Permanent magnet synchronous motor adaptive control method and system, electronic device and medium

Abstract

The application discloses a permanent magnet synchronous motor adaptive control method and system, electronic equipment and medium, and relates to the technical field of engineering control. The method comprises the following steps: a forward Euler method is used to discretize a voltage equation of the permanent magnet synchronous motor to obtain a discretization model at a current time and a discretization model at a previous time; the two discretization models are subtracted to obtain an incremental model; a parameter identification model is obtained according to the incremental model; the values of each to-be-identified model parameter at the current time are calculated according to the identification values; the incremental correction prediction model at the current time is obtained according to the parameter identification model and the values of each to-be-identified model parameter at the current time; the permanent magnet synchronous motor at the current time is controlled by using the incremental correction prediction model at the current time, and the values of each to-be-identified model parameter at the current time are calculated according to the identification values, so that the precision of the permanent magnet synchronous motor control can be improved.

Description

Technical Field

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

[0002] Permanent magnet synchronous motors (PMSMs), as highly efficient and energy-saving motors, are characterized by their small size, high power factor, and high torque-to-current ratio, and are widely used in aerospace, intelligent robotics, electric vehicles, high-speed trains, and other production and daily life fields. In recent years, the development of new power electronic devices, intelligent control, and high-frequency magnetic technologies has further expanded the breadth and depth of applications for PMSM-related control methods. Model predictive control not only boasts advantages such as fast dynamic response, simple control structure, and wide applicability, but also enables the optimization of multiple objectives through nonlinear constraints, thus becoming one of the popular control methods applied to motor control systems.

[0003] However, when the motor is running, factors such as winding magnetic saturation and changes in ambient temperature can cause changes in inductance and resistance parameters, leading to model parameter mismatch. The control effect of model predictive control is affected by the accuracy of the model parameters. Inaccurate prediction of model parameters will affect the estimation accuracy of current, torque or rotor position. However, existing control methods do not take these situations into account, and the model parameters do not automatically adjust over time, resulting in low control accuracy of permanent magnet synchronous motors, which in turn degrades the system control performance and increases steady-state error. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive control method, system, electronic device, and medium for permanent magnet synchronous motors, which can improve the control accuracy of permanent magnet synchronous motors.

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

[0006] An adaptive control method for a permanent magnet synchronous motor includes:

[0007] In a two-phase rotating coordinate system, the voltage equation of the permanent magnet synchronous motor is obtained;

[0008] The voltage equations of the permanent magnet synchronous motor are discretized using the forward Euler method to obtain the discretized model at the current moment and the discretized model at the previous moment.

[0009] The incremental model of the permanent magnet synchronous motor is obtained by subtracting the discretized model at the current moment from the discretized model at the previous moment.

[0010] The parameter identification model of the permanent magnet synchronous motor is obtained based on the incremental model of the permanent magnet synchronous motor.

[0011] The values ​​of each parameter of the permanent magnet synchronous motor to be identified at the current moment are calculated based on the identification values. The identification values ​​include: the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the previous moment, the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the current moment, the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the moment before last, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the previous moment, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the current moment, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the moment before last, the voltage component of the optimal voltage vector in the q-axis at the previous moment, the voltage component of the optimal voltage vector in the d-axis at the moment before last, and the voltage component of the optimal voltage vector in the d-axis at the moment before last.

[0012] Based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of the parameters of each model to be identified at the current time in the parameter identification model of the permanent magnet synchronous motor, the incremental correction prediction model of the permanent magnet synchronous motor at the current time is obtained.

[0013] With the goal of minimizing the performance index function value, the incremental correction prediction model of the permanent magnet synchronous motor at the current moment is used to control the permanent magnet synchronous motor at the current moment, and the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor calculated based on the identification value at the current moment are updated.

[0014] Optionally, obtaining the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time specifically includes:

[0015] Based on the values ​​of the parameters of each model to be identified at the current moment in the parameter identification model of the permanent magnet synchronous motor and the parameter identification model of the permanent magnet synchronous motor, the incremental prediction model of the permanent magnet synchronous motor at the current moment is obtained.

[0016] Based on the incremental prediction model of the permanent magnet synchronous motor at the current moment, the incremental update prediction model of the permanent magnet synchronous motor at the current moment is obtained.

[0017] The incremental correction prediction model of the permanent magnet synchronous motor at the current moment is obtained based on the incremental update prediction model of the permanent magnet synchronous motor at the current moment.

[0018] Optionally, the parameter identification model of the permanent magnet synchronous motor is specifically as follows:

[0019] Among them, i d(k) represents the current component of the stator current on the d-axis of the permanent magnet synchronous motor at the current moment, i q (k) represents the current component of the stator current in the q-axis at the current moment of the permanent magnet synchronous motor, i d (k-1) represents the d-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i q (k-1) represents the q-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i d (k-2) represents the stator current component along the d-axis at time i. q (k-2) represents the q-axis current component of the stator current at time u of the permanent magnet synchronous motor. d (k-1) represents the voltage component of the optimal voltage vector along the d-axis at the previous moment, u q (k-1) represents the voltage component of the optimal voltage vector along the q-axis at the previous moment, u d (k-2) represents the voltage component of the optimal voltage vector along the d-axis at the previous time step, u q (k-2) represents the voltage component of the optimal voltage vector on the q-axis at the previous time step, a represents the first model parameter to be identified, b represents the second model parameter to be identified, ω represents the electric angular velocity of the permanent magnet synchronous motor rotor, and T s Indicates the sampling period.

