Mechanical parameter identification method, system and equipment of motor and storage medium

Abstract

The invention discloses a mechanical parameter identification method, system and device for a motor and a storage medium, and is applied to the technical field of motor control, and the method comprises the steps: determining real-time motor operation data at a current sampling moment; the real-time motor operation data comprises motor acceleration; determining a target storage space for storing real-time motor operation data from each candidate storage space according to the motor acceleration; the capacities of the candidate storage spaces are different, and the capacity of the target storage space is positively correlated with the absolute value of the acceleration of the motor; and storing the real-time motor operation data to the target storage space, and performing mechanical parameter identification of the motor based on the motor operation data stored in the target storage space and the non-target storage space. By means of the scheme, timeliness and accuracy of mechanical parameter identification of the motor are guaranteed, and the maximum storage space needing to be occupied can be determined in advance.

Description

Technical Field

[0001] The present invention relates to the field of motor control technology, and in particular to a method, system, device and storage medium for identifying mechanical parameters of a motor. Background Art

[0002] In motor control systems, moment of inertia, load torque, viscous friction coefficient, and Coulomb friction are all important mechanical parameters. If mechanical parameters that do not match the current system are set in the vector control algorithm of a servo motor or inverter, vibration or poor response may occur, affecting positioning time and accuracy, causing speed fluctuations and significantly impacting production sites. Therefore, real-time mechanical parameter identification is necessary in situations such as new equipment installation, variable mechanical inertia, variable load torque, and frequent forward and reverse speed cycles.

[0003] Traditionally, mechanical parameter identification has typically been performed using either the traditional least squares method or the recursive least squares method. However, the traditional least squares method requires a large amount of data to be accumulated before a single calculation can be performed, making real-time mechanical parameter identification impossible. While the recursive least squares method can achieve real-time calculations, the selection of parameters significantly impacts the algorithm, resulting in lower accuracy in mechanical parameter identification.

[0004] In summary, how to effectively realize the mechanical parameter identification of the motor and improve the accuracy is a technical problem that those skilled in the art urgently need to solve. Summary of the Invention

[0005] The object of the present invention is to provide a method, system, device and storage medium for identifying the mechanical parameters of a motor, so as to effectively realize the mechanical parameter identification of the motor and improve the accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for identifying mechanical parameters of a motor, comprising:

[0008] Determining real-time motor operation data at a current sampling moment; the real-time motor operation data includes motor acceleration;

[0009] determining, from each candidate storage space, a target storage space for storing the real-time motor operation data according to the motor acceleration; wherein each candidate storage space has a different capacity, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration;

[0010] The real-time motor operation data is stored in the target storage space, and the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space.

[0011] In one embodiment, determining a target storage space for storing the real-time motor operation data from candidate storage spaces based on the motor acceleration includes:

[0012] Determining a target acceleration interval to which the motor acceleration belongs; the target acceleration interval being any one of a plurality of preset acceleration intervals;

[0013] Determining a target storage space corresponding to the target acceleration interval from each of the candidate storage spaces;

[0014] Each of the candidate storage spaces corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the middle value of the corresponding preset acceleration interval.

[0015] In one embodiment, the motor operation data includes a motor speed; and identifying the mechanical parameters of the motor based on the motor operation data stored in the target storage space and the non-target storage space, respectively, includes:

[0016] determining first operating data and second operating data from the motor operating data stored in the target storage space and the non-target storage space, respectively, based on the motor speed; the motor speed in the first operating data is a positive number, and the motor speed in the second operating data is a negative number;

[0017] Calculating forward mechanical parameters based on the first operating data;

[0018] calculating a negative mechanical parameter based on the second operating data;

[0019] A target mechanical parameter of the motor is determined based on the positive mechanical parameter and the negative mechanical parameter.

[0020] In one embodiment, the first operating data includes a plurality of operating parameters; and the forward mechanical parameters are calculated based on the first operating data, including:

[0021] Performing statistics on each operating parameter in the first operating data to obtain a statistical value corresponding to each operating parameter;

[0022] Based on the statistical values ​​corresponding to the operating parameters, the forward mechanical parameters are calculated.

