An intelligent control method for operation of a permanent magnet brushless motor

Abstract

The application relates to the field of motor control, in particular to an intelligent operation control method based on a permanent magnet brushless motor, which comprises the following steps: defining the input and output of a recursive least square method; calculating the cosine similarity of an input vector at a current moment and all historical input vectors, selecting the prediction error of a historical moment with the highest similarity to calculate a forgetting factor at the current moment, and updating a coefficient vector at the current moment based on the forgetting factor; according to the coefficient vector, the working condition characteristic vector of the motor at the current moment and the constraint region of the control parameter vector, calculating the predicted output efficiency at the current moment and selecting a group of control parameter vectors with the maximum predicted output efficiency to control the operation of the motor at the next moment. The application can track the time-varying characteristics of the motor and maintain stability under similar working conditions by dynamically adjusting the forgetting factor, improve the adaptability of the model, and inversely solve the optimal control parameters to control the operation of the motor in real time.

Description

Technical Field

[0001] This invention relates to the field of electric motor control, and more particularly to an intelligent control method for the operation of a permanent magnet brushless electric motor. Background Technology

[0002] Permanent magnet brushless motors have advantages such as high efficiency, high power density, and fast response speed, and are widely used in industrial drives, new energy vehicles, smart homes, and other fields. Motor operating efficiency is one of the core indicators for measuring its performance, and efficiency is affected by both operating conditions (speed, load, temperature, etc.) and control parameters (d-axis and q-axis current).

[0003] Chinese Patent No. CN114337427B discloses a method for identifying the moment of inertia using recursive least squares with a forgetting factor. The method includes: writing the motion equations of an asynchronous motor in recursive least squares form; determining the output variables, parameters to be identified, and the observation matrix; defining the observation magnitude, forgetting factor, initializing the covariance matrix, and identifying parameters; calculating the gain matrix at the current time; calculating the covariance at the current time; updating the parameter estimates; updating the objective function value; comparing the calculated objective function value with the preset objective function value, continuously updating, and finally calculating the moment of inertia information of the motor.

[0004] Recursive least squares (RLS) can update model parameters in real time, but traditional RLS uses a fixed forgetting factor, which is not adaptable to time-varying operating conditions: if the forgetting factor is too large, it will cause the model update to lag, and if it is too small, it will introduce noise interference, resulting in poor adaptability of motor control methods and insufficient efficiency optimization. Summary of the Invention

[0005] To address the aforementioned technical problems of poor adaptability to motor control methods and insufficient efficiency optimization, this invention provides an intelligent control method for operation based on a permanent magnet brushless motor.

[0006] A method for intelligent control of operation based on a permanent magnet brushless motor, comprising:

[0007] The current operating condition feature vector of the motor and the control parameter vector output at the previous moment are used as inputs to the recursive least squares method, and the actual output efficiency of the motor at the current moment is used as the output of the recursive least squares method.

[0008] Calculate the cosine similarity between the input vector at the current time and the input vectors at all historical times, select the historical time with the highest similarity as the reference time, obtain the error between the actual output efficiency and the predicted output efficiency at the reference time, calculate the forgetting factor at the current time based on the error, and update the coefficient vector at the current time based on the forgetting factor.

[0009] Based on the updated coefficient vector, the motor's current operating condition characteristic vector, and the constraint region of the set control parameter vector, the predicted output efficiency at the current moment is calculated, and the set of control parameter vectors with the highest predicted output efficiency is selected to control the motor's operation at the next moment.

[0010] This invention updates the model's coefficient vector in real time using the recursive least squares method and dynamically adjusts the forgetting factor by combining cosine similarity and historical error. This enables the model to track the time-varying characteristics of the motor and maintain stability under similar operating conditions, thus improving the model's adaptability. Furthermore, by maximizing the motor's output efficiency, the optimal control parameters are solved, which not only requires less computation but also meets the requirements of real-time control.