[0020] Optionally, the incremental correction prediction model for the permanent magnet synchronous motor is as follows:

[0021] in, This represents the predicted d-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted q-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted d-axis current at the current moment, corresponding to the optimal voltage vector at the previous moment. This represents the predicted q-axis current at the current moment, corresponding to the optimal voltage vector at the previous moment. This represents the d-axis voltage corresponding to the basic voltage vector of the permanent magnet synchronous motor at the current moment. This represents the q-axis voltage corresponding to the basic voltage vector of the permanent magnet synchronous motor at the current moment.

[0022] An adaptive control system for a permanent magnet synchronous motor includes:

[0023] The acquisition module is used to obtain the voltage equation of the permanent magnet synchronous motor in a two-phase rotating coordinate system.

[0024] The discrete module is used to discretize the voltage equation of the permanent magnet synchronous motor using the forward Euler method to obtain the discretized model at the current moment and the discretized model at the previous moment.

[0025] The incremental model determination module is used to obtain the incremental model of the permanent magnet synchronous motor by subtracting the discretized model at the current moment from the discretized model at the previous moment.

[0026] The parameter identification model determination module is used to obtain the parameter identification model of the permanent magnet synchronous motor based on the incremental model of the permanent magnet synchronous motor.

[0027] The module for determining parameters of the model to be identified is used to calculate the values ​​of each parameter of the model to be identified at the current moment in the parameter identification model of the permanent magnet synchronous motor based on the identification values. The identification values ​​include: the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the previous moment, the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the current moment, the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the moment before last, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the previous moment, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the current moment, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the moment before last, the voltage component of the optimal voltage vector in the q-axis at the previous moment, the voltage component of the optimal voltage vector in the d-axis at the moment before last, and the voltage component of the optimal voltage vector in the d-axis at the moment before last.

[0028] The incremental correction prediction model determination module is used to obtain the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of the parameters of each model to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time.

[0029] The control module is used to control the permanent magnet synchronous motor at the current moment by using the incremental correction prediction model of the permanent magnet synchronous motor at the current moment with the goal of minimizing the performance index function value, and to update the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor at the current moment, which is returned based on the identification value.

[0030] Optionally, the incremental correction prediction model determination module specifically includes:

[0031] An incremental prediction model determination unit is used to obtain the incremental prediction model of the permanent magnet synchronous motor at the current time based on the values ​​of the parameters of each model to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time and the parameter identification model of the permanent magnet synchronous motor.

[0032] The incremental update prediction model determination unit is used to obtain the incremental update prediction model of the permanent magnet synchronous motor at the current time based on the incremental prediction model of the permanent magnet synchronous motor at the current time.

[0033] The feedback corrector is used to obtain the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the incremental update prediction model of the permanent magnet synchronous motor at the current time.

[0034] Optionally, the parameter identification model of the permanent magnet synchronous motor is specifically as follows:

[0035] Among them, i d (k) represents the current component of the stator current on the d-axis of the permanent magnet synchronous motor at the current moment, i q (k) represents the current component of the stator current in the q-axis at the current moment of the permanent magnet synchronous motor, i d (k-1) represents the d-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i q (k-1) represents the q-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i d (k-2) represents the stator current component along the d-axis at time i. q (k-2) represents the q-axis current component of the stator current at time u of the permanent magnet synchronous motor. d (k-1) represents the voltage component of the optimal voltage vector along the d-axis at the previous moment, u q (k-1) represents the voltage component of the optimal voltage vector along the q-axis at the previous moment, u d (k-2) represents the voltage component of the optimal voltage vector along the d-axis at the previous time step, u q (k-2) represents the voltage component of the optimal voltage vector on the q-axis at the previous time step, a represents the first model parameter to be identified, b represents the second model parameter to be identified, ω represents the electric angular velocity of the permanent magnet synchronous motor rotor, and T s Indicates the sampling period.

[0036] Optionally, the incremental correction prediction model for the permanent magnet synchronous motor is as follows:

[0037] in,

[0038] This represents the predicted d-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. surface

[0039] This shows the predicted q-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. Indicated above

[0040] The optimal voltage vector at a given moment corresponds to the predicted d-axis current at the current moment. Indicates the previous moment

[0041] The optimal voltage vector corresponds to the predicted q-axis current at the current moment. Indicates permanent magnet synchronous motor

[0042] The d-axis voltage corresponding to the fundamental voltage vector at the current moment. Indicates the current moment of the permanent magnet synchronous motor

[0043] The q-axis voltage corresponding to the basic voltage vector.

