A mine belt conveyor motor control method based on an improved pulse high-frequency injection method

Abstract

The application discloses a kind of mine belt conveyor outer rotor permanent magnet synchronous motor position sensorless control method, fusion model reference adaptive parameter identification and pulse high-frequency voltage injection.Step: (1) collect motor synchronous rotation d-q axis voltage, current original signal;(2) after high-low frequency decoupling filtering, input model reference adaptive module, online joint identification stator resistance, inductance, output real-time parameter;(3) to identification parameter in turn amplitude limiting, first-order smoothing preprocessing, output stable parameter;(4) with preprocessed parameter instead of nominal value input pulse high-frequency injection module, analyze high-frequency response current to obtain rotor position error, and phase-locked loop is estimated with dynamic parameter adjustment rotor position angle and electric angular velocity;(5) estimated position angle is fed back to motor double closed loop system, and position, speed observation value is updated in real time iteration.The application is targeted to solve the problem of position observation deviation and excessive speed ripple caused by motor parameter drift under the condition of heavy load mutation and harsh working condition of coal mine underground belt conveyor.

Description

Technical Field

[0001] This invention relates to the field of motor control for mining belt conveyors, specifically a sensorless motor control method based on model adaptive reference parameter identification and injection of pulsed high-frequency signals. Background Technology

[0002] As a key transportation device in coal mine production systems, the operational reliability of mine belt conveyors directly affects the overall production efficiency of the mine. Traditional permanent magnet synchronous motor (PMSM) control systems for belt conveyors mostly use mechanical position sensors to obtain rotor position information. However, in the harsh working conditions of underground coal mines with high dust and humidity, physical position sensors are easily interfered with or even damaged, increasing system maintenance costs and reducing equipment reliability. Therefore, researching high-performance sensorless control technology suitable for mine belt conveyors has significant theoretical and engineering application value.

[0003] Mining conveyor belts operate at relatively low speeds, and in zero-speed, sensorless environments, the most common method is high-frequency pulse signal injection. This method injects a high-frequency signal, such as a high-frequency voltage signal, into a synchronous rotating coordinate system. This yields the high-frequency current response generated by the rotor's salient-pole characteristics, thus determining the rotor position. However, when applied to heavy-load starts and drastic load changes, such as in mining conveyor belts, the traditional pulse high-frequency injection method suffers from position estimation accuracy being easily affected by load disturbances. Therefore, introducing Model Reference Adaptive Control (MRAC) identification methods to identify motor parameters effectively improves the robustness of the motor control system. Summary of the Invention

[0004] The purpose of this invention is to provide a sensorless motor control method based on model adaptive reference parameter identification and pulse high-frequency signal injection to solve the problems of the prior art.

[0005] To achieve the above objectives, the technical solution adopted by this invention is as follows: a method for parameter identification of model adaptive reference and injection of pulse high-frequency signal into a sensorless motor control system, comprising the following steps:

[0006] S1. Establish a mathematical model of the permanent magnet synchronous motor in the synchronous rotating coordinate system dq, and obtain the original signal parameters such as d-axis voltage and current during the operation of the permanent magnet synchronous motor.

[0007] S2. The original d-axis voltage and current signals are processed by second-order low-pass filtering to remove the high-frequency harmonics generated by the high-frequency injection of the pulsation. The filtered fundamental signal is then sent to the model adaptive reference module. Based on the proportional-integral adaptive law, the stator resistance and flux linkage of the motor are identified online to obtain the real-time identification parameters.

[0008] S3. Mining conveyor belts frequently experience sudden changes from no-load to half-load and full-load conditions, causing real-time variations in motor temperature rise and magnetic saturation. Relying solely on a fixed-parameter phase-locked loop will result in significant speed pulsations. Therefore, this invention sequentially performs amplitude limiting and first-order smoothing filtering preprocessing on the real-time identification parameters output by the model adaptive reference module, and then sends the preprocessed stable identification parameters into the pulsation high-frequency injection module.

[0009] S4. The pulse high-frequency injection module uses the pre-processed stator resistance and flux linkage parameters to correct the high-frequency current amplitude calculation model and rotor position error demodulation formula. It combines the high-frequency response current to estimate the motor speed and rotor position angle, and feeds the rotor position angle back to the motor dual closed-loop PI controller.

