A permanent magnet synchronous motor predictive control method, system and device

Abstract

The application discloses a permanent magnet synchronous motor predictive control method, system and device, and relates to the technical field of engineering control. The method comprises the following steps: obtaining a reference value of motor q-axis current according to a current speed and a set speed value; obtaining the value of each to-be-identified model parameter according to the output current of the motor d-axis and the motor q-axis at the current moment, and the predicted parameters of the motor d-axis and the motor q-axis at the previous moment; obtaining the PWM wave of the optimal basic voltage vector at the current moment based on the output current of the motor d-axis and the motor q-axis at the current moment, the predicted current of the motor d-axis and the motor q-axis at the current moment corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor q-axis current, the value of each to-be-identified model parameter, and the output voltage of the motor q-axis and the motor d-axis corresponding to each basic voltage vector at the current moment; and controlling the motor according to the PWM wave of the optimal basic voltage vector at the current moment. The application can accurately control the motor.

Description

Technical Field

[0001] This invention relates to the field of engineering control technology, and in particular to a predictive control method, system and equipment for a permanent magnet synchronous motor. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are characterized by their small size, high power density, high power factor, and high torque-to-current ratio, making them highly efficient, energy-saving, and comprehensive performance-optimized drive motors widely used in high-performance drive systems. Traditional PMSM speed control systems primarily employ vector control or direct torque control strategies to achieve high control performance. In recent years, with the development of power electronics, microcomputer control, and control theory, PMSM control technology has made significant progress. Model predictive control (MMC) offers advantages such as simple control concepts and ease of handling nonlinear constraints, attracting widespread attention from scholars both domestically and internationally, and leading to its application in motor speed control systems.

[0003] However, existing model predictive control predicts motor behavior based on the motor's mathematical model. This method is highly dependent on the parameters of the permanent magnet synchronous motor model, making it impossible for the predictive model to avoid the influence of unmodeled dynamic factors. Because the existing motor mathematical model is inaccurate, the dynamic performance of the permanent magnet synchronous motor drive system is poor and the motor control accuracy is low when control is performed based on the existing motor mathematical model. Summary of the Invention

[0004] The purpose of this invention is to provide a predictive control method, system, and device for permanent magnet synchronous motors, which can accurately control the motor.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A predictive control method for a permanent magnet synchronous motor includes:

[0007] The system acquires the following parameters: the current output current of the motor's d-axis, the current output current of the motor's q-axis, the current output voltage of the motor's q-axis corresponding to each basic voltage vector, the current output voltage of the motor's d-axis corresponding to each basic voltage vector, the predicted parameters of the motor's d-axis at the previous moment, the predicted parameters of the motor's q-axis at the previous moment, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, and the actual rotational speed of the motor at the current moment. The predicted parameters include the output voltage and the output current.

[0008] The reference value of the motor q-axis current is obtained based on the current rotational speed and the set rotational speed value;

[0009] The values ​​of each model parameter to be identified are obtained based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted parameters of the motor d-axis at the previous moment, and the predicted parameters of the motor q-axis at the previous moment.

[0010] Based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor's q-axis current, the values ​​of each parameter of the model to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment, the PWM wave of the optimal basic voltage vector at the current moment is obtained, and the motor is controlled according to the PWM wave of the optimal basic voltage vector at the current moment.

[0011] Optionally, the step of obtaining the reference value of the motor q-axis current based on the current rotational speed and the set rotational speed specifically involves:

[0012] The reference value of the motor q-axis current is obtained by processing the current rotational speed and the set rotational speed value using the PI algorithm.

[0013] Optionally, the step of obtaining the values ​​of each model parameter to be identified based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted parameters of the motor's d-axis at the previous moment, and the predicted parameters of the motor's q-axis at the previous moment specifically involves:

[0014] The identification equation is obtained by inputting the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted parameters of the motor's d-axis at the previous moment, and the predicted parameters of the motor's q-axis at the previous moment into the linear regression model of the identification model.

[0015] The recursive least squares estimation method with a forgetting factor is used to solve the identification equation to obtain the values ​​of the parameters of each model to be identified.