[0011] Preferably, before performing the recursive least squares method, the method further includes obtaining the initial coefficient vector and initial covariance matrix of the recursive least squares method based on the basic least squares method, and setting an initial forgetting factor. The process of obtaining the initial coefficient vector and initial covariance matrix includes:

[0012] Historical operating data of the motor is collected to obtain operating condition feature vectors, control parameter vectors, and actual output efficiency. The operating condition feature vectors and control parameter vectors obtained at each acquisition moment are used as input vectors. A historical data matrix is ​​constructed using the input vectors at all acquisition moments, and the combined vector of actual output efficiency at all acquisition moments is used as the output vector. The optimal coefficients and covariance matrix are calculated based on the basic least squares method to obtain the initial coefficient vector and the initial covariance matrix.

[0013] The initial coefficient vector of the recursive least squares method is obtained by using the basic least squares method, so that the starting point of the recursion is as close as possible to the true parameters. This avoids large initial prediction errors and slow convergence caused by starting from zero vectors or random values ​​in the online stage. The uncertainty of offline coefficient estimation is quantified by calculating the initial covariance matrix, ensuring that the gain matrix of the recursive least squares method is within a reasonable range in the first calculation, neither overshooting nor sluggish. An initial forgetting factor is given as a historical data dependency benchmark when the recursive least squares method is started.

[0014] Preferably, the calculation process of the forgetting factor at the current moment includes:

[0015] Construct a decay function with the error at the reference time as the exponent; set the maximum and minimum values ​​of the forgetting factor, multiply the difference between the maximum and minimum values ​​by the decay function, and sum the resulting product with the minimum value to obtain the forgetting factor at the current time.

[0016] The difference between the maximum and minimum values ​​of the forgetting factor is used as a coefficient for adjusting the degree of adjustment, avoiding model instability due to an excessively small forgetting factor. The forgetting factor is used to control the degree of learning from historical data. If the error is large, it indicates that the current operating condition of the motor deviates significantly from the model of similar historical operating conditions, requiring more reliance on new data, thus reducing the forgetting factor. If the error is small, it indicates that the current operating condition of the motor deviates slightly from the model of similar historical operating conditions, allowing more reliance on historical data, thus increasing the forgetting factor; thereby improving the model's adaptability.

[0017] Preferably, the update process of the coefficient vector at the current moment includes:

[0018] Calculate the error between the actual output efficiency and the predicted output efficiency at the current moment to obtain the efficiency error; calculate the gain matrix at the current moment; sum the product of the gain matrix and the efficiency error with the coefficient vector at the previous moment to obtain the updated coefficient vector.

[0019] Preferably, the calculation process for the efficiency error includes:

[0020] The predicted output efficiency is calculated based on the input vector at the current moment and the coefficient vector at the previous moment. The difference between the actual output efficiency at the current moment and the predicted output efficiency is then calculated to obtain the efficiency error.

[0021] Preferably, the calculation process of the gain matrix includes:

[0022] Obtain the covariance matrix of the previous time step, multiply the covariance matrix by the input vector of the current time step as the numerator, and use the sum of the updated forgetting factor and the quadratic form of the product as the denominator to obtain the gain matrix.

[0023] Preferably, after updating the forgetting factor at the current time step, the covariance matrix at the current time step is updated. The update process includes:

[0024] The correction term is calculated based on the covariance matrix of the previous time step, the input vector of the current time step, and the forgetting factor of the current time step. The correction term is then subtracted from the covariance matrix of the previous time step and multiplied by the reciprocal of the forgetting factor of the current time step to obtain the covariance matrix of the current time step.

[0025] Preferably, the control parameter vector includes d-axis current and q-axis current, and the process of setting the constraint region of the control parameter vector includes:

[0026] The range of the d-axis current and q-axis current is determined based on the rated parameters of the motor. At the same time, the combined current amplitude of the d-axis current and q-axis current cannot exceed the set current upper limit, thereby obtaining the constraint range of the control parameters.

[0027] Preferably, the process of calculating the prediction output efficiency at the current moment and selecting the set of control parameter vectors with the highest prediction output efficiency is as follows:

[0028] Based on the updated coefficient vector at the current moment, a linear formula for the output efficiency and the input vector is obtained; the operating condition feature vector at the current moment is a fixed value, and the optimal control parameters are solved by linear programming based on the constraint region of the control parameters.

[0029] Preferably, the operating condition feature vector includes the motor speed, load torque, motor temperature, and DC bus voltage.

