulation strategies. The redundant positive and negative voltage vectors will cause the neutral point potential of the DC side of the three-level six-phase voltage source inverter to shift in different directions. However, the reasonable allocation of the action time of the positive and negative small vectors with opposite effects can effectively balance the neutral point potential. For the decoupling model, the vector space decoupling modeling method is usually used to map the various variables of the motor to the variables related to electromechanical energy conversion. α-β subspace and other subspaces not related to electromechanical energy conversion, but faces the problem of a huge set of pre-selected vectors after decoupling; and the double dq The transformation decouples the dual three-phase permanent magnet synchronous motor with isolated neutral points into two independent three-phase permanent magnet synchronous motors, which not only reduces the order of the original model but also reduces the number of pre-selected vectors from 3 to 1. 6 Simplified to 2×3 3 The traditional control methods for permanent magnet synchronous motors include vector control and direct torque control. However, the PI parameter adjustment process of vector control is relatively cumbersome, while the steady-state performance of direct torque control is poor. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a cascade model predictive control method, system, device and medium for dual three-phase motors, which achieves neutral point potential balance of the three-phase motors and efficient control of the dual three-phase motors.

[0004] The present invention provides a cascade model predictive control method for dual three-phase motors, comprising: S1: determining a target motor, collecting motor operating parameters of the target motor, and establishing an initial operating equation of the target motor based on the motor operating parameters; S2: Obtain the dual three-phase electrical angles of the target motor, establish a dual dq coordinate transformation matrix of the target motor according to the dual three-phase electrical angles, obtain an intermediate operating equation through the dual dq coordinate transformation matrix and the initial operating equation, decouple the intermediate operating equation to obtain a motor operating equation, and establish a state space expression of the target motor through the motor operating equation; S3: determining an operating sector of the target motor according to the motor operating parameters, and obtaining a time adjustment factor of the target motor in the operating sector; S4: constructing a cost function, obtaining an input matrix set of the target motor through the state space expression, and constructing a quadratic programming equation through the input matrix set and the cost function; S5: determining initial parameters of the quadratic programming equation, extracting initial input from the quadratic programming equation, and iteratively solving the quadratic programming equation using the initial parameters and the initial input using a gradient descent method to obtain a vector action time; S6: Controlling the target motor through the vector action time, the state space expression and the time adjustment factor.

[0005] According to the cascade model predictive control method for dual three-phase motors provided by the present invention, in step S1, after collecting the motor operating parameters of the target motor, the initial operating equation of the target motor is established according to the motor operating parameters: in, is the phase voltage vector matrix, is the phase current vector matrix, is the resistance coefficient matrix, t is the operating time of the target motor in the current operating cycle, is the phase flux vector matrix, is the inductance matrix, is the flux linkage coefficient matrix, is the flux linkage amplitude of the permanent magnet of the target motor.

[0006] According to the cascade model predictive control method for dual three-phase motors provided by the present invention, step S2 specifically includes: S21: Acquire the dual three-phase electrical angles of the target motor, construct a multidimensional transformation matrix, and establish a dual dq coordinate transformation matrix of the target motor through the dual three-phase electrical angles and the multidimensional transformation matrix; S22: multiplying the dual dq coordinate transformation matrix by both ends of the initial operating equation to obtain the intermediate operating equation, simplifying the intermediate operating equation to complete decoupling and obtain the motor operating equation; S23: Selecting state variables, performing formula transformation on the motor operation equation, and performing derivatives on the state variables in the motor operation equation after formula transformation to establish a state space expression of the target motor.

[0007] According to the cascade model predictive control method for dual three-phase motors provided by the present invention, step S3 specifically includes: S31: solving the future neutral point potential of the target motor according to the motor operating parameters, and determining the operating sector of the target motor; S32: Obtain a neutral point current expression through the future neutral point potential, and obtain a time adjustment factor of the target motor through the neutral point current expression in the operating sector.

[0008] According to the cascade model predictive control method for dual three-phase motors provided by the present invention, step S4 specifically includes: S41: Constructing a cost function, obtaining the input matrix set of the target motor through the state space expression, and constructing the system state equation of the operating sector through the input matrix set; the cost function of the kth operating cycle The expression is: in, is the actual value of the dq axis current in the k+1th operating cycle, is the theoretical value of the dq axis current in the k+1th operating cycle, Indicates taking the Euclidean norm; S42: rewriting the cost function by using the system state equation, and constructing the quadratic programming equation by using the rewritten cost function.