[0016] Optionally, the step of obtaining the PWM wave of the optimal basic voltage vector at the current moment based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor's q-axis current, the values ​​of each of the model parameters to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment, and controlling the motor according to the PWM wave of the optimal basic voltage vector at the current moment specifically includes:

[0017] Based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted current of the motor d-axis corresponding to the optimal basic voltage vector of the previous moment, the predicted current of the motor q-axis corresponding to the optimal basic voltage vector of the previous moment, the values ​​of each parameter of the model to be identified, the output voltage of the motor q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor d-axis corresponding to each basic voltage vector at the current moment, the predicted current of the motor q-axis corresponding to each basic voltage vector at the current moment and the predicted current of the motor d-axis corresponding to each basic voltage vector at the current moment are obtained.

[0018] Based on the reference value of the motor q-axis current, the predicted current of the motor q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor d-axis at the next moment corresponding to each basic voltage vector at the current moment, the PWM wave of the optimal basic voltage vector at the current moment is obtained, and the motor is controlled according to the PWM wave of the optimal basic voltage vector at the current moment.

[0019] Optionally, the step of obtaining the predicted current of the motor q-axis at the next moment and the predicted current of the motor d-axis at the next moment based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted current of the motor q-axis corresponding to the optimal basic voltage vector at the previous moment, the values ​​of each of the model parameters to be identified, the output voltage of the motor q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor d-axis corresponding to each basic voltage vector at the current moment, specifically involves:

[0020] According to the formula Calculate the predicted current of the motor's q-axis at the next moment corresponding to the current basic voltage vector and the predicted current of the motor's d-axis at the next moment corresponding to the current basic voltage vector. This represents the predicted current along the motor's d-axis at the next moment, corresponding to the basic voltage vector at the current moment. i represents the predicted current of the motor's q-axis at the next moment, corresponding to the basic voltage vector at the current moment. d (k) represents the current output current of the motor's d-axis at the current moment, i q (k) represents the output current of the motor's q-axis at the current moment. This represents the output voltage of the motor along the d-axis corresponding to the basic voltage vector at the current moment. This represents the output voltage of the motor along the q-axis corresponding to the basic voltage vector at the current moment. This represents the predicted current of the motor's d-axis at the current moment, corresponding to the optimal basic voltage vector at the previous moment. This represents the predicted current of the motor's q-axis at the current moment, corresponding to the optimal basic voltage vector at the previous moment. 'a' represents the first model parameter to be identified, 'b' represents the second model parameter to be identified, 'c' represents the third model parameter to be identified, 'w' represents the electric angular velocity, and 'T' represents the electric angular velocity. s This indicates the system's sampling period.

[0021] Optionally, the step of obtaining the PWM wave of the optimal basic voltage vector at the current moment based on the reference value of the motor q-axis current, the predicted current of the motor q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor d-axis at the next moment corresponding to each basic voltage vector at the current moment, and controlling the motor according to the PWM wave of the optimal basic voltage vector at the current moment specifically includes:

[0022] The reference value of the motor q-axis current, the predicted current of the motor q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor d-axis at the next moment corresponding to each basic voltage vector at the current moment are substituted into the performance index function, and the optimal basic voltage vector at the current moment is obtained with the minimum value of the performance index function as the objective.

[0023] The motor is controlled based on the PWM wave of the optimal basic voltage vector at the current moment.

[0024] Optionally, the calculation formula for the performance index function is as follows: Where g represents the performance index, i qref i is the reference value for the q-axis current of the motor. dref This is the reference value for the d-axis current. This represents the predicted current along the motor's d-axis at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted current of the motor's q-axis at the next moment, corresponding to the basic voltage vector at the current moment.

[0025] A predictive control system for a permanent magnet synchronous motor, comprising:

[0026] The acquisition module is used to acquire the current output current of the motor's d-axis, the current output current of the motor's q-axis, the current output voltage of the motor's q-axis corresponding to each basic voltage vector, the current output voltage of the motor's d-axis corresponding to each basic voltage vector, the predicted parameters of the motor's d-axis at the previous moment, the predicted parameters of the motor's q-axis at the previous moment, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, and the actual rotational speed of the motor at the current moment; the predicted parameters include output voltage and output current.

[0027] A PI controller is used to obtain a reference value for the q-axis current of the motor based on the current rotational speed and the set rotational speed value.

[0028] The parameter identifier is used to obtain the values ​​of each model parameter to be identified based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted parameters of the motor d-axis at the previous moment, and the predicted parameters of the motor q-axis at the previous moment.