[0030] The present invention has the following effects:

[0031] 1. This invention updates the model's coefficient vector in real time using the recursive least squares method, and dynamically adjusts the forgetting factor by combining cosine similarity and historical error, so that the model can not only track the time-varying characteristics of the motor, but also maintain stability under similar working conditions, thereby improving the model's adaptability.

[0032] 2. This invention is based on linear programming to solve for the optimal control parameters. By traversing the vertices of the constraint region of the control parameters, the maximum efficiency can be found quickly. This not only reduces the amount of computation and maximizes the output efficiency of the motor, but also meets the requirements of real-time control. Attached Figure Description

[0033] Figure 1 This is a flowchart of steps S1-S5 in an intelligent control method for operation based on a permanent magnet brushless motor according to an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0035] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0036] Traditional recursive least squares methods for controlling permanent magnet brushless motors typically pre-set a constant forgetting factor. When the preset forgetting factor is too large, the model relies too heavily on historical data, failing to promptly track changes in operating conditions such as motor aging, temperature rise, and sudden load changes, resulting in lagging efficiency optimization. Conversely, when the preset forgetting factor is too small, it overemphasizes the latest data, amplifying sensor noise and transient disturbances, causing parameter oscillations and unstable motor operation. This invention addresses the issue that a constant forgetting factor prevents the model from accurately assessing the similarity between current and historical operating conditions, leading to poor adaptability and insufficient efficiency optimization. It proposes a scheme to adjust the forgetting factor based on historical prediction errors. This approach maintains rapid model tracking while suppressing noise interference under time-varying conditions, achieving continuous optimal motor efficiency.

[0037] Reference Figure 1 A method for intelligent control of operation based on a permanent magnet brushless motor includes steps S1-S5, as detailed below:

[0038] S1. Obtain the initial coefficient vector and initial covariance matrix of the recursive least squares method based on the basic least squares method, and set the initial forgetting factor.

[0039] Before performing the recursive least squares method, it is necessary to set initialization parameters, including the initial coefficient vector, the initial covariance matrix, and the initial forgetting factor, to provide the recursive least squares method with a starting point that is both close to reality and robust enough, so that the subsequent recursive process can converge quickly and is not prone to divergence once it goes online.

[0040] Historical operating data of the motor was collected using a motor testing platform, and key information was extracted, including operating condition data: speed, load torque, motor temperature, and DC bus voltage; control parameters: d-axis current and q-axis current; and the motor's input and output power. For each acquisition point, a feature vector of operating conditions was constructed: [speed] Load torque Motor temperature DC bus voltage ]; Construct the control parameter vector: [d-axis current q-axis current This allows for the control of the motor's torque and speed; it also calculates the motor's historical actual output efficiency: output power / input power. .

[0041] The basic least squares method is used to solve linear regression problems, that is, to find a linear model that best fits the relationship between input and output data. This involves constructing a linear model using the basic least squares method. ,in This indicates the motor's output efficiency. This represents the input vector for the basic least squares method. This represents the coefficient vector.

[0042] The input vector for the basic least squares method is defined as including the operating condition feature vector and the control parameter vector. The input vector for the basic least squares method is expressed as:

[0043]

[0044] In the formula, This represents the input vector for the basic least squares method. It is a constant term. Indicates rotational speed. Indicates load torque. Indicates motor temperature. Indicates the DC bus voltage. Represents the d-axis current. This represents the q-axis current.

[0045] Construct a historical data matrix from the input vectors at all acquisition times. , This represents the number of input vectors, and the historical data matrix is... The matrix.

[0046] The combined vector of the actual output efficiencies of the motor at all acquisition times is used as the output vector of the basic least squares method. The output vector of the basic least squares method is expressed as:

[0047]

[0048] By using the basic least squares method, the optimal coefficient estimate under the historical operating data is calculated by minimizing the cumulative error of the historical operating data, which is the initial coefficient vector of the recursive least squares method.

[0049] The objective function is to find a coefficient vector that minimizes the sum of squared errors between the model's predicted and actual output efficiencies. Therefore, the objective function can be expressed as:

[0050]

[0051] In the formula, Represents the objective function value. Indicates the first The actual output efficiency corresponding to each input vector. The model represents the first The efficiency of predicting the output of an input vector. This indicates the number of input vectors.

