A PMSM Model Reference Adaptive Speed Estimation Method Based on Calibration Current Prediction

Abstract

The present invention discloses a PMSM model reference adaptive speed estimation method based on corrected current prediction, belonging to the technical field of motor control. First, the present invention derives the current state equation of PMSM in the synchronous rotating coordinate system, and discretizes the current state equation by using the Taylor formula to obtain the current prediction control equation; secondly, the d-q axis currents of the controller are optimized by combining a correction factor, and the corrected current prediction controller is used to replace the traditional current prediction controller; at the same time, compensation integral terms are respectively introduced into the d axis and the q axis to weaken the current deviation of the d-q axis; finally, a reference model and an adjustable model including the motor speed information are derived, the current error model is analyzed through the Popov hyperstability theory to obtain the speed adaptability rate of the model reference adaptive system, and the improved control voltage is substituted into the speed estimation link to estimate the motor speed. The present invention improves the speed prediction accuracy and dynamic performance of the sensorless speed regulation system.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and more specifically, to a PMSM model reference adaptive speed estimation method based on corrected current prediction. Background Art

[0002] As a power source for electrical appliances and various other machines, motors are widely used in various industries. According to statistics, the electricity consumption of motors accounts for about 42%-50% of the total electricity consumption, and motors with a power of 37KW and below account for about 50% of the total electricity consumption of motors. As a leading type of motor, the permanent magnet synchronous motor has been favored by researchers in various fields in recent years due to its advantages such as simple structure, high efficiency, large power density, low noise, and fast dynamic response. The research on new control technologies for this type of motor has also attracted the attention of scholars in related fields. In traditional permanent magnet synchronous motor control systems, mechanical sensors are generally used to obtain rotor position information and speed information to achieve the purpose of speed control. The use of mechanical sensors will increase the volume of the motor, raise the cost, and reduce the control stability. Therefore, sensorless control technologies have emerged.

[0003] Since sensorless control technologies use current and voltage signals to estimate the rotor position and have the advantages of high reliability and low cost, this technology has been widely concerned by scholars in various fields since the 1970s. In 1975, the A. Abbondnati team designed a slip frequency identification strategy based on induced voltage and first applied it to induction motors. However, its AC speed regulation accuracy and dynamic performance are difficult to meet actual requirements. In 1979, scholars such as M. Ishdia deduced a speed identification algorithm based on tooth harmonic detection using the relationship between the motor structure characteristics and the speed signal. However, its identification range is limited by the control performance of digital chips. In 1983, Ro. Joetten first transplanted sensorless technology onto the vector control of AC induction motors and laid a certain theoretical foundation for the development of sensorless technology. It wasn't until 1989 that scholars such as Jonees L.A first applied sensorless technology to permanent magnet synchronous motors. Since then, sensorless technology has become a major field of application for various motors and has also achieved certain results. According to the operating speed of the motor, the sensorless control algorithms for permanent magnet synchronous motors (PMSMs) are divided into two categories: one category is applicable to the medium and high speed stages (the speed is 10% and above of the rated speed), such as the sliding mode observer method, the model adaptive method, the extended Kalman filter method, etc.; the other category is applicable to the zero and low speed (the speed is below 10% of the rated speed), such as the high frequency signal injection method, the zero sequence voltage method, the constant voltage frequency ratio method, and the constant current frequency ratio method, etc.

[0004] In the current loop of the traditional sensorless control system of permanent magnet synchronous motors (PMSMs), the proportional-integral control method is often used. Although this method is simple and easy to implement, due to its low-pass filtering characteristics, problems such as phase lag and poor dynamic performance may occur. In some application scenarios with high requirements for dynamic performance, such as automated machine tools, high-performance electric vehicles, and aerospace, etc., it is necessary to improve the dynamic response ability of the current loop in the PMSM double-closed-loop vector control system, and the predictive current control method can well meet this requirement.