[0029] A predictive controller is used to obtain the PWM wave of the optimal basic voltage vector at the current moment based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor's q-axis current, the values ​​of each parameter of the model to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment, and to control the motor according to the PWM wave of the optimal basic voltage vector at the current moment.

[0030] An electronic device, comprising:

[0031] A memory and a processor, the memory being used to store a computer program, the processor running the computer program to cause the electronic device to perform the predictive control method for a permanent magnet synchronous motor as described above.

[0032] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: The present invention obtains the values ​​of each model parameter to be identified based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted parameters of the motor d-axis at the previous moment, and the predicted parameters of the motor q-axis at the previous moment, thereby obtaining an accurate motor model. Based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted current of the motor d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor q-axis current, the values ​​of each model parameter to be identified, the output voltage of the motor q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor d-axis corresponding to each basic voltage vector at the current moment, the PWM wave of the optimal basic voltage vector at the current moment is obtained. The motor is controlled according to the PWM wave of the optimal basic voltage vector at the current moment. The predicted parameters of the motor d-axis and the predicted parameters of the motor q-axis are used to correct the error of the PWM wave of the voltage vector at the next moment, making the PWM wave of the voltage vector more accurate. Thus, the motor is controlled more accurately based on the PWM wave of the optimal basic voltage vector at the current moment. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart of a predictive control method for a permanent magnet synchronous motor provided in an embodiment of the present invention;

[0035] Figure 2 A block diagram of a predictive control system for a permanent magnet synchronous motor provided in an embodiment of the present invention;

[0036] Figure 3 The diagram shows the identification results using inductance parameters as an example when the motor parameters remain unchanged.

[0037] Figure 4 The diagram shows the identification results, taking inductance parameters as an example, when motor parameters undergo a step change.

[0038] Figure 5 The diagram shows the identification results, taking the inductance parameter as an example, when the motor parameters undergo a sinusoidal jump.

[0039] Figure 6 A comparison chart showing the speed output curves of motors without a feedback corrector, with a feedback corrector, and with a traditional MPC during the motor startup and operation phases. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] like Figure 1 As shown, this embodiment of the invention provides a predictive control method for a permanent magnet synchronous motor, including:

[0043] Step 101: Obtain the current output current of the motor's d-axis, the current output current of the motor's q-axis, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment, the predicted parameters of the motor's d-axis at the previous moment, the predicted parameters of the motor's q-axis at the previous moment, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, and the actual speed of the motor at the current moment; the predicted parameters include output voltage and output current. The basic voltage vector is calculated by multiplying the inverter's DC voltage by a vector table (8 values ​​corresponding to 8 switches in sequence).

[0044] Step 102: Obtain the reference value of the motor q-axis current based on the current rotational speed and the set rotational speed value.

[0045] Step 103: Obtain the values ​​of each model parameter to be identified based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted parameters of the motor d-axis at the previous moment, and the predicted parameters of the motor q-axis at the previous moment.

[0046] Step 104: Based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted current of the motor d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor q-axis current, the values ​​of each of the model parameters to be identified, the output voltage of the motor q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor d-axis corresponding to each basic voltage vector at the current moment, the PWM wave of the optimal basic voltage vector at the current moment is obtained, and the motor is controlled according to the PWM wave of the optimal basic voltage vector at the current moment.

[0047] In practical applications, the reference value for the motor q-axis current is obtained based on the current rotational speed and the set rotational speed value, specifically as follows:

[0048] The reference value of the motor q-axis current is obtained by processing the current rotational speed and the set rotational speed value using the PI algorithm.

[0049] In practical applications, the process of obtaining the values ​​of each model parameter to be identified based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted parameters of the motor's d-axis at the previous moment, and the predicted parameters of the motor's q-axis at the previous moment is as follows:

[0050] The identification equation is obtained by inputting the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted parameters of the motor's d-axis at the previous moment, and the predicted parameters of the motor's q-axis at the previous moment into the linear regression model of the identification model.

[0051] The recursive least squares estimation method with a forgetting factor is used to solve the identification equation to obtain the values ​​of the parameters of each model to be identified.