[0052] Based on the above objective function, the optimal coefficient estimate is obtained by solving for the optimal coefficient estimate (initial coefficient vector):

[0053]

[0054] In the formula, This indicates the optimal coefficient estimate of the basic least squares method based on historical operating data. Represents a historical data matrix. Represents the transpose of the historical data matrix. The scale used to adjust the coefficient estimates ensures the uniqueness and optimality of the solution. This represents a combined vector of actual output efficiencies at all acquisition times. This represents the product of the transposed input historical data matrix and the output vector, used to calculate the optimal coefficient estimate.

[0055] After obtaining the optimal coefficient estimate, the variance of the error is further calculated. The variance of the error is calculated according to the following formula:

[0056]

[0057] In the formula, The variance represents the error; Indicates the number of input vectors; This represents the number of elements (including constant terms) in the input vector. In this embodiment, ; Indicates the first The actual output efficiency corresponding to each input vector. This indicates that the model is in optimal coefficient estimation. Next, for the first The efficiency of predicting the output of an input vector.

[0058] The covariance matrix is ​​calculated based on the variance of the error, which is the initial covariance matrix obtained using the recursive least squares method. The calculation of the covariance matrix (initial covariance matrix) satisfies the following formula:

[0059]

[0060] In the formula, This represents the covariance matrix (initial covariance matrix). Represents a historical data matrix. Represents the transpose of the historical data matrix. The variance represents the error.

[0061] The initial coefficient vector for the recursive least squares method is obtained using the basic least squares method, ensuring that the starting point of the recursion is as close as possible to the true parameters. This avoids large initial prediction errors and slow convergence caused by starting with a zero vector or random values ​​in the online phase. By calculating the initial covariance matrix, the uncertainty of the offline coefficient estimation is quantified, ensuring that the gain matrix of the recursive least squares method is within a reasonable range from the first calculation, neither overshooting nor sluggish.

[0062] In this embodiment, the initial forgetting factor is set to 0.95, which serves as the historical data dependency benchmark when the recursive least squares method is started.

[0063] S2. The current operating condition feature vector of the motor and the control parameter vector output at the previous moment are used as inputs to the recursive least squares method, and the actual output efficiency of the motor at the current moment is used as the output of the recursive least squares method.

[0064] Constructing a linear model of recursive least squares ,in This represents the actual output efficiency of the motor, which is used as the output of the recursive least squares method. This represents the input vector of the recursive least squares method at the current time step, including the motor's current operating condition characteristic vector and the control parameter vector output from the previous time step. Specifically, it can be represented as... ,in, Represents a constant term. This indicates the current speed of the motor. This indicates the current load torque of the motor. This indicates the current motor temperature. This represents the DC bus voltage of the motor at the current moment. This represents the d-axis current output at the previous moment. This represents the q-axis current output by the motor at the previous moment; This represents the vector of coefficients that need to be estimated.

[0065] It should be noted that when the recursive least squares method performs its first recursion, since there is no recursive data from the previous time step, it is necessary to manually set the initial control parameter vector so that the recursive least squares method can be started based on the initial control parameter vector, the initial coefficient vector, the initial covariance matrix and the initial forgetting factor in step S1 above.

[0066] S3. Calculate the cosine similarity between the input vector at the current time and the input vectors at all historical times. Select the historical time with the highest similarity as the reference time, obtain the error between the actual output efficiency and the predicted output efficiency at the reference time, and calculate the forgetting factor at the current time based on this error.

[0067] During the recursive least squares method, the cosine similarity between the current input vector and the input vectors of all previous historical input vectors is calculated. Cosine similarity measures the similarity between two input vectors in direction; the closer the value is to 1, the more similar they are. The historical input vector with the highest similarity is selected as the reference time, and the error between the actual output efficiency and the predicted output efficiency at that reference time is obtained. , This indicates the actual output efficiency of the motor at the reference time. The input vector at the reference time. This represents the coefficient vector updated at the previous time step, which is the reference time step. This indicates the model's prediction output efficiency for the input vector at the reference time.

