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

Abstract

The present invention provides a method and system for robust model predictive control of a permanent magnet synchronous motor, belonging to the field of permanent magnet synchronous motor control technology. The method comprises: establishing a state equation of a three-phase permanent magnet synchronous motor with d-q axis current as a state variable when parameters are mismatched; designing an extended state observer to observe and compensate for disturbances in the three-phase permanent magnet synchronous motor; introducing an event triggering technique into a robust model predictive control framework based on the extended state observer, and utilizing a threshold inequality design to adjust the pause and resume of the model predictive control scheme; substituting the predicted current of the d-q axis output by the robust model predictive control framework introducing the event triggering technique into a cost function, and selecting a voltage vector with the smallest cost function value as the optimal voltage vector to achieve control of the permanent magnet synchronous motor. The present invention can maintain a low switching frequency, enhance parameter robustness, and mitigate the influence of model uncertainty, thereby reducing energy loss.

Description

Technical Field

[0001] The present invention relates to the technical field of permanent magnet synchronous motor control, and in particular to a method and system for predictive control of a robust model of a permanent magnet synchronous motor. Background Art

[0002] In recent years, embedded permanent magnet synchronous motors (PMSMs) have been widely used in industrial applications such as CNC machine tools, household appliances, and subway and high-speed rail traction systems due to their high power factor, high efficiency, and high reliability. Compared to traditional PMSM drive control algorithms, such as proportional-integral (PI)-based vector control and direct torque control, model predictive control (MPC) offers strong constraint handling capabilities and adheres to optimal control principles, making it well-suited for industrial applications.

[0003] Common problems of model predictive control in permanent magnet synchronous motor drives are parameter mismatch and excessive switching frequency. The uncertainty of the model will affect the robustness of the motor control, and excessively high switching frequency will cause excessive switching losses and large energy losses. Summary of the Invention

[0004] The present invention provides a method and system for predictive control of a robust model of a permanent magnet synchronous motor, which is used to solve the problem in the prior art that the uncertainty of the model affects the robustness of motor control and causes large energy loss.

[0005] In the first aspect, the present invention provides a robust model predictive control method for a permanent magnet synchronous motor, comprising: establishing a state equation of a three-phase permanent magnet synchronous motor with dq axis current as a state variable when parameters are mismatched; designing an extended state observer to observe and compensate for disturbances in the three-phase permanent magnet synchronous motor; introducing event triggering technology into a robust model predictive control framework based on the extended state observer, and utilizing threshold inequality design to adjust the pause and resumption of the model predictive control scheme; substituting the predicted current of the dq axis output by the robust model predictive control framework that introduces the event triggering technology into the cost function, and selecting the voltage vector with the smallest cost function value as the optimal voltage vector to achieve control of the permanent magnet synchronous motor.

[0006] According to a robust model predictive control method for a permanent magnet synchronous motor provided by the present invention, a state equation of a three-phase permanent magnet synchronous motor with dq axis current as a state variable is established when the parameters are mismatched, including: establishing a voltage equation of the three-phase permanent magnet synchronous motor in the dq coordinate system:

[0007]

[0008] Among them, i d 、i q and u d 、u qare the current and voltage components in the dq coordinate system respectively; R s 、L d 、L q Respectively represent the stator resistance, stator d-axis inductance and stator q-axis inductance; ω e ,Ψ f are the rotor electrical angular velocity and permanent magnet flux linkage respectively;

[0009] The voltage equation is rewritten as a state equation with dq axis current as the state variable:

[0010]

[0011] Among them, x dq (t)=[i d i q ] T ,u dq (t)=[u d u q ] T , E dq (t) = [0 - ω e (t)Ψ f / L q ] T ,

[0012]

[0013] When considering parameter mismatch, the state equation is rewritten as:

[0014]

[0015] Among them, f dq (t)=[f d (t)f q (t)] T , f d (t) and f q (t) represents the total disturbance on the dq axis caused by parameter mismatch.