[0052] In practical applications, the process of obtaining the PWM wave of the optimal basic voltage vector at the current moment based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor's q-axis current, the values ​​of each parameter of the model to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment, and controlling the motor according to the PWM wave of the optimal basic voltage vector at the current moment, specifically includes:

[0053] Based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the values ​​of each parameter of the model to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment, the predicted current of the motor's q-axis corresponding to each basic voltage vector at the current moment and the predicted current of the motor's d-axis corresponding to each basic voltage vector at the current moment are obtained.

[0054] Based on the reference value of the motor q-axis current, the predicted current of the motor q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor d-axis at the next moment corresponding to each basic voltage vector at the current moment, the PWM wave of the optimal basic voltage vector at the current moment is obtained, and the motor is controlled according to the PWM wave of the optimal basic voltage vector at the current moment.

[0055] In practical applications, the method of obtaining the predicted current of the motor q-axis at the next moment based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted current of the motor d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor q-axis corresponding to the optimal basic voltage vector at the previous moment, the values ​​of the parameters of each model to be identified, the output voltage of the motor q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor d-axis corresponding to each basic voltage vector at the current moment, specifically involves:

[0056] According to the formula Calculate the predicted current of the motor's q-axis at the next moment corresponding to the current basic voltage vector and the predicted current of the motor's d-axis at the next moment corresponding to the current basic voltage vector. This represents the predicted current along the motor's d-axis at the next moment, corresponding to the basic voltage vector at the current moment. i represents the predicted current of the motor's q-axis at the next moment, corresponding to the basic voltage vector at the current moment. d (k) represents the current output current of the motor's d-axis at the current moment, i q (k) represents the output current of the motor's q-axis at the current moment. This represents the output voltage of the motor along the d-axis corresponding to the basic voltage vector at the current moment. This represents the output voltage of the motor along the q-axis corresponding to the basic voltage vector at the current moment. This represents the predicted current of the motor's d-axis at the current moment, corresponding to the optimal basic voltage vector at the previous moment. This represents the predicted current of the motor's q-axis at the current moment, corresponding to the optimal basic voltage vector at the previous moment. 'a' represents the first model parameter to be identified, 'b' represents the second model parameter to be identified, 'c' represents the third model parameter to be identified, 'w' represents the electric angular velocity, and 'T' represents the electric angular velocity. s This indicates the system's sampling period.

[0057] In practical applications, the PWM wave of the optimal basic voltage vector at the current moment is obtained based on the reference value of the motor's q-axis current, the predicted current of the motor's q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor's d-axis at the next moment corresponding to each basic voltage vector at the current moment. The motor is then controlled according to the PWM wave of the optimal basic voltage vector at the current moment, specifically including:

[0058] The reference value of the motor q-axis current, the predicted current of the motor q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor d-axis at the next moment corresponding to each basic voltage vector at the current moment are substituted into the performance index function, and the optimal basic voltage vector at the current moment is obtained with the minimum value of the performance index function as the objective.

[0059] The motor is controlled based on the PWM wave of the optimal basic voltage vector at the current moment.

[0060] In practical applications, when calculating the predicted current, eight basic voltage vectors need to be substituted. Each voltage vector corresponds to a set of predicted currents, so eight performance index function values ​​can be calculated. The PWM wave of the voltage vector corresponding to the smallest performance index function value is the PWM wave corresponding to the expected optimal voltage vector.

[0061] In practical applications, the calculation formula for the performance index function is as follows: Where g represents the performance index, i qref i is the reference value for the q-axis current of the motor. dref This is the reference value for the d-axis current. This represents the predicted current along the motor's d-axis at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted current of the motor's q-axis at the next moment, corresponding to the basic voltage vector at the current moment.

[0062] like Figure 2 As shown, in addition to the above method, this embodiment of the invention also provides a predictive control system for a permanent magnet synchronous motor, including:

[0063] The acquisition module is used to acquire the current output current of the motor's d-axis, the current output current of the motor's q-axis, the current output voltage of the motor's q-axis corresponding to each basic voltage vector, the current output voltage of the motor's d-axis corresponding to each basic voltage vector, the predicted parameters of the motor's d-axis at the previous moment, the predicted parameters of the motor's q-axis at the previous moment, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, and the actual rotational speed of the motor at the current moment; the predicted parameters include output voltage and output current.

[0064] PI controller is Figure 2 The PI in the equation is used to obtain a reference value for the q-axis current of the motor based on the current rotational speed and the set rotational speed value.

