Speed identification method of three-phase asynchronous motor based on speed sensorless vector control

Abstract

This invention provides a speed identification method for a three-phase asynchronous motor based on sensorless vector control, comprising: establishing a mathematical model of the three-phase asynchronous motor; determining the parameter matrix to be identified; defining the observation length, neuron factor, and initializing the model reference adaptive covariance matrix P(N); generating the observation matrix and its transpose matrix; calculating the current-time model reference adaptive covariance matrix P(N); using a fuzzy control algorithm to calculate the gain matrix K(N-1) of the previous time step; updating the estimated values ​​of the parameters to be identified; and updating the objective function value J. t (θ). This invention effectively reduces torque ripple, enabling a three-phase asynchronous motor to smoothly transition from low speed to high speed operation. Current ripple is significantly reduced, improving the system control performance of the three-phase asynchronous motor under varying load conditions. The technical solution of this invention addresses the technical problems of high parameter sensitivity and poor disturbance rejection capability in traditional model reference adaptive methods, as well as the difficulty in adjusting the parameters of traditional PID controllers for the speed loop.

Description

Technical Field

[0001] This invention relates to the field of motor speed identification technology, and in particular to a method for identifying the speed of a three-phase asynchronous motor based on sensorless vector control. Background Technology

[0002] In modern high-performance AC motor speed control systems, vector control technology, with its superior performance and simple, reliable methods, has been widely applied to the high-performance control of various AC motors. The key to vector control lies in decoupling, which presupposes accurate flux linkage estimation. The accuracy of flux linkage estimation, in turn, heavily depends on motor parameters. Therefore, motor parameter identification plays a crucial role in vector control technology. Besides being limited by the accuracy of motor parameter identification, it is also affected by load characteristics. Among the load's torque and speed characteristics, speed has a significant impact on the dynamic performance of the motor. For example, in multi-axis motion robots widely used in industrial control, the motor load speed changes when transporting objects. If the speed cannot be identified in real time, it will affect the system's dynamic performance. Therefore, online speed identification of AC motor control systems is an effective means to improve the performance of the control system.

[0003] Sensorless identification is a common and fundamental method. The idea is to select the model's state and observed variables, calculate the sum of squares of the errors between the observed and actual values, and adjust the model parameters to minimize this sum. At this point, the model parameters can be considered equal to the actual system parameters. This method is widely applicable to both dynamic and static systems, suitable for both offline and online identification, and the identification results are characterized by unbiasedness, consistency, and effectiveness. However, traditional model reference adaptive methods suffer from high parameter sensitivity and poor disturbance rejection capabilities. Furthermore, the speed loop in existing vector control systems using traditional PID controllers presents challenges in parameter adjustment. Summary of the Invention

[0004] This invention provides a speed identification method for a three-phase asynchronous motor based on sensorless vector control, which can solve the problems of high parameter sensitivity and poor anti-disturbance suppression capability of traditional model reference adaptive methods, and the technical problem that the speed loop of existing vector control systems using traditional PID controllers has difficulty in parameter adjustment.

[0005] According to one aspect of the present invention, a speed identification method for a three-phase asynchronous motor based on sensorless vector control is provided. The speed identification method for a three-phase asynchronous motor based on sensorless vector control includes: step S1, establishing a mathematical model of the three-phase asynchronous motor and determining the parameter matrix to be identified. Step S2, based on the parameter matrix to be identified Define the observation length and neuron factor, and initialize the model reference adaptive covariance matrix P(N); Step S3, generate the transpose matrix of the observation matrix. Step S4: Based on the model reference adaptive covariance matrix P(N) and the transpose of the observation matrix... Calculate the model reference adaptive covariance matrix P(N) at the current time; Step S5, based on the transpose of the observation matrix in Step S3. In step S4, the current time-instance model reference adaptive covariance matrix P(N) is used to calculate the previous time-instance gain matrix K(N-1) using a fuzzy control algorithm; in step S6, the estimated values ​​of the parameters to be identified are updated based on the previous time-instance gain matrix K(N-1) from step S5. Step S7, based on the estimated values ​​of the parameters to be identified Update the objective function value J t (θ) is used to identify the rotational speed of the system by the output electromagnetic torque of the three-phase asynchronous motor and the mechanical angular velocity of the rotor.

