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

Abstract

The invention discloses a parameter-free model prediction control method and system for a built-in permanent magnet synchronous motor, and the method comprises the steps: building a prediction model for the built-in permanent magnet synchronous motor, building an incremental model according to the prediction model, collecting the position, speed, current and reference signals of the built-in permanent magnet motor, and storing the signals in a register; and predicting the current of each switching state at the next moment according to the current prediction equation and storing the current prediction value corresponding to the switching state, calculating the cost value of each switching state according to the cost function, taking the switching state corresponding to the minimum cost value as the optimal switching state, and sending the optimal switching state as a control sequence to the inverter. And outputting the inverter state with the minimum cost value so as to control the operation of the permanent magnet synchronous motor. The prediction control function independent of motor parameters is achieved, the system has high robustness, and compared with the prior art, the speed regulation range of the motor is expanded.

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] In the control system of permanent magnet synchronous motor, model predictive control has great application potential due to its advantages such as simple structure, good dynamic performance, and easy handling of constraint problems. At present, model predictive control has been widely studied in the field of motor control, but it has not been widely used in industry. Specifically, model predictive control is sensitive to parameters. The parameters of the motor will be perturbed when it is working, so model mismatch will occur and affect the control effect. To solve the above problems, many scholars have designed observer algorithms to observe motor parameters, but the observer will significantly increase the calculation pressure of the microprocessor and increase the cost. In order to solve the technical problems of model mismatch and calculation pressure, there is a parameter-free model predictive control method that does not involve complex observer algorithms. However, the setting of its initial parameters depends on the rated speed, and this method can only be used for surface-mounted permanent magnet synchronous motors.

[0003] The built-in permanent magnet synchronous motor has a higher power density and can make full use of the magnetic resistance torque generated by the asymmetric rotor magnetic circuit, with a higher torque density. At the same time, the built-in permanent magnet synchronous motor has a greater rotor strength and is suitable for the ultra-high speed working conditions of electric vehicles. Therefore, a more versatile parameter-free model predictive control method that can work in the speed regulation system 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 motor parameters and does not require speed data for initialization parameters, thereby 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 internal permanent magnet synchronous motor, characterized in that it includes the following steps:

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

[0007]

[0008] In the formula, 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] In order 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 in adjacent cycles, and the incremental model is obtained:

[0010]

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

[0012] Step 2.1: Set variables and Rewrite the above formula to get:

[0013]

[0014] Since the values ​​of resistance and current are very small compared to the voltage value, 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 variable Q des and Q qes ; Initialize adjustment coefficient H d and H q ; For the predicted current and the 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 by the present invention:

[0019]

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

[0021] Step 2.4: Normalize the order of 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 , substitute into the current prediction equation, make current prediction for the eight switching states [s1…s8] of the three-phase two-level inverter and evaluate it 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] In the formula, i d Represents the direct axis current; i q represents the quadrature axis current; It represents the predicted value of 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 Represents direct-axis inductance; L q Represents the quadrature-axis inductance; R s Represents stator resistance; T s represents the control cycle; s represents the switch state; the superscript P represents the predicted value; ω e represents the rotor electrical angular velocity; k represents the current cycle, k+1 represents the next cycle, and k-1 represents 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] In the formula, 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 switch state with the smallest cost value is taken as the optimal switch state; the optimal switch state is sent to the inverter as a control sequence, the output is the one with the smallest cost value 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] In the formula, 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

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

[0038]

[0039] In the formula, 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; 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 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 transformed into finding 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 go 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 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 is:

[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 a controlled object; the encoder is used to obtain a rotor position signal, and the signal is differentiated to obtain a mechanical angular velocity; the speed controller calculates i according to a 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 output the collected current; the current prediction control module includes two new variables and two adjustment coefficients for representing the motor state instead of the motor parameters, the current model for model prediction, the update algorithm of the new variables, the calculation method of the adjustment coefficient and the iterative optimization process of 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 internal permanent magnet synchronous motors.

[0061] (4) No observer with large computational complexity 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 vehicle applications 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 the interior permanent magnet synchronous motor.

[0064] Figure 2 It is a 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 a current waveform diagram of the prior art at different speeds.