[0044] An electronic device includes a memory and a processor, the memory storing a computer program and the processor running the computer program to cause the electronic device to perform the adaptive control method for a permanent magnet synchronous motor as described above.

[0045] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the adaptive control method for a permanent magnet synchronous motor as described above.

[0046] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0047] This invention obtains an incremental model based on the discretized models of the previous and current time points, and then obtains an identification model based on the incremental model. Based on the identification model and the real-time identification values, the identification parameters are calculated in real time to obtain a real-time incremental correction prediction model, which can perform adaptive control of the motor and thus improve the control accuracy of the permanent magnet synchronous motor. Attached Figure Description

[0048] 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.

[0049] Figure 1 A flowchart of the adaptive control method for permanent magnet synchronous motors provided by the present invention;

[0050] Figure 2 A block diagram of the adaptive control system for permanent magnet synchronous motors provided by the present invention;

[0051] Figure 3 The figure shows the identification results of the incremental model of the permanent magnet synchronous motor provided by this invention, taking the inductance parameter as an example, without parameter mismatch.

[0052] Figure 4 The figure shows the identification results of the incremental model of the permanent magnet synchronous motor provided by this invention when the motor parameters undergo a step change, taking the inductance parameter as an example.

[0053] Figure 5 The diagram shows the identification results of the incremental model of the permanent magnet synchronous motor provided by this invention when the motor parameters undergo a sinusoidal jump, taking the inductance parameter as an example.

[0054] Figure 6 This is a comparison chart of the speed simulation results between the control method of this invention and the traditional model predictive control method. Detailed Implementation

[0055] 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.

[0056] 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.

[0057] When a motor is running, it causes changes in the ambient temperature, a significant reason for variations in the motor's resistance. Saturation or coupling in the motor's magnetic circuit can also cause changes in its inductance parameters. The rotor flux linkage determines the stator current vector, and its changes affect the stator current and thus the motor's control performance. Therefore, this invention provides an adaptive control method for permanent magnet synchronous motors (PMSMs). This method primarily utilizes the motor's real-time voltage and current signals to identify the parameters of the incremental model of the PMSM online, resulting in a real-time and accurate incremental correction prediction model. Based on this model, a model predictive control algorithm is used for predictive control, achieving overall control of the PMSM system and realizing adaptive predictive control.

[0058] like Figure 1 As shown, this embodiment of the invention provides an adaptive control method for a permanent magnet synchronous motor, applicable to a permanent magnet synchronous motor control system when motor parameters are mismatched. The method includes:

[0059] Step 101: Obtain the voltage equation of the permanent magnet synchronous motor in a two-phase rotating coordinate system.

[0060] Step 102: Discretize the voltage equation of the permanent magnet synchronous motor using the forward Euler method to obtain the discretized model at the current moment and the discretized model at the previous moment.

[0061] Step 103: Subtract the discretized model at the current moment from the discretized model at the previous moment to obtain the incremental model of the permanent magnet synchronous motor.

[0062] Step 104: Obtain the parameter identification model of the permanent magnet synchronous motor based on the incremental model of the permanent magnet synchronous motor.

[0063] Step 105: Calculate the values ​​of each parameter of the permanent magnet synchronous motor in the parameter identification model at the current moment based on the identification values; the identification values ​​include: the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the previous moment, the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the current moment, the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the moment before last, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the previous moment, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the current moment, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the moment before last, the voltage component of the optimal voltage vector in the q-axis at the previous moment, the voltage component of the optimal voltage vector in the d-axis at the moment before last, and the voltage component of the optimal voltage vector in the d-axis at the moment before last.

[0064] Step 106: Obtain the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time.

[0065] Step 107: With the goal of minimizing the performance index function value, the incremental correction prediction model of the permanent magnet synchronous motor at the current moment is used to control the permanent magnet synchronous motor at the current moment, and the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor calculated based on the identification value at the current moment are updated.

[0066] In practical applications, the voltage equation for a permanent magnet synchronous motor is:

[0067]

[0068] In the formula, u d and u q Let i be the components of the motor voltage along the dq axis in a two-phase rotating coordinate system. d and i q These are the components of the motor current along the dq axis in a two-phase rotating coordinate system, R. sFor surface-mounted permanent magnet synchronous motors, the stator armature resistance is L, and the inductances of the d and q axes are the same, so we set them uniformly as L. ω is the electric angular velocity, and ψ is the stator armature resistance. f Let t represent the rotor permanent magnet flux linkage, and t represent the current time.

[0069] In practical applications, the step of obtaining the incremental correction prediction model of the permanent magnet synchronous motor at the current moment based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor at the current moment specifically includes:

[0070] Based on the values ​​of the parameters of each model to be identified at the current moment in the parameter identification model of the permanent magnet synchronous motor and the parameter identification model of the permanent magnet synchronous motor, the incremental prediction model of the permanent magnet synchronous motor at the current moment is obtained.

[0071] Based on the incremental prediction model of the permanent magnet synchronous motor at the current moment, the incremental update prediction model of the permanent magnet synchronous motor at the current moment is obtained.