[0006] Further, step S1 specifically includes: step S11, constructing the system regression equation; step S12, transforming the system regression equation into matrix form to obtain the measurement equation; step S13, establishing the error based on the measurement equation using an objective function; and step S14, selecting a set of estimated values ​​for the parameter matrix θ to be identified. Minimize the objective function J to obtain the parameters to be identified. The fuzzy estimation formula.

[0007] Further, step S2 specifically includes: step S21, taking the parameter to be identified The fuzzy estimation method is discretized and described in the form of difference equations; step S22, the parameters to be identified are... The fuzzy estimation formula is augmented with an observation time point, where the input is u(n+N+1) and the output is y(n+N+1), to obtain the observation matrix for the next time point. Step S23, based on the observation matrix at the next time step Obtain the parameter estimation matrix; Step S24, introduce the matrix inversion lemma into the parameter estimation matrix to obtain the recursive fuzzy identification formula; Step S25, introduce the neuron factor into the recursive fuzzy identification formula to initialize the model reference adaptive covariance matrix P(N).

[0008] Furthermore, in step S3, the transpose of the observation matrix... for (n+N-1) and (N) represent the current time, y() is the system output sample value, and u() is the system input sample value.

[0009] Furthermore, in step S4, the model reference adaptive covariance matrix P(N) at the current time is... in, This is the transpose of the matrix.

[0010] Furthermore, in step S5, the gain matrix K(N-1) of the previous time step is Where P(N-1) is the model reference adaptive covariance matrix at time N-1, and λ is the single neuron factor.

[0011] Further, in step S6, the updated estimated value of the parameter to be identified... for in, The estimated values ​​of the parameters to be identified at time N-1 are... Let y(n+N) be the estimated value of the parameter to be identified at time N, and let y(n+N) be the sampled value of the system output at time n+N.

[0012] Further, in step S7, the objective function value J t (θ) is The mechanical equation of a three-phase asynchronous motor is: In the formula, J t (θ) is the objective function, ω r It is the mechanical angular velocity of the motor rotor, T e It is the electromagnetic torque output by the motor, T L is the load torque, B is the damping coefficient, and e is the error.

[0013] According to another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the steps of the speed identification method for a three-phase asynchronous motor based on sensorless vector control as described above.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the speed identification method for a three-phase asynchronous motor based on sensorless vector control as described above.

[0015] Applying the technical solution of this invention, a speed identification method for a three-phase asynchronous motor based on sensorless vector control is provided. This method uses a single-neuron PID control method to improve the starting performance of the sensorless motor, and introduces a model reference adaptive algorithm to optimize the speed regulator for abnormal fluctuations in current and speed waveforms. Simulation results show that this fuzzy-single-neuron PID control system can optimize overshoot and reduce fluctuations. Moreover, it has good dynamic performance. Compared with the prior art, the speed identification method for a three-phase asynchronous motor based on sensorless vector control provided by this invention has the following beneficial effects: (1) This invention utilizes an advanced model reference adaptive sensorless control algorithm to improve the load-carrying capacity of the system, achieving the effect of flexible start and flexible stop; (2) This invention introduces a fuzzy single-neuron PID control algorithm, which effectively reduces torque pulsation and overshoot. In actual operation, it can smoothly switch from low speed to high speed, and the current ripple is significantly reduced. Attached Figure Description

[0016] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0017] Figure 1 A flowchart of a speed identification method for a three-phase asynchronous motor based on sensorless vector control according to a specific embodiment of the present invention is shown;

