Permanent Magnet Synchronous Motor Control Method and System Based on Reference Voltage Prediction Model

Abstract

The present invention discloses a permanent magnet synchronous motor control method and system based on a reference voltage prediction model. The method constructs a discrete disturbance observer to provide an accurate reference amplitude of the stator flux linkage. Secondly, a reference voltage prediction model is constructed by analyzing the relationship between the stator flux linkage amplitude and the stator reference voltage vector to obtain the stator reference voltage vector of the cost function. Finally, the deadbeat principle is used to construct the cost function to select the optimal voltage vector combination. The reference voltage prediction model provided by the present invention removes the inductance and permanent magnet flux linkage parameters, and suppresses the influence of resistance mismatch by multiplying the resistance by the current difference at adjacent moments, effectively enhancing the ability of the system to resist parameter mismatch. In addition, a discrete disturbance observer is used to replace the PI speed controller. The discrete disturbance observer acts on the speed outer loop to provide an accurate reference flux linkage for the prediction model, enhancing the anti-disturbance ability of the system and ensuring fast dynamic performance.

Description

Technical Field

[0001] The present invention belongs to the field of predictive control of permanent magnet synchronous motors, and particularly relates to a control method and system for permanent magnet synchronous motors based on a reference voltage prediction model. Background Art

[0002] Figure 1 The following is the control flow of the control diagram of the traditional model predictive control method based on a PI speed controller: First, the current signal and position signal of the permanent magnet synchronous motor are measured, and the speed signal is obtained through calculation. The speed error is input into the PI controller to obtain the torque reference value, and the maximum torque current ratio control strategy is adopted to obtain the stator flux reference value; and the current signal at time k is input into the full-order observer to obtain the measured value of the stator flux at time k. According to signals such as the motor voltage and current, the model predictive controller predicts the torque and stator flux values at time k + 1. After the prediction is completed, the prediction results are input into the cost function minimization to obtain the optimal switching state sequence.

[0003] In addition to the above traditional predictive control method, exploring other feasible methods to achieve model predictive control is one of the purposes of the present invention. Summary of the Invention

[0004] Different from the traditional control flow, the purpose of the present invention is to explore other control ideas, provide other feasible methods to achieve model predictive control, and further provide a control method and system for permanent magnet synchronous motors based on a reference voltage prediction model. Among them, the method of the present invention constructs a reference voltage prediction model to predict the stator reference voltages of the α-axis and β-axis at the next moment, and constructs a cost function J related to the stator reference voltages of the α-axis and β-axis at the next moment. By minimizing the cost function, the optimal switching state sequence is obtained, realizing the model predictive control of the permanent magnet synchronous motor, and providing a brand-new control idea / control means.

[0005] Therefore, the technical solution of the present invention to achieve the above purpose is as follows:

[0006] On the one hand, a control method for a permanent magnet synchronous motor based on a reference voltage prediction model is provided, including the following steps:

[0007] Step 1: Perform real-time sampling on the permanent magnet synchronous motor to obtain the sampled values of the stator current, stator voltage, and motor speed of the α-axis and β-axis in the αβ coordinate system;

[0008] Step 2: Calculate the stator flux reference values of the α-axis and β-axis in the αβ coordinate system based on the motor speed given value and the motor speed sampled value;

[0009] Step 3: Input the stator flux reference values and stator voltage sampling values of the α-axis and β-axis into the constructed reference voltage prediction model to predict the stator reference voltages of the α-axis and β-axis at the next moment;

[0010] Step 4: Construct a cost function J, and use the stator reference voltages of the α-axis and β-axis at the next moment to obtain the optimal switching state sequence by minimizing the cost function, which is used to control the permanent magnet synchronous motor.