[0065] The parameter identifier is Figure 2The parameter identification in the model is used to obtain the values ​​of each model parameter to be identified based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted parameters of the motor's d-axis at the previous moment, and the predicted parameters of the motor's q-axis at the previous moment.

[0066] Predictive controller is Figure 2 The predictive control in the model is used to obtain the PWM wave of the optimal basic voltage vector at the current moment based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor's q-axis current, the values ​​of each parameter of the model to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment. The PWM wave of the optimal basic voltage vector at the current moment is then used to... Figure 2 The PMSM in the system is used for control.

[0067] In practical applications, for permanent magnet synchronous motors, for ease of study, the following assumptions are generally made: core magnetic saturation is ignored, the electromotive force is sinusoidal, harmonic factors are ignored, iron losses caused by eddy currents and hysteresis are ignored, and three-phase symmetry is assumed. In the rotor rotating coordinate system, the dynamic voltage model of the permanent magnet synchronous motor can be written as follows:

[0068]

[0069] In the formula, L d and L q These are the inductances along the d-axis and q-axis, respectively. For a surface-mounted permanent magnet synchronous motor, the inductances along the d-axis and q-axis are the same, so we set them all to L. Therefore, the motor model is as follows:

[0070]

[0071] Due to the saturation of the internal magnetic field of the motor, in actual operation, the motor's inductance will affect the motor control effect due to changes in stator current. Prolonged motor operation will cause temperature changes, thus affecting the motor's resistance value, and the motor's flux linkage will also deviate from the factory value. Therefore, this invention designs an algorithm based on online data updates of motor model parameters to obtain a real-time and accurate motor model, and then uses a model predictive control algorithm to achieve overall control of the permanent magnet synchronous motor system.

[0072] The PI controller takes the set speed value and the actual output speed value of the permanent magnet synchronous motor as input data. It performs proportional and integral calculations on the deviation between the two values ​​to output a reference current signal for the motor. This signal primarily utilizes the set speed value N of the permanent magnet synchronous motor. refThe reference current signal of the motor is calculated by linearly combining the proportional and integral values ​​of the deviation from the actual output speed N. To achieve static decoupling of the d-axis and q-axis currents, the reference value i of the d-axis current is used. dref Set to 0.

[0073] According to formula i qref =K p (N ref (t)-N(t))+K i ∫(N ref (t)-N(t))dt(3) Calculate the reference value of the q-axis current, where i qref K is the reference value for the q-axis current. p K is the control coefficient for proportional control. i N represents the control coefficient for integral control. ref N(t) represents the set speed of the motor at the current moment, and N(t) represents the actual speed of the motor at the current moment, where t represents the time. The proportional control is based on the deviation signal of the motor speed, using the control coefficient K. p Adjust the control output to make the controlled variable change in the direction of reducing the deviation, K p The larger the value, the faster the deviation decreases, but it is also more prone to oscillations. Integral control can eliminate steady-state errors and increase the system's resistance to high-frequency interference. The integral term with respect to the error depends on the integral over time; as time increases, the integral term increases. Thus, even if the error is small, the integral term will increase with time, and this is further compounded by the control coefficient K. i The controller's output increases, further reducing the steady-state error until it equals zero.

[0074] In practical applications, the parameter identifier mainly uses the actual output speed and voltage / current signals of the permanent magnet synchronous motor to complete the online identification of the motor model parameters and output the real-time and accurate motor model parameters. By linearizing the mathematical model (2) of the motor, the motor identification model shown in formula (4) can be defined.

[0075]

[0076] In this invention, the parameters to be identified are designed as follows:

[0077]

[0078]

[0079]

[0080] In the formula, a, b, and c are the parameters of the motor model to be identified, where a represents the first parameter of the model to be identified, b represents the second parameter of the model to be identified, and u d(k-1) represents the output voltage of the motor's d-axis at time k-1, u q (k-1) represents the output voltage of the motor's q-axis at time k-1, c represents the third parameter of the model to be identified, and T s R is the sampling period of the system. s Let L be the stator armature resistance of the motor, L be the inductance of the motor's d-axis or q-axis, and ψ be the inductance of the motor's d-axis or q-axis. f This refers to the permanent magnet flux linkage of the motor rotor. Parameter identification allows us to obtain the motor's model parameters, enabling the acquisition of an accurate motor model in real time.