[0010] S5. Build the corresponding model on the Simulink simulation platform and verify the feasibility of the proposed method under the following conditions: low-speed steady-state operation, sudden load increase / decrease, and parameter drift.

[0011] Preferably, in step S1, a mathematical model of the permanent magnet synchronous motor is established in the synchronous rotating coordinate system dq to obtain the d-axis and q-axis voltages and currents of the permanent magnet synchronous motor. Stator voltage equation:

[0012]

[0013] Stator flux linkage equation:

[0014]

[0015] Electromagnetic torque equation:

[0016]

[0017] Where R is the stator resistance, i d i q These are the d-axis and q-axis stator currents, respectively, L d L q These are the d-axis and q-axis inductances, respectively, and ω e It is the electric angular velocity, ω. f It is a permanent magnet flux linkage, p n It is an extreme logarithm.

[0018] Preferably, in step S2, the voltage and current of the permanent magnet synchronous motor are fed into the model adaptive reference module to identify the stator resistance and inductance of the motor. Wherein:

[0019] S21. Model reference adaptation involves constructing a reference model and an adjustable model. The error between the outputs of the two models drives an adaptive law, adjusting the parameters of the adjustable model in real time to make its output approximate the output of the reference model. The reference model is the physical entity of the permanent magnet synchronous motor, while the adjustable model is a mathematical model established based on the motor parameters.

[0020] S22. This patent uses a salient-pole permanent magnet synchronous motor, i.e., L... d =L q =L, then the stator current equation of the motor can be obtained as follows:

[0021]

[0022] S23. This formula is the reference model constructed by model reference adaptation. It can be simplified to:

[0023]

[0024] in, , , , , .

[0025] S24. Establish an adjustable model containing the parameters to be identified:

[0026]

[0027] in, , It is the current estimated by the adjustable model. , These are the resistance and inductance parameters to be identified.

[0028] S25. The adaptive law based on Popov's hyperstability theory has a short adjustment time, smaller fluctuations in estimation results, and less workload; therefore, it is chosen to design the adaptive law. Define the error. Then the error equation is:

[0029]

[0030] in, , , .

[0031] If we define the error equation in state-space form, we get:

[0032]

[0033] in, , .

[0034] By designing an adaptive law, MRAS can satisfy the Popov stability theory:

[0035]

[0036] in, C is the output constant matrix. y is the integral of the inner product of the input and output, y is the input vector of the feedback loop, and W is the output vector.

[0037] S26. By referencing the adaptive law of the design model, the identification formula for inductance L is obtained:

[0038]

[0039] Formula for identifying stator resistance R:

[0040]

[0041] Where k L1 k L2 k R1 k R2 It is the proportional integral constant, e d e q It is the current error, i d i q This is the obtained current value of the permanent magnet synchronous motor. The identification results of the stator resistance and flux linkage of the permanent magnet synchronous motor are sent to the pulse high-frequency injection module.

[0042] Preferably, in step S3, the inductance identification result obtained by the model reference adaptive module is sent to the sensorless control module, i.e., the pulse high-frequency injection module. The relationship between the actual coordinate axes and the estimated coordinate axes is as follows:

[0043]

[0044] in, The error angle is estimated for the rotor.

[0045] S31, in In a coordinate system, the relationship between current and voltage is as follows:

[0046]

[0047] in, , , , yes High-frequency voltage and high-frequency current in a coordinate system.

[0048] Preferably, in step S4, the pulsation-free high-frequency injection module, without a position sensor, estimates the motor speed and rotor position angle based on the already obtained motor parameters. Among these parameters... Injecting high-frequency sinusoidal signals into the shaft:

[0049]

[0050] in, It is the amplitude of high-frequency voltage. It is the angular frequency of high-frequency voltage.

[0051] S41. After inputting a high-frequency voltage signal, the high-frequency response current is:

[0052]

[0053] in, , , .exist After injecting a high-frequency sinusoidal signal into the d-axis, the inductances of the surface-mount permanent magnet synchronous motor on the d and q axes become different. It is not equal to 0.

[0054] S42, when When it approaches 0, It also approaches 0, so continue processing. The rotor position is obtained after the high-frequency shaft current signal passes through a low-pass filter.

