Anti-interference motor speed regulation method and system based on motor vector modulation

Abstract

The invention relates to an anti-interference motor speed regulation method and system based on motor vector modulation, and belongs to the technical field of motor control. The method comprises the following steps: acquiring initial position information of a rotor through a motor mathematical model and a photoelectric encoder, generating initial state sampling data, and transmitting the initial state sampling data to a vector control module; constructing a discrete space state model according to the initial state sampling data of the vector control module, and predicting a system state according to the discrete space state model to obtain an initial state control quantity; tracking an input signal through a differentiator to dynamically monitor the real-time running state of the motor, and dynamically adjusting a prediction model through a state feedback controller and a cascade optimization model according to a monitoring result; and the frequency conversion speed regulation module receives the output of the model prediction unit and the prediction correction unit and controls the on-off state of the inverter so as to regulate the power supply voltage and frequency of the motor. Accurate control over a motor speed regulation system is achieved, and the operation efficiency and stability of the motor are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of motor control, and in particular relates to an anti-interference motor speed regulation method and system based on motor vector modulation. Background Art

[0002] The motor is regarded as an important driving source of precision control system. Its speed control system is used to realize the controlled object's rotation angle and displacement to accurately track the change of input command, and reflect the performance of the speed control system through the main performance indicators such as speed ratio, static error, torque pulsation rate and system bandwidth. In automatic control theory, accurate mathematical model of the controlled object is the prerequisite for perfect control. As the controlled object of the speed control system, the mathematical model of the permanent magnet synchronous motor is very complex. Generally, it has different degrees of nonlinear factors, and the parameters are coupled with each other, which is difficult to express in accurate mathematical language. Due to the nonlinear characteristics of the inverter, especially at low speed, it is easy to cause torque pulsation, which will affect the stability and comfort of the motor. The nonlinear characteristics of the inverter will distort the current and affect the running performance of the motor. At high speed, weak magnetic control is needed to maintain the high efficiency and high performance of the motor. However, the implementation of weak magnetic control is relatively complex, and precise control strategies and algorithms are required to ensure the stability and response speed of the system. The permanent magnet synchronous motor speed control system needs to take into account the dynamic performance and anti-interference of the system. For example, in dual closed-loop vector control, an adaptive inverse control method is needed to improve the system's anti-disturbance performance and stability.

[0003] With the diversification and complexity of application scenarios, existing technologies are evaluated and optimized through methods such as vector control, sensorless control, direct torque control and neural network control to achieve more accurate and efficient control. In order to reduce the volume, cost and inconvenience of installation of mechanical position or speed sensors, sensorless control technology is widely used. However, the key to achieving sensorless control lies in the accurate estimation of the motor rotor position, which puts higher requirements on the control algorithm; other control methods such as internal model control (IMC) and extended state observer (ESO) composite control can effectively suppress periodic interference and non-periodic slow-changing interference, but there are still problems such as large data volume and poor effect on simultaneously suppressing periodic interference and non-periodic slow-changing interference of permanent magnet synchronous motors; although the improved active disturbance suppression control (I-ADRC) enhances the interference suppression ability of the permanent magnet synchronous motor speed controller, and has a smaller steady-state error and stronger interference suppression ability, the nonlinear function used in its design has obvious inflection points and non-smoothness, which will reduce the robustness and anti-interference ability of the system.

[0004] The main problems faced by motor-based speed control systems at this stage include: insufficient anti-interference ability of speed control methods based on model predictive control, the impact of feedforward parameter accuracy on system performance, and the system robustness problem caused by the design of nonlinear functions in the improved control strategy. Specifically, the integral link is embedded in the model predictive control strategy, which has a strong suppression ability for slow-changing disturbances such as motor parameter changes. However, the controller current sampling error, inverter dead zone effect and permanent magnet flux harmonics will introduce periodic disturbances in the closed-loop system, and these disturbances do not have accurate mathematical models, so the classical continuous set prediction model cannot model them, resulting in periodic fluctuations in the motor output speed and torque, which affects the steady-state performance of the continuous set model predictive control strategy. These problems require further research and optimization to improve the overall performance and reliability of the system. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides an anti-interference motor speed regulation method and system based on motor vector modulation; The purpose of the present invention can be achieved through the following technical solutions: An anti-interference motor speed control method based on motor vector modulation, comprising: According to the stator winding of the motor, the current space vector, the voltage space vector, and the flux space vector are obtained through the motor mathematical model; the initial position information of the rotor is obtained through the photoelectric encoder, and the current space vector, the voltage space vector, the flux space vector and the initial position information of the rotor are combined with the data sampling time to generate initial state sampling data and transmit it to the vector control module; Constructing a discrete space state model according to the initial state sampling data of the vector control module, and predicting the system state according to the discrete space state model to obtain the initial state control quantity; The real-time operating status of the motor is dynamically monitored by tracking the input signal through the differentiator, and the prediction model is dynamically adjusted through the state feedback controller and cascade optimization model according to the monitoring results; The outputs of the model prediction unit and the prediction correction unit are received through the variable frequency speed regulation module, and a corresponding PWM signal is generated to control the switching state of the inverter, thereby adjusting the power supply voltage and frequency of the motor.