[0023] In one embodiment, the positive mechanical parameters include positive moment of inertia, positive disturbance torque, and positive viscous friction coefficient; the operating parameters include motor acceleration ω′, motor torque T e , motor speed ω, ω′T e,ω′ω,T e ω, ω′ 2 and ω 2 The forward mechanical parameters are calculated based on the statistical values ​​of the operating parameters, including:

[0024] Substituting the statistical values ​​of the operating parameters into a preset identification formula group to calculate the positive disturbance torque Tp, the positive moment of inertia Jp, and the positive viscous friction coefficient Bp;

[0025] The preset identification formula group includes:

[0026]

[0027] a + represents the statistical value corresponding to ω′, b + Indicates T e The corresponding statistical value, c + represents the statistical value corresponding to ω, d + represents ω′T e The corresponding statistical value, e + represents the statistical value corresponding to ω′ω, f + Indicates T e The statistical value corresponding to ω, g + Represents ω′ 2 The corresponding statistical value, h + Represents ω 2 Corresponding statistical value; M represents the total number of the first running data and the second running data.

[0028] In one embodiment, storing the real-time motor operation data in the target storage space and performing motor mechanical parameter identification based on the motor operation data stored in the target storage space and the non-target storage space includes:

[0029] After storing the real-time motor operation data in the target storage space, determining whether a preset identification condition is met based on the motor operation data stored in the target storage space and the non-target storage space;

[0030] If so, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0031] In one embodiment, the preset identification conditions include:

[0032] The total amount of motor operation data currently stored in the target storage space and the non-target storage space is greater than a first threshold, and the difference between the largest space number and the smallest space number among the space numbers corresponding to the storage spaces storing the motor operation data exceeds a second threshold;

[0033] The N storage spaces are numbered in sequence from 1 to N.

[0034] In a second aspect, the present invention provides a motor mechanical parameter identification system, comprising:

[0035] A data sampling module, configured to determine real-time motor operation data at a current sampling moment; the real-time motor operation data includes motor acceleration;

[0036] a data classification module, configured to determine, from among candidate storage spaces, a target storage space for storing the real-time motor operation data based on the motor acceleration; wherein the capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration;

[0037] The identification module is used to store the real-time motor operation data in the target storage space, and perform mechanical parameter identification of the motor based on the motor operation data stored in the target storage space and the non-target storage space.

[0038] In a third aspect, the present invention provides a device for identifying mechanical parameters of a motor, comprising:

[0039] memory for storing computer programs;

[0040] A processor is used to execute the computer program to implement the steps of the motor mechanical parameter identification method as described above.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method for identifying the mechanical parameters of a motor as described above are implemented.

[0042] By applying the technical solution provided by the embodiment of the present invention, whenever a sampling moment is reached, the real-time motor operation data of the current sampling moment will be determined, and the real-time motor operation data includes motor acceleration, so that the target storage space for storing the real-time motor operation data can be determined from each candidate storage space according to the magnitude of the motor acceleration in the real-time motor operation data of the current sampling moment. In other words, for the real-time motor operation data of a certain sampling moment, the real-time motor operation data will be placed in the corresponding target storage space for storage based on the magnitude of the motor acceleration. Then, the mechanical parameters of the motor can be identified based on the motor operation data stored in the target storage space and the non-target storage space, which means that each time new real-time motor operation data is obtained, the present application can perform one motor mechanical parameter identification, which ensures the timeliness of the motor mechanical parameter identification. Moreover, since the larger the absolute value of the motor acceleration, the more accurate the real-time motor operation data obtained, by setting each candidate storage space to a different capacity, and the capacity of the target storage space is positively correlated with the motor acceleration, the larger the capacity of the storage space, the larger the absolute value of the motor acceleration stored, and the larger the capacity, the more data that can be stored. Therefore, all the motor operation data used for the mechanical parameter identification of the motor will contain more motor operation data with larger acceleration absolute values, and less motor operation data with smaller acceleration absolute values, which is beneficial to ensure the accuracy of the identification results.

[0043] In summary, the solution of the present application realizes the mechanical parameter identification of the motor and ensures the real-time and accuracy of the identification. In addition, since the capacity of each candidate storage space is pre-set, the solution of the present application can pre-determine the maximum storage space required. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 1 is a flowchart of a method for identifying mechanical parameters of a motor according to an embodiment of the present invention;

[0046] Figure 2 A schematic diagram illustrating the division of acceleration intervals and the corresponding differences in storage space capacity in a specific embodiment of the present invention;

[0047] Figure 3 A schematic diagram of a storage method in a specific embodiment of the present invention;

[0048] Figure 4 Schematic diagram of identification results in a specific embodiment of the present invention;

[0049] Figure 5 Schematic diagram of the structure of a mechanical parameter identification system for a motor in one embodiment of the present invention. DETAILED DESCRIPTION

[0050] The core of the present invention is to provide a method for identifying the mechanical parameters of a motor, which realizes the identification of the mechanical parameters of the motor and ensures the timeliness and accuracy of the identification.