[0009] According to the cascade model predictive control method for dual three-phase motors provided by the present invention, step S5 specifically includes: S51: setting a linear constraint value, an initial solution, an initial step size, and a tolerance, and using the linear constraint value, the initial solution, the initial step size, and the tolerance as the initial parameters, extracting an initial input matrix and an initial input vector from the quadratic programming equation, and using the initial input matrix and the initial input vector as the initial input; S52: Calculate the gradient using the initial input and the initial parameters, and calculate the update amount using the gradient and the projection function; S53: Iterating the gradient, the initial solution, the update amount and the initial step size until an iteration end condition is reached, and obtaining the vector action time through the iterated initial solution.

[0010] According to the cascade model predictive control method for dual three-phase motors provided by the present invention, the multidimensional transformation matrix is ​​a Clark-Park transformation matrix.

[0011] The present invention also provides a cascade model predictive control system for dual three-phase motors, comprising: An operating equation module is used to determine a target motor, collect motor operating parameters of the target motor, and establish an initial operating equation of the target motor based on the motor operating parameters; A state-space expression module is configured to obtain the dual three-phase electrical angles of the target motor, establish a dual DQ coordinate transformation matrix of the target motor based on the dual three-phase electrical angles, obtain an intermediate operating equation by combining the dual DQ coordinate transformation matrix with the initial operating equation, perform decoupling through the intermediate operating equation to obtain a motor operating equation, and establish a state-space expression of the target motor through the motor operating equation. A time adjustment factor module is configured to determine an operating sector of the target motor according to the motor operating parameters, and obtain a time adjustment factor of the target motor in the operating sector; A quadratic programming equation module is used to construct a cost function, obtain an input matrix set of the target motor through the state space expression, and construct a quadratic programming equation through the input matrix set and the cost function; An equation solving module is used to determine initial parameters of the quadratic programming equation, extract initial input from the quadratic programming equation, and iteratively solve the quadratic programming equation using the initial parameters and the initial input using a gradient descent method to obtain a vector action time; Motor control module: used for controlling the target motor through the vector action time, the state space expression and the time adjustment factor.

[0012] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the cascade model predictive control method for a dual three-phase motor as described above are implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the cascade model predictive control method for a dual three-phase motor as described above are implemented.

[0014] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: The present invention provides a dual three-phase motor cascade model predictive control method, system, device and medium, by using dual dqThe coordinate transformation matrix decouples the dual three-phase permanent magnet synchronous motor into two three-phase motors, significantly simplifying control. Furthermore, a time adjustment factor is introduced into the inner control loop to adjust the duration of the positive and negative small vectors, effectively balancing the neutral point potential of the three-phase motor. The gradient descent method is used to solve a quadratic programming problem, predicting the duration of each vector in the control cycle and improving motor control.

[0015] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a flow chart of the cascade model predictive control method for dual three-phase motors provided by the present invention.

[0018] Figure 2 It is a topological diagram of the target motor of the cascade model predictive control method of the dual three-phase motor provided by the present invention.

[0019] Figure 3 It is a schematic diagram of experimental verification results of the cascade model predictive control method for dual three-phase motors provided by the present invention.

[0020] Figure 4 It is a structural diagram of the cascade model predictive control system of the dual three-phase motor provided by the present invention.

[0021] Figure 5 It is a structural schematic diagram of the cascade model predictive control device for dual three-phase motors provided by the present invention.

[0022] Reference numerals: 100, operating equation module; 200, state space expression module; 300, time adjustment factor module; 400, quadratic programming equation module; 500, equation solving module; 600, motor control module; 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0023] To make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below. Obviously, the embodiments described are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0024] In the description of the embodiments of the present invention, it should be noted that the terms “first”, “second” and “third” are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0025] In the description of the embodiments of the present invention, it should be noted that, unless otherwise specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; and direct connections or indirect connections through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on the specific circumstances.

[0026] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0027] The following combination Figures 1 to 5 Describe the specific embodiments of the present invention: Figure 1This is a flow chart of the cascade model predictive control method for dual three-phase motors provided by the present invention. First, the target motor is determined, the motor operating parameters are obtained, and the initial operating equation is established; then a dual dq coordinate transformation matrix is ​​established, the intermediate operating equation is obtained and decoupled, and a state space expression is established; then the operating sector of the target motor is established to obtain a time adjustment factor, and then a cost function is constructed, and an input matrix set is obtained to construct a quadratic programming equation; then the initial parameters and initial inputs are determined, and the gradient descent method is used for iterative solution to obtain the vector action time; finally, the target motor is controlled by the vector action time, the state space expression and the time adjustment factor.