[0011] Further optionally, the formula for obtaining the stator reference voltages (also the predicted voltages) of the α-axis and β-axis at the next moment based on the reference voltage prediction model is as follows:

[0012]

[0013] There is:

[0014] u s (k)=[u α (k),u β (k)] T , i s (k)=[i α (k),i β (k)] T

[0015] In the formula, represents the matrix composed of the stator reference voltages of the α-axis and β-axis , represents the matrix composed of the stator flux reference values of the α-axis and β-axis , ψ s (k) is the stator flux observation value at the kth moment, u s (k) is the stator voltage sampling value u α (k), u β (k) form a matrix, i s (k) is the stator current sampling value i α (k), i β (k) form a matrix, i s (k - 1) and i s (k - 2) respectively correspond to the matrices composed of the stator current sampling values at the (k - 1)th moment and the (k - 2)th moment, R s is the stator resistance, T s is the control period, k corresponds to the sampling moment or sampling point, and T is the matrix transpose mark.

[0016] Model predictive control depends on accurate motor parameters. During the actual operation of the motor, its inductance, resistance, and flux linkage parameters are time-varying. When there is a deviation between the parameters in the predictive control equation and the actual parameters of the motor, it will deteriorate the control performance of the current loop, resulting in phenomena such as current tracking error, increased current harmonics, and increased torque ripple, and even cause the control system to become unstable. Therefore, the actual parameters of the motor seriously affect the control accuracy of predictive current control. The reference voltage prediction model proposed by the technical solution of the present invention removes the inductance and permanent magnet flux linkage parameters through simple calculations and only includes resistance, but suppresses the influence of resistance mismatch by multiplying the resistance by the current difference at adjacent moments. Compared with the existing solutions, the parameter mismatch suppression of the reference voltage prediction model does not use parameter identification, disturbance estimation, or complex look-up tables.

[0017] Further optionally, in step 2, the motor speed given value and the motor speed sampled value are input into the constructed discrete disturbance observer to obtain the reference values of the stator flux linkage on the α-axis and β-axis in the αβ coordinate system.

[0018] Further optionally, the reference value of the q-axis stator flux linkage is calculated using the discrete disturbance observer, and the formula is expressed as:

[0019]

[0020] In the formula, ψ q ref is the reference value of the q-axis stator flux linkage, λ is a constant coefficient, there is n p representing the number of pole pairs, J is the moment of inertia, L q is the q-axis inductance, ψ f is the permanent magnet flux linkage, is the motor speed given value ω e ref the derivative with respect to time, k 1 and k 2 are both constants greater than 0, s is the sliding mode surface function, sign is the sign function, d(t) is the lumped disturbance, α > 0 represents the design parameter, and the speed tracking error ω e is the motor speed sampled value.

[0021] Further optionally, the discrete disturbance observer is provided with a disturbance compensation module, and the disturbance compensation module performs feedforward compensation on the lumped disturbance of the reference value of the q-axis stator flux linkage. The feedforward compensation is expressed as:

[0022]

[0023] In the formula, is the motor speed estimated value, ω e (k) is the motor speed sampled value, and e(k) is the motor speed estimation error. is the lumped disturbance estimate value, γ 1 and γ 2 are both gain parameters, T ds is the speed sampling period, and k corresponds to the sampling moment or sampling point.

[0024] Further optionally, the gain parameters γ 1 and γ 2 are expressed as:

[0025] where ω 0 represents the bandwidth, and there is: T is the matrix transpose mark.

[0026] The discrete disturbance observer provided by the technical solution of the present invention acts on the speed outer loop, provides an accurate reference magnetic flux for the prediction model, can eliminate the influence of motor parameter disturbance and load disturbance, and provides an accurate reference magnetic flux value, that is, effectively suppresses the disturbance.

[0027] Further optionally, the cost function J is expressed as:

[0028]

[0029] where u α|sw , u β|sw are the values of the basic voltage vector u sw of the inverter corresponding to the α-axis and β-axis, sw = 0 to 7, and k corresponds to the sampling moment or sampling point.

[0030] Among them, the cost function aims to minimize the square difference of the objective function, can compare the directly predicted voltage with the candidate voltage vectors, construct a cost function without a weighting factor, and select the best switching state. It should be noted that there are many types of cost functions, and different objectives result in different constructions of the cost function. The cost function is generally divided into two categories, namely, without a weighting factor and with a weighting factor. Among them, the cost function without a weighting factor has the advantage of better steady-state performance and does not require the tuning of weighting factor parameters. The present invention preferably uses the difference between the reference (predicted) voltage and the basic voltage u α|sw , u β|sw to be minimized as the objective, makes the predicted voltage approach the basic voltage, and the stator reference voltage is essentially the predicted voltage. In other feasible embodiments, the cost function can be adjusted according to the application objective, and the present invention does not specifically limit this.