[0005] At present, there are some problems in the model reference adaptive speed regulation system of PMSMs based on traditional current predictive control: the performance of the current predictive controller extremely depends on the motor parameters, and the mismatch of the motor parameters will affect the stability of the system, cause the current to oscillate, and thus lead to problems such as low rotational speed estimation accuracy and poor dynamic performance. Summary of the Invention

[0006] 1. Technical problems to be solved by the invention

[0007] Aiming at the problems of low rotational speed estimation accuracy and poor dynamic performance caused by the current oscillation in the control system of the traditional PMSM model reference adaptive control speed regulation system based on current predictive control, the present invention proposes a PMSM model reference adaptive rotational speed estimation method based on corrected current prediction, which can realize the sensorless vector control of PMSMs. In practical applications, it can effectively track the rotor position and speed of the motor, and improve the steady-state accuracy and dynamic performance of the system while reducing the cost of the motor control system.

[0008] 2. Technical solutions

[0009] To achieve the above object, the technical solution provided by the present invention is as follows:

[0010] A PMSM model reference adaptive rotational speed estimation method based on corrected current prediction proposed by the present invention, the steps of which are as follows:

[0011] Step 1: Establish the mathematical model of the PMSM in the synchronous rotating coordinate system d-q, deduce the current state equation, and discretize the current state equation by using the Taylor formula to obtain the current predictive control equation;

[0012] Step 2: Optimize the d-q axis current of the controller by combining the correction factor, and replace the traditional current predictive controller with the corrected current predictive controller to reduce the current oscillation and improve the stability of the system;

[0013] Step 3: Introduce compensation integral terms in the d-axis and q-axis respectively to weaken the current deviation of the d-q axis, improve the current prediction accuracy, and improve the d-q axis control voltage value;

[0014] Step 4: Derive the reference model and adjustable model containing motor speed information, analyze the current error model through Popov hyperstability theory to obtain the speed adaptation rate of the model reference adaptive system, and introduce the improved d-q axis control voltage value into the speed estimation link to complete the accurate estimation of the motor speed.

[0015] 3. Beneficial effects

[0016] Adopting the technical solution provided by the present invention, compared with the existing well-known technologies, it has the following remarkable effects:

[0017] A PMSM model reference adaptive speed estimation method based on corrected current prediction proposed by the present invention effectively solves the problems of low speed estimation accuracy and poor dynamic performance caused by current oscillation in the traditional sensorless permanent magnet synchronous motor control system. The control system has strong robustness and can accurately estimate the motor rotor position and speed in the medium and high speed stages. Compared with the MRAS based on traditional current prediction, it overcomes the existing current oscillation problem and improves the speed estimation accuracy and system dynamic performance. Description of the drawings

[0018] Figure 1 is the sensorless vector control block diagram of PMSM based on the method proposed by the present invention;

[0019] Figure 2 in (a) is the root locus diagram in the Z domain of the predicted current control system proposed by the present invention; Figure 2 in (b) is the root locus diagram in the Z domain of the traditional predicted current control system;

[0020] Figure 3 in (a)-(d) are the d-q axis current simulation waveform diagrams of the control system proposed by the present invention under the parameter mismatches of 0.5 times the magnetic flux, 1.5 times the magnetic flux, 0.5 times the inductance, and 1.5 times the inductance respectively;

[0021] Figure 4 in (a)-(d) are the d-q axis current simulation waveform diagrams of the traditional control system under the parameter mismatches of 0.5 times the magnetic flux, 1.5 times the magnetic flux, 0.5 times the inductance, and 1.5 times the inductance respectively;

[0022] Figure 5 in (a) is the simulation comparison waveform diagram of the predicted speed and the actual speed when the load speed of the control system proposed by the present invention suddenly changes; Figure 5 in (b) is the simulation comparison waveform diagram of the predicted speed and the actual speed when the load speed of the traditional control system suddenly changes;

[0023] Figure 6 in (a) is the simulation comparison waveform diagram of the error between the predicted speed and the actual speed of the control system proposed by the present invention; Figure 6In (b) is the simulation comparison waveform diagram of the predicted speed and the actual speed error of the traditional control system;

[0024] Figure 7 In (a) is the simulation waveform diagram of the A-phase stator current of the control system proposed by the present invention; Figure 7 In (b) is the simulation waveform diagram of the A-phase stator current of the traditional control system;

[0025] Figure 8 Is the simulation comparison waveform diagram of the predicted rotor position angle and the actual rotor position angle of the control system proposed by the present invention. Detailed implementation manners

[0026] To further understand the content of the present invention, the present invention will be described in detail in conjunction with the accompanying drawings and embodiments.