[0018] Figure 2 A system block diagram of a speed identification method for a three-phase asynchronous motor based on sensorless vector control according to a specific embodiment of the present invention is shown. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. 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 a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. 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.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0021] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0022] like Figure 1 and Figure 2 As shown, a specific embodiment of the present invention provides a speed identification method for a three-phase asynchronous motor based on sensorless vector control. This speed identification method includes: Step S1, establishing a mathematical model of the three-phase asynchronous motor and determining the parameter matrix to be identified. Step S2, based on the parameter matrix to be identified Define the observation length and neuron factor, and initialize the model reference adaptive covariance matrix P(N); Step S3, generate the transpose matrix of the observation matrix. Step S4: Based on the model reference adaptive covariance matrix P(N) and the transpose of the observation matrix... Calculate the model reference adaptive covariance matrix P(N) at the current time; Step S5, based on the transpose of the observation matrix in Step S3. In step S4, the current time-instance model reference adaptive covariance matrix P(N) is used to calculate the previous time-instance gain matrix K(N-1) using a fuzzy control algorithm; in step S6, the estimated values ​​of the parameters to be identified are updated based on the previous time-instance gain matrix K(N-1) from step S5. Step S7, based on the estimated values ​​of the parameters to be identified Update the objective function value J t(θ) is used to identify the rotational speed of the system by the output electromagnetic torque of the three-phase asynchronous motor and the mechanical angular velocity of the rotor.

[0023] This invention provides a speed identification method for a three-phase asynchronous motor based on sensorless vector control. This method employs a single-neuron PID control approach to improve the starting performance of the sensorless motor, and introduces a model reference adaptive algorithm to optimize the speed regulator for abnormal fluctuations in the current and speed waveforms. Simulation results show that this fuzzy-single-neuron PID control system can optimize overshoot and reduce fluctuations, and also exhibits good dynamic performance. Compared with existing technologies, the speed identification method for a three-phase asynchronous motor based on sensorless vector control provided by this invention has the following advantages:

[0024] (1) This invention utilizes an advanced sensorless speed control algorithm based on model reference adaptation to improve the system's load-bearing capacity and achieve the effect of flexible start and flexible stop.

[0025] (2) The present invention introduces a fuzzy single-neural PID control algorithm, which effectively reduces torque ripple and overshoot. In actual operation, it can smoothly switch from low speed to high speed, and the current ripple is significantly reduced.

[0026] In this invention, step S1 specifically includes: step S11, constructing a system regression equation; step S12, converting the system regression equation into matrix form to obtain a measurement equation; step S13, establishing an error based on the measurement equation using an objective function; and step S14, selecting a set of estimated values ​​for the parameter matrix θ to be identified. Minimize the objective function J to obtain the parameters to be identified. The fuzzy estimation formula.

[0027] Further, step S2 specifically includes: step S21, taking the parameter to be identified The fuzzy estimation method is discretized and described in the form of difference equations; step S22, the parameters to be identified are... The fuzzy estimation formula is augmented with an observation time point, where the input is u(n+N+1) and the output is y(n+N+1), to obtain the observation matrix for the next time point. Step S23, based on the observation matrix at the next time step Obtain the parameter estimation matrix; Step S24, introduce the matrix inversion lemma into the parameter estimation matrix to obtain the recursive fuzzy identification formula; Step S25, introduce the neuron factor into the recursive fuzzy identification formula to initialize the model reference adaptive covariance matrix P(N).

[0028] Furthermore, in step S3, the transpose of the observation matrix... for (n+N-1) and (N) represent the current time, y() is the system output sample value, and u() is the system input sample value.

[0029] Furthermore, in step S4, the model reference adaptive covariance matrix P(N) at the current time is... in, This is the transpose of the matrix.

[0030] Furthermore, in step S5, the gain matrix K(N-1) of the previous time step is Where P(N-1) is the model reference adaptive covariance matrix at time N-1, and λ is the single neuron factor.

[0031] Further, in step S6, the updated estimated value of the parameter to be identified... for in, The estimated values ​​of the parameters to be identified at time N-1 are... Let y(n+N) be the estimated value of the parameter to be identified at time N, and let y(n+N) be the sampled value of the system output at time n+N.

[0032] Further, in step S7, the objective function value J t (θ) is The mechanical equation of a three-phase asynchronous motor is: In the formula, J t (θ) is the objective function, ω r It is the mechanical angular velocity of the motor rotor, T e It is the electromagnetic torque output by the motor, T L is the load torque, B is the damping coefficient, and e is the error.

[0033] According to another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the speed identification method for a three-phase asynchronous motor based on sensorless vector control as described above.

[0034] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, characterized in that, when executed by a processor, the computer program implements the steps of the speed identification method for a three-phase asynchronous motor based on sensorless vector control as described above.

[0035] To gain a further understanding of the present invention, the following description is provided in conjunction with... Figure 1 and Figure 2 The present invention provides a detailed description of the speed identification method for a three-phase asynchronous motor based on sensorless vector control.

