Low-dimensional explicit predictive current control method, device and system for permanent magnet synchronous motor

Abstract

The application discloses a low-dimension explicit predictive current control method, device and system of a permanent magnet synchronous motor, which comprises the following steps: constructing an explicit model predictive current controller containing an explicit optimal control law and an extended state observer; obtaining an actual current value of the permanent magnet synchronous motor and inputting the actual current value into the extended state observer in the explicit model predictive current controller to obtain a current loop lumped disturbance observation value of the permanent magnet synchronous motor; inputting a current preset given reference instruction, the current loop lumped disturbance observation value and the actual current value of the permanent magnet synchronous motor into the explicit optimal control law to obtain a reference voltage, so as to realize the explicit model predictive current control of the closed loop feedback of the permanent magnet synchronous motor. The application can reduce the optimization dimension of the explicit model predictive controller on the basis of guaranteeing the good current control performance of the permanent magnet synchronous motor, and can significantly reduce the memory space requirement of a microprocessor, enhance the practicability and competitiveness of the algorithm.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of current control, and particularly relates to a low-dimensional explicit predictive current control method, device and system for a permanent magnet synchronous motor. BACKGROUND

[0002] The permanent magnet synchronous motor has been widely applied to fields such as rail transportation, electric vehicles, aerospace, etc. due to advantages such as high power density, high torque density and high reliability. The torque control performance of the permanent magnet synchronous motor is determined by the control performance of the d-axis current and the q-axis current. Therefore, synchronous control of the d-axis current and the q-axis current with high dynamic response and high steady-state accuracy is the key to realizing high-performance control of the permanent magnet synchronous motor.

[0003] In recent years, model predictive control has the ability to directly design a single controller for a multi-input, multi-output system to realize multi-objective collaborative control, and has attracted extensive attention and research of researchers in the field of motor control. The basic principle of model predictive control is to convert a multi-objective control problem into a quadratic programming problem, and an optimal voltage vector that satisfies system constraints is obtained by solving the quadratic programming problem in the entire state space with the help of a quadratic programming problem solver, and the motor is driven by using modulation technology. Although researchers have developed many efficient online solvers for constrained quadratic programming problems based on interior point methods, active set methods, etc., the huge real-time computing burden is still not suitable for systems with high control frequency such as motor drives. Therefore, the existing model predictive control suitable for motors usually converts the quadratic programming problem that needs to be solved online into a multi-parameter quadratic programming problem that is pre-solved offline. Such model predictive control is called explicit model predictive control.

[0004] Due to the mathematical principle of the explicit model predictive control algorithm, the computational complexity is closely related to the state dimension of the optimization problem. When the state dimension increases, the number of critical regions obtained by offline partitioning the state space increases exponentially, which leads to high requirements for the memory capacity and computing power of microprocessors for storage and table lookup, thereby limiting the application of this method in motor drive occasions with limited hardware resources. SUMMARY

[0005] In order to solve the problems in the background art, the application provides a low-dimensional explicit predictive current control method, device and system for a permanent magnet synchronous motor, which can significantly reduce the memory space requirement of a microprocessor while ensuring good current control performance of the permanent magnet synchronous motor.

[0006] The technical scheme provided by the application is as follows:

[0007] In a first aspect, the application provides a low-dimensional explicit predictive current control method for a permanent magnet synchronous motor, which comprises:

[0008] A discrete model of a permanent magnet synchronous motor is established, which explicitly contains cross-coupling terms of the product of speed, current and inductance, to provide a prediction basis of system dynamic characteristics for explicit model predictive control;

[0009] An explicit model predictive current controller is constructed, which comprises an explicit optimal control law and an extended state observer; the explicit optimal control law comprises a linear prediction model, a value function and system constraints; in the solving process, the double current tracking task realized by a quadratic weighted penalty term in the value function is converted into an equality hard constraint;

[0010] Actual current values and speed values of the permanent magnet synchronous motor are obtained and input into the extended state observer in the explicit model predictive current controller to obtain current loop lumped disturbance observation values of the permanent magnet synchronous motor;

[0011] The current preset given reference instruction, the current loop lumped disturbance observation values and the actual current values of the permanent magnet synchronous motor are input into the explicit optimal control law in the explicit model predictive current controller to obtain reference voltages of the permanent magnet synchronous motor, which are used to realize the explicit model predictive current control of the closed-loop feedback of the permanent magnet synchronous motor.