[0051] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0052] Please refer to Figure 1 , Figure 1 FIG. 1 is a flowchart of a method for identifying the mechanical parameters of a motor in one embodiment of the present invention. The method for identifying the mechanical parameters of a motor may include the following steps:

[0053] Step S101: determining the real-time motor operation data at the current sampling moment; the real-time motor operation data includes the motor acceleration.

[0054] Specifically, in practical applications, sampling can be performed periodically, and as will be described below, each time a sample is taken, the motor's mechanical parameters can be identified. Therefore, the parameter sampling period can be set based on the identification requirements. Typically, the parameter sampling period is set relatively short to ensure that the mechanical parameter identification of this application can be highly real-time. Of course, in other specific implementations, other sampling triggering methods can be set as needed without affecting the implementation of the present invention.

[0055] When a sampling moment arrives, it is necessary to determine the real-time motor operation data at the sampling moment. The real-time motor operation data at the sampling moment usually includes multiple operation parameters. Of course, the specific operation parameters can be set and adjusted as needed, as long as the mechanical parameters of the motor can be identified based on the motor operation data at multiple sampling moments. For example, in one embodiment, the real-time motor operation data at a sampling moment specifically includes the motor speed ω, the motor torque T at the sampling moment. eAs well as the motor acceleration ω′. In another embodiment described below, the motor operation data at a sampling moment specifically includes 8 operation parameters to improve the calculation convenience during the mechanical parameter identification.

[0056] When a sampling moment arrives, real-time motor operating data at that sampling moment can be obtained. Since real-time motor operating data typically includes multiple operating parameters, for ease of description, the real-time motor operating data at that sampling moment is referred to as a data set, that is, a data set can be obtained at each sampling moment. The operating parameters in this data set can all be obtained through parameter sampling, or some of the operating parameters can be obtained through parameter sampling, while the remaining operating parameters are obtained through calculation. This does not affect the implementation of the present invention, and the specific method for obtaining each operating parameter can be set accordingly based on the operating parameters specifically included in the real-time motor operating data in actual applications.

[0057] In a specific embodiment of the present invention, step S101 may include the following two steps:

[0058] Step 1: Determine the motor speed ω and motor torque T at the current sampling time e and the motor acceleration ω′;

[0059] Step 2: Based on the motor speed ω and motor torque T at the current sampling moment e and the motor acceleration ω′, which will include ω′, T e ,ω,ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 The data group is used as the real-time motor operation data at the current sampling moment.

[0060] In this embodiment, for the current sampling moment, the motor speed ω and the motor torque T at the sampling moment are first determined. e And the motor acceleration ω′. In practical applications, the motor speed ω and the motor torque T e Usually, the data can be collected by sensors, and the motor acceleration ω′ can be calculated by the motor speed ω at adjacent sampling moments, or directly collected by relevant sensors, which does not affect the implementation of the present invention. Of course, the more common implementation method is to collect the motor speed ω and the motor torque T by sensors. e The motor acceleration ω′ is collected and calculated. In addition, it can be understood that, since the solution of this application requires the motor speed ω and motor torque T at the same sampling moment, e As well as the motor acceleration ω′, so in practical applications, data synchronization processing is usually performed when sampling data.

[0061] In this embodiment, one data set needs to include 8 operating parameters, that is, at a single sampling moment, the real-time motor operating data at the sampling moment includes 8 operating parameters, in addition to the motor speed ω, the motor torque T e In addition to the three operating parameters of motor acceleration ω′, the remaining five operating parameters ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 All can be obtained based on these three operating parameters.

[0062] It can be seen that when step S101 is executed according to the requirements of this embodiment, real-time motor operating data at each sampling moment can be obtained, and the real-time motor operating data at each sampling moment is a data set containing eight operating parameters. When using this embodiment, based on these eight operating parameters in the real-time motor operating data at each sampling moment, the mechanical parameters of the motor can be conveniently and effectively identified.

[0063] Step S102: determining a target storage space for storing real-time motor operation data from each candidate storage space according to the motor acceleration; each candidate storage space has a different capacity, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration.

[0064] After determining the real-time motor operation data at the current sampling moment, it is necessary to store the real-time motor operation data at the current sampling moment. In the solution of the present application, N different storage spaces are provided. Therefore, it is necessary to determine the target storage space for storing the real-time motor operation data from each candidate storage space.

[0065] The capacity of each candidate storage space is different, and the capacity of the determined target storage space is positively correlated with the absolute value of the motor acceleration. In other words, in the solution of this application, the motor acceleration is graded according to the absolute value of the motor acceleration of the real-time motor operation data at the current sampling moment, and then the decision is made as to which of the candidate storage spaces is selected as the target storage space to store the real-time motor operation data.