[0028] The present invention provides a cascade model predictive control method for dual three-phase motors, comprising: S1: determining a target motor, collecting motor operating parameters of the target motor, and establishing an initial operating equation of the target motor based on the motor operating parameters; Furthermore, the purpose of this stage is to collect the operating parameters of the dual three-phase motor and establish the initial operating equation of the dual three-phase motor through the operating parameters, so as to facilitate subsequent calculation of the initial operating equation and further establish the state space expression of the dual three-phase motor. After collecting the motor operating parameters of the target motor, the initial operating equation of the target motor is established through the motor operating parameters: in, is the phase voltage vector matrix, is the phase current vector matrix, is the resistance coefficient matrix, t is the operating time of the target motor in the current operating cycle, is the phase flux vector matrix, is the inductance matrix, is the flux linkage coefficient matrix, is the flux linkage amplitude of the permanent magnet of the target motor.

[0029] Regarding the above steps, the specific implementation methods in this embodiment are as follows: First, determine the dual three-phase motor that needs to be controlled and use it as the target motor. The topology of the target motor is as follows: Figure 2 As shown, the motor operating parameters of the target motor are then collected. The motor operating parameters include the current of each phase of the target motor, the resistance of the stator, the electrical angle of the rotor, the self-inductance and mutual inductance of the winding, etc. The initial operating equation of the target motor is expressed as: in, ,and is the phase voltage vector matrix, where Indicates the voltage of phase A, Indicates the voltage of phase B, Indicates the voltage of phase C, Indicates the voltage of the U phase, Indicates the voltage of the V phase, Indicates the voltage of the W phase, Indicates taking the transpose of the matrix; ,and is the phase current vector matrix, where Indicates the current of phase A, Indicates the current of phase B, Indicates the current of phase C, Indicates the current of phase U, Indicates the current of the V phase, Indicates the current of phase W; = ,and is the resistivity matrix, where diag[] represents the diagonal matrix. Indicates the stator resistance value, and the resistance value of each stator is the same; t is the running time of the dual three-phase motor in the current operating cycle, ,and is the phase flux vector matrix, where Indicates the flux linkage of phase A, Indicates the flux linkage of phase B, Indicates the flux linkage of phase C, Indicates the flux linkage of the U phase, Indicates the flux linkage of the V phase, Represents the flux linkage of W phase; ,and is the inductance matrix, where Indicates the self-inductance and mutual inductance inside the first set of three-phase windings of the dual three-phase motor, Indicates the self-inductance and mutual inductance inside the second set of three-phase windings of the dual three-phase motor, Indicates the mutual inductance between the first set of three-phase windings and the second set of three-phase windings of the dual three-phase motor, Represents the mutual inductance between the second set of three-phase windings and the first set of three-phase windings of a dual three-phase motor; is the flux linkage coefficient matrix, in, is the electrical angle between the rotor pole position and the axis of the A-phase winding; is the flux linkage amplitude of the permanent magnet of the target motor.

[0030] S2: Obtain the dual three-phase electrical angles of the target motor, establish a dual dq coordinate transformation matrix of the target motor based on the dual three-phase electrical angles, obtain an intermediate operating equation through the dual dq coordinate transformation matrix and the initial operating equation, decouple the intermediate operating equation to obtain the motor operating equation, and establish the state space expression of the target motor through the motor operating equation. Furthermore, the purpose of this stage is to establish the dual dq coordinate transformation matrix of the target motor, and decouple it through the dual dq coordinate transformation matrix to obtain the motor operation equation, and finally obtain the state space expression of the target motor. Step S2 specifically includes: S21: Acquire the dual three-phase electrical angles of the target motor, construct a multidimensional transformation matrix, and establish a dual dq coordinate transformation matrix of the target motor through the dual three-phase electrical angles and the multidimensional transformation matrix; S22: multiplying the dual dq coordinate transformation matrix by both ends of the initial operating equation to obtain the intermediate operating equation, simplifying the intermediate operating equation to complete decoupling and obtain the motor operating equation; S23: Selecting state variables, performing formula transformation on the motor operation equation, and taking derivatives of the state variables in the motor operation equation after the formula transformation to establish a state space expression for the target motor. The multidimensional transformation matrix is ​​a Clarke-Park transformation matrix.