[0012] In an embodiment, the discrete model of the permanent magnet synchronous motor is:

[0013] ,

[0014] In the formula, and respectively represent the d-axis actual current of the permanent magnet synchronous motor at the sampling time and the sampling time . and respectively represent the q-axis actual current of the permanent magnet synchronous motor at the sampling time and the sampling time . represents the electrical angular velocity of the permanent magnet synchronous motor at the sampling time k; represents a sampling period; and respectively represent the nominal values of the d-axis and q-axis inductance of the permanent magnet synchronous motor; and respectively represent the d-axis voltage and q-axis voltage of the permanent magnet synchronous motor at the sampling time . and respectively represent the d-axis and q-axis current loop lumped disturbance of the permanent magnet synchronous motor at the sampling time . and are cross-coupling terms of the product of speed, current and inductance.

[0015] In an embodiment, the double current tracking task implemented by a quadratic weighted penalty term in the cost function is converted into an equality hard constraint implementation, specifically including:

[0016] The quadratic weighted penalty term in the cost function is canceled and converted into an equality hard constraint to implement dq-axis double current synchronous tracking, without balancing the priority of current tracking in two axes by a weight factor.

[0017] In an embodiment, the equality hard constraint includes: adding the following two equality constraints at the end of the prediction horizon:

[0018] ,

[0019] In the formula, and respectively represent the d-axis and q-axis actual currents of the permanent magnet synchronous motor at the sampling time . and respectively represent the d-axis and q-axis current preset given reference commands of the permanent magnet synchronous motor at the sampling time . represents the prediction horizon.

[0020] In an embodiment, the linear prediction model in the explicit optimal control law is:

[0021] ,

[0022] In the formula, and respectively represent the product of the electrical angular velocity and the q-axis actual current of the permanent magnet synchronous motor at the sampling time k+1 and the sampling time . and respectively represent the product of the electrical angular velocity and the d-axis actual current of the permanent magnet synchronous motor at the sampling time and the sampling time . and respectively represent the d-axis actual current of the permanent magnet synchronous motor at the sampling time and the sampling time . and respectively represent the q-axis actual current of the permanent magnet synchronous motor at the sampling time and the sampling time . represents the sampling period; and respectively represent the nominal values of the d-axis and q-axis inductances of the permanent magnet synchronous motor; and respectively represent the d-axis and q-axis actual currents of the permanent magnet synchronous motor at the sampling time d-axis voltage and q-axis voltage of the permanent magnet synchronous motor at the sampling time and respectively represent d-axis current loop lumped disturbance observation value of the permanent magnet synchronous motor at the sampling time and the sampling time ; and respectively represent q-axis current loop lumped disturbance observation value of the permanent magnet synchronous motor at the sampling time and the sampling time ; and respectively represent d-axis current preset given reference instruction of the permanent magnet synchronous motor at the sampling time and the sampling time ; and respectively represent q-axis current preset given reference instruction of the permanent magnet synchronous motor at the sampling time and the sampling time ; and respectively represent d-axis and q-axis voltage of the permanent magnet synchronous motor at the sampling time ; and respectively represent d-axis and q-axis voltage variation value of the permanent magnet synchronous motor at the sampling time .

[0023] In an embodiment, the value function in the explicit optimal control law is:

[0024] ,

[0025] wherein, represents the value function value of the permanent magnet synchronous motor at the sampling time , is an integer variable, represents a prediction time domain, and respectively represent d-axis and q-axis voltage variation of the permanent magnet synchronous motor at the sampling time .

[0026] In an embodiment, the system constraint in the explicit optimal control law is:

[0027] ,

[0028] wherein, and respectively represent d-axis and q-axis actual current of the permanent magnet synchronous motor at the sampling time ; and respectively represent d-axis and q-axis current variation of the permanent magnet synchronous motor at the sampling time d-axis and q-axis current preset given reference commands of the permanent magnet synchronous motor; and respectively represent the d-axis and q-axis voltage of the permanent magnet synchronous motor at the sampling time represents the inverter output voltage limiting value, A u represents the irregular hexagonal constraint matrix; E is a 6x1 unit column vector.