[0016] According to a robust model predictive control method for a permanent magnet synchronous motor provided by the present invention, an extended state observer is designed to observe and compensate for disturbances of a three-phase permanent magnet synchronous motor, including:

[0017] Establish the extended state observer equation for parameter mismatch:

[0018]

[0019] Among them, z 1dq (t) represents the dq-axis current estimated by the extended state observer; z 2dq (t) represents the total disturbance f dq(t); c1 and c2 are the gains of the extended state observer, c1=2ω c , ω c is the ideal bandwidth of the extended state observer;

[0020] The extended state observer equation is discretized using the first-order forward Euler method to obtain the extended state observer discrete equation:

[0021]

[0022] Among them, T s Indicates the sampling time;

[0023] Considering the interference caused by parameter mismatch, the current equation at time k+2 is:

[0024]

[0025] Where I represents the identity matrix.

[0026] According to a robust model predictive control method for a permanent magnet synchronous motor provided by the present invention, an event triggering technology is introduced into a robust model predictive control framework based on an extended state observer, and a threshold inequality design is used to adjust the pause and resumption of the model predictive control scheme, including: defining a time interval [t n ,t n+1 ), where t n+1 is the trigger time when the activation trigger condition occurs;

[0027] Define the error in estimating the state variables and establish an upper bound on the error:

[0028]

[0029] in, represents the error in estimating the state variables, z 1dq (t n ) represents the estimated state variables that are not updated during the time interval, z 1dq (t) represents the dq-axis current estimated by the extended state observer;

[0030] At the triggering time t n+1 , the upper limit of the error of the state variable can be updated as:

[0031]

[0032] The inequality design of the trigger condition is:

[0033]

[0034] Among them, ζ is the adjustment parameter.

[0035] According to a robust model predictive control method for a permanent magnet synchronous motor provided by the present invention, before the trigger condition is applied to the finite set model predictive control, a discretization process with a sampling period of Ts is performed, specifically:

[0036]

[0037] According to a robust model predictive control method for a permanent magnet synchronous motor provided by the present invention, the predicted current of the dq axes output by the robust model predictive control framework introducing the event triggering technology is substituted into the cost function, specifically:

[0038]

[0039] Among them, g represents the cost function, i d (k+2) and i q (k+2) is the predicted current of the dq axis at time k+2, and is the given value of the dq axis current.

[0040] In a second aspect, the present invention further provides a permanent magnet synchronous motor robustness model predictive control system, comprising:

[0041] The motor mathematical model establishment module is used to establish the state equation of the three-phase permanent magnet synchronous motor with the dq axis current as the state variable when the parameters are mismatched;

[0042] The observation and compensation module is used to design an extended state observer to observe and compensate disturbances of the three-phase permanent magnet synchronous motor;

[0043] The trigger condition setting module is used to introduce event triggering technology into the robust model predictive control framework based on the extended state observer, and use threshold inequality design to adjust the pause and resumption of the model predictive control scheme;

[0044] The control output module is used to substitute the predicted current of the dq axes output by the robust model predictive control framework that introduces event-triggered technology into the cost function, and select the voltage vector with the smallest cost function value as the optimal voltage vector to realize the control of the permanent magnet synchronous motor.

[0045] In a third aspect, the present invention provides 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 program, the steps of the permanent magnet synchronous motor robust model predictive control method as described above are implemented.

[0046] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the permanent magnet synchronous motor robust model predictive control method as described in any one of the above.

[0047] The robust model predictive control method and system for permanent magnet synchronous motors provided by this invention utilize an event-triggered control technique. By introducing a threshold inequality design to regulate the pause and start of the model predictive control scheme, the system switching frequency is reduced, minimizing unnecessary energy loss. Furthermore, considering the impact of model uncertainty on system robustness, an extended state observer (ESO) is combined with a control framework based on event-triggered control techniques. The ESO is used to observe and compensate for disturbances caused by model mismatch, maintaining a low switching frequency while enhancing parameter robustness and mitigating the impact of model uncertainty, thereby reducing energy loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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.