[0055]

[0056] S43, when the error angle When it is small, it can be approximated as and Consider it as directly proportional:

[0057]

[0058] if only Adjusting to 0 estimates the position angle. It will approach the true position angle The calculation result can be used as rotor position information and fed back to the PI controller of the dual closed-loop control system.

[0059] S44, the pulsed high-frequency voltage injection algorithm will reduce the position error. By inputting a phase-locked loop and using a PI controller to bring the error to zero, the observed rotor electrical angular velocity can be obtained.

[0060]

[0061] in, , These are the proportional and integral adjustment coefficients of the phase-locked loop.

[0062] Preferably, in step S5, a simulation model is established on the Simulink simulation platform, and three simulation conditions are set: low-speed steady-state operation, sudden load increase and decrease, and parameter drift, to verify the feasibility of the proposed method.

[0063] Through the above technical solutions, the beneficial effects of the present invention are: (1) The stator resistance and inductance of the permanent magnet synchronous motor are identified by model reference adaptation, thereby improving the accuracy of positionless control; (2) The permanent magnet synchronous motor of the mining belt conveyor is controlled by the pulse high-frequency voltage injection method without position sensors, which has a simple algorithm structure and makes it easy to obtain the speed; (3) The combination of parameter identification and positionless control can effectively improve the robustness of the permanent magnet synchronous motor control system. Attached Figure Description

[0064] Figure 1 This is the control block diagram for the adaptive reference model in the implementation case of this invention.

[0065] Figure 2 This is a schematic diagram of the sensorless control system for a permanent magnet synchronous motor based on an improved pulsed high-frequency voltage injection method, which is an embodiment of the present invention.

[0066] Figure 3 This is the output speed of the permanent magnet synchronous motor in the simulation model of the embodiment of this invention under low-speed steady-state operation.

[0067] Figure 4 It is the rotor position angle of the permanent magnet synchronous motor in the simulation model of the embodiment of the present invention under low-speed steady-state operation.

[0068] Figure 5 This is the output speed of the permanent magnet synchronous motor in the simulation model of the implementation case of this invention under the condition of sudden increase or decrease in load.

[0069] Figure 6 The simulation model of the present invention shows the rotor position angle of a permanent magnet synchronous motor under sudden load increases or decreases.

[0070] Figure 7 The simulation model of the permanent magnet synchronous motor in this invention demonstrates the output speed under parameter drift conditions.

[0071] Figure 8 The simulation model of the permanent magnet synchronous motor in the embodiment of this invention shows the rotor position angle under parameter drift conditions. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0073] This invention was run in a Windows 10 environment and analyzed using Matlab 2022a software.

[0074] To address some shortcomings of existing technologies, this invention provides a method for parameter identification using a model adaptive reference and injection of pulsed high-frequency signals into a sensorless motor control system. The identification method includes the following steps:

[0075] S1. Establish a permanent magnet synchronous motor and its main circuit on the simulation platform Matlab, and set the parameters of the permanent magnet synchronous motor.

[0076] S2. Build a vector control dual closed-loop module to obtain parameters such as d, q voltage, and current of the permanent magnet synchronous motor.

[0077] S3. Establish a model reference adaptive module on the simulation platform, input the voltage and current of the permanent magnet synchronous motor into the model adaptive reference module, and identify the parameters of the motor's stator resistance and flux linkage.

[0078] S4. Establish a pulse high-frequency injection module on the simulation platform, and send the identification results to the sensorless control module.

[0079] S5, the sensorless module, combines the obtained motor parameters to estimate the motor speed and rotor position angle.

[0080] S6. The simulation model is set to run under three operating conditions: low-speed steady-state operation, sudden load increase and decrease, and parameter drift.

[0081] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, a method for parameter identification of a model adaptive reference and injection of a pulsed high-frequency signal into a sensorless motor control system provided by the embodiments of the present invention will be described in detail with reference to the accompanying drawings. The method for parameter identification of a model adaptive reference and injection of a pulsed high-frequency signal into a sensorless motor control system provided by the embodiments of the present invention includes a control block diagram as shown below. Figure 2 As shown:

[0082] S1. Establish a permanent magnet synchronous motor and its main circuit on the simulation platform Matlab, and set the parameters of the permanent magnet synchronous motor: The permanent magnet synchronous motor control in the simulation experiment adopts i drefThe control mode is set to 0, the sampling frequency is set to 10kHz, and the DC power supply voltage is 500V. The specific parameters of the permanent magnet synchronous motor are as follows: motor power is 1KW; rated speed is 500 rpm; number of pole pairs is 8; permanent magnet flux is 0.2Wb; stator resistance is 1Ω; stator inductance is 0.0056mH.