[0029] In an embodiment, the extended state observer in the explicit model predictive current controller is:

[0030] ,

[0031] In the formula, and respectively represent the observed values of the d-axis current of the permanent magnet synchronous motor at the sampling time and the sampling time and respectively represent the observed values of the q-axis current of the permanent magnet synchronous motor at the sampling time and the sampling time , , and respectively represent the first d-axis gain coefficient, the first q-axis gain coefficient, the second d-axis gain coefficient, and the second q-axis gain coefficient of the extended state observer.

[0032] In a second aspect, the present application provides a low-dimensional explicit predictive current control device of a permanent magnet synchronous motor, which is used to realize the low-dimensional explicit predictive current control method of the permanent magnet synchronous motor described above, and the device comprises:

[0033] a current acquisition and coordinate transformation module, which is used to acquire three-phase currents output by an inverter and obtain actual current values of the permanent magnet synchronous motor through coordinate transformation;

[0034] a speed detection module, which is used to acquire a speed value of the permanent magnet synchronous motor;

[0035] a discrete model of the permanent magnet synchronous motor, which is used to provide a prediction basis of system dynamic characteristics for the explicit model predictive current control, and the discrete model of the permanent magnet synchronous motor explicitly comprises cross-coupling terms of speed, current and inductance multiplication;

[0036] an explicit model predictive current controller, which comprises an extended state observer and an explicit optimal control module; wherein the extended state observer is used to obtain an observed value of current loop lumped disturbance of the permanent magnet synchronous motor by receiving the actual current values and the speed value of the permanent magnet synchronous motor;

[0037] ​​​The explicit optimal control module is configured to receive a current preset given reference instruction of the permanent magnet synchronous motor, a current loop lumped disturbance observation value, and an actual current value, and derive a reference voltage of the permanent magnet synchronous motor based on an explicit optimal control law.

[0038] The explicit optimal control module comprises a linear prediction model, a value function, and system constraints; and in a solving process, the explicit optimal control law converts a double current tracking task realized by a quadratic weighted penalty term in the value function into an equality hard constraint.

[0039] In a third aspect, the application provides a permanent magnet synchronous motor control system, which comprises the low-dimensional explicit prediction current control device of the permanent magnet synchronous motor.

[0040] The application has the following beneficial effects:

[0041] (1) In order to exert the advantage of the explicit model prediction current control in the cooperative optimization control of dq-axis currents, the application breaks through the traditional path, and eliminates the adverse effect of the cross-coupling term on the current dynamic performance by retaining the cross-coupling term in the prediction model.

[0042] (2) In order to solve the problem that the critical region obtained by offline partitioning the multi-dimensional state space of the traditional explicit model prediction current control method is too much, the memory requirement of the controller is too high, and the method is difficult to be practically applied, the soft penalty term in the value function is replaced by an equality hard constraint, and the double current tracking is realized based on the constraint.

[0043] (3) The application fully exerts the constraint processing capability of the explicit model prediction algorithm, simplifies the traditional complex four-dimensional space convex optimization problem into a two-dimensional plane convex optimization problem, and can significantly reduce the memory space requirement of the microprocessor on the basis of guaranteeing the good current control performance of the permanent magnet synchronous motor. BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings, which are part of this application, serve to further understand the application, and the illustrative embodiments of the application and the description thereof serve to explain the application, but do not constitute an improper limitation on the application.

[0045] Figure 1 A flowchart of a low-dimensional explicit prediction current control method of a permanent magnet synchronous motor provided for an embodiment of the application is shown in FIG. 4.

[0046] Figure 2 A reduction of the optimization dimension is shown in FIG. 5.

[0047] Figure 3 A block diagram of a permanent magnet synchronous motor control system provided for an embodiment of the application is shown in FIG. 6.

[0048] Figure 4A comparison chart of experimental results of d-axis and q-axis currents of a permanent magnet synchronous motor provided in an embodiment of the present application is shown in FIG. 1, in which, Figure 4 (a) is a chart of experimental results of a conventional method, Figure 4 (b) is a chart of experimental results of the method of the present application.