[0072] The incremental correction prediction model of the permanent magnet synchronous motor at the current moment is obtained based on the incremental update prediction model of the permanent magnet synchronous motor at the current moment.

[0073] In practical applications, the parameter identification model of the permanent magnet synchronous motor is specifically Equation (8) in the following system embodiment.

[0074] In practical applications, the incremental correction prediction model of the permanent magnet synchronous motor is specifically Equation (20) in the following system embodiment.

[0075] This invention also provides an adaptive control system for a permanent magnet synchronous motor corresponding to the above-described method. The system's input data includes the permanent magnet synchronous motor's speed reference value, real-time speed value, voltage, and current signals. The output data is the switching sequence of the control inverter, such as... Figure 2 As shown, the system includes: an acquisition module and a parameter identifier ( Figure 2 The parameter identifier includes a discrete module, an incremental model determination module, a parameter identification model determination module, and a parameter determination module for the model to be identified.

[0076] The acquisition module is used to obtain the voltage equation of the permanent magnet synchronous motor in a two-phase rotating coordinate system.

[0077] The discrete module is used to discretize the voltage equation of the permanent magnet synchronous motor using the forward Euler method to obtain the discretized model at the current moment and the discretized model at the previous moment.

[0078] The incremental model determination module is used to obtain the incremental model of the permanent magnet synchronous motor by subtracting the discretized model at the current moment from the discretized model at the previous moment.

[0079] The parameter identification model determination module is used to obtain the parameter identification model of the permanent magnet synchronous motor based on the incremental model of the permanent magnet synchronous motor.

[0080] The module for determining parameters of the model to be identified is used to calculate the values ​​of each parameter of the model to be identified at the current moment in the parameter identification model of the permanent magnet synchronous motor based on the identification values. The identification values ​​include: the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the previous moment, the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the current moment, the current component of the stator current of the permanent magnet synchronous motor in the q-axis at the moment before last, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the previous moment, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the current moment, the current component of the stator current of the permanent magnet synchronous motor in the d-axis at the moment before last, the voltage component of the optimal voltage vector in the q-axis at the previous moment, the voltage component of the optimal voltage vector in the d-axis at the previous moment, and the voltage component of the optimal voltage vector in the d-axis at the previous moment.

[0081] The incremental correction prediction model determination module is used to obtain the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of the parameters of each model to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time.

[0082] The control module is used to control the permanent magnet synchronous motor at the current moment by using the incremental correction prediction model of the permanent magnet synchronous motor at the current moment with the goal of minimizing the performance index function value, and to update the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor at the current moment, which is returned based on the identification value.

[0083] In practical applications, the adaptive control system for permanent magnet synchronous motors also includes:

[0084] PI controller ( Figure 2 PI in the formula is used to calculate i qref =K p (N ref (t)-N(t))+K i ∫(N ref (t)-N(t))dt(2) Calculate the reference value i of the q-axis current qref In the formula, K p K represents the control coefficient for the proportional control to be tuned. i N represents the control coefficient for the integral control to be tuned.ref N(t) is the reference value of the motor speed at the current moment, and N(t) is the actual value of the motor speed at the current moment.

[0085] Referring to the concept of "active damping", the principle for tuning the coefficients can be designed as follows:

[0086]

[0087] In the formula, β is the desired bandwidth of the speed loop, J is the moment of inertia of the motor, and p is the number of pole pairs of the motor. Compared with the typical Type II system parameter tuning method, this method is more theoretical and simpler to adjust. The PI controller is the controller on the outer speed loop of the permanent magnet synchronous control system. The input signal of the PI controller is the reference value and actual value of the permanent magnet synchronous motor speed. It mainly obtains the reference current signal of the inner current loop through proportional-integral control based on the reference value and actual value of the motor speed. Among them, the inner loop uses the common i d The control method with a value of 0 essentially achieves static decoupling of the d-q axis current. Therefore, the main purpose of a PI controller is to obtain a reference value for the q-axis current.

[0088] In practical applications, the incremental correction prediction model determination module specifically includes: a prediction controller ( Figure 2 Adaptive control and feedback corrector (in the context of adaptive control) and feedback corrector ( Figure 2 (Feedback correction in the process); wherein, the prediction controller includes an incremental prediction model determination unit and an incremental update prediction model determination unit.

[0089] The incremental prediction model determination unit is used to obtain the incremental prediction model of the permanent magnet synchronous motor at the current time based on the values ​​of the parameters of each model to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time and the parameter identification model of the permanent magnet synchronous motor.

[0090] The incremental update prediction model determination unit is used to obtain the incremental update prediction model of the permanent magnet synchronous motor at the current time based on the incremental prediction model of the permanent magnet synchronous motor at the current time.

[0091] The feedback corrector is used to obtain the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the incremental update prediction model of the permanent magnet synchronous motor at the current time.