[0066] In practical applications, there are many specific ways to implement step S102, as long as it can ensure that the capacity of the selected target storage space is positively correlated with the absolute value of the motor acceleration. For example, in a specific embodiment of the present invention, considering that the acceleration range to which the absolute value of the motor acceleration belongs is determined, the corresponding candidate storage space can be selected accordingly. Therefore, step S102 may specifically include:

[0067] Determine a target acceleration interval to which the motor acceleration belongs; the target acceleration interval is any one of a plurality of preset acceleration intervals;

[0068] Determine a target storage space corresponding to the target acceleration range from each candidate storage space;

[0069] Each candidate storage space corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the middle value of the corresponding preset acceleration interval.

[0070] In this embodiment, a plurality of acceleration intervals are preset, which are conveniently described as N preset acceleration intervals. The specific interval range of each of the N acceleration intervals can be set and adjusted according to actual needs, and in actual applications, in order to facilitate interval division, the interval lengths of each acceleration interval are usually set to be the same. For example, in a specific embodiment of the present invention, the preset N acceleration intervals are N acceleration intervals that are pre-divided evenly within the range of a_min to a_max. Among them, a_min is the preset minimum acceleration threshold, and a_max is the preset maximum acceleration threshold. Both a_min and a_max can be pre-set based on the experience of the staff or experimental data. That is to say, in this example, for the range of a_min to a_max, according to the principle of average division, N continuous acceleration intervals can be obtained. For easy understanding, please refer to Figure 2 , Figure 2 In the example, the range from a_min to a_max is evenly divided into 8 acceleration intervals, which are marked as the 1st acceleration interval to the 8th acceleration interval.

[0071] At the current sampling moment, it is necessary to determine the target acceleration interval to which the motor acceleration belongs based on the motor acceleration in the real-time motor operation data. It is understandable that since the motor acceleration values ​​are different in different situations, the target acceleration interval can be any one of the N preset acceleration intervals. Figure 2 For example, at the current sampling moment, the target acceleration interval to which the motor acceleration belongs may be any one of the acceleration intervals from the 1st acceleration interval to the 8th acceleration interval.

[0072] The acceleration range to which the motor acceleration belongs is called the target acceleration range. After determining the target acceleration range, the target storage space corresponding to the target acceleration range can be determined from each candidate storage space, and then the real-time motor operation data at the current sampling moment can be stored in the target storage space. For example, in one case, Figure 2For example, if the motor acceleration in the real-time motor operation data at a certain sampling moment belongs to the first acceleration interval, the real-time motor operation data at the sampling moment needs to be stored in the target storage space corresponding to the first acceleration interval. For another example, if the motor acceleration in the motor operation data at a certain sampling moment belongs to the fifth acceleration interval, the motor operation data at the sampling moment needs to be stored in the target storage space corresponding to the fifth acceleration interval.

[0073] For example Figure 3 In the example, R(i) refers to the target storage space corresponding to the i-th acceleration interval. It can be seen that Figure 3 In the example, the motor operation data at a single sampling moment specifically includes ω′, T e ,ω,ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 These 8 operating parameters, Figure 3 The subscripts of the various operating parameters in represent the sampling time, for example, ω k ′ represents the motor acceleration ω′ at the kth sampling moment, and Te k+2 It represents the motor torque at the k+2th sampling moment.

[0074] In the solution of the present application, there are N preset acceleration intervals, where N is a positive integer not less than 2. Each candidate storage space corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the median value of the corresponding preset acceleration interval. That is, among the N acceleration intervals, the larger the absolute value of the median value of the acceleration interval, the larger the capacity of the candidate storage space corresponding to the acceleration interval. In short, the capacity of the candidate storage space is positively correlated with the absolute value of the median value of the acceleration interval corresponding to the storage space.

[0075] It can be understood that the larger the capacity of the storage space is, the more data groups can be stored in the storage space, that is, more real-time motor operation data at the sampling time can be stored. Since the subsequent steps are based on all the motor operation data in the current storage spaces to identify the mechanical parameters of the motor, it means that the larger the storage space capacity, the greater the impact on the identification results. The storage space with a larger capacity stores real-time motor operation data with a larger absolute value of acceleration. The larger the absolute value of acceleration, the higher the accuracy of the group of real-time motor operation data. Therefore, such a setting is conducive to improving the identification accuracy of the present application solution.