[0049] It should be noted that the drawings and the written description are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0050] The present application will be described in detail below with reference to the drawings and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present application, and gives a detailed implementation and specific operation process, and is not intended to limit its use, and the present application is not limited to the following embodiments.

[0051] In order to deepen the understanding and understanding of the present application, the technical solution of the present application will be further introduced below in combination with the drawings and specific embodiments.

[0052] As shown in FIG. 1, the embodiment of the present application provides a low-dimensional explicit predictive current control method for a permanent magnet synchronous motor, which specifically includes the following steps: Figure 1 Step S100: Establish a discrete model of a permanent magnet synchronous motor which explicitly contains cross-coupling terms of speed, current and inductance multiplication, to provide a prediction basis for system dynamic characteristics of explicit model predictive control.

[0053] The cross-coupling terms of speed, current and inductance multiplication are explicitly retained in the discrete model to solve the problem of deterioration of current dynamic performance caused by the cross-coupling terms in the conventional method.

[0054] Further, the discrete model of the permanent magnet synchronous motor is established as follows:

[0055]

[0056] ,

[0057] In the formula, and respectively represent the d-axis actual current of the permanent magnet synchronous motor at the sampling time and the sampling time ; and respectively represent the q-axis actual current of the permanent magnet synchronous motor at the sampling time and the sampling time ; represents the electrical angular velocity of the permanent magnet synchronous motor at the sampling time k; represents the sampling period; and​ These represent the nominal inductance values ​​of the d-axis and q-axis of the permanent magnet synchronous motor, respectively. and These represent the permanent magnet synchronous motor at the sampling time. d-axis voltage and q-axis voltage; and These represent the permanent magnet synchronous motor at the sampling time. Lumped disturbances in the d-axis and q-axis current loops; and It is a cross-coupled term that multiplies the rotational speed, current, and inductance.

[0058] Step S200: Construct an explicit model predictive current controller that includes an explicit optimal control law and an extended state observer.

[0059] In the embodiments of this application, the explicit optimal control law includes a linear prediction model, a value function, and system constraints. During the solution process, the explicit optimal control law transforms the dual-current tracking task, implemented by a quadratic weighted penalty term in the value function, into an equality-based hard constraint implementation.

[0060] Furthermore, the dual-current tracking task, implemented by a quadratic weighted penalty term in the value function, is transformed into an equality hard constraint implementation, specifically including:

[0061] The quadratic weighted penalty term in the value function is removed, and it is transformed into an equality hard constraint to achieve synchronous tracking of the dual currents on the dq axes, without the need to balance the tracking priority of the two-axis currents through a weight factor.

[0062] In a preferred embodiment, the equality hard constraint includes adding the following two equality constraints at the prediction time domain endpoint:

[0063] ,

[0064] In the formula, and These represent the permanent magnet synchronous motor at the sampling time. The actual currents along the d-axis and q-axis; and These represent the permanent magnet synchronous motor at the sampling time. The d-axis and q-axis current preset reference commands are given. This indicates the prediction time domain.

[0065] Furthermore, the linear prediction model in the explicit optimal control law is as follows:

[0066] ,

[0067] In the formula, and respectively denote the actual d-axis and q-axis currents of the permanent magnet synchronous motor at the sampling instant the product of the electrical angular velocity and the actual q-axis current; and respectively denote the actual d-axis and q-axis currents of the permanent magnet synchronous motor at the sampling instant and the sampling instant the product of the electrical angular velocity and the actual d-axis current; and respectively denote the actual d-axis and q-axis currents of the permanent magnet synchronous motor at the sampling instant and the sampling instant of the permanent magnet synchronous motor; and respectively denote the actual d-axis and q-axis currents of the permanent magnet synchronous motor at the sampling instant and the sampling instant of the permanent magnet synchronous motor; denotes the sampling period; and respectively denote the nominal values of the d-axis and q-axis inductances of the permanent magnet synchronous motor; and respectively denote the d-axis and q-axis voltages of the permanent magnet synchronous motor at the sampling instant ; and respectively denote the d-axis and q-axis current loop lumped disturbance observation values of the permanent magnet synchronous motor at the sampling instant and the sampling instant ; and respectively denote the d-axis and q-axis current loop lumped disturbance observation values of the permanent magnet synchronous motor at the sampling instant and the sampling instant ; and respectively denote the d-axis and q-axis current preset given reference commands of the permanent magnet synchronous motor at the sampling instant and the sampling instant ; and respectively denote the d-axis and q-axis current preset given reference commands of the permanent magnet synchronous motor at the sampling instant and the sampling instant ; and respectively denote the d-axis and q-axis voltages of the permanent magnet synchronous motor at the sampling instant ; and respectively denote the d-axis and q-axis voltage variation values of the permanent magnet synchronous motor at the sampling instant . , is the mechanical angular velocity, and p is the number of pole pairs of the motor.

