Parameter-free model predictive control method and system for interior permanent magnet synchronous motor

Abstract

The present invention discloses a parameter-free model predictive control method and system for an internal permanent magnet synchronous motor. The method includes establishing a predictive model for the internal permanent magnet synchronous motor and establishing an incremental model based on the predictive model. The method also collects the position, speed, current, and reference signals of the internal permanent magnet synchronous motor and stores them in registers. The method predicts the current of each switching state at the next moment based on a current prediction equation and stores the current prediction value corresponding to the switching state. The method calculates the cost value of each switching state based on a cost function, selects the corresponding switching state with the smallest cost value as the optimal switching state, and transmits the optimal switching state as a control sequence to an inverter. The method outputs the inverter state with the smallest cost value to control the operation of the permanent magnet synchronous motor. The method implements predictive control functions that are independent of motor parameters, making the system more robust and expanding the motor's speed regulation range compared to the prior art.

Description

Technical Field

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

[0002] Model predictive control (MPC) holds great potential for application in permanent magnet synchronous motor (PMSM) control systems due to its simple structure, excellent dynamic performance, and ease of handling constraints. While MPC has been extensively researched in the motor control field, it has not yet been widely adopted in industry. Specifically, MPC is sensitive to parameters, and parameters can be perturbed during motor operation, resulting in model mismatch that impacts control effectiveness. To address these issues, many researchers have designed observer algorithms to measure motor parameters. However, these observers significantly increase microprocessor computational burden and cost. To address these technical challenges of model mismatch and computational burden, a parameter-free MPC method has emerged that does not involve a complex observer algorithm. However, the initial parameter tuning depends on the rated speed, and this method is only applicable to surface-mounted PMSMs.

[0003] Interior permanent magnet synchronous motors (IPMS) offer higher power density and can fully exploit the reluctance torque generated by the asymmetric rotor magnetic circuit, resulting in higher torque density. They also have greater rotor strength, making them suitable for the ultra-high speeds of electric vehicles. Therefore, a more versatile parameter-free model predictive control method suitable for speed regulation systems is urgently needed. Summary of the Invention

[0004] Purpose of the invention: In order to solve the existing technical problems, the present invention provides a parameter-free model predictive control method for a built-in permanent magnet synchronous motor, which is independent of the motor parameters and does not require speed data for initialization parameters, enabling the system to have strong robustness and can be well applied to motor speed regulation systems.

[0005] Technical solution: A parameter-free model predictive control method for an interior permanent magnet synchronous motor, characterized by comprising the following steps:

[0006] Step 1: Establish a prediction model for the interior permanent magnet synchronous motor; its current prediction equation in the synchronous rotating coordinate system is:

[0007]

[0008] Where i d and i q is the direct and quadrature axis current; u d and u q is the DC and Q axis voltage; L d and L q is the direct and quadrature axis inductance; R sis the stator resistance; T s is the control period; the superscript P is the predicted value; the k in the brackets represents the current period, and k+1 is the next period; ψ f is the permanent magnet flux; ω e is the rotor electrical angular velocity;

[0009] To eliminate the influence of permanent magnet linkage parameters, the current equations at two adjacent moments are subtracted. At the same time, since the electrical time constant is much smaller than the mechanical time constant, it can be assumed that the speed remains unchanged during several adjacent cycles, resulting in an incremental model:

[0010]

[0011] Step 2: Replace the motor parameters in the prediction equation with the new variable Q d and Q q The replacement is performed to obtain an incremental prediction model that does not depend on motor parameters; the specific steps are:

[0012] Step 2.1: Set variables and Rewriting the above formula, we get:

[0013]

[0014] Since the values ​​of resistance and current are very small compared to the voltage, we can ignore the resistance multiplied by the current in the last term of the equation and further simplify it to:

[0015]

[0016] Step 2.2: Use Q d , Q q The estimated value of Q des , Q qes Replace Q d , Q q , and obtain the estimated value driven forecast model:

[0017]

[0018] Step 2.3: Initialize the variable Q des and Q qes ; Initialize adjustment coefficient H d and H q ; Comparison of predicted current and actual current, Q des and Q qes The relationship between the current prediction error and the actual parameters of the motor is analyzed; the following function is constructed to evaluate the current prediction error and parameter estimation error of the prediction model proposed in the present invention:

[0019]