[0076] exist Figure 2In the example, the number of squares is used to represent the capacity of the candidate storage space corresponding to different acceleration intervals. It can be seen that the fourth acceleration interval is close to 0, so the capacity of the candidate storage space corresponding to it is relatively low. Figure 2 In the figure, two squares are used to represent the capacity of the corresponding storage space. The same is true for the 5th acceleration interval and the 4th acceleration interval. From the 6th to the 8th acceleration interval, since the absolute value of the middle value of the acceleration interval gradually increases, the capacity of the candidate storage space corresponding to these three acceleration intervals will also gradually increase. Figure 2 Similarly, from the third acceleration interval to the first acceleration interval, since the absolute value of the intermediate value of the acceleration interval gradually increases, the capacity of the candidate storage space corresponding to these three acceleration intervals will also gradually increase.

[0077] It should also be noted that, since the capacity of each candidate storage space is pre-set, in actual operation, as the motor operation data is continuously generated, for any one candidate storage space, the storage space may be full. Therefore, in one embodiment, each storage space can adopt a storage method of circular storage, which is also the solution usually adopted in actual applications. For example, if a candidate storage space can store 1000 data groups, then as the motor operation data is continuously generated, real-time motor operation data will be continuously divided into the candidate storage space for storage, and the storage address will continuously move to the end of the storage space until the storage space has stored 1000 data groups. If there are more data groups that need to be stored in the candidate storage space later, the storage address will return to the head of the candidate storage space, so that the new data group overwrites the old data group. It is understandable that in this example, after this time, in the subsequent process, the candidate storage space will always store the most recent 1000 data groups. Figure 3 In the example, the circular storage method is also used. Figure 3 The sliding shown in , means that the storage address will continuously move from the head to the tail of the storage space and then return to the head, thus continuously circulating.

[0078] In addition, in the case where the storage space is full, in addition to the above-mentioned circular storage method, other methods can be set as needed. For example, in one case, whenever a candidate storage space is full, all data in the candidate storage space is directly cleared.

[0079] Step S103: storing the real-time motor operation data into the target storage space and the non-target storage space, and performing motor mechanical parameter identification based on the motor operation data stored in the target storage space and the non-target storage space.

[0080] In the solution of the present application, the mechanical parameters of the motor can be identified once every time the data in any storage space is updated to ensure the timeliness of the identification. The specific identification method can be set and adjusted as needed.

[0081] As described above, based on the motor acceleration, the target storage space for storing real-time motor operation data can be determined from each candidate storage space, and then the real-time motor operation data can be stored in the target storage space. At this time, the mechanical parameter identification of the motor can be performed once. During the identification, it is necessary to use each data group in all the storage spaces, that is, the mechanical parameter identification of the motor is performed based on the motor operation data stored in the target storage space and the non-target storage space. It can be understood that the non-target storage space described here refers to other storage spaces in each candidate storage space except the target storage space.

[0082] In a specific embodiment of the present invention, step S103 may specifically include:

[0083] The first step is to determine first operating data and second operating data from the motor operating data stored in the target storage space and the non-target storage space respectively based on the motor speed; the motor speed in the first operating data is a positive number, and the motor speed in the second operating data is a negative number;

[0084] The second step: based on the first operating data, calculate the forward mechanical parameters;

[0085] The third step: calculating negative mechanical parameters based on the second operating data;

[0086] The fourth step is to determine the target mechanical parameters of the motor based on the positive mechanical parameters and the negative mechanical parameters.

[0087] This implementation method takes into account that when performing mechanical parameter identification of the motor, the motor operation data stored in the target storage space and the non-target storage space can be divided into two categories according to the different motor speeds, respectively referred to as first operation data and second operation data.

[0088] The motor speed in the first operating data is a positive number, while the motor speed in the second operating data is a negative number. That is, for any set of motor operating data in the entire storage space, when the motor speed in that set of motor operating data is a positive number, that set of motor operating data belongs to the first operating data; conversely, when the motor speed in that set of motor operating data is a negative number, that set of motor operating data belongs to the second operating data. Furthermore, in a small number of cases where the motor speed in the motor operating data is exactly 0, that set of motor operating data can be considered to belong to either the first operating data or the second operating data. For example, it is usually set to be considered to belong to the first operating data.

[0089] Based on the first operating data, positive mechanical parameters can be calculated, and based on the second operating data, negative mechanical parameters can be calculated. Finally, the motor's target mechanical parameters can be determined based on the positive and negative mechanical parameters. The specific contents of the positive and negative mechanical parameters can be set and adjusted according to actual conditions. Ultimately, the motor's target mechanical parameters can be effectively determined, thus achieving motor mechanical parameter identification.