[0031] Regarding the above steps, the specific implementation methods in this embodiment are as follows: First, the dual three-phase electrical angle of the dual three-phase motor is obtained from the motor operating parameters. Since the stator winding of the dual three-phase motor consists of two sets of three-phase symmetrical windings, it can be regarded as a combination of two three-phase subsystems. Traditional three-phase motor coordinate transformation is applied to each subsystem, thus constructing two Clarke-Park transformation matrices as multidimensional transformation matrices. Here, the dual three-phase electrical angle refers to the electrical angle between the two three-phase subsystems, which is 30° in this embodiment. Then, the dual dq coordinate transformation matrix T of the target motor can be established using the dual three-phase electrical angle and the multidimensional transformation matrix. 61 : Where P1 is the first Clark-Parker transform matrix, P2 is the second Clark-Parker transform matrix, and ; Next, multiply the dual dq coordinate transformation matrix with both ends of the initial operating equation to obtain the intermediate operating equation: in, is the inverse matrix of the double dq coordinate transformation matrix, and d represents the differential.

[0032] By simplifying and solving the intermediate operating equation, the initial operating equation can be decoupled to the dq plane and the motor operating equation can be obtained: in, is the voltage component on the d-axis after decoupling of phases A, B, and C. is the component of the voltage on the q axis after phase A, phase B, and phase C are decoupled. is the component of the voltage on the d-axis after the U phase, V phase, and W phase are decoupled. is the component of the voltage on the q axis after the U phase, V phase and W phase are decoupled, is the component of the current on the d-axis after phase A, phase B, and phase C are decoupled. is the component of the current on the q axis after phase A, phase B, and phase C are decoupled. is the component of the current on the d-axis after the U phase, V phase, and W phase are decoupled. is the component of the current on the q axis after the U phase, V phase and W phase are decoupled, is the electrical angular velocity of the rotor, is the component of the magnetic flux on the d-axis after decoupling of phases A, B, and C, is the component of the magnetic flux on the q axis after decoupling of phases A, B, and C, is the component of the magnetic flux on the d-axis after the U phase, V phase and W phase are decoupled, is the component of the magnetic flux on the q axis after the U phase, V phase and W phase are decoupled, for The derivative of for The derivative of for The derivative of for The derivative of L d is the component of inductance on the d-axis, L q is the component of inductance on the q axis, L dd is the self-inductance of the d-axis inductor, L qq is the self-inductance of the q-axis inductor.

[0033] Next, a state variable is selected. In this embodiment, the selected state variable is , transform the motor operation equation and derive the state variables in the transformed motor operation equation to establish the state space expression of the target motor: in, for The derivative of for The derivative of for The derivative of for The derivative of is the mutual inductance between the q-axis inductance and the d-axis inductance, L dq is the mutual inductance between the d-axis inductance and the q-axis inductance, is the current input matrix, is the voltage input matrix, Enter the matrix for the flux linkage.

[0034] S3: determining an operating sector of the target motor according to the motor operating parameters, and obtaining a time adjustment factor of the target motor in the operating sector; Furthermore, the purpose of this stage is to determine the operating sector of the target motor and calculate the time adjustment factor of the target motor in the operating sector. Step S3 specifically includes: S31: solving the future neutral point potential of the target motor according to the motor operating parameters, and determining the operating sector of the target motor; S32: Obtain a neutral point current expression through the future neutral point potential, and obtain a time adjustment factor of the target motor through the neutral point current expression in the operating sector.

[0035] Regarding the above steps, the specific implementation methods in this embodiment are as follows: First, since we hope that the future neutral point potential is 0, we can solve the future neutral point potential of the target motor in the next operating cycle by using Kirchhoff's law and motor operating parameters. U np : in, is the voltage of the first capacitor on the DC side of the target motor in the next operation cycle, that is, the k+1th operation cycle, is the voltage of the second capacitor on the DC side of the target motor in the next operation cycle, is the voltage of the second capacitor of the target motor in the current operation cycle, is the voltage of the first capacitor of the target motor in the current operation cycle, that is, the kth operation cycle, is the length of an operating cycle, is the current of the second capacitor of the target motor when charging and discharging in the current operation cycle, is the current of the first capacitor of the target motor when charging and discharging in the current operation cycle, are the capacitance values ​​of the first capacitor and the second capacitor, is the neutral point potential of the target motor in the current operating cycle, that is, the measured value of the neutral point potential, is the neutral point current, that is, the measured value of the current at the neutral point of the target motor.

[0036] Here, according to the topology diagram of the target motor, the neutral point is located between the first capacitor and the second capacitor, and there are no other components between the first capacitor and the second capacitor. Therefore, the neutral point potential is 1 / 2 of the voltage difference between the first capacitor and the second capacitor, and the current flowing into the neutral point is defined as positive.

[0037] Next, determine the operating sector of the target motor. For the operation of a dual three-phase motor, there are six large sectors in the three phases, and each large sector has six small sectors. When the target motor load is constant, as the motor rotor rotates, it will operate in several determined small sectors in turn. These small sectors are the operating sectors of the target motor.