[0020] Where H d and H q is the adjustment coefficient, term1 represents the difference between the current prediction value and the actual value, term2 represents Q des and Q qes The amount of change;

[0021] Step 2.4: Normalize the magnitude of term1 and term2;

[0022] Step 3: Collect the mechanical angular velocity of the rotor position signal, the current of each phase of the built-in permanent magnet synchronous motor, and the reference signal of the current value;

[0023] Step 4: Based on the Q calculated above des and Q qes , substituted into the current prediction equation, the current of the eight switching states [s1…s8] of the three-phase two-level inverter is predicted and evaluated according to the cost function;

[0024] Step 4.1: Save the collected data at the current moment in the register, and predict the current of each switch state at the next moment according to the current prediction equation;

[0025] The current prediction equation is:

[0026]

[0027] Where i d Represents the direct axis current; i q represents the quadrature-axis current; Indicates the predicted value of the direct-axis current in the next cycle; Indicates the predicted value of the quadrature-axis current in the next cycle; u d Indicates the direct axis voltage; u q Indicates the quadrature axis voltage; L d Indicates direct-axis inductance; L q Represents the quadrature-axis inductance; R s represents the stator resistance; T s represents the control period; s represents the switch state; the superscript P represents the predicted value; ω e Indicates the rotor electrical angular velocity; k represents the current cycle, k+1 is the next cycle, and k-1 is the previous cycle;

[0028] Step 4.2: Loop through step 4.1 and save the current prediction values ​​corresponding to the switch states s1…s8 to i dq1 …i dq8 , calculate the cost value of each switch state according to the cost function and save it to J1…J8. The cost function is:

[0029]

[0030] Where, J represents the cost value of the switch state; Indicates the predicted value of the direct-axis current in the current cycle; Indicates the predicted value of the quadrature-axis current in the current cycle; Indicates i d Reference value of Indicates i q Reference value of

[0031] Step 5: The corresponding switching state with the smallest cost value is taken as the optimal switching state; the optimal switching state is sent to the inverter as a control sequence, and the output with the smallest cost value is used to control the operation of the permanent magnet synchronous motor, and then return to step 2 for control at the next moment.

[0032] Furthermore, the initialization in step 2.3 specifically includes:

[0033] Q des and Q qes Initialize, the initialization formula is:

[0034] Q dN =T s / L dN ;

[0035] Q qN =T s / L qN ;

[0036] Where Q d Indicates the motor direct axis parameters; Q des represents the estimated value of the direct-axis motor parameters; T s Indicates the control cycle; L d Indicates direct-axis inductance; Q q Indicates the motor quadrature axis parameter; Q qes Represents the estimated value of the quadrature-axis motor parameters; L q represents the quadrature-axis inductance; Represents Q des Initialization value of Indicates Q qes Initialization value of

[0037] H d and H q Initialize, the initialization formula is:

[0038]

[0039] Where i N Indicates rated current; H d Indicates the adjustment coefficient of the motor direct axis weight; H q Indicates the adjustment coefficient of the motor quadrature axis weight; L dNIndicates the rated direct-axis inductance; Represents Q des Initialization value of Represents Q qes Initialization value of L qN Indicates the rated quadrature-axis inductance; λ d represents the weight coefficient of the direct axis term2; λ q Represents the weight coefficient of the cross axis term2;

[0040] Furthermore, when term1 approaches 0, it can be considered that the current prediction value is infinitely close to the actual current value, and when term2 approaches 0, it can be considered that Q des and Q qes It is very close to the actual motor parameters; the control purpose is to find the minimum value of the function G. Taking the partial derivative and setting the derivative function to 0 gives:

[0041]

[0042] The iterative formula can be solved:

[0043]

[0044] Since it is impossible to obtain the predicted current at time k+1 at time k, we step back one cycle:

[0045]

[0046] Furthermore, the normalization process in step 2.4 is:

[0047] To make the current parameter, Q des and Q qes The unit of is normalized and can be rewritten as:

[0048]

[0049] i N is the rated current, Q dN =T s / L dN , L dN is the direct-axis rated inductance, H d It can be calculated as follows:

[0050]

[0051] Similarly, we can get H q The expression:

[0052]

[0053] where Q qN =T s / LqN , L qN is the quadrature-axis rated inductance;

[0054] Furthermore, the control system of the present invention uses the motor nameplate parameters only during initialization and does not involve the motor parameters at other times.