[0027] Embodiment 1

[0028] Figure 1 Is the sensorless vector control block diagram of PMSM based on the algorithm proposed in this embodiment. As Figure 1 shown, the control system mainly consists of a motor body, a three-phase inverter, an SVPWM module, a PI speed outer loop, a corrected current prediction module, a flux regulator, an inductance regulator, and an MRAS module, etc. After the inverse Park transformation, the αβ-axis given voltages u α , u β are used as the input values of the voltage space vector modulation SVPWM, and at the same time, the on-off of the thyristors of the inverter is controlled by adjusting the PWM switching waveform, so as to realize the sensorless vector speed regulation control of the permanent magnet synchronous motor.

[0029] Sample the three-phase current i abc , transform it into i dq through Clark / Park transformation, and at the same time call the d-q axis control voltages u d , u q optimized by the corrected current prediction module proposed by the present invention, substitute the two into the model reference adaptive speed estimation link to obtain a reference model and an adjustable model including the motor speed parameters, subtract the estimated adjustable model current equation from the actual adjustable model current equation to obtain a current error equation, and then estimate the speed of the motor through the Popov hyperstability theory and the rotor position

[0030] The specific steps of this embodiment are as follows:

[0031] Step 1: Establish the mathematical model of the permanent magnet synchronous motor in the synchronous rotating coordinate system d-q, deduce the current state equation, and discretize the current state equation by using the Taylor formula to obtain the current prediction control equation:

[0032] For simplicity of analysis, assume that the PMSM is an ideal motor. Through coordinate transformation, the mathematical model of the permanent magnet synchronous motor in the two-phase rotating coordinate system is shown in Equation (1).

[0033]

[0034] In Equation (1): u d and u q are the components of the stator voltage u s on the d-axis and q-axis; i d and i q are the components of the stator current i s on the d-axis and q-axis; R s is the stator resistance; ψ d and ψ q are the components of the stator flux linkage on the d-axis and q-axis; L d and L q are the components of the stator inductance on the d-axis and q-axis; w e is the electrical angular velocity; ψ f is the permanent magnet flux linkage.

[0035] Substitute the values of ψ d and ψ q into the stator voltage equation of the permanent magnet synchronous motor in the two-phase rotating coordinate system, and derive the current state equation:

[0036]

[0037] Since the sampling period T s is small enough, the Taylor formula is used to discretize the current state equation, and the equations of i d(k) and i q(k) with respect to i d(k+1) and i q(k+1) are:

[0038]

[0039] Where: i d(k+1) and i q(k+1) and i d(k) and i q(k) are the given values of the d-q axis current at the (k + 1)-th moment and the k-th moment respectively.

[0040] Combining Equation (2) and Equation (3), the discrete current predictive control equation of the permanent magnet synchronous motor is:

[0041]

[0042] Taking i d(k) and i q(k) as the input quantities i (k+1) at the next moment Td(k+1) , i q(k+1) , where i d(k) , i q(k) is the d-q axis current reference value. By adopting the vector control strategy with i d = 0, the voltage equation in the synchronous rotating coordinate system at time k can be written as:

[0043]

[0044] Step 2: Optimize the d-q axis currents of the controller by combining the correction factor, and replace the traditional current prediction controller with a corrected current prediction controller to reduce current oscillation and improve the stability of the system:

[0045] Analyze the factors affecting the stability of the prediction control system. Substituting the true values of the controller motor model parameters into the PMSM voltage and current equations, we get:

[0046]

[0047] In the above formula:

[0048]

[0049] are the components of the true inductance of the motor on the d-q axis, is the actual rated magnetic flux of the motor. Let the actual value of u (k) be equal to the value of u (k) in Equation (5), then the relationship between the true current and the reference current in the prediction current controller can be obtained:

[0050]

[0051] In Equation (7):

[0052] Since the motor speed changes relatively slowly compared to the current, taking w e(k) as the disturbance term and performing Z-transform on Equation (7), the discrete-domain closed-loop transfer function of the traditional current prediction control system is obtained:

[0053]

[0054] Combined with Figure 2 (b), when the motor rated inductance value is within the range of 0 to 2 times the true inductance value, the system is stable; if it exceeds this range, the closed-loop poles of the system will be located in the right half-plane of the S-plane, resulting in system oscillation, and the stability margin of the system is relatively small at this time. To improve the stability margin of the system and reduce system current oscillation, a correction factor α (0 < α < 1) is introduced to process the d-q axis current feedback value:

[0055]

[0056] In the above formula: is the current reference value of the controller, and i dq(k) is the actual value of the controller current.