[0092] In practical applications, the parameter identifier mainly utilizes the real-time voltage and current signals of the permanent magnet synchronous motor to perform online identification of the model parameters of the incremental model of the motor. The main working process of the parameter identifier is as follows:

[0093] The forward Euler method is a method for discretizing state equations, suitable for most situations. It uses forward difference operations to approximate the differential terms in the equations, as shown in equation (4):

[0094]

[0095] In the formula, i(k) represents the current value at the k-th sampling time (k = 1, 2, 3, ..., M), i(k+1) represents the current value at the (k+1)-th sampling time (k = 1, 2, 3, ..., M), and the sampling period is set to T. s After discretizing the voltage equation (1), the discretized model of the permanent magnet synchronous motor can be obtained as the current time discretized model, as shown in equation (5):

[0096]

[0097] In the formula, i d (k+1) and i q (k+1) represent the current components of the stator current along the dq axis at the next moment, i d (k) and i q (k) represent the current components of the stator current on the dq axis at the current moment.

[0098] From this, we can derive the discretized model of the motor at the previous moment:

[0099]

[0100] In the formula, i d (k-1) and i q (k-1) represent the current components of the stator current along the dq axis at the previous moment, u d (k-1) and u q (k-1) represent the voltage components of the optimal voltage vector on the dq axis at the previous moment.

[0101] By subtracting the discretized models of the current time and the previous time, that is, subtracting equation (6) from equation (5), we can obtain the incremental model of the permanent magnet synchronous motor:

[0102]

[0103] Correspondingly, this is organized into an identification model to obtain the parameter identification model of the permanent magnet synchronous motor:

[0104]

[0105] In the formula, i d (k) represents the current component of the stator current on the d-axis of the permanent magnet synchronous motor at the current moment, iq (k) represents the current component of the stator current in the q-axis at the current moment of the permanent magnet synchronous motor, i d (k-1) represents the d-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i q (k-1) represents the q-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i d (k-2) represents the stator current component along the d-axis at time i. q (k-2) represents the q-axis current component of the stator current at time u of the permanent magnet synchronous motor. d (k-1) represents the voltage component of the optimal voltage vector along the d-axis at the previous moment, u q (k-1) represents the voltage component of the optimal voltage vector along the q-axis at the previous moment, u d (k-2) represents the voltage component of the optimal voltage vector along the d-axis at the previous time step, u q (k-2) represents the voltage component of the optimal voltage vector on the q-axis at the previous time step, a represents the first model parameter to be identified, b represents the second model parameter to be identified, ω represents the electric angular velocity of the permanent magnet synchronous motor rotor, and T s The sampling period is represented by , and a and b represent the parameters of the two motor models to be identified, as shown in equation (9):

[0106]

[0107] Therefore, the incremental model parameters of the permanent magnet synchronous motor can be identified through the parameter identification algorithm studied.

[0108] The parameter identification model of the permanent magnet synchronous motor is reorganized into a linear regression model, as shown in equation (10):

[0109]

[0110] In the formula, Y(k) represents the system's output signal matrix, and Φ(k) represents the data matrix containing system information. This is the parameter matrix of the model to be identified. Combining equation (9) with the identification model equation (8) of the motor, we can obtain equation (11):

[0111]

[0112] The specific parameters for each item are as follows:

[0113]

[0114]

[0115]

[0116] In the formula, Y(k) is the motor output information vector, Φ T (k) is the transpose of a data vector containing motor input and output information.

[0117] This invention employs a recursive least squares method with a forgetting factor for online identification. The identification principle is as follows:

[0118]

[0119] In the formula, This is a data vector containing motor input and output data. I is the identity matrix, P(k) is the covariance matrix at the current time step, P(k-1) is the covariance matrix at the previous time step, λ is the forgetting factor, K(k) is the intermediate matrix at the current time step (with no special meaning), and y(k) is the motor output data at the current time step. The initial values ​​are the identified values ​​from the previous time step. The covariance matrix and the parameters to be identified are set to 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.

[0120] In practical applications, this embodiment of the invention uses the motor identification model (8) obtained by the parameter identifier to design a predictive controller. The predictive controller uses the real-time voltage and current signals of the permanent magnet synchronous motor and the identification results of the parameter identifier to obtain the optimal control voltage vector at the current moment, and controls the permanent magnet synchronous motor according to its corresponding PWM wave. The dq-axis current at the current moment k can be sampled to calculate the dq-axis current at the next moment k+1 under different basic voltage vectors. The incremental predictive model of the permanent magnet synchronous motor can be summarized as follows:

[0121]

[0122] In the formula, the superscript p represents the predicted value. This represents the predicted d-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. i represents the predicted q-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. d (k) and i q (k) represent the current components of the stator current along the dq axis at the current moment, i d (k-1) and i q (k-1) represent the stator current components along the dq axis at the previous moment, and ω is the electric angular velocity of the motor rotor. These are the input state variables of the predictive controller. d (k) and u q(k) represent the components of the optimal voltage vector on the dq axis at the current moment, u d (k-1) and u q (k-1) represent the voltage components of the optimal voltage vector on the dq axis at the previous moment, which are the control variables for the predictive controller output.