[0068] Further, the value function in the explicit optimal control law is:

[0069]

[0070] wherein, represents the value function value of the permanent magnet synchronous motor at the sampling moment ; is an integer variable, represents a prediction time domain; and respectively represent the d-axis and q-axis voltage variation of the permanent magnet synchronous motor at the sampling moment .

[0071] It can be seen from the value function that the value function no longer contains the penalty term related to the dq-axis current, and the use of the weight coefficient is avoided, so that the parameter adjustment process is simplified.

[0072] Further, the system constraint in the explicit optimal control law is:

[0073]

[0074] wherein, and respectively represent the d-axis and q-axis actual currents of the permanent magnet synchronous motor at the sampling moment ; and respectively represent the d-axis and q-axis current preset given reference instructions of the permanent magnet synchronous motor at the sampling moment ; and respectively represent the d-axis and q-axis voltages of the permanent magnet synchronous motor at the sampling moment , represents an inverter output voltage amplitude value, A u represents an irregular hexagonal constraint matrix; E is a 6*1 unit column vector.

[0075] The method cancels the original quadratic weighted penalty term in the value function, converts it into an equation hard constraint to realize the dq-axis double current synchronous tracking, reduces the multi-dimensional space optimization problem involved in the explicit model predictive control by two dimensions, thereby greatly reducing the number of critical regions and the demand for microprocessor memory capacity. The complexity of the method and the critical region number analysis process are as follows: according to the principle of explicit model predictive control, the explicit model predictive algorithm divides the multi-dimensional space into different critical regions according to the different activated constraint combinations corresponding to different positions in the multi-dimensional state space, and calculates the optimal control law in each critical region offline. The traditional display model predictive model uses a 10-dimensional state, corresponding to a 10-dimensional state space, but ​​The six states remain unchanged in the prediction time domain when solving a multi-parameter quadratic programming problem; therefore, the 10-dimensional space optimization degenerates into a 4-dimensional space optimization. Traditional explicit model predictive current control solves a four-dimensional convex optimization problem, which has high complexity.

[0076] The method of this invention introduces... and Two hard equality constraints not only eliminate the penalty term in the value function, setting Q=0 and eliminating the need for a weighting factor, but also force the two pairs of states to be equal, transforming the original four-dimensional space... and Two dimensions degenerate into And in The projection at a certain point degenerates the four-dimensional optimization space into a two-dimensional plane. In summary, the method of this invention only involves a two-dimensional planar convex optimization problem, significantly reducing the complexity of the multi-parameter quadratic programming problem. A schematic diagram of the dimensionality reduction of the multi-dimensional space under equality constraints is shown below. Figure 2 As shown in the figure The numerical value used for a specific example.

[0077] Furthermore, the extended state observer in the explicit model predictive current controller is:

[0078] ,

[0079] In the formula, and These represent the permanent magnet synchronous motor at the sampling time. and sampling time The observed values ​​of the d-axis current, and These represent the permanent magnet synchronous motor at the sampling time. and sampling time The observed values ​​of the q-axis current; , , and These represent the first d-axis gain coefficient, the first q-axis gain coefficient, the second d-axis gain coefficient, and the second q-axis gain coefficient of the extended state observer, respectively.

[0080] Step S300: Obtain the actual current and speed values ​​of the permanent magnet synchronous motor and input them into the extended state observer in the explicit model predictive current controller to obtain the observed value of the current loop lumped disturbance of the permanent magnet synchronous motor.