[0068] The forgetting factor at the current time is adjusted based on the error at the reference time, and a decay function with the error at the reference time as the exponent is constructed. A maximum and minimum value for the forgetting factor are set, and the difference between the maximum and minimum values ​​is multiplied by the decay function. The resulting product is then summed with the minimum value to obtain the forgetting factor at the current time. The specific formula is as follows:

[0069]

[0070] In the formula, This represents the forgetting factor at the current moment; and These are the maximum and minimum values ​​of the forgetting factor, set according to the requirements of the permanent magnet brushless motor. The minimum value is generally determined based on the system noise level to prevent noise from being amplified when the forgetting factor is too small. The maximum value is generally set to 1, and the difference between the maximum and minimum values ​​of the forgetting factor is used as a coefficient for the degree of adjustment to avoid model instability due to the forgetting factor being too small. This represents the error between the actual output efficiency and the predicted output efficiency at the reference time. This represents a decay function with respect to the error at the reference time as an exponent. The increase, The value will decrease.

[0071] The forgetting factor controls the degree of learning from historical data. A larger forgetting factor indicates that more historical data should be learned, while a smaller factor indicates that learning from new data is needed. By adjusting the forgetting factor as described above, if the error is large, it means that the current operating condition of the motor deviates significantly from the model of similar historical operating conditions, requiring more reliance on new data; therefore, the forgetting factor should be decreased. Conversely, if the error is small, it means that the current operating condition of the motor deviates less significantly from the model of similar historical operating conditions, allowing more reliance on historical data; therefore, the forgetting factor should be increased.

[0072] S4. Update the coefficient vector at the current time based on the forgetting factor at the current time.

[0073] After obtaining the forgetting factor at the current moment, the efficiency error at the current moment is calculated: based on the input vector at the current moment and the coefficient vector at the previous moment, the predicted output efficiency is calculated, and the difference between the actual output efficiency collected at the current moment and the predicted output efficiency is obtained, thus yielding the efficiency error. The specific calculation formula is as follows:

[0074]

[0075] In the formula, This represents the efficiency error at the current moment. This indicates the actual output efficiency of the motor at the current moment; This represents the transpose of the input vector at the current time step; This represents the coefficient vector from the previous time step. This indicates the model's prediction output efficiency for the input vector at the current time step.

[0076] Calculate the gain matrix at the current time step: Obtain the covariance matrix from the previous time step. Multiply the covariance matrix from the previous time step by the input vector at the current time step, using this as the numerator. Sum the quadratic form of this product with the updated forgetting factor, using this as the denominator, to obtain the gain matrix. The specific calculation formula is as follows:

[0077]

[0078] In the formula, This represents the gain matrix at the current time. Represents the covariance matrix of the previous time step; This represents the input vector at the current moment; This represents the forgetting factor at the current time step. The forgetting factor appears in the denominator and affects the size of the gain matrix. A larger forgetting factor results in a smaller gain matrix, and new data has a smaller impact on the coefficient vector update; a smaller forgetting factor results in a larger gain matrix, and new data has a larger impact on the coefficient vector update.

[0079] Calculate the coefficient vector at the current time step: Sum the product of the gain matrix and the efficiency error with the coefficient vector from the previous time step to obtain the updated coefficient vector. The specific calculation formula is as follows:

[0080]

[0081] In the formula, Represents the coefficient vector at the current moment; This represents the coefficient vector from the previous time step. This represents the gain matrix at the current time. This represents the efficiency error at the current moment; This represents the update amount of the coefficient vector. The larger the value of each element in the gain matrix, the stronger the impact of the error at the current time step on the correction of the coefficient vector. The size of the gain matrix directly reflects the degree of influence of the new data on the coefficient vector.

[0082] The covariance matrix at the current time step is updated. The update process includes: calculating a correction term based on the covariance matrix at the previous time step, the input vector at the current time step, and the forgetting factor at the current time step; subtracting the correction term from the covariance matrix at the previous time step and multiplying it by the reciprocal of the forgetting factor at the current time step to obtain the covariance matrix at the current time step. Updating the covariance matrix at the current time step is for recursive calculation at the next time step. The formula for calculating the covariance matrix at the current time step is as follows:

[0083]

[0084] In the formula, Represents the covariance matrix at the current time. Represents the covariance matrix of the previous time step; This represents the forgetting factor at the current moment; This represents the input vector at the current moment; This represents the transpose of the input vector at the current time step.