[0038] At this time, if the future neutral point potential is set to 0, the neutral point current expression can be obtained: In the operating sector, different sectors have different vectors. Each sector has three vectors, and each vector has a corresponding action time. Taking the first small sector of the first large sector of the ABC phase as an example, there are redundant small vectors ONN and POO, small vector OON, and zero vector OOO. Let the total action time of the redundant small vectors ONN and POO be T1, the action time of small vector OON be T2, and the action time of zero vector OOO be T3. Based on the different phase currents involved in the different vectors, we have: And there are: Among them, each vector has different effects on the neutral point current, and the redundant small vector ONN is , POO's , the small vector OON is , the zero vector OOO is .

[0039] in, is the time adjustment factor of the sector, according to the above equation, Replace with , the time adjustment factor of the sector can be calculated as: The same method is used to determine the corresponding time adjustment factors for the other sectors within the operating sector, thereby obtaining the time adjustment factor for the target motor. Since there are two redundant small vectors, ONN and POO, and T1 is the total action time of the redundant small vectors ONN and POO, a time adjustment factor is required to control the action time of each of the two redundant small vectors. When controlling the target motor, the corresponding time adjustment factor is selected based on the sector the target motor passes through during operation.

[0040] S4: constructing a cost function, obtaining an input matrix set of the target motor through the state space expression, and constructing a quadratic programming equation through the input matrix set and the cost function; Furthermore, the purpose of this stage is to construct a cost function and obtain the input matrix set of the target motor to construct the quadratic programming equation so as to solve the quadratic programming equation. Step S4 specifically includes: S41: Constructing a cost function, obtaining the input matrix set of the target motor through the state space expression, and constructing the system state equation of the operating sector through the input matrix set; the cost function of the kth operating cycle The expression is: in, is the actual value of the dq axis current in the k+1th operating cycle, is the theoretical value of the dq axis current in the k+1th operating cycle, Indicates taking the Euclidean norm S42: rewriting the cost function by using the system state equation, and constructing the quadratic programming equation by using the rewritten cost function.

[0041] Regarding the above steps, the specific implementation methods in this embodiment are as follows: First, build the cost function : in, is the actual value of the dq axis current in the k+1th operating cycle, that is, the next operating cycle, is the theoretical value of the dq axis current in the k+1th operating cycle, Indicates taking the Euclidean norm.

[0042] Then, the input matrix set of the target motor is obtained through the voltage input matrix, current input matrix and flux input matrix of the state space expression. The input matrix set includes the sector voltage input matrix , sector current input matrix and sector flux input matrix , then in the operating sector, taking the three-phase motor model of the first small sector of the first large sector of phase A, phase B, and phase C as an example, the system state equation of the operating sector can be written as: in, is the current value on the d-axis in the k+1th operating cycle, is the current value on the q axis in the k+1th operating cycle, is the actual value of the current on the d-axis in the kth operating cycle, that is, the current operating cycle, is the actual value of the current on the q axis in the kth operating cycle, is the actual value of the voltage on the d-axis in the k-th operating cycle, is the actual value of the voltage on the q axis in the kth operating cycle.

[0043] Then the cost function is rewritten through the system state equation as: Among them, T park is the Park transformation matrix, T SVM is the vector magnitude matrix, is the dq axis current value of the kth operating cycle, is the matrix of the action time of each vector in the sector to be solved.

[0044] Construct the first auxiliary variable r, and and the second auxiliary variable M, and ,but: in, is the transpose of r, is the transpose of M, for Here, since r is a 2×1 matrix, M is a 2×3 matrix, and t is a 3×1 matrix, is a 1×1 matrix, that is, a scalar, It is also a 1×1 matrix, that is, a scalar, so the two scalars can be combined to get , at this time, the cost function can be transformed into a quadratic programming problem, and the quadratic programming equation can be constructed: Where H is the initial input matrix, and , f is the initial input vector, and , min() means finding the minimum value in the brackets, b means the linear constraint value of the quadratic programming equation, Including T1, T2 and T3.