[0049] Figure 1 1 is a flow chart of a robust model predictive control method for a permanent magnet synchronous motor provided by the present invention;

[0050] Figure 2 1 is a schematic structural diagram of the extended state observer provided by the present invention;

[0051] Figure 3 It is a logic block diagram of the event-triggered model predictive control algorithm provided by the present invention;

[0052] Figure 4 It is a logic flow chart of the event-triggered model predictive control algorithm provided by the present invention;

[0053] Figure 5 Schematic diagram of the structure of the robust model predictive control system of the permanent magnet synchronous motor provided by the present invention;

[0054] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0055] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0056] It should be noted that, in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include a ..." do not exclude the presence of other identical elements in the process, method, article or device comprising the elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0057] The terms "first," "second," and the like in this application are used to distinguish similar objects, and are not used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, the objects distinguished by "first," "second," and the like generally refer to a class of objects and do not limit the number of objects. For example, the first object may be one or more.

[0058] The following combination Figures 1-6 The present invention describes a method and system for robust model predictive control of a permanent magnet synchronous motor provided by an embodiment of the present invention.

[0059] Figure 1 FIG. 1 is a flow chart of a robust model predictive control method for a permanent magnet synchronous motor provided by the present invention, such as Figure 1 As shown, including but not limited to the following steps:

[0060] Step 1: Establish the state equation of the three-phase permanent magnet synchronous motor with dq axis current as the state variable when the parameters are mismatched.

[0061] Step 101: First, establish the voltage equation of the three-phase permanent magnet synchronous motor in the dq coordinate system:

[0062]

[0063] Among them, i d 、i q and u d 、uq are the current and voltage components in the dq coordinate system respectively; R s 、L d 、L q Respectively represent the stator resistance, stator d-axis inductance and stator q-axis inductance; ω e ,Ψ f are the rotor electrical angular velocity and permanent magnet flux linkage respectively.

[0064] Step 102: Rewrite the voltage equation into a state equation with dq axis current as the state variable:

[0065]

[0066] Among them, x dq (t)=[i d i q ] T ,u dq (t)=[u d u q ] T , E dq (t) = [0 - ω e (t)Ψ f / L q ] T ,

[0067]

[0068] ω e (t) can be considered as a constant in steady state, that is, ω e (t) = ω e (t-1).

[0069] Step 103: When considering parameter mismatch, the state equation can be rewritten as:

[0070]

[0071] Among them, f dq (t)=[f d (t)f q (t)] T , f d (t) and f q (t) represents the total disturbance on the dq axis caused by parameter mismatch.

[0072] Step 2: Design an extended state observer to observe and compensate disturbances of the three-phase permanent magnet synchronous motor.

[0073] Figure 2 is a schematic diagram of the structure of the extended state observer provided by the present invention, such as Figure 2As shown in Figure 1, the extended state observer (ESO) is used to estimate disturbances and unmodeled dynamics of a system. It estimates disturbances by observing the system's inputs and outputs, thereby improving control performance. Designing the ESO requires determining its parameters, such as bandwidth and gain, to ensure good observation performance and stability. The specific process is as follows:

[0074] Step 201: Establish the extended state observer equation when the parameters are mismatched:

[0075]

[0076] Among them, z 1dq (t) represents the dq-axis current estimated by the extended state observer; z 2dq (t) represents the total disturbance f dq (t); c1 and c2 are the gains of the extended state observer, c1=2ω c , ω c is the ideal bandwidth of the extended state observer;

[0077] When the observer gain is selected correctly, z 1dq (t) will converge to x dq (t), which means that the dq axis current estimated by the observer will be equal to the actual dq axis current; 2dq (t) will converge to f dq (t), which means that the dq-axis disturbance estimated by the observer will be equal to the actual dq-axis disturbance.

[0078] Step 202: Use the first-order forward Euler method to discretize the extended state observer equation to obtain the extended state observer discrete equation, which is specifically:

[0079]

[0080] Among them, T s Indicates the sampling time.

[0081] Step 203: After considering the interference caused by parameter mismatch, according to the extended state observer discrete equation, the current equation at time k+2 can be written as:

[0082] x dq (k+2)=T s (I+A dq (k))z 1dq (k+1)+T s z 2dq (k+1)

[0083] +T s B dq (k)u dq(k+1)+T s E dq (k)

[0084] Among them, the coefficient matrix A dq , B dq , E dq It should be the coefficient matrix A at time k+1 dq (k+1), B dq (k+1), E dq (k+1), but B dq is a constant matrix, so B dq =B dq (k), A dq and E dq The time-varying term in is given by ω e (k+1) determines that, due to the approximation, ω e (k+1)≈ω e (k), so A dq (k+1)≈A dq (k) and E dq (k+1)≈E dq (k); I represents the identity matrix.