[0031] On the other hand, a system based on the above control method provided by the present invention includes:

[0032] A sampling module, which is used to perform real-time sampling on a permanent magnet synchronous motor to obtain the stator current sampling values, stator voltage sampling values, and motor speed sampling values on the α-axis and β-axis in the αβ coordinate system;

[0033] A stator flux reference value calculation module, which is used to calculate the stator flux reference values on the α-axis and β-axis in the αβ coordinate system based on the given motor speed value and the motor speed sampling value;

[0034] A stator reference voltage prediction module, which is used to input the stator flux reference values on the α-axis and β-axis and the stator voltage sampling values into a constructed reference voltage prediction model to predict the stator reference voltages on the α-axis and β-axis at the next moment;

[0035] A control module, which is used to construct a cost function J, and use the stator reference voltages on the α-axis and β-axis at the next moment to obtain an optimal switching state sequence by minimizing the cost function, for controlling the permanent magnet synchronous motor.

[0036] In a third aspect, a computer-readable storage medium provided by the present invention stores a computer program, and the computer program is called by a processor to implement:

[0037] The steps of a control method for a permanent magnet synchronous motor based on a reference voltage prediction model.

[0038] Beneficial effects

[0039] 1. The control method for the permanent magnet synchronous motor provided by the technical solution of the present invention is different from the traditional control process. The technical solution of the present invention analyzes the relationship between the stator flux amplitude and the stator reference voltage vector, constructs a reference voltage prediction model to predict the stator reference voltages on the α-axis and β-axis at the next moment, and constructs a cost function J related to the stator reference voltages on the α-axis and β-axis at the next moment. By minimizing the cost function, an optimal switching state sequence is obtained to realize the model predictive control of the permanent magnet synchronous motor, providing a new control idea / control means.

[0040] 2. The reference voltage prediction model proposed by the further optimized technical solution of the present invention eliminates the inductance and permanent magnet flux parameters through simple operations, and suppresses the influence of resistance mismatch by multiplying the resistance by the current difference between adjacent moments, effectively enhancing the ability of the system to resist parameter mismatch.

[0041] 3. The further optimized technical solution of the present invention introduces a discrete disturbance observer to calculate the stator flux reference values on the α-axis and β-axis in the αβ coordinate system, replacing the PI speed controller in the traditional control process. In particular, a disturbance compensation module is provided to perform feedforward compensation on the lumped disturbance of the q-axis stator flux reference value, enhancing the anti-disturbance ability of the system and ensuring fast dynamic performance. Description of the drawings

[0042] Figure 1 It is a block diagram of traditional model predictive control based on a PI speed controller;

[0043] Figure 2 It is the overall control block diagram of the permanent magnet synchronous motor control method provided by the technical solution of the present invention;

[0044] Figure 3 It is the internal structure diagram of the discrete disturbance observer;

[0045] Figure 4 It is the experimental result of the comparison of the settling time between the traditional method and the method described in the present invention under the condition that the torque is increased from 0 to 4 N·m; among them, (a) is the schematic diagram of the result of traditional model predictive control, and (b) is the schematic diagram of the result of model predictive control of the present invention;

[0046] Figure 5 It is the experimental result of the settling time between the traditional method and the method described in the present invention under the condition that the rotational speed is increased from 500 rpm to 1000 rpm; among them, (a) is the schematic diagram of the result of traditional model predictive control, and (b) is the schematic diagram of the result of model predictive control of the present invention;

[0047] Figure 6 It is the experimental result of the steady-state operation between the traditional method and the method described in the present invention under the condition of a rotational speed of 500 rpm and a torque of 4 N·m; among them, (a) is the schematic diagram of the result of traditional model predictive control, and (b) is the schematic diagram of the result of model predictive control of the present invention. Detailed implementation manners

[0048] A permanent magnet synchronous motor control method based on a reference voltage prediction model provided by the technical solution of the present invention is optimized and improved on the basis of the traditional model predictive control method shown in Figure 1 , and a new technical idea / technical means is proposed to realize model predictive control. The present invention will be further described below in conjunction with embodiments.