[0123] By substituting the eight basic voltage vectors corresponding to different switching combinations of the inverter into the incremental prediction model (16), eight different predicted current values ​​for the next moment can be generated. These eight predicted current values ​​are then substituted into the designed optimization formula (18) for rolling optimization. The optimal voltage vector is selected, and its corresponding switching combination is transmitted to the inverter. Therefore, the prediction model can be updated to obtain the incremental update prediction model:

[0124]

[0125] In the formula, This represents the d-axis voltage of the motor at the current moment, corresponding to the basic voltage vector. This represents the current q-axis voltage of the motor corresponding to the basic voltage vector. As can be seen from equation (17), the term containing the permanent magnet flux linkage in the incremental prediction model has been removed. This ensures that changes in the permanent magnet flux linkage parameters will not interfere with the predictive control effect, thus improving the robustness of the predictive control.

[0126] The performance metric function g of the predictive controller is set as follows:

[0127]

[0128] In the formula, i dref and i qref These are the reference values ​​for the d-axis and q-axis currents of the motor, respectively. Based on i d =0 control method, set current i dref It is zero, while i qref This is the actual output of the outer speed loop of the permanent magnet synchronous motor control system. The predicted d-axis current of the motor at the next moment corresponds to the basic voltage vector at the current moment. This represents the predicted q-axis current of the motor at the next moment, corresponding to the current basic voltage vector. Substituting all the current values ​​mentioned above into equation (18), the optimal basic voltage vector can be obtained by minimizing the performance index function value.

[0129] In practical applications, by substituting all the current values ​​mentioned above into equation (18) and aiming to minimize the performance index function value, the optimal basic voltage vector can be obtained, which specifically includes:

[0130] For any current basic voltage vector, input the predicted d-axis current of the motor at the next time corresponding to the current basic voltage vector and the predicted q-axis current of the motor at the next time corresponding to the current basic voltage vector into equation (18) to obtain the performance index function value of the basic voltage vector;

[0131] The minimum value is selected from the performance index function values ​​of all basic voltage vectors, and then the basic voltage vector corresponding to the minimum value is determined as the optimal basic voltage vector.

[0132] In practical applications, during motor operation, sensor detection delays and inaccurate parameter identification results can affect the control performance of the predictive controller. The feedback corrector compares the predicted current value of the predictive controller with the actual current output of the motor and compensates for the prediction error in the predictive control. In this invention, the difference between the current actual motor current value and the predicted current value calculated at the previous moment is used as the prediction error for the current moment, and this error is used to correct the predictive controller. The designed feedback correction error e(k) is...

[0133]

[0134] This feedback is added to the current predicted value based on the prediction model, thereby correcting the prediction model and obtaining the incremental correction prediction model. Therefore, the prediction model with added feedback correction, i.e., the incremental correction prediction model for permanent magnet synchronous motors, is as follows:

[0135]

[0136] in, This represents the predicted d-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted q-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted d-axis current at the current moment, corresponding to the optimal voltage vector at the previous moment. This represents the predicted q-axis current at the current moment, corresponding to the optimal voltage vector at the previous moment. This represents the d-axis voltage corresponding to the basic voltage vector of the permanent magnet synchronous motor at the current moment. This represents the q-axis voltage corresponding to the basic voltage vector of the permanent magnet synchronous motor at the current moment.

[0137] In practical applications, the control module is also included in the predictive controller. It is used to control the permanent magnet synchronous motor at the current moment by using the incremental correction predictive model of the permanent magnet synchronous motor at the current moment with the goal of minimizing the performance index function value, and to update the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor at the current moment, which is calculated based on the identification value.

[0138] This invention provides an electronic device, comprising:

[0139] 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 adaptive control method for a permanent magnet synchronous motor as described above.

[0140] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the adaptive control method for a permanent magnet synchronous motor as described above.

[0141] To verify the advantages of the control method of the present invention over the traditional model predictive control method, the present invention provides an embodiment for simulation verification of a permanent magnet synchronous motor control system using MATLAB software.

[0142] The predictive models used in traditional model predictive control methods are as follows:

[0143]

[0144] Traditional model predictive control methods use fixed model parameters of the motor, while the control scheme of this invention uses real-time model parameters obtained through incremental online model identification, with the speed setpoint selected as 1000 rad / s. Simulation results are as follows. Figures 3-6 As shown.

[0145] Figure 3 , Figure 4 and Figure 5 In this paper, L(t) represents the inductance parameter value identified based on the incremental model of the permanent magnet synchronous motor provided by this invention, and L(ref) represents the inductance parameter reference value. Figure 3 It can be seen that the reference value of the motor model parameter is 0.006H. The identification result obtained by the method of the present invention has a very small error and can obtain a relatively accurate identification result.

[0146] from Figure 4 It can be seen that the method of the present invention can quickly track and identify the changes in inductance parameters when the inductance parameters undergo a step change, and obtain a real-time and accurate identification effect. Figure 5 This demonstrates that the method of the present invention can promptly detect changes in inductance parameters and provides accurate identification results.