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

[0069] The present invention is further described in detail below in conjunction with 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 built-in permanent magnet synchronous motor; its current prediction equation in the synchronous rotating coordinate system is:

[0072]

[0073] In the formula, 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] In order 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 in adjacent cycles, and the incremental model is obtained:

[0075]

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

[0077] Step 2.1: Set variables and Rewrite the above formula to get:

[0078]

[0079] Since the values ​​of resistance and current are very small compared to the voltage value, 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 variable Q des and Q qes ; Initialize adjustment coefficient H d and H q ; For the predicted current and the 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 by the present invention:

[0084]

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

[0086] Step 2.4: Normalize the order of 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 , substitute into the current prediction equation, make current prediction for the eight switching states [s1…s8] of the three-phase two-level inverter and evaluate it 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] In the formula, i d Represents the direct axis current; i q represents the quadrature axis current; It represents the predicted value of 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 Represents direct-axis inductance; L q Represents the quadrature-axis inductance; R s Represents stator resistance; T s represents the control cycle; s represents the switch state; the superscript P is the predicted value; ω e represents the rotor electrical angular velocity; k represents the current cycle, k+1 represents the next cycle, and k-1 represents 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] In the formula, 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 switch state with the smallest cost value is taken as the optimal switch state; the optimal switch state is sent to the inverter as a control sequence, the output is the one with the smallest cost value 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] In the formula, 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] In the formula, 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 transformed into finding 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 go 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 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 is:

[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 according to the variables and parameter data collected at the current moment, and obtain the corresponding switching state with the smallest 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 the parameter-free model predictive control method for the permanent magnet synchronous motor of the present invention is superior to the prior art in response speed in each speed range, thanks to the initialization process that does not rely on speed information and the incremental model that eliminates the influence of magnetic flux.

[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 relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within 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 built-in permanent magnet synchronous motor; its current prediction equation in the synchronous rotating coordinate system is: In the formula, 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; In order to eliminate the influence of permanent magnet linkage parameters, the incremental model is obtained by subtracting 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 adjacent cycles, the incremental model is obtained: Step 2: Substitute the motor parameters in the prediction equation with the new variable Q d and Q q The incremental prediction model that does not depend on the motor parameters is obtained by replacement; the specific steps are: Step 2.1: Set variables and Rewrite the above formula to get: Compared to the voltage, the resistance and current are very small, so ignoring the resistance multiplied by the current in the last term of the equation 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 variable Q des and Q qes ; Initialize adjustment coefficient H d and H q ; For the predicted current and the 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 by the present invention: Where H d and H q is the adjustment coefficient, term1 represents the difference between the predicted current value and the actual value, and term2 represents Q des and Q qes The amount of change; Step 2.4: Normalize the order of 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 , substitute into the current prediction equation, make current prediction for the eight switching states [s1…s8] of the three-phase two-level inverter and evaluate it 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: In the formula, i d Represents the direct axis current; i q represents the quadrature axis current; It represents the predicted value of 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 Represents direct-axis inductance; L q Represents quadrature-axis inductance; R s Represents stator resistance; T s represents the control cycle; s represents the switch state; the superscript P represents the predicted value; ω e represents the rotor electrical angular velocity; k represents the current cycle, k+1 represents the next cycle, and k-1 represents 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: In the formula, 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 switch state with the smallest cost value is taken as the optimal switch state; the optimal switch state is sent to the inverter as a control sequence, the output is the one with the smallest cost value 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, characterized in that: 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 ; In the formula, 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 H d and H q Initialize, the initialization formula is: In the formula, 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 cross axis.

3. The parameter-free model predictive control method for an interior permanent magnet synchronous motor according to claim 1, characterized in that: 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 transformed into finding 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 go back one cycle:

4. The parameter-free model predictive control method for an interior permanent magnet synchronous motor according to claim 1, characterized in that: The normalization process in step 2.4 is as follows: To make the current parameter, Q des and Q qes The unit of 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, H q The expression is: 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, characterized in that: 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 method for an interior permanent magnet synchronous motor according to any one of claims 1 to 5, a parameter-free model predictive control system for an interior permanent magnet synchronous motor, 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 reference value of 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 replace the motor parameters to express the motor state, the current model used for model prediction, the update algorithm of the new variables, the calculation method of the adjustment coefficient and the iterative optimization process of the output switch sequence; The inverter is used to receive the optimal switching state and then control the operation of the permanent magnet synchronous motor.

Images