[0085] Step 3: Introduce event triggering technology into the robust model predictive control framework based on the extended state observer, and use threshold inequality design to regulate the suspension and resumption of the model predictive control scheme.

[0086] Figure 3 This is a logic block diagram of the event-triggered model predictive control algorithm provided by the present invention. Figure 4 This is a logic flow chart of the event-triggered model predictive control algorithm provided by the present invention, see Figure 3 and Figure 4 Further explanation is given below.

[0087] Step 301: Define the time interval [t n ,t n+1 ), where t n+1 is the trigger time when the activation trigger condition occurs. n and t n+1 The relationship between them is as follows:

[0088]

[0089] Where N represents the number of cycles that the program runs to trigger the preset condition, usually 1; T et The preset sampling time.

[0090] If no trigger condition is activated within this time interval, the control framework sends the control action x(t n) will not be updated. When t=t n+1 When the trigger condition is activated, the control framework will update the control action to x(t n+1 ).

[0091] Step 302: Before determining the trigger condition, it is necessary to define the error of the estimated state variable and determine its upper limit:

[0092]

[0093] Among them, z 1dq (t n ) represents the estimated state variables that are not updated during the time interval, z 1dq (t) represents the dq-axis current estimated using the extended state observer.

[0094] To ensure input-state stability (ISS), the following inequality must be satisfied:

[0095]

[0096] where σ and τ are κ ∞ Class function, the values ​​of σ and τ can be selected based on stability.

[0097] Using the above inequality we can get:

[0098]

[0099] To facilitate subsequent expression, define:

[0100]

[0101] At this point, the inequality can be rewritten as:

[0102]

[0103] At t = t n , so:

[0104]

[0105] The solution to the inequality can be expressed as:

[0106]

[0107] Substituting the definition of Φ into the solution of the inequality, we can obtain the upper limit of the error of the state variable:

[0108]

[0109] Step 303: Determine the triggering condition:

[0110] At the triggering time t n+1 , the upper limit of the error of the state variable can be updated as:

[0111]

[0112] The trigger condition can be designed based on the inequality that satisfies the ISS condition:

[0113]

[0114] Here, ζ is adjustable to balance the tuning performance and the number of switching actions in practical applications.

[0115] Before applying the trigger condition to the finite set model predictive control, a sampling period of T is required. s Discretization of :

[0116]

[0117] When the triggering conditions are not met, the control framework keeps the control signal unchanged. Once the triggering conditions are met, the model predictive control scheme will be activated to update the optimal control signal.

[0118] Step 4: The predicted current of the dq axis output by the robust model predictive control framework with event triggering technology, i d (k+2) and i q (k+2), substitute it into the cost function, and select the voltage vector with the smallest cost function value as the optimal voltage vector to realize the control of the permanent magnet synchronous motor.

[0119] Among them, the predicted current of the dq axis output by the robust model predictive control framework that introduces event triggering technology is substituted into the cost function (price function), specifically:

[0120]

[0121] Among them, g represents the cost function, i d (k+2) and q (k+2) is the predicted current of the dq axis at time k+2, and is the given value of the dq axis current.

[0122] The voltage vector with the smallest cost function value is selected as the optimal voltage vector and applied to the motor by the inverter; at the same time, the predicted current i of the dq axis at this time is d (k+2) and i q (k+2) is used as the state variable at the start of the next event trigger cycle to ensure the continuity of the algorithm.

[0123] The robust model predictive control method for a permanent magnet synchronous motor, provided by this invention, employs an event-triggered control technique. By introducing a threshold inequality design to regulate the pause and start of the model predictive control scheme, the method reduces the system switching frequency and minimizes unnecessary energy loss. Furthermore, considering the impact of model uncertainty on system robustness, the method combines an extended state observer (ESO) with a control framework based on event-triggered control. The ESO is used to observe and compensate for disturbances caused by model mismatch, maintaining a low switching frequency while enhancing parameter robustness and mitigating the impact of model uncertainty, thereby reducing energy loss.