[0055] Furthermore, a parameter-free model predictive control method for a built-in permanent magnet synchronous motor, a parameter-free model predictive control system for a built-in permanent magnet synchronous motor, is characterized in that it includes: a built-in permanent magnet synchronous motor, an encoder, a current sensor, an abc / dq converter, an inverter, a current predictive control module and a speed controller.

[0056] Furthermore, a built-in permanent magnet synchronous motor is used as the controlled object; the encoder is used to obtain the rotor position signal and differentiate the signal to obtain the mechanical angular velocity; the speed controller calculates i according to the speed reference value d 、i q reference value; the current sensor is used to collect the three-phase current of the built-in permanent magnet synchronous motor; the abc / dq converter is used to convert the output of the collected current; the current prediction control module includes two new variables and two adjustment coefficients used to represent the motor state instead of motor parameters, a current model for model prediction, an update algorithm for new variables, a calculation method for adjustment coefficients, and an iterative optimization process for the output switching sequence; the inverter is used to receive the optimal switching state and then control the operation of the permanent magnet synchronous motor.

[0057] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0058] (1) The current prediction model adopts an incremental model, which eliminates the influence of motor flux parameters on the control system.

[0059] (2) Control parameter initialization does not require rated speed information, which is suitable for speed regulation systems.

[0060] (3) Considering the salient pole effect of the rotor magnetic circuit, it can be used to control both surface-mounted and built-in permanent magnet synchronous motors.

[0061] (4) No computationally intensive observer is required, the calculation is simple, and the requirements for the control chip are low.

[0062] The present invention is simple and easy to implement and has strong robustness. The built-in permanent magnet synchronous motor has high rotor strength and is suitable for new energy vehicles with high speed, high reliability and high performance requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of the parameter-free model predictive control method for interior permanent magnet synchronous motor.

[0064] Figure 2 It is the structural block diagram of the parameter-free model predictive control system of the built-in permanent magnet synchronous motor.

[0065] Figure 3 It is a speed performance diagram of the prior art.

[0066] Figure 4 It is the speed performance diagram of the present invention.

[0067] Figure 5 It is the current waveform diagram of the existing technology at different speeds.

[0068] Figure 6 1 is a current waveform diagram of the present invention at different speeds. DETAILED DESCRIPTION

[0069] The present invention is further described in detail below with reference to the accompanying drawings.

[0070] like Figure 1 As shown, embodiment 1 of the present invention provides a parameter-free model predictive control method for an interior permanent magnet synchronous motor, comprising the following steps:

[0071] Step 1: Establish a prediction model for the interior permanent magnet synchronous motor; its current prediction equation in the synchronous rotating coordinate system is:

[0072]

[0073] Where i d and i q is the direct and quadrature axis current; u d and u q is the DC and Q axis voltage; L d and L q is the direct and quadrature axis inductance; R s is the stator resistance; T s is the control period; the superscript P is the predicted value; the k in the brackets represents the current period, and k+1 is the next period; ψ f is the permanent magnet flux; ω e is the rotor electrical angular velocity;

[0074] To eliminate the influence of permanent magnet linkage parameters, the current equations at two adjacent moments are subtracted. At the same time, since the electrical time constant is much smaller than the mechanical time constant, it can be assumed that the speed remains unchanged during several adjacent cycles, resulting in an incremental model:

[0075]

[0076] Step 2: Replace the motor parameters in the prediction equation with the new variable Q d and Q q The replacement is performed to obtain an incremental prediction model that does not depend on motor parameters; the specific steps are:

[0077] Step 2.1: Set variables and Rewriting the above formula, we get:

[0078]

[0079] Since the values ​​of resistance and current are very small compared to the voltage, we can ignore the resistance multiplied by the current in the last term of the equation and further simplify it to:

[0080]

[0081] Step 2.2: Use Q d , Q q The estimated value of Q des , Q qes Replace Q d , Q q , and obtain the estimated value driven forecast model:

[0082]

[0083] Step 2.3: Initialize the variable Q des and Q qes ; Initialize adjustment coefficient H d and H q ; Comparison of predicted current and actual current, Q des and Q qes The relationship between the current prediction error and the actual parameters of the motor is analyzed; the following function is constructed to evaluate the current prediction error and parameter estimation error of the prediction model proposed in the present invention:

[0084]

[0085] Where H d and H q is the adjustment coefficient, term1 represents the difference between the current prediction value and the actual value, term2 represents Q des and Q qes The amount of change;

[0086] Step 2.4: Normalize the magnitude of term1 and term2;

[0087] Step 3: Collect the mechanical angular velocity of the rotor position signal, the current of each phase of the built-in permanent magnet synchronous motor, and the reference signal of the current value;

[0088] In this embodiment, preferably, the current of each phase of the built-in permanent magnet synchronous motor is collected by a current sensor, and the actual value i of the current in the synchronous rotating coordinate system is obtained by an abc / dq converter. d 、i q .