[0083] S2. Construct a vector control dual-closed-loop module to obtain parameters such as d-axis and q-axis voltage and current of the permanent magnet synchronous motor: 3 PI controller modules, including speed loop PI control, d-axis current PI control, and q-axis current PI control; Clark transform module, Park transform module, and inverse Park transform module. The d-axis and q-axis voltage and current obtained by the Park transform module are sent to the model reference adaptive module.

[0084] S3. The stator resistance and flux linkage of the permanent magnet synchronous motor are identified. The obtained d-axis current and q-axis voltage of the permanent magnet synchronous motor are substituted into the adaptive calculation formula of the model reference to obtain the parameter identification results of the inductor:

[0085]

[0086] The parameter identification results for the stator resistor are as follows:

[0087]

[0088] Where k L1 k L2 k R1 k R2 It is the proportional integral constant, e d e q It is the current error, i d i q It is the current value obtained from the permanent magnet synchronous motor.

[0089] S31. Design of adaptive reference modules for simulation models:

[0090] This module consists of two main parts: an adjustable model module and an adaptive law module. The adjustable model module is further divided into an adjustable d-axis model and an adjustable q-axis model. The d and q voltages and currents obtained by the Park transform are fed into the model reference adaptive module after passing through a second-order low-pass filter. The adaptive adjustment law then calculates and outputs the identification parameters: stator resistance and inductance.

[0091] S32. Under low-speed steady-state operation, the stator inductance identification error of the permanent magnet synchronous motor is 2.31%; the inductance identification error is 3.48%.

[0092] Under sudden load increases and decreases, the stator inductance identification error of the permanent magnet synchronous motor is 2.89%; the inductance identification error is 4.04%.

[0093] Under parameter drift conditions, the stator inductance identification error of the permanent magnet synchronous motor is 2.65%; the inductance identification error is 3.73%.

[0094] S4. Establish a pulsed high-frequency injection module on the simulation platform, and send the identification results to the sensorless control module: After inputting the high-frequency voltage signal, the high-frequency response current is:

[0095]

[0096] in, , , .

[0097] S41. Input design for the pulsed high-frequency injection module in the simulation model:

[0098] The parameters identified by the model reference adaptive module are processed by the Saturation limiting module to remove outlier data, preventing them from affecting the subsequent estimation of the permanent magnet synchronous motor's speed and position angle. After the parameters pass through a first-order smoothing filter module to eliminate high-frequency jitter, they are fed into the high-frequency injection demodulation subsystem.

[0099] S5, the sensorless module, combined with the obtained motor parameters, estimates the motor speed and rotor position angle:

[0100]

[0101] When the error angle When it is small, it can be approximated as and Consider it as directly proportional:

[0102]

[0103] The pulsed high-frequency voltage injection algorithm will reduce the position error. By inputting a phase-locked loop and using a PI controller to bring the error to zero, the observed rotor electrical angular velocity can be obtained.

[0104]

[0105] S51. Design of the pulsed high-frequency injection module in the simulation model:

[0106] S511. The pre-processed stator resistance and inductance of the permanent magnet synchronous motor replace the original fixed parameters and are connected to the high-frequency current reference amplitude calculation module to calculate the high-frequency response current after the input high-frequency voltage signal in real time.

[0107] S512, the smoothed stator resistor and inductor are connected to the position error calculation module through another route, and the adaptive position error is calculated in real time by combining the obtained high-frequency q-axis current.

[0108]

[0109] S513. The obtained adaptive position error is connected to the PI input terminal of the PLL phase-locked loop to replace the error signal calculated by the traditional inherent parameters, and outputs the speed and rotor position angle.

[0110] S6. The simulation model is set to run under three operating conditions: low-speed steady-state operation, sudden load increase and decrease, and parameter drift.

[0111] Operating Condition 1: T L =120 At t=0s, n=90r / min; at t=1.5s, n=120r / min.