[0036] like Figure 1 and Figure 2 As shown in the figure, a speed identification method for a three-phase asynchronous motor based on sensorless vector control is provided according to a specific embodiment of the present invention, so as to improve the system control performance of the motor under conditions such as speed changes.

[0037] The technical solution to achieve the purpose of this invention is: a speed identification method for a three-phase asynchronous motor based on sensorless vector control, comprising the following steps:

[0038] Step S1: Establish the mathematical model of the three-phase asynchronous motor and determine the parameter matrix to be identified.

[0039] Step S2: Define observation length, neuron factor, and initialize model reference adaptive covariance matrix P(N);

[0040] Step S3: Generate the observation matrix and its transpose matrix.

[0041] Step S4: Calculate the model reference adaptive covariance matrix P(N) at the current time.

[0042] Step S5: Calculate the gain matrix K(N-1) of the previous time step using a fuzzy control algorithm;

[0043] Step S6: Update the estimated values ​​of the parameters to be identified.

[0044] Step S7: Update the objective function value J t (θ).

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] (1) This invention utilizes an advanced sensorless speed control algorithm based on model reference adaptation to improve the system's load-bearing capacity and achieve the effect of flexible start and flexible stop.

[0047] (2) The present invention introduces a fuzzy single-neural PID control algorithm, which effectively reduces torque ripple and overshoot. In actual operation, it can smoothly switch from low speed to high speed, and the current ripple is significantly reduced.

[0048] Because the output shaft of the valve drive motor is equipped with a coded speed sensor, this increases cost and size, and reduces system stability. Introducing a sensorless vector control system based on model reference adaptation can reduce system cost and size. However, the speed loop in the vector control system using a traditional PID controller suffers from difficulty in parameter adjustment. Therefore, a single-neuron PID controller algorithm is introduced to replace the traditional PID controller. The self-learning capability of the single neuron improves system stability and response speed. To further enhance the performance of the single-neuron PID control, fuzzy control is introduced to correct the gain coefficient K, which enhances the system's load-carrying capacity and anti-interference performance. Combined with... Figure 1 and Figure 2 This invention proposes a speed identification method for a three-phase asynchronous motor based on sensorless vector control, comprising the following steps:

[0049] Step S1: Establish the mathematical model of the three-phase asynchronous motor and determine the parameter matrix to be identified.

[0050] Step S2: Based on the parameter matrix to be identified Define the observation length, neuron factor, and initialize the model reference adaptive covariance matrix P(N);

[0051] Step S3: Generate the observation matrix and its transpose matrix.

[0052] Step S4: Calculate the model reference adaptive covariance matrix P(N) at the current time.

[0053] Step S5: Calculate the gain matrix K(N-1) of the previous time step using a fuzzy control algorithm;

[0054] Step S6: Update the estimated values ​​of the parameters to be identified.

[0055] Step S7: Update the objective function value J t (θ).

[0056] Furthermore, the establishment of the mathematical model of the three-phase asynchronous motor and the determination of the parameter matrix θ to be identified in step S1 are as follows:

[0057] Step S11: Construct the system regression equation, specifically as follows:

[0058] y(i)=θ1u1(i)+θ2u2(i)+…+θ n u n (i)+ei=1,2,3,… (1)

[0059] In the formula, u and y represent the values ​​at t1, t2, ..., t3 respectively. m The system input and output are observed at all times; θi These are the parameters to be identified, also known as regression coefficients.

[0060] Step S12: Convert to matrix form, specifically:

[0061] Y = [y(1)y(2)...y(m)] T (2)

[0062] θ=[θ1θ2…θ m ] T (3)

[0063]

[0064] e = [e1 e2…e] m ] T (5)

[0065] There is a measurement equation:

[0066] Y = Φθ + e (6)

[0067] In the formula, Y is the system output matrix, Φ is the system input matrix, θ is the matrix of parameters to be identified, and e is the error.

[0068] Step S13: Establish the error using the objective function, and obtain...

[0069]

[0070] In the formula, J is the objective function, also known as the cost function.