[0081] Specifically, the permanent magnet synchronous motor is equipped with three current sensors, which collect the a-phase current of the permanent magnet synchronous motor. b-phase current and c-phase current The a-phase current of the permanent magnet synchronous motor b-phase current and c-phase current The actual currents of the d-axis and q-axis in the permanent magnet synchronous motor are obtained through Park coordinate transformation.

[0082] Step S400: inputting the current preset given reference instruction, current loop lumped disturbance observation value and actual current value of the permanent magnet synchronous motor into an explicit optimal control law in an explicit model predictive current controller to obtain a reference voltage of the permanent magnet synchronous motor, so as to realize the explicit model predictive current control of the closed loop feedback of the permanent magnet synchronous motor.

[0083] Referring to Figure 3 , the present application provides a low-dimensional explicit predictive current control device of a permanent magnet synchronous motor, which comprises:

[0084] a current acquisition and coordinate transformation module, which is used for acquiring three-phase currents output by an inverter and obtaining actual current values of the permanent magnet synchronous motor through coordinate transformation;

[0085] a speed detection module, which is used for obtaining a speed value of the permanent magnet synchronous motor;

[0086] a discrete model of the permanent magnet synchronous motor, which is used for providing a prediction basis of system dynamic characteristics for the explicit model predictive current control, and the discrete model of the permanent magnet synchronous motor explicitly comprises cross-coupling terms of speed, current and inductance multiplication;

[0087] an explicit model predictive current controller, which comprises an extended state observer and an explicit optimal control module; wherein the extended state observer is used for obtaining a current loop lumped disturbance observation value of the permanent magnet synchronous motor by receiving actual current values and speed values of the permanent magnet synchronous motor;

[0088] the explicit optimal control module is used for receiving a current preset given reference instruction, a current loop lumped disturbance observation value and actual current values of the permanent magnet synchronous motor, and obtaining a reference voltage of the permanent magnet synchronous motor based on an explicit optimal control law;

[0089] the explicit optimal control module comprises a linear prediction model, a value function and system constraints; in the solving process, the explicit optimal control law converts a double current tracking task realized by a quadratic form weighted penalty term in the value function into an equal equation hard constraint.

[0090] It should be noted that the low-dimensional explicit predictive current control device of the permanent magnet synchronous motor provided in the above embodiment is only exemplified by the above division of the functional modules when the low-dimensional explicit predictive current control method of the permanent magnet synchronous motor is performed, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the low-dimensional explicit predictive current control device of the permanent magnet synchronous motor provided in the above embodiment and the low-dimensional explicit predictive current control method of the permanent magnet synchronous motor embodiment belong to the same concept, and the implementation process is detailed in the low-dimensional explicit predictive current control method of the permanent magnet synchronous motor embodiment, which will not be repeated here.

[0091] With reference to the above Figure 3 As shown in FIG. 1, in one embodiment, a permanent magnet synchronous motor control system is proposed, which at least comprises: a low-dimensional explicit predictive current control device of the permanent magnet synchronous motor.

[0092] Based on the description of the low-dimensional explicit predictive current control device of the permanent magnet synchronous motor, reference is made to the description content of the above embodiment, which will not be repeated here.

[0093] To verify the effectiveness and superiority of the control method of the present application, experimental verification is carried out, and the specific implementation is as follows:

[0094] The permanent magnet synchronous motor d-axis current preset given reference instruction is first stepped from 0 A to -2 A, and then stepped to 0 A; the q-axis current preset given reference instruction is first stepped from 0 A to 5 A, and then stepped to 0 A. By comparing the number of critical regions and the amount of lookup table data contained in the traditional explicit model predictive current control algorithm and the proposed explicit model predictive current control algorithm of the permanent magnet synchronous motor, and the experimental waveforms of the actual d-axis current and q-axis current of the proposed control algorithm, the control effect of the low-dimensional explicit model predictive current control method of the permanent magnet synchronous motor in the present application is verified.

[0095] The experimental results are as follows:

[0096] As shown in Table 1, the number of critical regions and the amount of memory data occupied by the lookup table of the control method and the traditional control method of the present application.