[0057] The PMSM voltage equation after introducing the correction factor is obtained:

[0058]

[0059] Similarly, performing the Z-transform on the above formula, the discrete-domain closed-loop transfer function of the system after introducing the correction factor can be obtained:

[0060]

[0061] It can be obtained from Equation (11) that when the inductance parameter mismatch coefficient is in the range of [0, 2 / (1-α)], the system is stable, and compared with Equation (8), the method proposed in the present invention improves the stability margin of the system. Combining Figure 2 with (a) and (b) in, when α = 0.6, the stable range after the inductance parameter mismatch is increased from 0-2 to 0-5, so the stability margin of the system is improved.

[0062] Step 3: Introduce compensation integral terms in the d-axis and q-axis respectively to weaken the current deviation between the d-q axes, improve the current prediction accuracy, and improve the d-q axis control voltage value:

[0063] To analyze the reason for the deviation between the d-q axis command current and the actual current, a model regarding the d-q axis current deviation needs to be obtained. Since the sampling period is very short, within two adjacent current loop control periods, the current change of the d-q axis can be ignored. According to the relationship between the actual current and the reference current in Step 2, the d-q axis current deviation of the PMSM control system with the correction factor introduced is analyzed, and its deviation analysis model is as follows:

[0064]

[0065] In Equation (12), the factors affecting the d-axis current deviation are mainly the d-axis inductance parameter error and the correction factor parameter; the factors affecting the q-axis current deviation are mainly the q-axis inductance parameter error, the flux linkage error, and the correction factor parameter.

[0066] The generation of current deviation will reduce the current prediction accuracy. To solve the influence brought by this problem, the present invention proposes a compensation strategy to weaken the current deviation. First, based on the vector control strategy with i d = 0, to weaken the d-axis current deviation, a compensation integral term is introduced on the d-axis control voltage, that is:

[0067]

[0068] Among them, u' d (k) is the compensated d-axis control voltage, and k d is the d-axis current deviation compensation coefficient.

[0069] On the basis of completing Equation (13), the d-axis current deviation has been greatly weakened. At this time, the main factor affecting the q-axis current deviation is the magnetic flux linkage. Therefore, by adjusting the dynamic response of the q-axis current, the magnetic flux linkage parameters are adjusted to reduce the magnetic flux linkage error and weaken the q-axis current deviation. For this purpose, an integral term is introduced to make the magnetic flux linkage parameters of the controller motor model converge to the true value:

[0070]

[0071] Among them: ψ' f (k) is the magnetic flux linkage parameter of the compensated controller motor model, and k q is the magnetic flux linkage error compensation coefficient.

[0072] Combined with Figure 3 and Figure 4 it can be seen that the simulation results are consistent with the theoretical analysis results, and the control system with the compensated integral term can greatly weaken the current deviation, thereby improving the current prediction accuracy and further improving the speed estimation accuracy.

[0073] Step 4: Derive a reference model and an adjustable model containing motor speed information, analyze the current error model through the Popov hyperstability theory, and obtain the speed adaptation rate of the model reference adaptive system. Introduce the improved d-q axis control voltage value into the speed estimation link to complete the accurate estimation of the motor speed: [[ID= 31]]

[0074] In the said Step 4, to obtain an adjustable model containing motor speed parameters, the current state equation of the permanent magnet synchronous motor is rewritten as:

[0075]

[0076] Here, it is defined that: i' q = i q , u' q = u q .

[0077] The adjustable model of the PMSM model reference adaptive system can be obtained:

[0078]

[0079] Among them: w e is the adjustable parameter to be identified.