[0006] Specifically, the motor mathematical model is a basic mathematical model of a three-phase voltage equation, a flux equation, a mechanical angular displacement equation and a mechanical motion equation in a natural coordinate system.

[0007] Specifically, the vector control module includes a vector converter and a current controller. The vector converter is responsible for converting the three-phase current and voltage signals of the motor into two-phase orthogonal current and voltage signals; the current controller calculates the required voltage vector based on the output of the vector converter and the output of the model prediction unit and the prediction correction unit to generate a PWM signal to drive the motor.

[0008] Specifically, the discrete space state model performs predictive control based on the parameter model, and uses the discrete difference equation to describe the controlled object subject to random interference through the CARIMA model. The specific expression is: , Among them, u(k) is the control system input, y(k) is the control system output, ξ(k) is the random noise sequence affected by the interference signal, and z -1 Indicates a delay operation on the time series, A(z -1 ) is the delayed operation autoregressive term, B(z -1 ) is the moving average term of the delayed operation.

[0009] Specifically, the differentiator tracks the input signal according to the approximate discrete fastest feedback control function by solving the discrete form of the fastest tracking differentiator, and the discrete form of the fastest tracking differentiator is expressed as: , in, f is the fastest feedback control function, which realizes fast and accurate tracking of the input signal by adjusting the system state variables, r is the speed factor, sign is the sign function used to judge the positive and negative of the signal, x1(k) is the tracked input signal, v(k) is the input signal, x2(k) is the differential signal of the tracked input signal, x1(k+1) and x2(k+1) are the signals corresponding to the next time step, and h is the sampling time step.

[0010] Specifically, the state feedback controller includes a state observer and a compensator. The state observer is used to integrate the voltage and current measurement values ​​of the motor and the predicted values ​​of the motor model; the compensator is used to correct the control error caused by the change of motor parameters or external load disturbance, and ensure the stable performance of the motor by adjusting the control signal in real time.

[0011] Specifically, the cascade optimization model divides the motor control into a speed control layer and a current control layer through a hierarchical control strategy; the speed control layer is used to use the output of the model prediction unit as a reference speed, and generate a torque command for the motor through a speed controller using a PID control algorithm; the current control layer is used to convert the torque command into a stator current command for the motor through the current controller of the vector control module, to ensure that the motor operates at a predetermined torque and speed.

[0012] Specifically, the variable frequency speed regulation module adopts a dual PWM variable frequency speed regulation system, expands the speed regulation range through two independent pulse width modulation controllers, adopts a two-level inverter as the main circuit, converts DC power into AC power through PWM technology, and drives the permanent magnet synchronous motor to work.

[0013] Specifically, the dual PWM variable frequency speed regulation system is composed of a grid-side PWM rectifier and a machine-side PWM inverter connected via a DC bus. The grid-side PWM rectifier realizes a two-way flow of energy with the power grid, and the machine-side PWM inverter is responsible for converting DC power into AC power with adjustable frequency and voltage to drive the motor to operate; the inverter and the motor are equivalent to resistors connected in parallel at both ends of the DC bus capacitor, and the DC bus voltage is equivalent to a constant voltage source as the input of the system.