[0049] Embodiment 1:

[0050] A permanent magnet synchronous motor control method based on a reference voltage prediction model provided in this embodiment specifically includes the following steps:

[0051] Step 1: Perform real-time sampling on the permanent magnet synchronous motor to obtain the stator currents i α (k), i β (k) and stator voltages u α (k), u β (k) on the α-axis and β-axis in the αβ coordinate system, as well as the sampled value ω e (k) of the motor speed, where k corresponds to the sampling moment or sampling point.

[0052] Step 2: Calculate the reference values of the stator flux linkage on the α-axis and β-axis in the αβ coordinate system based on the given value of the motor speed and the sampled value of the motor speed. In this embodiment, it is preferable to construct a discrete disturbance observer for calculating the reference values of the stator flux linkage ψ α ref , ψ β ref , that is, the discrete disturbance observer replaces the Figure 1 PI speed controller in. However, it should be noted that in other feasible embodiments, without departing from the technical idea of Steps 1 - 4, other existing methods can also be used to obtain the reference values of the stator flux linkage ψ α ref , ψ β ref , for example Figure 1 PI speed controller, which also falls within the protection scope of the present invention, and the present invention does not specifically limit this.

[0053] The discrete disturbance observer of this embodiment includes a sliding mode controller and a disturbance compensation module.

[0054] When using for control measurement, the definition of the reference value of the d-axis stator flux linkage ψ f is the flux linkage of the permanent magnet. According to the relationship between the q-axis stator flux linkage and the torque, the q-axis stator flux linkage can be expressed as:

[0055]

[0056]

[0057] where, T e represents the electromagnetic torque, and the superscript ref represents the reference value; T l represents the load torque; J is the moment of inertia; n p represents the number of pole pairs; L q represents the q-axis flux linkage, and ω e represents the motor speed. It should be noted that formulas (1) and (2) and the following formulas are used to reflect the mathematical relationship between parameters, so the sampling time k is not added to the expressions.

[0058] Combining equations (1) and (2), we have:

[0059]

[0060] where, η = n p / J.

[0061] When considering the influence of motor parameter errors:

[0062]

[0063] The definitions of Δλ and Δη are the error values between the normal parameters and the error parameters of the motor. It can be seen from the above formula (4) that parameter mismatch and load disturbance will affect the accuracy of the flux reference value. Therefore, this embodiment proposes Figure 3 the discrete disturbance observer shown in the figure, designs a sliding mode controller to estimate the q-axis stator flux reference value, and performs feedforward compensation by observing the lumped disturbance.

[0064] The speed control equation considering the lumped disturbance can be written as:

[0065]

[0066] where d(t) is the lumped disturbance.

[0067] The speed tracking error ε is expressed as:

[0068]

[0069] where and ω e are the reference value of the motor speed and the actual value of the motor speed respectively.

[0070] Combined with Equation (6), the derivative of the speed tracking error can be rewritten as:

[0071]

[0072] The overall terminal sliding mode surface s is expressed as (8), which has a simple structure and excellent stability:

[0073]

[0074] where α > 0 represents the design parameter. Applying the exponential reaching law, there is:

[0075]

[0076] where k 1 and k 2 are both constants greater than 0. Combining Equations (5)-(9), the actual q-axis stator flux reference value can be expressed as:

[0077]

[0078] In order to suppress the chattering problem of the sign function in this embodiment, it is preferable to apply a continuous or smooth function to replace the sign function sign:

[0079]

[0080] Among them, ζ is a constant greater than 0, and the specific value is an empirical value.

[0081] In this embodiment, it is preferably provided with a disturbance compensation module in the discrete disturbance observation value, that is, the lumped disturbance d(t) needs to be compensated. The second-order system is constructed as:

[0082]

[0083] Among them, is the estimated value of the motor speed, is the estimated value of the lumped disturbance; γ 1 and γ 2 are gains, and a scaling and bandwidth parameterization method is adopted. The gain parameters are expressed as:

[0084]

[0085] Among them, ω 0 represents the bandwidth. Using the forward Euler discretization method to convert (12) to the discrete domain:

[0086]

[0087] It should be noted that due to the discretization process, the variable is added at the sampling time k. e(k) is the estimated error of the motor speed.