[0080] Taking the PMSM itself as the reference model, to complete the establishment of MRAS, a suitable adaptation rate is required. Replace the current value and motor speed value in the above formula with estimated values:

[0081]

[0082] The variable with a "^" represents the corresponding estimated value, and an adjustable model based on the estimated parameters of the motor can be obtained:

[0083]

[0084] Subtract the current equation of the estimated adjustable model from the current equation of the actual adjustable model to obtain the current error equation:

[0085]

[0086] Define here:

[0087] Rewrite Equation (18) in the following form:

[0088]

[0089] According to Popov hyperstability theory, the conditions for the system to be stable are as follows:

[0090] (1) The transfer function H (s) =(sI - A) -1 is a strictly positive definite matrix;

[0091] (2) γ0 is an arbitrary finite positive number. At this time, there is That is, MRAS is asymptotically stable.

[0092] Reverse solve the Popov integral inequality to obtain the MRAS adaptation rate:

[0093]

[0094] In the above formula: K p and K i are the proportional coefficient and integral coefficient of MRAS respectively.

[0095] Combined with Figure 1 , it is necessary to substitute the optimized d-q axis control voltage value in step three into the speed estimation model, where the speed estimation equation is:

[0096]

[0097] Integrate the above formula to obtain the estimated value of the rotor position as:

[0098]

[0099] The method design process of this embodiment is simulated and experimented through MATLAB / Simulink simulation and Pocket bench hardware-in-the-loop platform. Through simulation, the speed regulation system based on the PMSM speed estimation method proposed in the present invention is compared with the PMSM model reference adaptive speed regulation system based on traditional current prediction. The parameters of the permanent magnet synchronous motor are: rated speed Stator resistance R s = 0.4578 Ω, direct and quadrature axis inductances L d = L q = 3.34 mH, rotor magnetic flux Number of pole pairs P = 4, moment of inertia J = 14.69 kg·cm 2 . The motor starts with a load (T N = 10 N·m), and the initial given speed of the system is When the motor control system runs to 0.5 s, the speed suddenly changes from 1000 rad / min to 2000 rad / min, and the given torque of the system remains unchanged. The simulation running time is 1 s.

[0100] Analysis Figure 3 and Figure 4 show that the PMSM starts under a loaded state, and the deviation of the d-q axis current within 0.5 s is studied. Figure 4 In, when the inductance parameter is small, the feedback value of the q-axis response current is less than the given value, and the feedback value of the d-axis response current is greater than the given value; when the inductance parameter is large, the feedback values of the d-q axis response currents are both less than the given value, but the influence on the q-axis is relatively small. When the magnetic flux parameter is small, the feedback value of the q-axis response current is less than the given value, and the d-axis current has basically no deviation; when the magnetic flux parameter is large, the feedback value of the q-axis response current is greater than the given value, and the feedback value of the d-axis response current is slightly less than the given value. Similarly, Figure 3 in, analyze the current situation after introducing the deviation processing algorithm. Compared with Figure 4 , the d-q axis current deviation is greatly reduced, which proves the effectiveness of the proposed algorithm.

[0101] Analysis Figure 5 shows that the system starts and runs under a loaded state, and the given speed is And the rotational speed mutated to 2000 rad / min at 0.5 s. Both control methods can quickly reach the given value. It can be seen from the locally enlarged waveform diagram during the stable process that the fluctuation ranges of the rotational speed estimation values of the PMSM control system based on the method proposed in the present invention are 998 rad / min to 1002 rad / min and 1998 rad / min to 2002 rad / min respectively, and the jump range of the rotational speed amplitude after stabilization is within ±2 rad / min; the fluctuation ranges of the rotational speed estimation values of the PMSM based on the traditional algorithm control are 990 rad / min to 1010 rad / min and 1990 rad / min to 2020 rad / min respectively, and the jump range of the rotational speed amplitude after stabilization is within ±11 rad / min. However, the rotational speed waveform has a large vibration during startup and when the rotational speed mutates. From Figure 5 It can be seen that, compared with the control system based on the traditional method, the control system based on the rotational speed estimation method proposed in the present invention has a lower pulsation amplitude of the motor rotational speed estimation and a smaller vibration when the rotational speed jumps, and has better dynamic and steady-state performance.