[0014] An anti-interference motor speed control system based on motor vector modulation includes: a data acquisition unit, a model prediction unit, a prediction correction unit, and a power control unit; The data acquisition unit is used to obtain the current space vector, voltage space vector, and flux space vector according to the stator winding of the motor through the motor mathematical model; obtain the rotor initial position information through the photoelectric encoder, combine the current space vector, voltage space vector, flux space vector and the rotor initial position information with the data sampling time to generate initial state sampling data and transmit it to the vector control module; The model prediction unit constructs a discrete space state model according to the initial state sampling data of the vector control module, and predicts the system state according to the discrete space state model to obtain the initial state control quantity; The prediction correction unit dynamically monitors the real-time operating state of the motor by tracking the input signal through a differentiator, and dynamically adjusts the prediction model through a state feedback controller and a cascade optimization model according to the monitoring results; The power control unit receives the outputs of the model prediction unit and the prediction correction unit through the variable frequency speed regulation module, generates a corresponding PWM signal to control the switching state of the inverter, and further adjusts the power supply voltage and frequency of the motor.

[0015] The beneficial effects of the present invention are as follows: through the precise data acquisition unit, the current, voltage and flux space vector of the motor can be obtained in real time, and combined with the initial position information of the rotor, accurate initial state sampling data can be generated. This provides a reliable basis for subsequent vector control and ensures the accuracy of control; the introduction of the model prediction unit enables the system to predict the future state of the motor according to the discrete space state model, so as to make adjustments in advance, which greatly improves the response speed and control accuracy of the motor speed control system. The prediction correction unit dynamically monitors the real-time operating state of the motor through the differentiator, and dynamically adjusts the prediction model according to the monitoring results, which enables the system to adapt to various changes in the operation of the motor and enhances the robustness and adaptability of the system. The power control unit generates a PWM signal through the variable frequency speed control module, controls the switching state of the inverter, and adjusts the power supply voltage and frequency of the motor. This control method not only improves the energy efficiency of the motor, but also reduces the energy loss in the operation of the motor and prolongs the service life of the motor. The precise control of the motor speed control system is achieved, and the operating efficiency and stability of the motor are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 A schematic flow chart of an anti-interference motor speed regulation method based on motor vector modulation of the present invention; Figure 2 This is the basic structure diagram of model predictive control; Figure 3 It is the schematic diagram of the energy feedback circuit of the dual PWM variable frequency speed regulation system. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0019] See also Figure 1-3 , an anti-interference motor speed control method based on motor vector modulation, comprising: According to the stator winding of the motor, the current space vector, the voltage space vector, and the flux space vector are obtained through the motor mathematical model; the initial position information of the rotor is obtained through the photoelectric encoder, and the current space vector, the voltage space vector, the flux space vector and the initial position information of the rotor are combined with the data sampling time to generate initial state sampling data and transmit it to the vector control module; Constructing a discrete space state model according to the initial state sampling data of the vector control module, and predicting the system state according to the discrete space state model to obtain the initial state control quantity; The real-time operating status of the motor is dynamically monitored by tracking the input signal through the differentiator, and the prediction model is dynamically adjusted through the state feedback controller and cascade optimization model according to the monitoring results; The outputs of the model prediction unit and the prediction correction unit are received through the variable frequency speed regulation module, and a corresponding PWM signal is generated to control the switching state of the inverter, thereby adjusting the power supply voltage and frequency of the motor.

[0020] Specifically, the motor mathematical model is a basic mathematical model of a three-phase voltage equation, a flux equation, a mechanical angular displacement equation and a mechanical motion equation in a natural coordinate system.

[0021] Specifically, the vector control module includes a vector converter and a current controller. The vector converter is responsible for converting the three-phase current and voltage signals of the motor into two-phase orthogonal current and voltage signals; the current controller calculates the required voltage vector based on the output of the vector converter and the output of the model prediction unit and the prediction correction unit to generate a PWM signal to drive the motor.

[0022] Specifically, the discrete space state model performs predictive control based on the parameter model, and uses the discrete difference equation to describe the controlled object subject to random interference through the CARIMA model. The specific expression is: , Among them, u(k) is the control system input, y(k) is the control system output, ξ(k) is the random noise sequence affected by the interference signal, and z -1 Indicates a delay operation on the time series, A(z -1 ) is the delayed operation autoregressive term, B(z -1 ) is the moving average term of the delayed operation.