[0112] Operating condition 2: When n=90r / min and t=0s, T L =120 At t=1s, T L =250 At t=2s, T L =120 .

[0113] Operating condition 3: n=90r / min, T L =250 The stator resistance increases by 40% due to the heat generated during motor operation.

[0114] The specific simulation waveforms are shown in the attached diagram. Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 .

[0115] S61, such as Figure 3 What we see is the output motor speed under low-speed steady-state operation using the high-frequency signal injection method based on parameter identification. The black curve represents the actual speed of the permanent magnet synchronous motor, while the blue curve represents the speed output by the high-frequency signal injection module. At t=1.5s, the speed suddenly increases, and the output speed quickly catches up with the reference speed.

[0116] S62, such as Figure 4What we see are waveforms comparing the position angles of a permanent magnet synchronous motor under low-speed steady-state operation. The black sawtooth wave represents the actual rotor position angle, while the blue sawtooth wave represents the estimated rotor position angle. At t=1.5s, due to a sudden increase in speed, the curves show a separation phenomenon. Subsequently, the observer quickly completed the correction, indicating that the position-free observer converges rapidly and has high tracking accuracy.

[0117] S63, such as Figure 5 What we see is the output motor speed using the parameter-identified high-frequency signal injection method when the rotational speed is constant and the load first increases and then decreases. The black curve represents the actual speed of the permanent magnet synchronous motor, and the blue curve represents the output speed of the high-frequency signal injection module. At t=1s, the given load suddenly increases, and the output speed decreases; at t=2s, the given load suddenly decreases, and the output speed increases accordingly.

[0118] S64, such as Figure 6 What you see is a waveform comparison of the motor's position angle when the speed is constant and the load first increases and then decreases. The black sawtooth wave is the actual rotor position angle output by the permanent magnet synchronous motor, and the blue sawtooth wave is the rotor position angle estimated by the high-frequency signal injection method. As the load changes, the interval distance of the rotor position angle sawtooth waves also changes.

[0119] S65, such as Figure 7 What we see is the output motor speed using the high-frequency signal injection method based on parameter identification when the stator resistance increases due to parameter drift. The black curve represents the actual speed of the permanent magnet synchronous motor, and the blue curve represents the speed output by the high-frequency signal injection module. The overall speed waveform is relatively stable and can be fixed near the given speed.

[0120] S66, such as Figure 8 What you see is a waveform comparison of the motor's position angle during parameter drift. The black sawtooth wave is the actual rotor position angle output by the permanent magnet synchronous motor, and the blue sawtooth wave is the rotor position angle estimated by the high-frequency signal injection method.

[0121] The beneficial effects of this invention through the above technical solution are as follows: It employs model reference adaptation for parameter identification to obtain the stator resistance and inductance parameters of the permanent magnet synchronous motor, and combines this with sensorless control using pulsed high-frequency injection to output the speed and rotor position angle of the permanent magnet synchronous motor. This solves the problem that existing mining belt conveyors are easily affected by position sensors in harsh environments such as high temperatures and dust in mines. This method exhibits good output speed tracking, robustness, and strong anti-interference capability.

[0122] At the same time, it improves the operational reliability of mining belt conveyors.

[0123] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0124] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A sensorless control method based on a combination of pulsed high-frequency injection and model adaptive reference for parameter identification, applied to the external rotor permanent magnet synchronous motor of a mining belt conveyor, characterized in that... The identification method includes the following steps: S1. Establish a mathematical model of the permanent magnet synchronous motor in the synchronous rotating coordinate system dq, and obtain the original signal parameters such as d-axis voltage and current during the operation of the permanent magnet synchronous motor. S2. The original d-axis voltage and current signals are processed by second-order low-pass filtering to remove the high-frequency harmonics generated by the high-frequency injection of the pulsation. The filtered fundamental signal is then sent to the model adaptive reference module. Based on the proportional-integral adaptive law, the stator resistance and flux linkage of the motor are identified online to obtain the real-time identification parameters. S3. The real-time identification parameters output by the model adaptive reference module are sequentially subjected to amplitude limiting and first-order smoothing filtering preprocessing, and the preprocessed stable identification parameters are sent to the pulse high-frequency injection module. S4. The pulse high-frequency injection module uses the pre-processed stator resistance and flux linkage parameters to correct the high-frequency current amplitude calculation model and rotor position error demodulation formula. It combines the high-frequency response current to estimate the motor speed and rotor position angle, and feeds the rotor position angle back to the motor dual closed-loop PI controller. S5. Build the corresponding model on the Simulink simulation platform and verify the feasibility of the proposed method under no-load and variable-load variable-speed conditions.