[0147] Regarding the effect of speed control, such as Figure 6As shown, both the traditional model predictive control method (speed N0) and the control method of this invention (speed N1) can reach stability after a short overshoot. However, as can be seen from the smaller graph, the speed result of the method of this invention is closer to the set value of 1000 rad / s, reducing the steady-state error. Therefore, compared with the traditional MPC method, the method of this invention can identify accurate online motor model parameters and has superior speed control performance.

[0148] The present invention has the following beneficial effects:

[0149] 1. This invention can be designed and implemented based on the incremental model of the controlled motor.

[0150] 2. This invention can identify an accurate online motor model even when the motor model parameters are mismatched.

[0151] 3. The control concept of this invention is easy to understand, and the control structure is simple, flexible, and easy to implement.

[0152] 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.

[0153] 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. An adaptive control method for a permanent magnet synchronous motor, characterized in that, include: In a two-phase rotating coordinate system, the voltage equation of the permanent magnet synchronous motor is obtained; The voltage equations of the permanent magnet synchronous motor are discretized using the forward Euler method to obtain the discretized model at the current moment and the discretized model at the previous moment. The incremental model of the permanent magnet synchronous motor is obtained by subtracting the discretized model at the current moment from the discretized model at the previous moment. The parameter identification model of the permanent magnet synchronous motor is obtained based on the incremental model of the permanent magnet synchronous motor. Calculate the values ​​of each parameter of the permanent magnet synchronous motor in the parameter identification model at the current moment based on the identification values; The identification values ​​include: the stator current component of the permanent magnet synchronous motor on the q-axis at the previous moment, the stator current component of the permanent magnet synchronous motor on the q-axis at the current moment, the stator current component of the permanent magnet synchronous motor on the q-axis at the moment before last, the stator current component of the permanent magnet synchronous motor on the d-axis at the previous moment, the stator current component of the permanent magnet synchronous motor on the d-axis at the current moment, the stator current component of the permanent magnet synchronous motor on the d-axis at the moment before last, the voltage component of the optimal voltage vector on the q-axis at the previous moment, the voltage component of the optimal voltage vector on the d-axis at the moment before last, and the voltage component of the optimal voltage vector on the d-axis at the moment before last. Based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of the parameters of each model to be identified at the current time in the parameter identification model of the permanent magnet synchronous motor, the incremental correction prediction model of the permanent magnet synchronous motor at the current time is obtained. With the goal of minimizing the performance index function value, the incremental correction prediction model of the permanent magnet synchronous motor at the current moment is used to control the permanent magnet synchronous motor at the current moment, and the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor calculated based on the identification value at the current moment are updated.

2. The adaptive control method for a permanent magnet synchronous motor according to claim 1, characterized in that, The step of obtaining the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time specifically includes: Based on the values ​​of the parameters of each model to be identified at the current moment in the parameter identification model of the permanent magnet synchronous motor and the parameter identification model of the permanent magnet synchronous motor, the incremental prediction model of the permanent magnet synchronous motor at the current moment is obtained. Based on the incremental prediction model of the permanent magnet synchronous motor at the current moment, the incremental update prediction model of the permanent magnet synchronous motor at the current moment is obtained. The incremental correction prediction model of the permanent magnet synchronous motor at the current moment is obtained based on the incremental update prediction model of the permanent magnet synchronous motor at the current moment.

3. The adaptive control method for a permanent magnet synchronous motor according to claim 2, characterized in that, The parameter identification model of the permanent magnet synchronous motor is as follows: Among them, i d (k) represents the current component of the stator current on the d-axis of the permanent magnet synchronous motor at the current moment, i q (k) represents the current component of the stator current in the q-axis at the current moment of the permanent magnet synchronous motor, i d (k-1) represents the d-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i q (k-1) represents the q-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i d (k-2) represents the stator current component along the d-axis at time i. q (k-2) represents the q-axis current component of the stator current at time u of the permanent magnet synchronous motor. d (k-1) represents the voltage component of the optimal voltage vector along the d-axis at the previous moment, u q (k-1) represents the voltage component of the optimal voltage vector along the q-axis at the previous moment, u d (k-2) represents the voltage component of the optimal voltage vector along the d-axis at the previous time step, u q (k-2) represents the voltage component of the optimal voltage vector on the q-axis at the previous time step, a represents the first model parameter to be identified, b represents the second model parameter to be identified, ω represents the electric angular velocity of the permanent magnet synchronous motor rotor, and T s Indicates the sampling period.

4. The adaptive control method for a permanent magnet synchronous motor according to claim 3, characterized in that, The incremental correction prediction model for the permanent magnet synchronous motor is as follows: in, This represents the predicted d-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted q-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted d-axis current at the current moment, corresponding to the optimal voltage vector at the previous moment. This represents the predicted q-axis current at the current moment, corresponding to the optimal voltage vector at the previous moment. This represents the d-axis voltage corresponding to the basic voltage vector of the permanent magnet synchronous motor at the current moment. This represents the q-axis voltage corresponding to the basic voltage vector of the permanent magnet synchronous motor at the current moment.