[0023] Specifically, the differentiator tracks the input signal according to the approximate discrete fastest feedback control function by solving the discrete form of the fastest tracking differentiator, and the discrete form of the fastest tracking differentiator is expressed as: , in, f is the fastest feedback control function, which realizes fast and accurate tracking of the input signal by adjusting the system state variables, r is the speed factor, sign is the sign function used to judge the positive and negative of the signal, x1(k) is the tracked input signal, v(k) is the input signal, x2(k) is the differential signal of the tracked input signal, x1(k+1) and x2(k+1) are the signals corresponding to the next time step, and h is the sampling time step.

[0024] In this embodiment, the target input of the differentiator is made smoother without sudden changes, nonlinearity is introduced for analysis, and a second-order integrator series system is set: , Where x1(k) is the tracking input signal, x2(k) is the differential signal of the tracking input signal, k represents the time count, f is the fastest feedback control function, which controls the dynamic change of x2; and |f|≤r, r is the amplitude constraint of the control quantity, reflecting the saturation limit of the physical actuator; Set the initial conditions: x1(0)=z1, x2(0)=z2; Set the terminal conditions: x1(k f )=0,x2(k f )=0; Design time-optimal control through Hamiltonian function; , Among them, λ is the Lagrange multiplier in the Hamiltonian function, which is used to describe the relationship between the system state variables and the control variables; The optimal performance index in time is J=(k f -k0), the conditions for the performance index to reach the extreme value are: , The Hamiltonian function is a linear function of f, so the extreme values ​​are on the boundary, not at the point where the partial derivative is 0; Through the co-state equation: get: , When the minimum value is reached; λ2(k)f(k) reaches the minimum value; it is known that |f|≤r, and ; when hour, ; At this time, x1(k+1)=x2(k), x2(k+1)=-r, and according to the set initial conditions, we get: , Eliminating k gives: ; Similarly, when hour, ; Depending on the initial value, the optimal control appears to alternate between r and -r. Considering the curve passing through the origin, the above formula can be further simplified to: , Then, f can be expressed as: ; The differential equation is discretized into a difference equation by the forward Euler method: , h is the sampling time step; All solutions of the above equations satisfy the following conditions: , ; For any bounded measurable signal v(k), the fastest feedback control function can be adjusted to: , The first component x1(r,k) of the solution to the differential equation satisfies: .

[0025] Specifically, the state feedback controller includes a state observer and a compensator. The state observer is used to integrate the voltage and current measurement values ​​of the motor and the predicted values ​​of the motor model; the compensator is used to correct the control error caused by the change of motor parameters or external load disturbance, and ensure the stable performance of the motor by adjusting the control signal in real time.

[0026] In this embodiment, by introducing a tracking differentiator and a state feedback controller, precise control of the motor speed control system is achieved. The tracking differentiator is responsible for quickly and accurately tracking the changes in the input signal, while the state feedback controller ensures the stable operation of the motor under various working conditions through the coordinated work of the state observer and the compensator. The state observer uses the voltage and current measurement values ​​of the motor, combined with the predicted values ​​of the motor model, to estimate the internal state of the motor in real time. This observation method that combines actual measurement values ​​and model prediction values ​​improves the accuracy and robustness of state estimation. The compensator adjusts the control signal in real time based on the observed state information to compensate for the effects of motor parameter changes or external load disturbances, thereby ensuring the stable performance of the motor. In addition, the model prediction unit also has a simplified function of parameter adjustment; through optimized design, it can automatically adjust its internal parameters to adapt to different working environments and load conditions. This adaptive capability greatly reduces the need for manual intervention, making the motor speed control system more intelligent and automated.

[0027] In this embodiment, the application of motor vector modulation technology further enhances the anti-interference ability of the system. Vector modulation technology decomposes the voltage and current of the motor into independent vector components, so that the system can accurately control each component. This control method not only improves the dynamic response speed of the motor, but also effectively suppresses interference caused by factors such as power grid fluctuations and load mutations. In the vector modulation process, advanced space vector pulse width modulation (SVPWM) technology is used to reduce switching losses and improve the operating efficiency of the motor by accurately calculating and optimizing the switching sequence. In addition, SVPWM technology can also reduce the electromagnetic noise generated when the motor is running and improve the operating environment of the motor. In order to further improve the stability and reliability of the system, this embodiment also introduces fault diagnosis and adaptive control mechanisms. The fault diagnosis module can monitor the operating status of the motor in real time. Once an abnormal situation is detected, such as overheating, overload or short circuit, the system will automatically start the protection program, cut off the power supply or adjust the operating parameters in time to prevent the fault from expanding. The adaptive control mechanism dynamically adjusts the control strategy according to the real-time operating data of the motor to ensure that the motor can maintain optimal performance under different working conditions. This mechanism is particularly suitable for complex and changeable working environments and can significantly improve the adaptability and robustness of the motor speed control system.