[0081] This invention employs recursive least squares with a forgetting factor for online parameter identification. By comparing the derivation process of the recursive least squares method, the iterative formula for parameter estimation using recursive least squares with a forgetting factor can be obtained, as shown in the following formula:

[0082]

[0083]

[0084]

[0085] In the formula, the initial values ​​of the covariance matrix and the identified parameter are set as P(0) = αI. α is a sufficiently large positive real number, typically 10. 4 ~10 6 ε can be a sufficiently small positive real vector or the zero vector.

[0086] The motor identification model (4) can be reorganized into the form of the following linear regression model.

[0087]

[0088] In the formula, Y(k) is the output variable matrix, and Φ(k) is the regression information matrix containing the input and output data. The parameter vector to be identified is v(k), which is a white noise vector. Combining equation (11) with the motor identification model (4) yields the specific data information of the above matrix.

[0089]

[0090]

[0091]

[0092] Set the performance index function J of the parameter identifier to...

[0093]

[0094] In the formula, λ is the forgetting factor, which should be a positive number close to 1, usually not less than 0.9; if the system is linear, it should be 0.95≤λ≤1. Based on the performance index function J of the parameter identifier, the recursive least squares method is used to identify the parameters online to obtain the specific values ​​of a, b, and c.

[0095] In practical applications, this invention uses the motor identification model (4) obtained by the parameter identifier to design a current-based prediction model, as shown in formula (16):

[0096]

[0097] During motor operation, sensor detection delays and inaccurate parameter identification results can affect the control performance of the model predictive controller. In this invention, a feedback corrector is set as... Figure 2 In the feedback correction, the input data of the feedback corrector is the current prediction value calculated by the predictive controller at the previous time step. This value is compared with the actual current output value of the motor at the current time step, thus outputting the feedback correction value of the model predictive control. This value is then used to compensate the predictive model of the predictive controller. Through the feedback corrector, the error between the actual and predicted values ​​of the motor current is used to correct the current-based prediction model. The designed feedback correction error e(k) is...

[0098]

[0099] Therefore, the corrected current-based prediction model, i.e., the model predictive controller, is as follows:

[0100]

[0101] In practical applications, for a two-level three-phase inverter, the basic voltage vector u (the voltage vector synthesized from the d-axis output voltage and the q-axis output voltage) includes 6 non-zero vectors and 2 zero vectors. Therefore, based on the current prediction model (18), the predictive controller can predict the motor current at the next moment under the action of 8 different voltage vectors, and input the predicted current value into the performance index function (19).

[0102] The voltage vector with the smallest performance index function is selected as the optimal basic voltage vector at the current moment. The PWM wave corresponding to the optimal basic voltage vector at the current moment is sent to the inverter as the control voltage vector corresponding to the next moment, and then applied to the permanent magnet synchronous motor.

[0103] This invention also provides an electronic device, comprising:

[0104] A memory and a processor, wherein the memory stores a computer program and the processor runs the computer program to cause the electronic device to perform the predictive control method for a permanent magnet synchronous motor according to the above embodiments.

[0105] To verify the advantages of the control scheme of this invention compared with the traditional MPC method, this invention provides an example of using MATLAB software to simulate and verify the control method of permanent magnet synchronous motor:

[0106] The prediction model used in traditional MPC methods is as follows:

[0107]

[0108] Traditional MPC methods use the motor's factory default parameters as model parameters, while the control scheme of this invention uses real-time motor parameters obtained through online identification, with the speed setpoint set to 1000 rad / s. Simulation results are as follows. Figures 3-6 As shown.

[0109] Figure 3 This is the identification result of the present invention, taking inductance parameters as an example when the motor parameters have not changed. Figure 3 It can be seen that the identification result of the present invention has a very small error, and can obtain a relatively accurate identification result when the motor parameters have not changed.

[0110] Figure 4 and Figure 5 This is the identification result of the present invention when the motor parameters change over time, taking the inductance parameter as an example. Figure 4 As can be seen, the present invention can quickly identify changes in motor parameters when the motor undergoes a step change and obtain accurate motor parameters in real time; Figure 5 This invention demonstrates that it can quickly track sinusoidal changes in motor parameters, with rapid response and accurate results.