[0097] Table 1

[0098] Critical domain number Memory data amount Conventional method 231 1867 KB Invention method 1 19 KB

[0099] As can be seen from the data in the table, compared with the traditional explicit model predictive current control method, the proposed explicit model predictive current control method can significantly reduce the memory space requirement of the microprocessor.

[0100] As Figure 4The shown are experimental result graphs of the d-axis actual current and the q-axis actual current of the permanent magnet synchronous motor provided in the embodiment. Figure 4 The (a) in the table is an experimental result graph of a traditional method, Figure 4 The (b) in the table is an experimental result graph of the method of the present application. As can be seen from the experimental graph, the method of the present application can guarantee that the current of the permanent magnet synchronous motor realizes good dynamic and steady-state performance.

[0101] Through theoretical analysis and experimental results, it is proved that the low-dimensional explicit predictive current control method of the permanent magnet synchronous motor proposed in the present application can reduce the optimization dimension of the explicit model predictive controller on the basis of guaranteeing good current control performance of the permanent magnet synchronous motor, significantly reduce the microprocessor memory space requirement, and enhance the practicability and competitiveness of the algorithm.

[0102] The above-described embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A low-dimensional explicit predictive current control method for permanent magnet synchronous motor, characterized in that: The method comprises: establishing a discrete model of a permanent magnet synchronous motor which explicitly includes cross-coupling terms of multiplication of speed, current and inductance, to provide a prediction basis of system dynamic characteristics for explicit model predictive control; constructing an explicit model predictive current controller including an explicit optimal control law and an extended state observer; the explicit optimal control law includes a linear prediction model, a value function and system constraints; in the solving process, the explicit optimal control law converts a double current tracking task implemented by a quadratic weighted penalty term in the value function into an implementation of an equality hard constraint; the linear prediction model in the explicit optimal control law is: ， In the formula, and These represent the permanent magnet synchronous motor at sampling time k+1 and sampling time k+1, respectively. The product of electric angular velocity and actual q-axis current; and These represent the permanent magnet synchronous motor at the sampling time. and sampling time The product of electric angular velocity and actual current along the d-axis; and These represent the permanent magnet synchronous motor at the sampling time. and sampling time The actual current along the d-axis; and These represent the permanent magnet synchronous motor at the sampling time. and sampling time The actual q-axis current; Indicates the sampling period; and These represent the nominal inductance values ​​of the d-axis and q-axis of the permanent magnet synchronous motor, respectively. and These represent the permanent magnet synchronous motor at the sampling time. d-axis voltage and q-axis voltage; and These represent the permanent magnet synchronous motor at the sampling time. and sampling time Observations of lumped disturbance in the d-axis current loop; and These represent the permanent magnet synchronous motor at the sampling time. and sampling time Observations of lumped disturbance in the q-axis current loop; and These represent the permanent magnet synchronous motor at the sampling time. and sampling time The d-axis current is preset with a reference command. and These represent the permanent magnet synchronous motor at the sampling time. and sampling time The q-axis current is preset with a reference command. and These represent the permanent magnet synchronous motor at the sampling time. The d-axis and q-axis voltages; and These represent the permanent magnet synchronous motor at the sampling time. The voltage changes along the d-axis and q-axis; the value function in the explicit optimal control law is: ， In the formula, represents a value function value of the permanent magnet synchronous motor at a sampling time point , is an integer variable, represents a prediction time domain, and respectively represent d-axis and q-axis voltage variation amounts of the permanent magnet synchronous motor at a sampling time point . the system constraints in the explicit optimal control law are: ， wherein, and respectively represent the d-axis and q-axis actual currents of the permanent magnet synchronous motor at the sampling time ; and respectively represent the d-axis and q-axis current preset given reference commands of the permanent magnet synchronous motor at the sampling time ; and respectively represent the d-axis and q-axis voltages of the permanent magnet synchronous motor at the sampling time ; represents an inverter output voltage limiting value, A u represents an irregular hexagon constraint matrix; E is a 6-by-1 unit column vector; actual current values and speed values of the permanent magnet synchronous motor are obtained and input into the extended state observer in the explicit model predictive current controller to obtain current loop lumped disturbance observation values of the permanent magnet synchronous motor; the extended state observer in the explicit model predictive current controller is: ， wherein and respectively represent the observed values of the d-axis current of the permanent magnet synchronous motor at the sampling time and the sampling time , and respectively represent the observed values of the q-axis current of the permanent magnet synchronous motor at the sampling time and the sampling time ; , , and respectively represent the first d-axis gain coefficient, the first q-axis gain coefficient, the second d-axis gain coefficient and the second q-axis gain coefficient of the extended state observer. the current preset given reference instruction, the current loop lumped disturbance observation values and the actual current values of the permanent magnet synchronous motor are input into the explicit optimal control law in the explicit model predictive current controller to obtain reference voltages of the permanent magnet synchronous motor, which are used to implement the explicit model predictive current control of the closed-loop feedback of the permanent magnet synchronous motor.