[0090] In addition, the specific contents of the determined target mechanical parameters of the motor can also be set and adjusted according to actual needs. For example, in a specific embodiment of the present invention, considering that the moment of inertia, viscous friction coefficient, load torque and Coulomb friction of the motor are all important mechanical parameters, one or more of these four mechanical parameters can be identified according to actual needs. Figure 4 This figure shows the identification results for a specific embodiment, showing, from top to bottom, the motor's moment of inertia, viscous friction coefficient, load torque, and Coulomb friction. Therefore, in practical applications, the target motor mechanical parameters determined can be one or more of the motor's moment of inertia, viscous friction coefficient, load torque, and Coulomb friction.

[0091] In a specific embodiment of the present invention, the first operating data may include multiple operating parameters; the second step may specifically include:

[0092] Performing statistics on each operating parameter in the first operating data to obtain a statistical value corresponding to each operating parameter;

[0093] Based on the statistical values ​​corresponding to each operating parameter, the forward mechanical parameters are calculated.

[0094] This implementation method takes into account that when identifying the mechanical parameters of the motor, some traditional solutions require a large number of matrix operations, which consumes a large amount of computing resources and is not conducive to achieving real-time mechanical parameter identification. In this regard, the present application takes into account that in the process of calculating the positive mechanical parameters, statistics can be performed on each operating parameter in the first operating data to obtain the statistical value corresponding to each operating parameter, and then the positive mechanical parameters can be calculated based on the statistical value corresponding to each operating parameter. The operation of performing operating parameter statistics and the operation of calculating the positive mechanical parameters based on the statistical value corresponding to each operating parameter are relatively simple to implement, reducing the required computing resources and facilitating the implementation of the solution. Similarly, when calculating the negative mechanical parameters based on the second operating data, statistics can also be performed on each operating parameter in the second operating data. After obtaining the statistical value corresponding to each operating parameter, the negative mechanical parameters can be conveniently calculated based on this.

[0095] Still taking the positive mechanical parameters as an example, in a specific embodiment of the present invention, the positive mechanical parameters specifically include the positive moment of inertia Jp, the positive disturbance torque Tp, and the positive viscous friction coefficient Bp; the operating parameters include the motor acceleration ω′, the motor torque T e , motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 Based on the statistical values ​​of various operating parameters, the forward mechanical parameters are calculated, including:

[0096] Substitute the statistical values ​​of each operating parameter into the preset identification formula group to calculate the positive disturbance torque Tp, positive moment of inertia Jp, and positive viscous friction coefficient Bp;

[0097] The preset identification formula group includes:

[0098]

[0099] a + represents the statistical value corresponding to ω′, b + Indicates T e The corresponding statistical value, c + represents the statistical value corresponding to ω, d + represents ω′T e The corresponding statistical value, e + represents the statistical value corresponding to ω′ω, f + Indicates T e The statistical value corresponding to ω, g + Represents ω′ 2 The corresponding statistical value, h + Represents ω 2Corresponding statistical value; M represents the total number of the first running data and the second running data.

[0100] In this embodiment, for each data set in the first operating data, including motor acceleration ω′, motor torque T e , motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 These eight parameters are summed up to get the motor acceleration ω′ of each data group in the first running data, and the result is the statistical value a corresponding to ω′. + Similarly, all T in the first running data e The result of summing is T e The corresponding statistical value b + Sum all the ω in the first run data, and the result is the statistical value c corresponding to ω + . All ω′T in the first running data e The result of summing is ω′T e The corresponding statistical value d + Sum all ω′ω in the first run data, and the result is the statistical value e corresponding to ω′ω + . All T in the first run data e ω is summed, and the result is T e The statistical value f corresponding to ω + . All ω′ in the first running data 2 The result of summing is ω′ 2 The corresponding statistical value g + . All ω in the first run data 2 The result of summing is ω 2 The corresponding statistical value h + .

[0101] It can be seen that in this implementation, when calculating the positive disturbance torque Tp, the positive moment of inertia Jp, and the positive viscous friction coefficient Bp, the preset identification formula group used are all scalar multiplication and addition, and do not involve any matrix multiplication, division, inversion and other operations. Therefore, it is beneficial to reduce the calculation time of related programs and chips, and is conducive to realizing real-time identification of mechanical parameters.

[0102] Based on the same principle, statistics can be performed on each operating parameter in the second operating data to obtain statistical values ​​corresponding to each operating parameter, and then based on the statistical values ​​corresponding to each operating parameter, negative mechanical parameters can be calculated. Negative mechanical parameters may include negative moment of inertia, negative disturbance torque, and negative viscous friction coefficient.