[0089] The rotor position signal θ is obtained through the encoder, and the mechanical angular velocity ω is obtained by differentiating the signal r and obtain i through the speed controller d 、i q Reference value

[0090] Step 4: Based on the Q calculated above des and Q qes , substituted into the current prediction equation, the current of the eight switching states [s1…s8] of the three-phase two-level inverter is predicted and evaluated according to the cost function;

[0091] Step 4.1: Save the collected data at the current moment in the register, and predict the current of each switch state at the next moment according to the current prediction equation;

[0092] The current prediction equation is:

[0093]

[0094] Where i d Represents the direct axis current; i q represents the quadrature-axis current; Indicates the predicted value of the direct-axis current in the next cycle; Indicates the predicted value of the quadrature-axis current in the next cycle; u d Indicates the direct axis voltage; u q Indicates the quadrature axis voltage; L d Indicates direct-axis inductance; L q Represents the quadrature-axis inductance; R s represents the stator resistance; T s represents the control period; s represents the switch state; the superscript P is the predicted value; ω e Indicates the rotor electrical angular velocity; k represents the current cycle, k+1 is the next cycle, and k-1 is the previous cycle;

[0095] Step 4.2: Loop through step 4.1 and save the current prediction values ​​corresponding to the switch states s1…s8 to i dq1 …i dq8 , calculate the cost value of each switch state according to the cost function and save it to J1…J8. The cost function is:

[0096]

[0097] Where, J represents the cost value of the switch state; Indicates the predicted value of the direct-axis current in the current cycle; Indicates the predicted value of the quadrature-axis current in the current cycle; Indicates i dReference value of Indicates i q Reference value of

[0098] Step 5: The corresponding switching state with the smallest cost value is taken as the optimal switching state; the optimal switching state is sent to the inverter as a control sequence, and the output with the smallest cost value is used to control the operation of the permanent magnet synchronous motor, and then return to step 2 for control at the next moment.

[0099] Furthermore, the initialization in step 2.3 specifically includes:

[0100] Q des and Q qes Initialize, the initialization formula is:

[0101] Q dN =T s / L dN ;

[0102] Q qN =T s / L qN ;

[0103] Where Q d Indicates the motor direct axis parameters; Q des represents the estimated value of the direct-axis motor parameters; T s Indicates the control cycle; L d Indicates direct-axis inductance; Q q Indicates the motor quadrature axis parameter; Q qes Represents the estimated value of the quadrature-axis motor parameters; L q represents the quadrature-axis inductance; Indicates Q des Initialization value of Indicates Q qes Initialization value of

[0104] H d and H q Initialize, the initialization formula is:

[0105]

[0106] Where i N Indicates rated current; H d Indicates the adjustment coefficient of the motor direct axis weight; H q Indicates the adjustment coefficient of the motor quadrature axis weight; L dN Indicates the rated direct-axis inductance; Indicates Q des Initialization value of Indicates Q qes Initialization value of L qN Indicates the rated quadrature-axis inductance; λd represents the weight coefficient of the direct axis term2; λ q Represents the weight coefficient of the cross axis term2;

[0107] In this embodiment, preferably, λ d The value is 1, λ q The value is 1.

[0108] Furthermore, when term1 approaches 0, it can be considered that the current prediction value is infinitely close to the actual current value, and when term2 approaches 0, it can be considered that Q des and Q qes It is very close to the actual motor parameters; the control purpose is to find the minimum value of the function G. Taking the partial derivative and setting the derivative function to 0 gives:

[0109]

[0110] The iterative formula can be solved:

[0111]

[0112] Since it is impossible to obtain the predicted current at time k+1 at time k, we step back one cycle:

[0113]

[0114] Furthermore, the normalization process in step 2.4 is: to make the current parameter, Q des and Q qes The unit of is normalized and can be rewritten as:

[0115]

[0116] i N is the rated current, Q dN =T s / L dN , L dN is the direct-axis rated inductance, H d It can be calculated as follows:

[0117]

[0118] Similarly, we can get H q The expression:

[0119]

[0120] where Q qN =T s / L qN , L qN is the quadrature-axis rated inductance;

[0121] like Figure 2 As shown, embodiment 2 of the present invention provides a parameter-free model predictive control system for a built-in permanent magnet synchronous motor, including: a built-in permanent magnet synchronous motor, an encoder, a current sensor, an inverter, a current predictive control module and a speed controller.