[0071] Step S14: Select a set of estimated values ​​for θ To minimize the objective function J, we take the derivative of J with respect to θ and set the derivative to 0:

[0072]

[0073] We can obtain:

[0074]

[0075] The parameters to be identified The fuzzy estimation formula.

[0076] Furthermore, the definition of observation length, neuron factor, and initialization of model reference adaptive covariance matrix P(N) in step S2 is as follows:

[0077] Step S21: Discretize the fuzzy estimation formula and describe it in the form of a difference equation:

[0078]

[0079] In the formula, y(K) is the current output sample value of the system, U(K) is the current input sample value of the system, a is the coefficient of the current output sample value of the system, b is the coefficient of the current input sample value of the system, n is the final sample value at the current time, and K is the current time.

[0080] Step S22: Add an observation time to the fuzzy estimation formula (9) for the parameter matrix θ to be identified. The input quantity of the observation is u(n+N+1), and the output quantity is y(n+N+1), then:

[0081] θ=[a1 a2…a n b0 b1…b n ] T (11)

[0082] e = [e(n+1)e(n+2)...e(n+N+1)] T (12)

[0083] Add a row φ to Φ(N) T (N+1), Y(N) increases by a term y(n+N+1), we have:

[0084]

[0085] In the formula, The observation matrix for the next time step:

[0086]

[0087] Step S23: Obtain the parameter estimation matrix based on the observation matrix of the next time step in step S22, specifically as follows:

[0088]

[0089] Step S24: Introduce the matrix inversion lemma into the parameter estimation matrix, we have:

[0090]

[0091] In the formula, P(N)=[Φ T (N)Φ(N)] -1 Is seeking The obtained auxiliary matrix; K(N) is the correction matrix;

[0092] y(n+N+1) represents the new observation; It is an estimate The estimated value obtained later is the (N+1)th estimate.

[0093] Equations (17) to (19) are the recursive fuzzy identification formulas.

[0094] Step S25: Introduce neuron factors into the recursive fuzzy identification formula to initialize the model reference adaptive covariance matrix P(N), as follows:

[0095]

[0096] In the formula, λ is the single-neuron factor. When λ = 1, it degenerates into a common recursive fuzzy identification formula. The initialization model reference adaptive covariance matrix is ​​set to... The single neuron factor was set to λ = 0.7.

[0097] Furthermore, the transpose matrix of the observation matrix generated in step S3 Specifically as follows:

[0098]

[0099] Where (n+N-1) and (N) represent the time periods.

[0100] Further, in step S4, the reference adaptive covariance matrix P(N) at the current time is calculated based on the model reference adaptive covariance matrix P(N) in step S2 and the observation matrix in step S3, as follows:

[0101]

[0102] Furthermore, the calculation of the gain matrix K(N-1) of the previous time step using the fuzzy control algorithm in step S5 is as follows:

[0103]

[0104] Further, step S6 updates the estimated values ​​of the parameters to be identified. Specifically as follows:

[0105]

[0106] Furthermore, step S7 involves updating the objective function value J. t (θ), as follows:

[0107]

[0108] The mechanical equation of a three-phase asynchronous motor is:

[0109]

[0110] In the formula, J t (θ) is the objective function, ω r It is the mechanical angular velocity of the motor rotor, T e It is the electromagnetic torque output by the motor, T LB is the load torque, and B is the damping coefficient.

[0111] Ultimately, the output electromagnetic torque (T) of the three-phase asynchronous motor can be determined. e ) and rotor mechanical angular velocity (ω) r This is used to identify the rotational speed of the system.

[0112] In summary, this invention provides a speed identification method for a three-phase asynchronous motor based on sensorless vector control, comprising: establishing a mathematical model of the three-phase asynchronous motor and determining the parameter matrix to be identified. Define the observation length, neuron factor, and initialize the model reference adaptive covariance matrix P(N), then generate the observation matrix and its transpose. Calculate the model reference adaptive covariance matrix P(N) at the current time step, and use the fuzzy control algorithm to calculate the gain matrix K(N-1) at the previous time step to update the estimated values ​​of the parameters to be identified. Update the objective function value J t (θ). This invention effectively reduces torque ripple, enabling a three-phase asynchronous motor to smoothly transition from low speed to high speed operation, significantly reducing current ripple, and improving the system control performance of the three-phase asynchronous motor under conditions such as load rotation changes.