[0103] Operating parameters include motor acceleration ω′, motor torque T e , motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 Based on the statistical values ​​of various operating parameters, the negative mechanical parameters calculated may include:

[0104] Performing statistics on each operating parameter in the second operating data to obtain a statistical value corresponding to each operating parameter, substituting the statistical value of each operating parameter into a negative identification formula group to calculate the negative disturbance torque Tn, the negative moment of inertia Jn, and the negative viscous friction coefficient Bn;

[0105] Among them, the negative identification formula group includes:

[0106]

[0107] Sum up all ω′ in the second run data, and the result is the negative statistical value a corresponding to ω′ - . All T in the second run data e The result of summing is T e The corresponding negative statistical value b - Sum all the ω in the second run data, and the result is the negative statistic c corresponding to ω - . All ω′T in the second running data e The result of summing is ω′T e The corresponding negative statistical value d - Sum all ω′ω in the second run data, and the result is the negative statistical value e corresponding to ω′ω - . All T in the second run data e ω is summed, and the result is T e Negative statistical value f corresponding to ω - . All ω′ in the second run data 2 The result of summing is ω′ 2 The corresponding negative statistical value g - . All ω in the second run data 2 The result of summing is ω 2 The corresponding negative statistical value h - .

[0108] It can be seen that in this implementation, when calculating the negative disturbance torque Tn, the negative moment of inertia Jn, and the negative viscous friction coefficient Bn, the negative identification formula group used are all scalar multiplication and addition, and do not involve any matrix multiplication, division, inversion and other operations. Therefore, it is beneficial to reduce the computing time of related programs and chips, and is conducive to realizing real-time identification of mechanical parameters.

[0109] In addition, it should be noted that in this embodiment, based on the positive mechanical parameters and the negative mechanical parameters, the target mechanical parameters of the motor are determined, which can be expressed as follows: Calculations are performed to obtain the motor's moment of inertia J, viscous friction coefficient B, load torque TL, and Coulomb friction force Tc.

[0110] Specifically, in the above embodiment, it is considered that the mechanical motion equation of the motor can be expressed as Te = Jω′ + Bω + TL + Tc; wherein Te is the electromagnetic torque of the motor, J is the moment of inertia of the motor, B is the viscous friction coefficient, TL is the load torque, Tc is the Coulomb friction force, ω is the motor speed, and ω′ is the motor acceleration. In addition, Tc = sign(ω). That is, when the motor speed ω is greater than or equal to 0, Te = Jω′ + Bω + Tp, where Tp = TL + Tc, which is a positive disturbance torque. Correspondingly, when the motor speed ω is less than 0, Te = Jω′ + Bω + Tn, where Tn = TL - Tc, which is a negative disturbance torque.

[0111] Taking the case where the motor speed ω is greater than or equal to 0 as an example, in order to identify the positive moment of inertia Jp, the positive viscous friction coefficient Bp and the positive disturbance torque Tp, an objective function H can be set and converted into the problem of finding the minimum value of the objective function H. Finding the minimum value of the objective function H can be converted into the problem of finding the partial derivative of the function. In addition, since the scheme of the present application divides N acceleration levels, that is, N acceleration intervals (i=1~N), for any sampling moment, the motor acceleration in the motor operation data belongs to the acceleration interval, and the motor operation data at the sampling moment is stored in the storage space corresponding to the acceleration interval. Therefore, based on the above analysis, it can be finally obtained that: for each data group stored in any current storage space, the data group with the motor speed ω greater than or equal to 0 is called the first type of data group, and is represented by a + to h + In turn, T in all the first-class data groups e The sum of ω, the sum of ω′T e The sum of ω′ω, T e The sum of ω, ω′ 2 The sum of ω 2 The sum of , then solving the above equations together can be obtained:

[0112]

[0113] According to the same principle, when the motor speed ω is less than 0, we can get:

[0114] Thus, a negative disturbance torque Tn, a negative moment of inertia Jn and a negative viscous friction coefficient Bn are obtained.

[0115] Finally, through The calculation of the motor's moment of inertia J, viscous friction coefficient B, load torque TL, and Coulomb friction force Tc can be realized.

[0116] In a specific embodiment of the present invention, storing real-time motor operation data in a target storage space and performing motor mechanical parameter identification based on the motor operation data stored in the target storage space and the non-target storage space may specifically include:

[0117] After storing the real-time motor operation data in the target storage space and the non-target storage space, determining whether a preset identification condition is met based on the motor operation data stored in the target storage space and the non-target storage space;

[0118] If so, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0119] If not, the current motor mechanical parameter identification can be canceled.