[0088] Therefore, the transfer function of the second-order system is shown as:

[0089]

[0090] According to the July criterion, the stability condition is derived as:

[0091]

[0092] Combining (13) and (15), the stability condition is expressed as:

[0093]

[0094] In practice, a certain margin is usually reserved to ensure stability. Therefore, the upper limit is designed to be 1.5 / T ds .

[0095] Among them, the conversion formulas of the d-axis and q-axis stator flux reference values ψ d ref , ψ q ref and the α-axis and β-axis stator flux reference values ψ α ref , ψ β ref are as follows:

[0096]

[0097] Among them, θ is the rotor angle of the motor.

[0098] Step 3: Input the stator flux reference values and stator voltage sampling values of the α-axis and β-axis into the constructed reference voltage prediction model to predict the stator reference voltages of the α-axis and β-axis at the next moment. The design idea of the reference voltage prediction model in this embodiment is as follows:

[0099] Design of the reference voltage prediction model:

[0100] According to the traditional model prediction model method, the discretized output voltage vector can be expressed as:

[0101]

[0102] Among them, u s (k)=[u α (k), u β (k)] T , i s (k)=[i α (k), i β (k)] T ,

[0103] T s is the control period, which means that the values sampled at time 0 are used for calculation, and the next moment until T s is sampled again, and the sampled values are used for calculation, and the interval time is T s . The voltage vector at time (k + 1)T s can be expressed as:

[0104]

[0105] Among them, is the predicted value of the stator flux at time (k + 2)T s .

[0106] By subtracting the above two equations, the predicted voltage at time kT s is simplified to:

[0107]

[0108] The superscript p represents the predicted value. Due to the inherent one-shot delay in the actual digital system, the trajectory of the control variable cannot be controlled as expected, and there will be some deviations after the control period. To suppress the influence of the one-shot delay, the stator flux is used to calculate the output voltage at time k. T s Rs i s The item is very small in the steady state and can be ignored (T s R s i s ≈0). At the same time, the stator flux linkage is predicted based on the deadbeat control principle and its reference value are equal.

[0109] According to equations (19) and (20), the output voltage vector can be expressed as

[0110]

[0111] where is the expected value of the output voltage at time (k + 1)T s , ψ s (k) is the flux linkage observation value at kT obtained through a full-order observer s . The full-order observer is existing content, so no specific statement is made about it.

[0112] The current measurement value at time k + 1 is estimated by Lagrange extrapolation to obtain

[0113]

[0114] Therefore, the final output voltage vector can be expressed as:

[0115]

[0116] where, after obtaining at time k through a discrete disturbance observer,

[0117] s

[0118]

[0119] Step 4: Construct a cost function J, and use the stator reference voltages on the α-axis and β-axis at the next moment to obtain the optimal switching state sequence by minimizing the cost function for controlling the permanent magnet synchronous motor.

[0119] According to equation (23), the stator reference voltage vector can make the torque and stator flux track their reference values within a control period. Therefore, the cost function is defined as:

[0120]

[0121] Among them, u sw represents the basic voltage vector of the inverter, where sw = 0 to 7. u α|sw and u β|sw are the α-axis and β-axis components of the candidate voltage vector. Compared with the cost function of the traditional model predictive control method, the proposed cost function does not require a weighting factor.

[0122] Experimental verification:

[0123] Figures 4 to 6 are the experimental results of the traditional model predictive control method based on the PI speed controller and the enhanced model predictive control method described in the present invention, including three comparison conditions: loading, acceleration process, and motor parameter mismatch.

[0124] Figure 4 are the experimental comparison results of the two methods under the condition that the load torque suddenly increases from 0 to 4 N·m at a speed of 500 rpm. The settling time of the control method described in the present invention is 2 s, while the settling time of the traditional model predictive method exceeds 4.4 s, and the settling time increases by more than 110% compared with the control method described in the present invention.

[0125] Figure 5 are the experimental comparison results of the two methods under the condition of ramp acceleration of the speed from 500 rpm to 1000 rpm at a torque of 4 N·m. The settling time of the control method described in the present invention is 2 s, while the settling time of the traditional model predictive method is 2.6 s. The control method described in the present invention has excellent dynamic response performance.