[0124] Figure 5 Schematic diagram of the structure of the permanent magnet synchronous motor robust model predictive control system provided by the present invention, such as Figure 5 As shown, the system includes: a motor mathematical model building module 501, an observation compensation module 502, a trigger condition setting module 503 and a control output module 504;

[0125] The motor mathematical model establishment module 501 is used to establish a state equation of the three-phase permanent magnet synchronous motor with the dq axis current as the state variable when the parameters are mismatched;

[0126] An observation and compensation module 502 is used to design an extended state observer to observe and compensate disturbances of the three-phase permanent magnet synchronous motor;

[0127] A trigger condition setting module 503 is used to introduce event triggering technology into the robust model predictive control framework based on the extended state observer, and use threshold inequality design to adjust the pause and resume of the model predictive control scheme;

[0128] The control output module 504 is used to substitute the predicted current of the dq axes output by the robust model predictive control framework that introduces event triggering technology into the cost function, and select the voltage vector with the smallest cost function value as the optimal voltage vector to realize the control of the permanent magnet synchronous motor.

[0129] It should be noted that the permanent magnet synchronous motor robust model predictive control system provided in the embodiment of the present invention can execute the permanent magnet synchronous motor robust model predictive control method described in any of the above embodiments during specific operation, which will not be described in detail in this embodiment.

[0130] Figure 6 Schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 6As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 may call logic instructions in the memory 630 to execute a robust model predictive control method for a permanent magnet synchronous motor. The method includes: establishing a state equation for a three-phase permanent magnet synchronous motor with dq axis currents as state variables when parameters are mismatched; designing an extended state observer to observe and compensate for disturbances in the three-phase permanent magnet synchronous motor; introducing event triggering technology into a robust model predictive control framework based on the extended state observer, and utilizing threshold inequality design to regulate the pause and resume of the model predictive control scheme; substituting the predicted dq axis currents output by the robust model predictive control framework that introduces the event triggering technology into a cost function, and selecting a voltage vector with the smallest cost function value as the optimal voltage vector to achieve control of the permanent magnet synchronous motor.

[0131] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the permanent magnet synchronous motor robust model predictive control method provided by the above-mentioned embodiments, the method including: establishing a state equation of the three-phase permanent magnet synchronous motor with the dq axis current as the state variable when the parameters are mismatched; designing an extended state observer to observe and compensate for disturbances of the three-phase permanent magnet synchronous motor; introducing event triggering technology into the robust model predictive control framework based on the extended state observer, and using threshold inequality design to adjust the pause and resumption of the model predictive control scheme; substituting the predicted current of the dq axis output by the robust model predictive control framework introducing the event triggering technology into the cost function, and selecting the voltage vector with the smallest cost function value as the optimal voltage vector to achieve control of the permanent magnet synchronous motor.

[0132] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the robust model predictive control method for a permanent magnet synchronous motor provided in the above-mentioned embodiments, the method comprising: establishing a state equation of a three-phase permanent magnet synchronous motor with dq axis current as a state variable when parameters are mismatched; designing an extended state observer to observe and compensate for disturbances of the three-phase permanent magnet synchronous motor; introducing event triggering technology into a robust model predictive control framework based on an extended state observer, and utilizing threshold inequality design to adjust the pause and resumption of the model predictive control scheme; substituting the predicted current of the dq axis output by the robust model predictive control framework introducing the event triggering technology into the cost function, and selecting the voltage vector with the smallest cost function value as the optimal voltage vector to achieve control of the permanent magnet synchronous motor.