[0028] Specifically, the cascade optimization model divides the motor control into a speed control layer and a current control layer through a hierarchical control strategy; the speed control layer is used to use the output of the model prediction unit as a reference speed, and generate a torque command for the motor through a speed controller using a PID control algorithm; the current control layer is used to convert the torque command into a stator current command for the motor through the current controller of the vector control module, to ensure that the motor operates at a predetermined torque and speed.

[0029] Specifically, the variable frequency speed regulation module adopts a dual PWM variable frequency speed regulation system, expands the speed regulation range through two independent pulse width modulation controllers, adopts a two-level inverter as the main circuit, converts DC power into AC power through PWM technology, and drives the permanent magnet synchronous motor to work.

[0030] Specifically, the dual PWM variable frequency speed regulation system is composed of a grid-side PWM rectifier and a machine-side PWM inverter connected via a DC bus. The grid-side PWM rectifier realizes a two-way flow of energy with the power grid, and the machine-side PWM inverter is responsible for converting DC power into AC power with adjustable frequency and voltage to drive the motor to operate; the inverter and the motor are equivalent to resistors connected in parallel at both ends of the DC bus capacitor, and the DC bus voltage is equivalent to a constant voltage source as the input of the system.

[0031] In this embodiment, when the motor is in the drag mode, energy flows from the AC power grid through the rectifier to the intermediate DC bus capacitor for charging, and the inverter transfers the energy on the DC bus to the motor under PWM control, and the motor torque and speed are regulated through appropriate control strategies. When the motor is in the deceleration running state, due to the load inertia, the motor is converted into the power generation mode to generate regenerative energy, and the energy flows to the intermediate DC bus capacitor through the bidirectional switch network in the inverter, resulting in an increase in the intermediate DC voltage. At this time, the switch element in the PWM rectifier feeds energy back to the AC power grid under PWM control, thereby realizing the feedback of regenerative energy.

[0032] An anti-interference motor speed control system based on motor vector modulation includes: a data acquisition unit, a model prediction unit, a prediction correction unit, and a power control unit; The data acquisition unit is used to obtain the current space vector, voltage space vector, and flux space vector according to the stator winding of the motor through the motor mathematical model; obtain the rotor initial position information through the photoelectric encoder, combine the current space vector, voltage space vector, flux space vector and the rotor initial position information with the data sampling time to generate initial state sampling data and transmit it to the vector control module; The model prediction unit constructs a discrete space state model according to the initial state sampling data of the vector control module, and predicts the system state according to the discrete space state model to obtain the initial state control quantity; The prediction correction unit dynamically monitors the real-time operating state of the motor by tracking the input signal through a differentiator, and dynamically adjusts the prediction model through a state feedback controller and a cascade optimization model according to the monitoring results; The power control unit receives the outputs of the model prediction unit and the prediction correction unit through the variable frequency speed regulation module, generates a corresponding PWM signal to control the switching state of the inverter, and further adjusts the power supply voltage and frequency of the motor.

[0033] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An anti-interference motor speed control method based on motor vector modulation, characterized in that: include: According to the stator winding of the motor, the current space vector, the voltage space vector and the flux space vector are obtained through the motor mathematical model; Obtain the rotor initial position information through the photoelectric encoder, combine the current space vector, voltage space vector, flux space vector and the rotor initial position information with the data sampling time to generate initial state sampling data and transmit it to the vector control module; Constructing a discrete space state model according to the initial state sampling data of the vector control module, and predicting the system state according to the discrete space state model to obtain the initial state control quantity; The real-time operating status of the motor is dynamically monitored by tracking the input signal through the differentiator, and the prediction model is dynamically adjusted through the state feedback controller and cascade optimization model according to the monitoring results; The variable frequency speed regulation module receives the outputs of the model prediction unit and the prediction correction unit, generates corresponding PWM signals to control the switching state of the inverter, and then adjusts the power supply voltage and frequency of the motor.

2. The method according to claim 1, characterized in that The motor mathematical model is a basic mathematical model of three-phase voltage equations, flux equations, mechanical angular displacement equations and mechanical motion equations in a natural coordinate system.