[0045] S5: determining initial parameters of the quadratic programming equation, extracting initial input from the quadratic programming equation, and iteratively solving the quadratic programming equation using the initial parameters and the initial input using a gradient descent method to obtain a vector action time; Furthermore, the purpose of this stage is to use the gradient descent method to iteratively solve the quadratic programming equation to obtain the action time of each redundant vector for control. Specifically, step S5 specifically includes: S51: setting a linear constraint value, an initial solution, an initial step size, and a tolerance, and using the linear constraint value, the initial solution, the initial step size, and the tolerance as the initial parameters, extracting an initial input matrix and an initial input vector from the quadratic programming equation, and using the initial input matrix and the initial input vector as the initial input; S52: Calculate the gradient using the initial input and the initial parameters, and calculate the update amount using the gradient and the projection function; S53: Iterating the gradient, the initial solution, the update amount and the initial step size until an iteration end condition is reached, and obtaining the vector action time through the iterated initial solution.

[0046] Regarding the above steps, the specific implementation methods in this embodiment are as follows: In this embodiment, the linear constraint value b of the quadratic programming equation is first set, and b= Since the purpose of solving the quadratic programming equation is to obtain the action time of each vector in the sector to be solved, the initial solution x0 is set to include three values, and x0={1 / 3, 1 / 3, 1 / 3}, the initial step size is alpha_0, and alpha_0=0.1, the tolerance is tol, and the initial solution, initial step size and tolerance are used as initial parameters. The initial input matrix H and initial input vector f are extracted from the quadratic programming equation, and the initial input matrix and initial input vector are used as initial input.

[0047] Then calculate the gradient g through the initial input and initial parameters c : in, Represents matrix multiplication. And calculates the update z through gradient and projection function c : Among them, proj() represents the projection function that projects the content in the brackets onto the linear plane.

[0048] Then, the iterative initial solution x is calculated d : Calculate the iterative update amount z d and iterative gradient g d : Calculate the gradient difference y c And the initial step size alpha_1 of the iteration is calculated by the gradient difference: Among them, z c ' for z c The transpose of .

[0049] Then, let alpha_0 = alpha_1, x0 = x d 、g c =g d And return to step S51 to recalculate for iteration until the update amount is less than the tolerance or the number of iterations reaches the iteration threshold set according to experience, completing the iteration of the initial solution, and use the initial solution after iteration as the vector action time. The three values ​​included in the initial solution correspond to T1, T2 and T3 in the quadratic programming equation respectively.

[0050] The same method is used to solve the corresponding initial solutions for other sectors in the operating sector according to the different phases of the sectors, thereby obtaining the initial solution of the target motor. In the process of controlling the operation of the target motor, the corresponding initial solution can be selected according to the sector through which the target motor passes during operation.

[0051] S6: Controlling the target motor through the vector action time, the state space expression and the time adjustment factor.

[0052] Furthermore, in this stage, the vector action time and time adjustment factor are used to control the various vectors of the target motor in the operating sector according to the sectors passed by the target motor at different times during operation, and the working state of the target motor is characterized in combination with the state space expression to complete the control of the target motor.

[0053] In order to verify the effectiveness of the present invention, the cascade model predictive control method of the dual three-phase motor proposed in the present invention was experimentally verified on a dual three-phase motor drive experimental platform. The experimental results are as follows: Figure 3 As shown, in terms of phase current, taking the current of phase A as an example, Figure 3 As shown in (a), the horizontal axis is in seconds. Figure 3 The experimental data shown in (a) is input into the MATLB tool for analysis, and the total harmonic distortion value of the method provided by the present invention is obtained to be 3.25%. d Axis current and q-axis current, such as Figure 3 As shown in (b), the unit of the horizontal axis is seconds. Figure 3 The experimental data shown in (b) is input into the MATLB tool for analysis, and the method provided by the present invention can be obtained. d Current on the axis It fluctuates within the reference value of 0A, and the peak-to-peak ripple value is 0.57A. q Current on the axis It also fluctuates within the reference value range, and its peak-to-peak ripple is 0.41A. Figure 3 As shown in (c), the horizontal axis is in seconds. Figure 3 The experimental data shown in (c) is input into the MATLB tool for analysis, and it can be obtained that in terms of neutral point potential control, the neutral point potential is basically stabilized at 0V and the peak-to-peak ripple value is 0.95V through the method provided by the present invention. Figure 3 The error in the experiment is mainly due to the delay between the control command to the target motor and the actual operating status of the motor, which leads to the fluctuation of the operating status of the target motor.

[0054] The cascade model predictive control device for dual three-phase motors provided by the present invention is described below. The cascade model predictive control device for dual three-phase motors described below and the cascade model predictive control method for dual three-phase motors described above can be referred to in correspondence with each other.