[0133] The system 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. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0134] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0135] 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 robust model predictive control method for a permanent magnet synchronous motor, characterized in that: include: Establish a three-phase permanent magnet synchronous motor with parameter mismatch dq The state equation with shaft current as the state variable; Design an extended state observer to observe and compensate disturbances of a three-phase permanent magnet synchronous motor; An event-triggered technique is introduced into the robust model predictive control framework based on the extended state observer, and the threshold inequality design is used to regulate the pause and resume of the model predictive control scheme. The robust model predictive control framework that introduces event triggering technology will output dq Substitute the predicted current of the axis into the cost function, and select the voltage vector with the smallest cost function value as the optimal voltage vector to realize the control of the permanent magnet synchronous motor; Among them, the event triggering technology is introduced into the robust model predictive control framework based on the extended state observer, and the threshold inequality design is used to adjust the suspension and resumption of the model predictive control scheme, including: Define time intervals ,in is the trigger time when the activation trigger condition occurs; Define the error in estimating the state variables and establish an upper bound on the error: ; ； in, represents the error in estimating the state variables, represents the estimated state variables that are not updated during the time interval, represents the estimated value using the extended state observer dq Shaft current; At the trigger moment , the upper limit of the error of the state variable is updated as: ； The inequality design of the trigger condition is: ； in, ζ To adjust the parameters; in, i d 、 i q and u d 、 u q They are dq Current and voltage components in the coordinate system; R s 、 L d 、 L q Represent stator resistance, stator d Shaft inductance and stator q Shaft inductance; ω e 、 Ψ f are the rotor electrical angular velocity and permanent magnet flux linkage respectively; x dq ( t )=[ i d i q ] T ， u dq ( t )=[ u d u q ] T ， E dq ( t )=[0- ω e ( t ) Ψ f / L q ] T ， A dq ( t )= ， B dq (t)= ； The total disturbance f dq ( t ), c 1 is the gain of the extended state observer.

2. The method for robust model predictive control of a permanent magnet synchronous motor according to claim 1, characterized in that: Establish a three-phase permanent magnet synchronous motor with parameter mismatch dq The state equation with shaft current as the state variable includes: Establish a three-phase permanent magnet synchronous motor dq Voltage equation in coordinate system: ； Rewrite the voltage equation as dq The state equation with shaft current as the state variable is: ； When considering parameter mismatch, the state equation is rewritten as: ； in, f dq ( t )=[ f d ( t ) f q ( t )] T , f d ( t )and f q ( t ) represent the parameters mismatch caused by dq Total disturbance on the axis.

3. The method for robust model predictive control of a permanent magnet synchronous motor according to claim 2, characterized in that: Design an extended state observer to observe and compensate disturbances of a three-phase permanent magnet synchronous motor, including: Establish the extended state observer equation for parameter mismatch: ； in, represents the estimated value of the extended state observer dq Shaft current; c 2 is also the gain of the extended state observer, c 1=2 , c 2= , is the ideal bandwidth of the extended state observer; The extended state observer equation is discretized using the first-order forward Euler method to obtain the extended state observer discrete equation: ； in, T s Indicates the sampling time; Considering the interference caused by parameter mismatch, k The current equation at time +2 is: ； in, I Represents the identity matrix.

4. The method for robust model predictive control of a permanent magnet synchronous motor according to claim 1, wherein: Before the trigger condition is applied to the finite set model predictive control, a sampling period of T s The discretization process is as follows: 。 5. The method for robust model predictive control of a permanent magnet synchronous motor according to claim 4, characterized in that: The robust model predictive control framework that introduces event triggering technology will output dq The predicted current of the axis is substituted into the cost function, which is: ； in, represents the cost function, and for dq Axis The predicted current at the moment, and yes dq Set value of shaft current.

6. A robust model predictive control system for a permanent magnet synchronous motor, characterized in that: A method for implementing a robust model predictive control of a permanent magnet synchronous motor as claimed in any one of claims 1 to 5, comprising: The motor mathematical model building module is used to establish the three-phase permanent magnet synchronous motor with dq The state equation with shaft current as the state variable; The observation and compensation module is used to design an extended state observer to observe and compensate disturbances of the three-phase permanent magnet synchronous motor; The trigger condition setting module is used to introduce event triggering technology into the robust model predictive control framework based on the extended state observer, and use threshold inequality design to adjust the pause and resumption of the model predictive control scheme; The control output module is used to output the robust model predictive control framework that introduces event triggering technology. dq The predicted current of the shaft is substituted into the cost function, and the voltage vector with the smallest cost function value is selected as the optimal voltage vector to realize the control of the permanent magnet synchronous motor.

7. 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 permanent magnet synchronous motor robust model predictive control method according to any one of claims 1 to 5 are implemented.

8. 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 method for robust model predictive control of a permanent magnet synchronous motor as claimed in any one of claims 1 to 5 are implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for robust model predictive control of a permanent magnet synchronous motor as claimed in any one of claims 1 to 5 are implemented.

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