[0102] Analysis Figure 6 It can be known that when the motor is stable at the given rotational speed, the fluctuation ranges of the rotational speed estimation errors of the PMSM controlled by the method proposed in the present invention are all within the range of ±3 rad / min, while the fluctuation range of the rotational speed estimation error of the PMSM controlled by the traditional method is within ±15 rad / min. It can be seen that the PMSM control system based on the method proposed in the present invention has higher estimation accuracy and better stability.

[0103] Analysis Figure 7 It can be known that when the motor is stable at the given rotational speed, in the PMSM control speed regulation system based on the traditional method, local oscillations occur in the waveform of the stator current of phase A, and the current amplitude fluctuates; while in the PMSM speed regulation control system based on the method proposed in the present invention, there is basically no oscillation in the waveform of the stator current of phase A after the rotational speed is stable, the current amplitude is stable, and the current waveform is relatively smooth.

[0104] Experiments were carried out on the PMSM semi-physical platform based on Pocket Bench. This experimental platform mainly includes the PocketBench ultra-compact power converter hardware-in-the-loop real-time simulator, the TMS320F28335 control board and its YXDSP-XDS100V3 simulator, the upper computer CCS software, etc. Among them, the Pocket Bench simulator is directly powered by the computer USB interface. Through experiments, it can be obtained that when the given rotational speed of the motor is 1500 rad / min and the permanent magnet synchronous motor is started under the condition of the rated load of 10 N·m, it can be seen that the actual waveforms of the motor current and the rotor position are stable, and the actual rotational speed of the motor finally stabilizes at 1500 rad / min, verifying the correctness of the method proposed in the present invention.

[0105] The above has schematically described the present invention and its implementation manners. Such description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. Therefore, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, creatively design structural manners and embodiments similar to the technical solution, they shall fall within the protection scope of the present invention.

Claims

1. A method for estimating the rotational speed of a PMSM model reference adaptive based on predicted correction current, characterized in that, The steps are as follows: Step 1: Establish the mathematical model of the permanent magnet synchronous motor in the synchronous rotating coordinate system d-q, derive the current state equation, and discretize the current state equation using the Taylor formula to obtain the discretized current prediction control equation of the permanent magnet synchronous motor as follows: where, u d , u q are the components of the stator voltage u s on the d-axis and q-axis; i d , i q are the components of the stator current i s on the d-axis and q-axis; R s is the stator resistance; L d , L q are the components of the stator inductance on the d-axis and q-axis; w e is the electrical angular velocity; ψ f is the permanent magnet flux linkage; T s is the sampling period; Step 2: Optimize the d-q axis current of the controller by combining the correction factor, and replace the traditional current prediction controller with the corrected current prediction controller; the discrete domain closed-loop transfer function of the system after introducing the correction factor: α is the correction factor, 0 < α < 1; Step 3: Introduce compensation integral terms in the d-axis and q-axis respectively to weaken the current deviation of the d-q axis, improve the current prediction accuracy, and improve the d-q axis control voltage value; Step 4: Derive the reference model and adjustable model containing the motor speed information, analyze the current error model through the Popov hyperstability theory and obtain the speed adaptation rate of the model reference adaptive system, and introduce the improved d-q axis control voltage value into the speed estimation link to complete the accurate estimation of the motor speed; among them, to obtain the adjustable model containing the motor speed parameters, rewrite the current state equation of the permanent magnet synchronous motor to get: Defined herein: i ′ q = i q , u ′ q = u q ; The adjustable model of the PMSM model reference adaptive system can be obtained as: where: w e is an adjustable parameter to be identified; Taking the PMSM itself as the reference model, if a model reference adaptive system is to be established, a suitable adaptation rate is required; replace the above current value and motor speed value with estimated values: The variable with a ^ represents the corresponding estimated value, and the adjustable model based on the estimated parameters of the motor can be obtained: Subtract the estimated adjustable model current equation from the actual adjustable model current equation to obtain the current error equation: Defined herein: Rewrite Equation (18) into the following form: According to the Popov hyperstability theory, and perform reverse solution on the Popov integral inequality to obtain the MRAS adaptation rate: In the above formula: K p and K i are the proportional coefficient and integral coefficient of MRAS, respectively; Substitute the improved d-q axis control voltage value into the speed estimation model, where the speed estimation equation is: Integrate the above formula to obtain the estimated value of the rotor position: Thus, sensorless PMSM model reference adaptive speed estimation is realized.