5. An adaptive control system for a permanent magnet synchronous motor, characterized in that, include: The acquisition module is used to obtain the voltage equation of the permanent magnet synchronous motor in a two-phase rotating coordinate system. The discrete module is used to discretize the voltage equation of the permanent magnet synchronous motor using the forward Euler method to obtain the discretized model at the current moment and the discretized model at the previous moment. The incremental model determination module is used to obtain the incremental model of the permanent magnet synchronous motor by subtracting the discretized model at the current moment from the discretized model at the previous moment. The parameter identification model determination module is used to obtain the parameter identification model of the permanent magnet synchronous motor based on the incremental model of the permanent magnet synchronous motor. The module for determining parameters of the model to be identified is used to calculate the values ​​of each parameter of the model to be identified at the current time in the parameter identification model of the permanent magnet synchronous motor based on the identification values. The identification values ​​include: the stator current component of the permanent magnet synchronous motor on the q-axis at the previous moment, the stator current component of the permanent magnet synchronous motor on the q-axis at the current moment, the stator current component of the permanent magnet synchronous motor on the q-axis at the moment before last, the stator current component of the permanent magnet synchronous motor on the d-axis at the previous moment, the stator current component of the permanent magnet synchronous motor on the d-axis at the current moment, the stator current component of the permanent magnet synchronous motor on the d-axis at the moment before last, the voltage component of the optimal voltage vector on the q-axis at the previous moment, the voltage component of the optimal voltage vector on the d-axis at the moment before last, and the voltage component of the optimal voltage vector on the d-axis at the moment before last. The incremental correction prediction model determination module is used to obtain the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the parameter identification model of the permanent magnet synchronous motor and the values ​​of the parameters of each model to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time. The control module is used to control the permanent magnet synchronous motor at the current moment by using the incremental correction prediction model of the permanent magnet synchronous motor at the current moment with the goal of minimizing the performance index function value, and to update the values ​​of each parameter to be identified in the parameter identification model of the permanent magnet synchronous motor at the current moment, which is returned based on the identification value.

6. The adaptive control system for a permanent magnet synchronous motor according to claim 5, characterized in that, The incremental correction prediction model determination module specifically includes: An incremental prediction model determination unit is used to obtain the incremental prediction model of the permanent magnet synchronous motor at the current time based on the values ​​of the parameters of each model to be identified in the parameter identification model of the permanent magnet synchronous motor at the current time and the parameter identification model of the permanent magnet synchronous motor. The incremental update prediction model determination unit is used to obtain the incremental update prediction model of the permanent magnet synchronous motor at the current time based on the incremental prediction model of the permanent magnet synchronous motor at the current time. The feedback corrector is used to obtain the incremental correction prediction model of the permanent magnet synchronous motor at the current time based on the incremental update prediction model of the permanent magnet synchronous motor at the current time.

7. The adaptive control system for a permanent magnet synchronous motor according to claim 6, characterized in that, The parameter identification model of the permanent magnet synchronous motor is as follows: Among them, i d (k) represents the current component of the stator current on the d-axis of the permanent magnet synchronous motor at the current moment, i q (k) represents the current component of the stator current on the q-axis at the current moment of the permanent magnet synchronous motor, i d (k-1) represents the d-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i q (k-1) represents the q-axis current component of the stator current of the permanent magnet synchronous motor at the previous moment, i d (k-2) represents the stator current component along the d-axis at time i. q (k-2) represents the q-axis current component of the stator current at time u of the permanent magnet synchronous motor. d (k-1) represents the voltage component of the optimal voltage vector along the d-axis at the previous moment, u q (k-1) represents the voltage component of the optimal voltage vector along the q-axis at the previous moment, u d (k-2) represents the voltage component of the optimal voltage vector along the d-axis at the previous time step, u q (k-2) represents the voltage component of the optimal voltage vector on the q-axis at the previous time step, a represents the first model parameter to be identified, b represents the second model parameter to be identified, ω represents the electric angular velocity of the permanent magnet synchronous motor rotor, and T s Indicates the sampling period.

8. The adaptive control system for a permanent magnet synchronous motor according to claim 7, characterized in that, The incremental correction prediction model for the permanent magnet synchronous motor is as follows: in, This represents the predicted d-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted q-axis current of the motor at the next moment, corresponding to the basic voltage vector at the current moment. This represents the predicted d-axis current at the current moment, corresponding to the optimal voltage vector at the previous moment. This represents the predicted q-axis current at the current moment, corresponding to the optimal voltage vector at the previous moment. This represents the d-axis voltage corresponding to the basic voltage vector of the permanent magnet synchronous motor at the current moment. This represents the q-axis voltage corresponding to the basic voltage vector of the permanent magnet synchronous motor at the current moment.

9. An electronic device, characterized in that, include: 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 adaptive control method for a permanent magnet synchronous motor according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the adaptive control method for permanent magnet synchronous motors as described in any one of claims 1 to 4.

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