[0126] Figure 6 are the comparison experimental results of the two methods under the condition of motor parameter mismatch, where the speed is 500 rpm and the load torque is 4 N·m. When the motor parameters are reduced by 50%, the torque ripple and current THD of the traditional model predictive method are as high as 2.2 N·m and 32.75% respectively. The torque ripple and current THD of the control method described in the present invention are reduced by 45.5% and 64.0% respectively.

[0127] Example 2:

[0128] This embodiment provides a system based on the above permanent magnet synchronous motor control method, including: a sampling module, a stator flux reference value calculation module, a stator reference voltage prediction module, and a control module.

[0129] Among them, the sampling module is used to perform real-time sampling on the permanent magnet synchronous motor to obtain the stator current sampling values, stator voltage sampling values, and motor speed sampling values on the α-axis and β-axis in the αβ coordinate system; the stator flux reference value calculation module is used to calculate the stator flux reference values on the α-axis and β-axis in the αβ coordinate system based on the motor speed given value and the motor speed sampling value; the stator reference voltage prediction module is used to input the stator flux reference values on the α-axis and β-axis and the stator voltage sampling values into the constructed reference voltage prediction model to predict the stator reference voltages on the α-axis and β-axis at the next moment; the control module is used to construct a cost function J, and use the stator reference voltages on the α-axis and β-axis at the next moment to obtain the optimal switching state sequence by minimizing the cost function, for controlling the permanent magnet synchronous motor.

[0130] It should be understood that the implementation process of each module can refer to the content description of the foregoing method. The above division of functional modules is only a division of logical functions. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. At the same time, the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0131] Embodiment 3:

[0132] This embodiment provides a computer-readable storage medium storing a computer program, and the computer program is called by a processor to implement: the steps of a method for controlling a permanent magnet synchronous motor based on a reference voltage prediction model. Specifically, it performs:

[0133] Step 1: Perform real-time sampling on the permanent magnet synchronous motor or obtain the real-time sampling values of the permanent magnet synchronous motor to obtain the stator current sampling values, stator voltage sampling values, and motor speed sampling values on the α-axis and β-axis in the αβ coordinate system;

[0134] Step 2: Calculate the stator flux reference values on the α-axis and β-axis in the αβ coordinate system based on the motor speed given value and the motor speed sampling value;

[0135] Step 3: Input the stator flux reference values on the α-axis and β-axis and the stator voltage sampling values into the constructed reference voltage prediction model to predict the stator reference voltages on the α-axis and β-axis at the next moment;

[0136] Step 4: Construct a cost function J, and use the stator reference voltages on the α-axis and β-axis at the next moment to obtain the optimal switching state sequence by minimizing the cost function, for controlling the permanent magnet synchronous motor.

[0137] Please refer to the description of the foregoing method for the specific implementation process of each step.

[0138] The readable storage medium is a computer-readable storage medium, which may be an internal storage unit of the controller described in any of the foregoing embodiments, such as the hard disk or memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk equipped on the controller, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the readable storage medium may also include both the internal storage unit of the controller and the external storage device. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store the data that has been output or is to be output.

[0139] Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing readable storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc.

[0140] It should be emphasized that the examples described in the present invention are illustrative rather than restrictive. Therefore, the present invention is not limited to the examples described in the specific embodiments. Any other embodiments obtained by those skilled in the art according to the technical solution of the present invention, without departing from the purpose and scope of the present invention, whether modified or replaced, equally belong to the protection scope of the present invention.