[0111] Regarding the speed control effect, the traditional MPC method (speed N0) exhibits significant oscillations. The control schemes in this invention, without a feedback corrector (speed N1) and with a feedback corrector (speed N2), achieve more ideal control results after a brief overshoot. Figure 6 As shown. From Figure 6 As can be seen in the inset, during the motor startup phase, the speed overshoot with the feedback corrector is slightly smaller than without it, demonstrating that the feedback corrector achieves the designed effect. Therefore, compared to the traditional MPC method, the method of this invention can identify accurate online motor model parameters and possesses superior speed control performance.

[0112] This invention provides a predictive control method based on recursive least squares identification for permanent magnet synchronous motor control systems with unknown or time-varying motor model parameters. The method uses the input and output data of the controlled object to identify the motor parameters of the permanent magnet synchronous motor model online.

[0113] The present invention has the following technical effects:

[0114] 1. This invention is a data-based predictive control method that can be designed and implemented based on the real-time input and output data of the controlled permanent magnet synchronous motor system.

[0115] 2. This invention can identify a real-time and accurate motor model when the motor model parameters are unknown or time-varying.

[0116] 3. The control concept of this invention is easy to understand, the control structure is simple and flexible, easy to implement, and easy to apply and promote in practical engineering.

[0117] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0118] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A predictive control method for a permanent magnet synchronous motor, characterized in that, include: The following parameters are obtained: the current output current of the motor's d-axis, the current output current of the motor's q-axis, the current output voltage of the motor's q-axis corresponding to each basic voltage vector, the current output voltage of the motor's d-axis corresponding to each basic voltage vector, the predicted parameters of the motor's d-axis at the previous moment, the predicted parameters of the motor's q-axis at the previous moment, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, and the actual speed of the motor at the current moment. The prediction parameters include output voltage and output current; The reference value of the motor q-axis current is obtained based on the current rotational speed and the set rotational speed value; The values ​​of each model parameter to be identified are obtained based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted parameters of the motor d-axis at the previous moment, and the predicted parameters of the motor q-axis at the previous moment. Based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor's q-axis current, the values ​​of each parameter of the model to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment, the PWM wave of the optimal basic voltage vector at the current moment is obtained, and the motor is controlled according to the PWM wave of the optimal basic voltage vector at the current moment.

2. The predictive control method for permanent magnet synchronous motors according to claim 1, characterized in that, The reference value for the motor q-axis current is obtained based on the current rotational speed and the set rotational speed value, specifically as follows: The reference value of the motor q-axis current is obtained by processing the current rotational speed and the set rotational speed value using the PI algorithm.

3. The predictive control method for permanent magnet synchronous motors according to claim 1, characterized in that, The process of obtaining the values ​​of each model parameter to be identified based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted parameters of the motor's d-axis at the previous moment, and the predicted parameters of the motor's q-axis at the previous moment is as follows: The identification equation is obtained by inputting the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted parameters of the motor's d-axis at the previous moment, and the predicted parameters of the motor's q-axis at the previous moment into the linear regression model of the identification model. The recursive least squares estimation method with a forgetting factor is used to solve the identification equation to obtain the values ​​of the parameters of each model to be identified.

4. The predictive control method for permanent magnet synchronous motors according to claim 1, characterized in that, The process involves obtaining the PWM wave of the optimal basic voltage vector at the current moment based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor's q-axis current, the values ​​of each parameter of the model to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment. Controlling the motor based on the PWM wave of the optimal basic voltage vector at the current moment specifically includes: Based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted current of the motor d-axis corresponding to the optimal basic voltage vector of the previous moment, the predicted current of the motor q-axis corresponding to the optimal basic voltage vector of the previous moment, the values ​​of each parameter of the model to be identified, the output voltage of the motor q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor d-axis corresponding to each basic voltage vector at the current moment, the predicted current of the motor q-axis corresponding to each basic voltage vector at the current moment and the predicted current of the motor d-axis corresponding to each basic voltage vector at the current moment are obtained. Based on the reference value of the motor q-axis current, the predicted current of the motor q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor d-axis at the next moment corresponding to each basic voltage vector at the current moment, the PWM wave of the optimal basic voltage vector at the current moment is obtained, and the motor is controlled according to the PWM wave of the optimal basic voltage vector at the current moment.