[0113] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0114] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A speed identification method for a three-phase asynchronous motor based on sensorless vector control, characterized in that, The speed identification method for a three-phase asynchronous motor based on sensorless vector control includes: Step S1: Establish the mathematical model of the three-phase asynchronous motor and determine the parameter matrix to be identified. Step S2, based on the parameter matrix to be identified Define the observation length and neuron factor, and initialize the model reference adaptive covariance matrix P(N); Step S3: Generate the transpose of the observation matrix. Step S4: Based on the model reference adaptive covariance matrix P(N) and the transpose of the observation matrix... Calculate the model reference adaptive covariance matrix P(N) at the current time step; Step S5, based on the transpose of the observation matrix in step S3 And the current time-time model reference adaptive covariance matrix P(N) in step S4, and the gain matrix K(N-1) of the previous time-time is calculated using a fuzzy control algorithm; Step S6: Update the estimated values ​​of the parameters to be identified based on the gain matrix K(N-1) from the previous time step in step S5. Step S7, based on the estimated value of the parameter to be identified Update the objective function value J t (θ) is used to identify the rotational speed of the system by the output electromagnetic torque of the three-phase asynchronous motor and the mechanical angular velocity of the rotor.

2. The speed identification method for a three-phase asynchronous motor based on sensorless vector control according to claim 1, characterized in that, Step S1 specifically includes: Step S11: Construct the system regression equation; Step S12: Transform the system regression equation into matrix form to obtain the measurement equation; Step S13: Based on the measurement equation, establish the error in the form of an objective function; Step S14: Select a set of estimated values ​​for the parameter matrix θ to be identified. Minimize the objective function J to obtain the parameters to be identified. The fuzzy estimation formula.

3. The speed identification method for a three-phase asynchronous motor based on sensorless vector control according to claim 2, characterized in that, Step S2 specifically includes: Step S21, the parameter to be identified The fuzzy estimation method is discretized and described in the form of difference equations; Step S22, in the parameters to be identified The fuzzy estimation formula is augmented with an observation time point, where the input is u(n+N+1) and the output is y(n+N+1), to obtain the observation matrix for the next time point. Step S23, based on the observation matrix of the next time moment Obtain the parameter estimation matrix; Step S24: Introduce the matrix inversion lemma into the parameter estimation matrix to obtain the recursive fuzzy identification formula; Step S25: Introduce neuron factors into the recursive fuzzy identification formula to initialize the model reference adaptive covariance matrix P(N).

4. The speed identification method for a three-phase asynchronous motor based on sensorless vector control according to claim 3, characterized in that, In step S3, the transpose of the observation matrix for (n+N-1) and (N) represent the current time, y() is the system output sample value, and u() is the system input sample value.

5. The speed identification method for a three-phase asynchronous motor based on sensorless vector control according to claim 4, characterized in that, In step S4, the current time-time model reference adaptive covariance matrix P(N) is: in, This is the transpose of the matrix.

6. The speed identification method for a three-phase asynchronous motor based on sensorless vector control according to claim 5, characterized in that, In step S5, the gain matrix K(N-1) of the previous time step is Where P(N-1) is the model reference adaptive covariance matrix at time N-1, and λ is the single neuron factor.

7. The speed identification method for a three-phase asynchronous motor based on sensorless vector control according to claim 6, characterized in that, In step S6, the updated estimated value of the parameter to be identified for in, The estimated values ​​of the parameters to be identified at time N-1 are... Let y(n+N) be the estimated value of the parameter to be identified at time N, and let y(n+N) be the sampled value of the system output at time n+N.

8. The speed identification method for a three-phase asynchronous motor based on sensorless vector control according to claim 7, characterized in that, In step S7, the objective function value J t (θ) is The mechanical equation of a three-phase asynchronous motor is: In the formula, J t (θ) is the objective function, ω r It is the mechanical angular velocity of the motor rotor, T e It is the electromagnetic torque output by the motor, T L is the load torque, B is the damping coefficient, and e is the error.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the three-phase asynchronous motor speed identification method based on sensorless vector control as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the speed identification method for a three-phase asynchronous motor based on sensorless vector control as described in any one of claims 1 to 7.

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