[0055] Figure 4 The structural diagram of the cascade model predictive control system of dual three-phase motors is shown as an example. Figure 4 As shown, the method for executing the cascade model predictive control of the dual three-phase motor as described above includes: Operation equation module 100: used to determine a target motor, collect motor operation parameters of the target motor, and establish an initial operation equation of the target motor based on the motor operation parameters; State-space expression module 200: used to obtain the dual three-phase electrical angles of the target motor, establish a dual DQ coordinate transformation matrix of the target motor based on the dual three-phase electrical angles, obtain an intermediate operating equation by combining the dual DQ coordinate transformation matrix with the initial operating equation, perform decoupling through the intermediate operating equation to obtain a motor operating equation, and establish a state-space expression for the target motor using the motor operating equation; The time adjustment factor module 300 is configured to determine an operating sector of the target motor according to the motor operating parameters, and obtain a time adjustment factor of the target motor in the operating sector; A quadratic programming equation module 400 is configured to construct a cost function, obtain an input matrix set of the target motor through the state space expression, and construct a quadratic programming equation through the input matrix set and the cost function; Equation solving module 500: used to determine initial parameters of the quadratic programming equation, extract initial input from the quadratic programming equation, and iteratively solve the quadratic programming equation using the initial parameters and the initial input using a gradient descent method to obtain a vector action time; The motor control module 600 is configured to control the target motor through the vector action time, the state space expression and the time adjustment factor.

[0056] on the other hand, Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the cascade model predictive control method for the dual three-phase motor, which includes: S1: determining a target motor, collecting motor operating parameters of the target motor, and establishing an initial operating equation of the target motor based on the motor operating parameters; S2: Obtain the dual three-phase electrical angles of the target motor, establish a dual dq coordinate transformation matrix of the target motor according to the dual three-phase electrical angles, obtain an intermediate operating equation through the dual dq coordinate transformation matrix and the initial operating equation, decouple the intermediate operating equation to obtain a motor operating equation, and establish a state space expression of the target motor through the motor operating equation; S3: determining an operating sector of the target motor according to the motor operating parameters, and obtaining a time adjustment factor of the target motor in the operating sector; S4: constructing a cost function, obtaining an input matrix set of the target motor through the state space expression, and constructing a quadratic programming equation through the input matrix set and the cost function; S5: determining initial parameters of the quadratic programming equation, extracting initial input from the quadratic programming equation, and iteratively solving the quadratic programming equation using the initial parameters and the initial input using a gradient descent method to obtain a vector action time; S6: Controlling the target motor through the vector action time, the state space expression and the time adjustment factor.

[0057] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0058] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for cascade model predictive control of a dual three-phase motor provided by the above methods is implemented. The method includes: S1: determining a target motor, collecting motor operating parameters of the target motor, and establishing an initial operating equation of the target motor based on the motor operating parameters; S2: Obtain the dual three-phase electrical angles of the target motor, establish a dual dq coordinate transformation matrix of the target motor according to the dual three-phase electrical angles, obtain an intermediate operating equation through the dual dq coordinate transformation matrix and the initial operating equation, decouple the intermediate operating equation to obtain a motor operating equation, and establish a state space expression of the target motor through the motor operating equation; S3: determining an operating sector of the target motor according to the motor operating parameters, and obtaining a time adjustment factor of the target motor in the operating sector; S4: constructing a cost function, obtaining an input matrix set of the target motor through the state space expression, and constructing a quadratic programming equation through the input matrix set and the cost function; S5: determining initial parameters of the quadratic programming equation, extracting initial input from the quadratic programming equation, and iteratively solving the quadratic programming equation using the initial parameters and the initial input using a gradient descent method to obtain a vector action time; S6: Controlling the target motor through the vector action time, the state space expression and the time adjustment factor.

[0059] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0060] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A cascade model predictive control method for dual three-phase motors, characterized in that: include: S1: determining a target motor, collecting motor operating parameters of the target motor, and establishing an initial operating equation of the target motor according to the motor operating parameters; S2: Acquire the dual three-phase electrical angles of the target motor, establish a dual dq coordinate transformation matrix of the target motor according to the dual three-phase electrical angles, obtain an intermediate operation equation through the dual dq coordinate transformation matrix and the initial operation equation, decouple through the intermediate operation equation to obtain a motor operation equation, and establish a state space expression of the target motor through the motor operation equation; S3: determining an operating sector of the target motor according to the motor operating parameters, and obtaining a time adjustment factor of the target motor in the operating sector; S4: constructing a cost function, obtaining an input matrix set of the target motor through the state space expression, and constructing a quadratic programming equation through the input matrix set and the cost function; S5: determining initial parameters of the quadratic programming equation, extracting initial input from the quadratic programming equation, iteratively solving the quadratic programming equation using the initial parameters and the initial input using a gradient descent method to obtain a vector action time; S6: Controlling the target motor through the vector action time, the state space expression and the time adjustment factor.