3. The method according to claim 1, characterized in that The vector control module includes a vector converter and a current controller. The vector converter is responsible for converting the three-phase current and voltage signals of the motor into two-phase orthogonal current and voltage signals; the current controller calculates the required voltage vector based on the output of the vector converter and the outputs of the model prediction unit and the prediction correction unit to generate a PWM signal to drive the motor.

4. The method according to claim 1, characterized in that: The discrete space state model performs predictive control based on the parameter model, and uses the discrete difference equation to describe the controlled object subject to random interference through the CARIMA model. The specific expression is: ， Among them, u(k) is the control system input, y(k) is the control system output, ξ(k) is the random noise sequence affected by the interference signal, and z -1 Indicates a delay operation on the time series, A(z -1 ) is the delayed operation autoregressive term, B(z -1 ) is the moving average term of the delay operation, and Δ is the signal sampling period.

5. The method according to claim 1, characterized in that The differentiator tracks the input signal according to the approximate discrete fastest feedback control function by solving the discrete form of the fastest tracking differentiator, and the discrete form of the fastest tracking differentiator is expressed as: ， in, f is the fastest feedback control function, which realizes fast and accurate tracking of the input signal by adjusting the system state variables, r is the speed factor, sign is the sign function used to judge the positive and negative of the signal, x1(k) is the tracked input signal, v(k) is the input signal, x2(k) is the differential signal of the tracked input signal, x1(k+1) and x2(k+1) are the signals corresponding to the next time step, and h is the sampling time step.

6. The method according to claim 1, characterized in that The state feedback controller includes a state observer and a compensator. The state observer is used to fuse the voltage and current measurement values ​​of the motor and the predicted values ​​of the motor model; the compensator is used to correct the control error caused by the change of motor parameters or external load disturbance, and ensure the stable performance of the motor by adjusting the control signal in real time.

7. The method according to claim 6, characterized in that The cascade optimization model divides the motor control into a speed control layer and a current control layer through a hierarchical control strategy; the speed control layer is used to use the output of the model prediction unit as a reference speed, and generate a torque command for the motor through a speed controller using a PID control algorithm; the current control layer is used to convert the torque command into a stator current command for the motor through the current controller of the vector control module, to ensure that the motor operates according to a predetermined torque and speed.

8. The method according to claim 1, characterized in that The variable frequency speed regulation module adopts a dual PWM variable frequency speed regulation system, expands the speed regulation range through two independent pulse width modulation controllers, adopts a two-level inverter as the main circuit, converts the DC power supply into AC power through PWM technology, and drives the permanent magnet synchronous motor to work.

9. The method according to claim 8, characterized in that The dual PWM variable frequency speed regulation system is composed of a grid-side PWM rectifier and a machine-side PWM inverter connected via a DC bus. The grid-side PWM rectifier realizes a two-way flow of energy with the power grid, and the machine-side PWM inverter is responsible for converting DC power into AC power with adjustable frequency and voltage to drive the motor to operate; the inverter and the motor are equivalent to resistors connected in parallel at both ends of the DC bus capacitor, and the DC bus voltage is equivalent to a constant voltage source as the input of the system.

10. An anti-interference motor speed control system based on motor vector modulation, used to execute any method according to claims 1-9, characterized in that: include: Data acquisition unit, model prediction unit, prediction correction unit, power control unit; The data acquisition unit is used to obtain the current space vector, voltage space vector, and flux space vector according to the stator winding of the motor through the motor mathematical model; obtain the rotor initial position information through the photoelectric encoder, combine the current space vector, voltage space vector, flux space vector and the rotor initial position information with the data sampling time to generate initial state sampling data and transmit it to the vector control module; The model prediction unit constructs a discrete space state model according to the initial state sampling data of the vector control module, and predicts the system state according to the discrete space state model to obtain the initial state control quantity; The prediction correction unit dynamically monitors the real-time operating state of the motor by tracking the input signal through a differentiator, and dynamically adjusts the prediction model through a state feedback controller and a cascade optimization model according to the monitoring results; The power control unit receives the outputs of the model prediction unit and the prediction correction unit through the variable frequency speed regulation module, generates a corresponding PWM signal to control the switching state of the inverter, and further adjusts the power supply voltage and frequency of the motor.

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