2. The sensorless control method based on pulse high-frequency injection and model adaptive reference parameter identification according to claim 1, characterized in that: In step S1, a control model of the permanent magnet synchronous motor is established in Matlab / Simulink, and the motor control uses i dref The control mode of =0 is used to obtain the d-axis voltage and current of the permanent magnet synchronous motor.

3. The sensorless control method combining pulsed high-frequency injection and model adaptive reference parameter identification according to claim 1, characterized in that: In step S2, the acquired d-axis and q-axis voltages and currents of the permanent magnet synchronous motor are fed into the model adaptive reference module to identify the stator resistance and flux linkage of the motor. The second-order low-pass filter used for decoupling high and low frequency signals has a cutoff frequency of 100Hz; the operating frequency of the pulsed high-frequency injection signal is set to 1000Hz.

4. The sensorless control method combining pulsed high-frequency injection and model adaptive reference parameter identification according to claim 1, characterized in that: In step S3, the model adaptive reference module designs a proportional-integral adaptive law based on the Popov superstability theory to obtain the parameter identification results of the inductor: ； Stator resistor parameter identification results: 。 Where k L1 k L2 k R1 k R2 It is the proportional integral constant specific to the adaptive law, e d e q It is the error value between the filtered fundamental current and the model-predicted current, i d i q These are the filtered fundamental currents of the d-axis and q-axis of the permanent magnet synchronous motor. The identified stator resistance and flux linkage are only used to correct the demodulation model of the pulsation high-frequency injection module and do not participate in the parameter update of the motor body model. When the identification parameters are limited, the parameter range is: stator resistance range is 0.5Ω to 2Ω, and stator inductance range is 5mH to 15mH.

5. The sensorless control method combining pulsed high-frequency injection and model adaptive reference parameter identification according to claim 1, characterized in that: In step S4, the pulsed high-frequency voltage injection method injects a high-frequency sinusoidal voltage signal into the d-axis of the estimated dq synchronous rotating coordinate system, while there is no high-frequency voltage input on the q-axis. The expression for the injected signal is: Where, ω h U is the frequency of the injected pulsed high-frequency signal; h The amplitude of the injected pulsed high-frequency signal. After inputting a high-frequency voltage signal to the external rotor permanent magnet synchronous motor, the high-frequency response currents of the d and q axes are obtained as follows: in, , , , It is the rotor position error angle. After injecting a high-frequency sinusoidal signal into the d-axis, the inductances of the surface-mount permanent magnet synchronous motor on the d and q axes become different. It is not equal to 0. Then, the high-frequency response current is separated through coordinate transformation. By demodulating the q-axis component of the excitation high-frequency response current, when calculating the high-frequency impedance term and rotor position error term, the original fixed nominal parameters are replaced throughout the calculation. The stator resistance, inductance, and real-time identification parameters, which have been preprocessed by limiting and first-order smoothing filtering, are substituted to obtain the estimated rotor position. When the error angle When it is small, it can be approximated as and Consider it as directly proportional: The motor control system enables closed-loop regulation to Adjusting to 0 estimates the position angle. It will approach the true position angle infinitely. The calculation result can be used as rotor position information and fed back to the PI controller of the dual closed-loop control system. Estimated rotor electric angular velocity: in, , These are the proportional and integral adjustment coefficients of the phase-locked loop. The dynamic adjustment rule for the phase-locked loop (PLL) PI coefficient is as follows: when the stator resistance identified by MRAS increases and the inductance decreases, the PLL proportional coefficient k is increased simultaneously. p Reduce the integral coefficient k i This reduces the steady-state speed fluctuations caused by motor temperature rise and magnetic saturation.

6. The sensorless control method combining pulsed high-frequency injection and model adaptive reference parameter identification according to claim 1, characterized in that: In step S5, a simulation model is built on the Simulink simulation platform to verify the feasibility of the proposed method.

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