Claims

1. A permanent magnet synchronous motor control method based on a reference voltage prediction model, characterized in that: It includes the following steps: Step 1: Perform real-time sampling on the permanent magnet synchronous motor to obtain the stator current sampling values, stator voltage sampling values, and motor speed sampling values on the α-axis and β-axis in the αβ coordinate system; Step 2: Calculate the stator flux reference values on the α-axis and β-axis in the αβ coordinate system based on the motor speed given value and the motor speed sampling value; Step 3: Input the stator flux reference values on the α-axis and β-axis and the stator voltage sampling value into the constructed reference voltage prediction model to predict the stator reference voltages on the α-axis and β-axis at the next moment; Step 4: Construct a cost function J, and use the stator reference voltages on the α-axis and β-axis at the next moment to obtain the optimal switching state sequence by minimizing the cost function for controlling the permanent magnet synchronous motor; The formula for obtaining the stator reference voltages on the α-axis and β-axis at the next moment based on the reference voltage prediction model is as follows: There is: u s (k) = [u α (k), u β (k)] T , i s (k) = [i α (k), i β (k)] T wherein, represents the stator reference voltages of the α-axis and β-axis to form a matrix, represents the reference values of the stator flux linkage of the α-axis and β-axis to form a matrix, ψ s (k) is the observed value of the stator flux linkage at the k-th moment, u s (k) is the sampled value of the stator voltage u α (k), u β (k) to form a matrix, i s (k) is the sampled value of the stator current i α (k), i β (k) to form a matrix, i s (k - 1) and i s (k - 2) respectively correspond to the matrices formed by the sampled values of the stator current at the (k - 1)-th moment and the (k - 2)-th moment, R s is the stator resistance, T s is the control period, k corresponds to the sampling moment or sampling point, and T is the matrix transpose marker; wherein, in Step 2, the motor speed given value and the motor speed sampling value are input into the constructed discrete disturbance observer to obtain the stator flux reference values on the α-axis and β-axis in the αβ coordinate system, and the q-axis stator flux reference value is calculated using the discrete disturbance observer, and the formula is expressed as: where ψ q ref is the reference value of the q - axis stator flux linkage, λ is a constant coefficient, there exists n p representing the number of pole pairs, J is the moment of inertia, L q is the q - axis inductance, ψ f is the permanent - magnet flux linkage, is the given value of the motor speed ω e ref the derivative with respect to time, k 1 and k 2 are both constants greater than 0, s is the sliding - mode surface function, sign is the sign function, d(t) is the lumped disturbance, α > 0 represents the design parameter, the speed tracking error ω e is the sampled value of the motor speed.

2. The method according to claim 1, characterized in that: The discrete disturbance observer is provided with a disturbance compensation module, and the disturbance compensation module performs feedforward compensation on the lumped disturbance of the q-axis stator flux reference value, and the feedforward compensation is expressed as: Wherein, is the estimated value of the motor speed, ω e (k) is the sampled value of the motor speed, and e(k) is the estimated error of the motor speed, is the estimated value of the lumped disturbance, γ 1 and γ 2 are both gain parameters, T ds is the speed sampling period, and k corresponds to the sampling moment or sampling point.

3. The method according to claim 2, characterized in that: Gain parameter γ 1 and γ 2 is expressed as: ω 0 represents the bandwidth, and there is: T is the matrix transpose symbol.

4. The method according to claim 1, characterized in that: The cost function J is expressed as: Among them, u α|sw , u β|sw is the basic voltage vector u of the inverter sw corresponding to the values on the α-axis and β-axis. sw = 0 to 7, and k corresponds to the sampling time or sampling point is the stator reference voltage on the α-axis and β-axis at the (k + 1)-th moment.

5. A system based on the method according to any one of claims 1-4, characterized in that: It includes: A sampling module for performing real-time sampling on the permanent magnet synchronous motor to obtain the stator current sampling values, stator voltage sampling values, and motor speed sampling values on the α-axis and β-axis in the αβ coordinate system; A stator flux reference value calculation module for calculating the stator flux reference values on the α-axis and β-axis in the αβ coordinate system based on the motor speed given value and the motor speed sampling value; A stator reference voltage prediction module for inputting the stator flux reference values on the α-axis and β-axis and the stator voltage sampling value into the constructed reference voltage prediction model to predict the stator reference voltages on the α-axis and β-axis at the next moment; A control module for constructing a cost function J, and using the stator reference voltages on the α-axis and β-axis at the next moment to obtain the optimal switching state sequence by minimizing the cost function for controlling the permanent magnet synchronous motor.

6. A computer-readable storage medium, characterized in that: It stores a computer program, and the computer program is called by a processor to implement: The steps of the method according to any one of claims 1-4.

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