2. The method for estimating the rotational speed of a PMSM model reference adaptive based on corrected current prediction according to claim 1, wherein: In the above Step 1, through coordinate transformation, the mathematical model of the permanent magnet synchronous motor in the two-phase rotating coordinate system is obtained as shown in Equation (1), In Equation (1): u d and u q are the components of the stator voltage u s on the d-axis and q-axis; i d and i q are the components of the stator current i s on the d-axis and q-axis; R s is the stator resistance; ψ d and ψ q are the components of the stator flux linkage on the d-axis and q-axis; L d and L q are the components of the stator inductance on the d-axis and q-axis; w e is the electrical angular velocity; ψ f is the permanent magnet flux linkage; Substitute the values of ψ d and ψ q into the stator voltage equation of the permanent magnet synchronous motor in the two-phase rotating coordinate system, and the current state equation is obtained as follows: The Taylor formula is used to discretize Equation (2) to obtain i d(k) , i q(k) Regarding i d(k+1) , i q(k+1) The equation is as follows: where: i d(k+1) , i q(k+1) and i d(k) , i q(k) are the reference values of the d-q axis currents at the (k + 1)-th and k-th instants respectively; Combine Equation (2) and Equation (3) to obtain the discretized current prediction control equation of the permanent magnet synchronous motor; Take i d(k) and i q(k) as the input quantity i (k+1) at the next moment T d(k+1) , where i q(k+1) , and i d(k) and i q(k) are the d-q axis current reference values. Adopting the vector control strategy with i d = 0, the voltage equation in the synchronous rotating coordinate system at the kth moment can be written as:

3. A method for estimating the rotational speed of a PMSM model reference adaptive based on corrected current prediction according to claim 2, characterized in that: In the above Step 2, analyze the factors affecting the stability of the prediction control system, and substitute the true value of the controller motor model parameters into the PMSM voltage and current equations to obtain: In the above formula: are the components of the true inductance of the motor in the d-q axes, is the actual nominal flux linkage of the motor. Let u (k) The actual value of is equal to the u in Equation (5), (k) and the relationship between the true current and the given current in the current predictive controller can be obtained: In formula (7): Since the motor speed changes relatively slowly compared to the current, is used as a disturbance term, and the Z - transformation is performed on Equation (7), that is, the discrete - domain closed - loop transfer function of the traditional current prediction control system is obtained: Introduce the correction factor α, 0 < α < 1, and process the d-q axis current feedback value: In the above formula: is the current set value of the controller, and i dq(k) is the actual value of the controller current; Obtain the PMSM voltage equation after introducing the correction factor: Similarly, perform Z transformation on the above formula to obtain the discrete domain closed-loop transfer function of the system after introducing the correction factor.

4. A method for estimating the rotational speed of a PMSM model reference adaptive based on corrected current prediction according to claim 3, characterized in that: In the above Step 3, within two adjacent current loop control periods, ignore the current change of the d-q axis, and analyze the d-q axis current deviation of the PMSM control system with the correction factor according to the relationship between the true current and the given current in Step 2. The d-q axis current deviation model is as follows: In Equation (12), the factors affecting the d-axis current deviation are mainly the d-axis inductance parameter error and the correction factor parameter; the factors affecting the q-axis current deviation are mainly the q-axis inductance parameter error, the flux linkage error, and the correction factor parameter; Based on the vector control strategy with i d = 0, to weaken the d-axis current deviation, a compensation integral term is introduced to the d-axis control voltage, that is: where u ′ d (k) is the compensated d-axis control voltage, and k d is the d-axis current deviation compensation coefficient; An integral term is introduced to make the flux linkage parameter of the controller motor model converge to the true value: Where: ψ ′ f (k) is the flux linkage parameter of the compensated controller motor model, and k q is the flux linkage error compensation coefficient.

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