[0085] The forgetting factor appears in the denominator and affects the magnitude of the change in the covariance matrix. A larger forgetting factor results in a smaller change in the covariance matrix, indicating that new data has a smaller impact on the covariance matrix. Because of the larger forgetting factor and smaller change in the covariance matrix, the gain matrix at the next time step will be smaller, resulting in a smaller update of the coefficient vector at the next time step. This indicates that the model is relatively stable, or that the new data is consistent with historical data and does not require significant adjustments. Conversely, a smaller forgetting factor results in a larger change in the covariance matrix, indicating that new data has a greater impact on the covariance matrix. Because of the smaller forgetting factor and larger change in the covariance matrix, the gain matrix at the next time step will be larger, resulting in a larger update of the coefficient vector at the next time step. This indicates that the new data differs significantly from historical data, or that the model needs to quickly adapt to the new situation.

[0086] Therefore, by adjusting the forgetting factor, when the motor's operating conditions change or the load is disturbed, the forgetting factor adaptively decreases, making the recursive least squares model more "sensitive," and the coefficient vector can quickly track new characteristics, avoiding model lag. When the motor's operating conditions are stable and the error at the reference time is small, the forgetting factor automatically increases, the model "trusts" historical data more, suppresses measurement noise and sensor drift, and prevents coefficient vector oscillation. The forgetting factor adjusts in real time with the error, eliminating the need for repeated manual parameter tuning and additional filters. The entire closed-loop system is self-maintaining and self-tuning. Moreover, an accurate forgetting factor ensures that the model always maintains the minimum prediction error, thereby obtaining the truly optimal control parameters in the subsequent linear programming stage and improving motor operating efficiency.

[0087] S5. Based on the updated coefficient vector, the motor's current operating condition characteristic vector, and the constraint region of the set control parameter vector, calculate the predicted output efficiency at the current moment and select the set of control parameter vectors with the highest predicted output efficiency to control the motor's operation at the next moment.

[0088] The control parameter vector includes d-axis current and q-axis current. The range of d-axis current and q-axis current is determined based on the rated parameters of the motor. At the same time, the combined current amplitude of d-axis current and q-axis current cannot exceed the set upper limit of current, thus defining the boundary constraints of the current.

[0089] ; ;

[0090] In the formula, Represents the d-axis current; This represents the minimum value of the d-axis current based on the motor's rated parameters. This indicates the maximum value of the d-axis current based on the motor's rated parameters; Represents the q-axis current; This represents the minimum value of the q-axis current based on the motor's rated parameters. This indicates the maximum value of the q-axis current based on the motor's rated parameters; This indicates the maximum permissible value of the combined current amplitude, which is usually equal to or slightly higher than the rated current of the motor.

[0091] The constrained region for the current is a polygonal region in two-dimensional space, defined by the boundary constraints described above. Specifically, the constrained region is defined by the following inequalities:

[0092]

[0093] After obtaining the coefficient vector at the current moment, we obtain a linear formula for the output efficiency versus the input vector:

[0094]

[0095] In the formula, This indicates the motor's output efficiency. These are the values ​​of each element in the coefficient vector at the current moment.

[0096] The operating condition feature vector of the motor at the current moment is determined. In order to maximize the output efficiency of the motor, given the operating condition feature vector, it is necessary to solve for the optimal control parameters through linear programming. Find the valid intersection points of the linear formula within the constraint region, including all boundary vertices of the constraint region and the intersection points of the line with the region. For each found intersection point ( Substitute the values ​​into the linear formula to calculate the corresponding predicted output efficiency, and select the point with the highest predicted output efficiency as the optimal control parameter vector for the current time step. This is used to control the operation of the motor in the next moment, thereby maximizing the efficiency of the permanent magnet brushless motor.

[0097] Repeat steps S2-S5 at the next moment to form a real-time control closed loop for the permanent magnet brushless motor, ensuring that the control parameters are always adapted to the current operating conditions.

[0098] This invention is based on linear programming to solve for optimal control parameters. By traversing the vertices of the constraint region of the control parameters, the maximum efficiency can be found quickly. This not only reduces the amount of computation and maximizes the output efficiency of the motor, but also meets the requirements of real-time control.