5. The predictive control method for a permanent magnet synchronous motor according to claim 4, characterized in that, The method of obtaining the predicted current of the motor q-axis at the next moment based on the current output current of the motor d-axis, the current output current of the motor q-axis, the predicted current of the motor d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor q-axis corresponding to the optimal basic voltage vector at the previous moment, the values ​​of the parameters of each model to be identified, the output voltage of the motor q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor d-axis corresponding to each basic voltage vector at the current moment, specifically involves: According to the formula Calculate the predicted current of the motor's q-axis at the next moment corresponding to the current basic voltage vector and the predicted current of the motor's d-axis at the next moment corresponding to the current basic voltage vector. This represents the predicted current along the motor's d-axis at the next moment, corresponding to the basic voltage vector at the current moment. i represents the predicted current of the motor's q-axis at the next moment, corresponding to the basic voltage vector at the current moment. d (k) represents the current output current of the motor's d-axis at the current moment, i q (k) represents the output current of the motor's q-axis at the current moment. This represents the output voltage of the motor along the d-axis corresponding to the basic voltage vector at the current moment. This represents the output voltage of the motor along the q-axis corresponding to the basic voltage vector at the current moment. This represents the predicted current of the motor's d-axis at the current moment, corresponding to the optimal basic voltage vector at the previous moment. This represents the predicted current of the motor's q-axis at the current moment, corresponding to the optimal basic voltage vector at the previous moment. 'a' represents the first model parameter to be identified, 'b' represents the second model parameter to be identified, 'c' represents the third model parameter to be identified, 'w' represents the electric angular velocity, and 'T' represents the electric angular velocity. s This indicates the system's sampling period.

6. The predictive control method for a permanent magnet synchronous motor according to claim 4, characterized in that, The process involves obtaining the PWM wave of the optimal basic voltage vector at the current moment based on the reference value of the motor's q-axis current, the predicted current of the motor's q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor's d-axis at the next moment corresponding to each basic voltage vector at the current moment. Controlling the motor based on the PWM wave of the optimal basic voltage vector at the current moment specifically includes: The reference value of the motor q-axis current, the predicted current of the motor q-axis at the next moment corresponding to each basic voltage vector at the current moment, and the predicted current of the motor d-axis at the next moment corresponding to each basic voltage vector at the current moment are substituted into the performance index function, and the optimal basic voltage vector at the current moment is obtained with the minimum value of the performance index function as the objective. The motor is controlled based on the PWM wave of the optimal basic voltage vector at the current moment.

7. The predictive control method for a permanent magnet synchronous motor according to claim 6, characterized in that, The formula for calculating the performance index function is as follows: Where g represents the performance index, i qref i is the reference value for the q-axis current of the motor. dref This is the reference value for the d-axis current. This represents the predicted current along the motor's d-axis at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted current of the motor's q-axis at the next moment, corresponding to the basic voltage vector at the current moment.

8. A predictive control system for a permanent magnet synchronous motor, characterized in that, include: The acquisition module is used to acquire the current output current of the motor's d-axis, the current output current of the motor's q-axis, the current output voltage of the motor's q-axis corresponding to each basic voltage vector, the current output voltage of the motor's d-axis corresponding to each basic voltage vector, the predicted parameters of the motor's d-axis at the previous moment, the predicted parameters of the motor's q-axis at the previous moment, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, and the actual speed of the motor at the current moment. The prediction parameters include output voltage and output current; A PI controller is used to obtain a reference value for the q-axis current of the motor based on the current rotational speed and the set rotational speed value. The parameter identifier is used to obtain the values ​​of each model parameter to be identified based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted parameters of the motor's d-axis at the previous moment, and the predicted parameters of the motor's q-axis at the previous moment. A predictive controller is used to obtain the PWM wave of the optimal basic voltage vector at the current moment based on the current output current of the motor's d-axis, the current output current of the motor's q-axis, the predicted current of the motor's d-axis corresponding to the optimal basic voltage vector at the previous moment, the predicted current of the motor's q-axis corresponding to the optimal basic voltage vector at the previous moment, the reference value of the motor's q-axis current, the values ​​of each parameter of the model to be identified, the output voltage of the motor's q-axis corresponding to each basic voltage vector at the current moment, and the output voltage of the motor's d-axis corresponding to each basic voltage vector at the current moment, and to control the motor according to the PWM wave of the optimal basic voltage vector at the current moment.

9. An electronic device, characterized in that, include: A memory and a processor, the memory for storing a computer program, the processor for running the computer program to cause the electronic device to perform the predictive control method for a permanent magnet synchronous motor according to any one of claims 1 to 7.

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