2. The low-dimensional explicit predictive current control method of a permanent magnet synchronous motor according to claim 1, characterized in that: the discrete model of the permanent magnet synchronous motor is: ， wherein, and denote the d-axis actual currents of the permanent magnet synchronous motor at sampling instants and sampling instants respectively; and denote the q-axis actual currents of the permanent magnet synchronous motor at sampling instants and sampling instants respectively; denotes the electrical angular speed of the permanent magnet synchronous motor at sampling instant k; denotes the sampling period; and denote the nominal values of the d-axis and q-axis inductances of the permanent magnet synchronous motor respectively; and denote the d-axis and q-axis voltages of the permanent magnet synchronous motor at sampling instants respectively; and denote the d-axis and q-axis current loop lumped disturbances of the permanent magnet synchronous motor at sampling instants respectively; and are cross-coupling terms of the speed, currents and inductances.

3. The low-dimensional explicit predictive current control method of a permanent magnet synchronous motor according to claim 1, characterized in that: the conversion of the double current tracking task implemented by the quadratic weighted penalty term in the value function into the implementation of the equality hard constraint specifically comprises: the quadratic weighted penalty term in the value function is cancelled and converted into the equality hard constraint to implement the dq-axis double current synchronous tracking, and the priority of the two-axis current tracking does not need to be balanced through a weight factor.

4. The low-dimensional explicit predictive current control method of a permanent magnet synchronous motor according to claim 3, characterized in that: the equality hard constraint includes: two equality constraints are added at the end of the prediction time domain: ， wherein, and respectively represent the d-axis and q-axis actual currents of the permanent magnet synchronous motor at the sampling time ; and respectively represent the d-axis and q-axis current preset given reference commands of the permanent magnet synchronous motor at the sampling time ; represents a prediction time domain.

5. A low-dimensional explicit predictive current control device for permanent magnet synchronous motor, characterized by: the device is used to implement the low-dimensional explicit predictive current control method of the permanent magnet synchronous motor according to any one of claims 1-4, and the device comprises: a current acquisition and coordinate transformation module, which is used to acquire three-phase currents output by an inverter and obtain actual current values of the permanent magnet synchronous motor through coordinate transformation; a speed detection module, which is used to obtain speed values of the permanent magnet synchronous motor; a discrete model of the permanent magnet synchronous motor, which is used to provide a prediction basis of system dynamic characteristics for explicit model predictive current control, and the discrete model of the permanent magnet synchronous motor explicitly includes cross-coupling terms of multiplication of speed, current and inductance; an explicit model predictive current controller, which includes an extended state observer and an explicit optimal control module; the extended state observer is used to obtain current loop lumped disturbance observation values of the permanent magnet synchronous motor by receiving actual current values and speed values of the permanent magnet synchronous motor; the explicit optimal control module is used to receive a current preset given reference instruction, current loop lumped disturbance observation values and actual current values of the permanent magnet synchronous motor, and obtain reference voltages of the permanent magnet synchronous motor based on an explicit optimal control law; the explicit optimal control module includes a linear prediction model, a value function and system constraints; in the solving process, the explicit optimal control law converts a double current tracking task implemented by a quadratic weighted penalty term in the value function into an implementation of an equality hard constraint.

6. A permanent magnet synchronous motor control system, characterized by: The system comprises the low-dimensional explicit predictive current control device of the permanent magnet synchronous motor of claim 5.

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