[0099] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for intelligent control based on the operation of a permanent magnet brushless motor, characterized by, The application relates to a method for predicting the output efficiency of a motor. The method comprises the following steps: The current working condition feature vector and the previous time output control parameter vector of the motor are collected as the input of the recursive least square method, and the actual output efficiency of the motor at the current time is collected as the output of the recursive least square method; The cosine similarity of the input vector at the current time and the input vectors at all historical times is calculated, the historical time with the highest similarity is selected as a reference time, the error between the actual output efficiency and the predicted output efficiency at the reference time is obtained, the forgetting factor at the current time is calculated based on the error, and the coefficient vector at the current time is updated based on the forgetting factor; 2. The method of claim 1, wherein the method further comprises: The predicted output efficiency at the current time is calculated according to the updated coefficient vector, the working condition feature vector of the motor at the current time and the set constraint region of the control parameter vector, and a control parameter vector with the maximum predicted output efficiency is selected to control the operation of the motor at the next time. Before the recursive least square method is recursively performed, the initial coefficient vector and the initial covariance matrix of the recursive least square method are obtained based on the basic least square method, and an initial forgetting factor is set, and the initial coefficient vector and the initial covariance matrix are obtained in the following way:

3. The method of claim 1, wherein the method further comprises: The historical operation data of the motor are collected to obtain the working condition feature vector, the control parameter vector and the actual output efficiency, the working condition feature vector and the control parameter vector obtained at each collection time are taken as the input vector, the input vectors at all collection times are used to construct a historical data matrix, and the combination vector of the actual output efficiency at all collection times is taken as the output vector; the optimal coefficient and the covariance matrix are calculated based on the basic least square method, so that the initial coefficient vector and the initial covariance matrix are obtained. The forgetting factor at the current time is calculated in the following way:

4. The method of claim 2, wherein the method further comprises: An attenuation function with the error at the reference time as the index is constructed, the maximum value and the minimum value of the forgetting factor are set, the difference between the maximum value and the minimum value is multiplied by the attenuation function, the product is summed with the minimum value, and the forgetting factor at the current time is obtained. The coefficient vector at the current time is updated in the following way:

5. The method of claim 4, wherein the method further comprises: The error between the actual output efficiency and the predicted output efficiency at the current time is calculated to obtain an efficiency error, the gain matrix at the current time is calculated, and the updated coefficient vector is obtained by summing the product of the gain matrix and the efficiency error and the coefficient vector at the previous time. The efficiency error is calculated in the following way:

6. The method of claim 4, wherein the method further comprises: The predicted output efficiency is calculated according to the input vector at the current time and the coefficient vector at the previous time, and the difference between the actual output efficiency and the predicted output efficiency at the current time is calculated to obtain the efficiency error. The gain matrix is calculated in the following way:

7. The method of intelligent control of operation based on a permanent-magnet brushless motor according to claim 1, characterized in that, The covariance matrix at the previous time is obtained, the product of the covariance matrix and the input vector at the current time is taken as the numerator, and the sum of the updated forgetting factor and the second form of the product is taken as the denominator to obtain the gain matrix. After the forgetting factor at the current time is updated, the covariance matrix at the current time is updated in the following way: A correction term is calculated based on the covariance matrix at the previous time, the input vector at the current time and the forgetting factor at the current time, and the covariance matrix at the current time is obtained by multiplying the covariance matrix at the previous time minus the correction term by the inverse of the forgetting factor at the current time.

8. The method of intelligent control of operation based on a permanent-magnet brushless motor according to claim 7, characterized in that, The control parameter vector includes d-axis current and q-axis current, and the setting process of the constraint region of the control parameter vector includes: The range of the d-axis current and the q-axis current is determined based on the rated parameters of the motor, and the combined current amplitude of the d-axis current and the q-axis current cannot exceed the set current upper limit, so as to obtain the constraint region of the control parameter.

9. The method of intelligent control of operation based on a permanent-magnet brushless motor according to claim 1, characterized in that, The process of calculating the predicted output efficiency at the current moment and selecting the control parameter vector with the maximum predicted output efficiency is as follows: According to the updated coefficient vector at the current moment, a linear formula of the output efficiency and the input vector is obtained; the working condition characteristic vector at the current moment is a fixed value, and based on the constraint region of the control parameter, the optimal control parameter is solved by linear programming.

10. The method of claim 1, wherein the method further comprises: The composition of the working condition characteristic vector includes the speed of the motor, the load torque, the temperature of the motor, and the DC bus voltage.

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