[0122] The built-in permanent magnet synchronous motor is used as the controlled object; the current sensor is used to collect the three-phase current i of the built-in permanent magnet synchronous motor abc The encoder is used to obtain the rotor position signal θ, and the signal is differentiated to obtain the mechanical angular velocity ω r ; The speed controller is based on the speed reference value ω r * Calculate i d 、i q Reference value The current prediction module is used to predict and optimize each of the eight switching states [s1…s8] of the three-phase two-level inverter based on the variables and parameter data collected at the current moment, and obtain the corresponding switching state with the lowest cost as the optimal switching state. The inverter is used to receive the optimal switching state and then control the operation of the permanent magnet synchronous motor.

[0123] Figures 3 to 6 The speed response and current curves of the present invention and the prior art under a parameter perturbation of 30% are compared. It can be seen that thanks to the initialization process that does not rely on speed information and the incremental model that eliminates the influence of magnetic flux, the parameter-free model predictive control method of the permanent magnet synchronous motor described in the present invention has a better response speed than the prior art in each speed range.

[0124] 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 above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A parameter-free model predictive control method for an interior permanent magnet synchronous motor, characterized in that: The following steps are involved: Step 1: Establish a prediction model for the interior permanent magnet synchronous motor; its current prediction equation in the synchronous rotating coordinate system is: Where i d and i q is the direct and quadrature axis current; u d and u q is the DC and Q axis voltage; L d and L q is the direct and quadrature axis inductance; R s is the stator resistance; T s is the control period; the superscript P is the predicted value; the k in the brackets represents the current period, and k+1 is the next period; ψ f is the permanent magnet flux; ω e is the rotor electrical angular velocity; To eliminate the influence of permanent magnet linkage parameters, the incremental model is obtained by taking the difference between the current equations at two adjacent moments. At the same time, the electrical time constant is much smaller than the mechanical time constant. Assuming that the speed remains unchanged in several adjacent cycles, the incremental model is obtained: Step 2: Replace the motor parameters in the prediction equation with the new variable Q d and Q q The replacement is performed to obtain an incremental prediction model that does not depend on motor parameters; the specific steps are: Step 2.1: Set variables and Rewriting the above formula, we get: Compared to the voltage, the resistance and current are very small. Ignoring the resistance multiplied by the current in the last term of the equation, we can further simplify it to: Step 2.2: Use Q d , Q q The estimated value of Q des , Q qes Replace Q d , Q q , and obtain the estimated value driven forecast model: Step 2.3: Initialize the variable Q des and Q qes ; Initialize adjustment coefficient H d and H q ; Comparison of predicted current and actual current, Q des and Q qes The relationship between the current prediction error and the actual parameters of the motor is analyzed; the following function is constructed to evaluate the current prediction error and parameter estimation error of the prediction model proposed in the present invention: Where H d and H q is the adjustment coefficient, term1 represents the difference between the current prediction value and the actual value, term2 represents Q des and Q qes The amount of change; Step 2.4: Normalize the magnitude of term1 and term2; Step 3: Collect the mechanical angular velocity of the rotor position signal, the current of each phase of the built-in permanent magnet synchronous motor, and the reference signal of the current value; Step 4: Based on the Q calculated above des and Q qes , substituted into the current prediction equation, the current of the eight switching states [s1…s8] of the three-phase two-level inverter is predicted and evaluated according to the cost function; Step 4.1: Save the collected data at the current moment in the register, and predict the current of each switch state at the next moment according to the current prediction equation; The current prediction equation is: Where i d Represents the direct axis current; i q represents the quadrature-axis current; Indicates the predicted value of the direct-axis current in the next cycle; Indicates the predicted value of the quadrature-axis current in the next cycle; u d Indicates the direct axis voltage; u q Indicates the quadrature axis voltage; L d Indicates direct-axis inductance; L q Represents the quadrature-axis inductance; R s represents the stator resistance; T s represents the control period; s represents the switch state; the superscript P represents the predicted value; ω e Indicates the rotor electrical angular velocity; k represents the current cycle, k+1 is the next cycle, and k-1 is the previous cycle; Step 4.2: Loop through step 4.1 and save the current prediction values ​​corresponding to the switch states s1…s8 to i dq1 …i dq8 , calculate the cost value of each switch state according to the cost function and save it to J1…J8. The cost function is: Where, J represents the cost value of the switch state; Indicates the predicted value of the direct-axis current in the current cycle; Indicates the predicted value of the quadrature-axis current in the current cycle; Indicates i d Reference value of Indicates i q Reference value of Step 5: The corresponding switching state with the smallest cost value is taken as the optimal switching state; the optimal switching state is sent to the inverter as a control sequence, and the output with the smallest cost value is used to control the operation of the permanent magnet synchronous motor, and then return to step 2 for control at the next moment.