2. The cascade model predictive control method for dual three-phase motors according to claim 1, characterized in that: In step S1, after the motor operating parameters of the target motor are collected, the initial operating equation of the target motor is established according to the motor operating parameters: in, is the phase voltage vector matrix, is the phase current vector matrix, is the resistance coefficient matrix, t is the operating time of the target motor in the current operating cycle, is the phase flux vector matrix, is the inductance matrix, is the flux linkage coefficient matrix, is the flux amplitude of the permanent magnet of the target motor.

3. The cascade model predictive control method for dual three-phase motors according to claim 1, characterized in that: Step S2 specifically includes: S21: Acquire the dual three-phase electrical angles of the target motor, construct a multidimensional transformation matrix, and establish a dual dq coordinate transformation matrix of the target motor through the dual three-phase electrical angles and the multidimensional transformation matrix; S22: multiplying the dual dq coordinate transformation matrix and both ends of the initial operation equation to obtain the intermediate operation equation, simplifying the intermediate operation equation, thereby completing decoupling and obtaining the motor operation equation; S23: Selecting state variables, performing formula transformation on the motor operation equation, and taking derivatives of the state variables in the motor operation equation after the formula transformation to establish a state space expression of the target motor.

4. The cascade model predictive control method for dual three-phase motors according to claim 1, characterized in that: Step S3 specifically includes: S31: solving the future neutral point potential of the target motor through the motor operating parameters, and determining the operating sector of the target motor; S32: Obtain a neutral point current expression through the future neutral point potential, and obtain a time adjustment factor of the target motor through the neutral point current expression in the operating sector.

5. The cascade model predictive control method for dual three-phase motors according to claim 1, characterized in that: Step S4 specifically includes: S41: construct a cost function, obtain the input matrix set of the target motor through the state space expression, and construct the system state equation of the operating sector through the input matrix set; the cost function of the kth operating cycle The expression is: in, is the actual value of the dq axis current in the k+1th operating cycle, is the theoretical value of the dq axis current in the k+1th operating cycle, Indicates taking the Euclidean norm; S42: rewriting the cost function through the system state equation, and constructing the quadratic programming equation through the rewritten cost function.

6. The cascade model predictive control method for dual three-phase motors according to claim 1, characterized in that: Step S5 specifically includes: S51: setting a linear constraint value, an initial solution, an initial step length and a tolerance, and using the linear constraint value, the initial solution, the initial step length and the tolerance as the initial parameters, extracting an initial input matrix and an initial input vector from the quadratic programming equation, and using the initial input matrix and the initial input vector as the initial input; S52: Calculate a gradient through the initial input and the initial parameters, and calculate an update amount through the gradient and a projection function; S53: iterating the gradient, the initial solution, the update amount and the initial step size until an iteration end condition is reached, and obtaining the vector action time through the iterated initial solution.

7. The cascade model predictive control method for dual three-phase motors according to claim 3, characterized in that: The multidimensional transformation matrix is ​​a Clark-Park transformation matrix.

8. A cascade model predictive control system for dual three-phase motors, used to execute the cascade model predictive control method for dual three-phase motors as claimed in any one of claims 1 to 7, characterized in that: include: Operation equation module: used to determine the target motor, collect the motor operation parameters of the target motor, and establish the initial operation equation of the target motor according to the motor operation parameters; State space expression module: used to obtain the dual three-phase electrical angle of the target motor, establish the dual dq coordinate transformation matrix of the target motor according to the dual three-phase electrical angle, obtain the intermediate operation equation through the dual dq coordinate transformation matrix and the initial operation equation, decouple through the intermediate operation equation to obtain the motor operation equation, and establish the state space expression of the target motor through the motor operation equation; A time adjustment factor module: used to determine the operation sector of the target motor according to the motor operation parameters, and obtain the time adjustment factor of the target motor in the operation sector; A quadratic programming equation module: used to construct a cost function, obtain an input matrix set of the target motor through the state space expression, and construct a quadratic programming equation through the input matrix set and the cost function; Equation solving module: used for determining initial parameters of the quadratic programming equation, extracting initial input from the quadratic programming equation, iteratively solving the quadratic programming equation by using the initial parameters and the initial input and using the gradient descent method to obtain the vector action time; Motor control module: used for controlling the target motor through the vector action time, the state space expression and the time adjustment factor.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the cascade model predictive control method for a dual three-phase motor as described in any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the cascade model predictive control method for a dual three-phase motor as claimed in any one of claims 1 to 7 are implemented.

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