2. The parameter-free model predictive control method for an interior permanent magnet synchronous motor according to claim 1, wherein: The initialization in step 2.3 specifically includes: Q des and Q qes Initialize, the initialization formula is: Q dN =T s / L dN ; Q qN =T s / L qN ; Where Q d Indicates the motor direct axis parameters; Q des represents the estimated value of the direct-axis motor parameters; T s Indicates the control cycle; L d Indicates direct-axis inductance; Q q Indicates the motor quadrature axis parameter; Q qes Represents the estimated value of the quadrature-axis motor parameters; L q represents the quadrature-axis inductance; Represents Q des Initialization value of Represents Q qes Initialization value of H d and H q Initialize, the initialization formula is: Where i N Indicates rated current; H d Indicates the adjustment coefficient of the motor direct axis weight; H q Indicates the adjustment coefficient of the motor quadrature axis weight; L dN Indicates the rated direct-axis inductance; Indicates Q des Initialization value of Indicates Q qes Initialization value of L qN Indicates the rated quadrature-axis inductance; λ d represents the weight coefficient of the direct axis term2; λ q Represents the weight coefficient of the term2 term on the quadrature axis.

3. The parameter-free model predictive control method for an interior permanent magnet synchronous motor according to claim 1, wherein: When term1 approaches 0, it can be considered that the current prediction value is infinitely close to the actual current value. When term2 approaches 0, it can be considered that Q des and Q qes It is very close to the actual motor parameters; the control purpose is to find the minimum value of the function G. Taking the partial derivative and setting the derivative function to 0 gives: The iterative formula can be solved: Since it is impossible to obtain the predicted current at time k+1 at time k, we step back one cycle:

4. The parameter-free model predictive control method for an interior permanent magnet synchronous motor according to claim 1, wherein: The normalization process in step 2.4 is as follows: To make the current parameter, Q des and Q qes The unit of is normalized and can be rewritten as: i N is the rated current, Q dN =T s / L dN , L dN is the direct-axis rated inductance, H d It can be calculated as follows: Similarly, we can get H q The expression: where Q qN =T s / L qN , L qN is the quadrature-axis rated inductance.

5. The parameter-free model predictive control method for an interior permanent magnet synchronous motor according to claim 1, wherein: The control system of the present invention uses the motor nameplate parameters only during initialization and does not involve the motor parameters at other times.

6. A parameter-free model predictive control system for an interior permanent magnet synchronous motor using the parameter-free model predictive control method for an interior permanent magnet synchronous motor according to any one of claims 1 to 5, characterized in that: include: Built-in permanent magnet synchronous motor, encoder, current sensor, ABC / DQ converter, inverter, current prediction control module and speed controller.

7. The parameter-free model predictive control system for an interior permanent magnet synchronous motor according to claim 6, characterized in that: The built-in permanent magnet synchronous motor is used as the controlled object; the encoder is used to obtain the rotor position signal and differentiate the signal to obtain the mechanical angular velocity; the speed controller calculates i according to the speed reference value d 、i q The current sensor is used to collect the three-phase current of the built-in permanent magnet synchronous motor; the abc / dq converter is used to convert the output of the collected current; the current prediction control module includes two new variables and two adjustment coefficients used to represent the motor state instead of motor parameters, a current model for model prediction, an update algorithm for the new variables, a calculation method for the adjustment coefficients, and an iterative optimization process for the output switching sequence; The inverter is used to receive the optimal switching state and then control the operation of the permanent magnet synchronous motor.

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