[0120] As can be seen from the above description, each time the data in any storage space is updated, a mechanical parameter identification can be performed. This embodiment takes into account that in some cases, such as when the total amount of data in each storage space is small, it is not conducive to ensuring the accuracy of the identification result. Therefore, after storing the real-time motor operation data in the target storage space, this embodiment will determine whether the preset identification conditions are met based on the motor operation data stored in the target storage space and the non-target storage space. If so, the subsequent operation can be performed normally, that is, the motor mechanical parameter identification is performed based on the motor operation data stored in the target storage space and the non-target storage space. Conversely, if it is not met, the current mechanical parameter identification can be canceled.

[0121] The specific content of the preset identification conditions can be set and adjusted according to actual needs, but it can be understood that when the set identification conditions are met, based on the current situation, the mechanical parameter identification of the motor should be able to be accurately and effectively realized. In any case, when the set identification conditions are not met, it means that based on the current situation, the accuracy and reliability of the identification cannot be guaranteed.

[0122] For example, in a specific embodiment of the present invention, the preset identification conditions may specifically include:

[0123] The total amount of motor operation data stored in the current target storage space and the non-target storage space is greater than a first threshold, and the difference between the largest space number and the smallest space number among the space numbers corresponding to the storage spaces storing the motor operation data is greater than a second threshold;

[0124] The N storage spaces are numbered in sequence from 1 to N.

[0125] In this embodiment, the total number of motor operation data stored in the current target storage space and non-target storage space is required to be greater than the first threshold, that is, the total number of data groups stored in all current storage spaces is required to exceed the first threshold, that is, if the total number M of data groups stored in all current storage spaces is low, it is not conducive to accurate and effective mechanical parameter identification of the motor, so the mechanical parameter identification of the motor can be canceled. As the motor runs, the total number of data groups stored in all storage spaces will continue to increase, which will make the identification condition meet the requirements.

[0126] This embodiment also requires that, among the space numbers corresponding to the storage spaces storing the data groups, the difference between the largest space number and the smallest space number is greater than the second threshold value. Since the N storage spaces are numbered in order from 1 to N, and the size of the space number is positively correlated with the median value of the acceleration interval corresponding to the storage space, it is easy to understand the above. Figure 2 For example, at the current moment, only the sixth storage space corresponding to the sixth acceleration interval stores several data sets, while the storage spaces corresponding to all other acceleration intervals are empty. In this example, the difference between the maximum and minimum numbers is 0. For example, if the second threshold is 2, then since 0 does not exceed 2, the identification condition is not met, and the current motor mechanical parameter identification can be canceled. Similarly, as the motor continues to operate, data sets will be gradually stored in each storage space, and the identification condition will be met.

[0127] For example, in one scenario, at the current moment, the second storage space corresponding to the second acceleration interval, the third storage space corresponding to the third acceleration interval, and the sixth storage space corresponding to the sixth acceleration interval all store several data groups, and the storage spaces corresponding to the remaining acceleration intervals are all empty. In this example, the difference between the maximum number and the minimum number is 4. For example, the second threshold is 2. Since 4 exceeds 2, the identification condition meets the requirements.

[0128] And if the cyclic storage implementation method described above is adopted, in actual applications, after the motor runs for a certain period of time, each storage space will successively realize the storage of full data volume, that is, each storage space is full and continuously overwrites old data with new data.

[0129] By applying the technical solution provided by the embodiment of the present invention, whenever a sampling moment is reached, the real-time motor operation data of the current sampling moment will be determined, and the real-time motor operation data includes motor acceleration, so that the target storage space for storing the real-time motor operation data can be determined from each candidate storage space according to the magnitude of the motor acceleration in the real-time motor operation data of the current sampling moment. In other words, for the real-time motor operation data of a certain sampling moment, the real-time motor operation data will be placed in the corresponding target storage space for storage based on the magnitude of the motor acceleration. Then, the mechanical parameters of the motor can be identified based on the motor operation data stored in the target storage space and the non-target storage space, which means that each time new real-time motor operation data is obtained, the present application can perform one motor mechanical parameter identification, which ensures the timeliness of the motor mechanical parameter identification. Moreover, since the larger the absolute value of the motor acceleration, the more accurate the real-time motor operation data obtained, by setting each candidate storage space to a different capacity, and the capacity of the target storage space is positively correlated with the motor acceleration, the larger the capacity of the storage space, the larger the absolute value of the motor acceleration stored, and the larger the capacity, the more data that can be stored. Therefore, all the motor operation data used for the mechanical parameter identification of the motor will contain more motor operation data with larger acceleration absolute values, and less motor operation data with smaller acceleration absolute values, which is beneficial to ensure the accuracy of the identification results.

[0130] In summary, the solution of the present application realizes the mechanical parameter identification of the motor and ensures the real-time and accuracy of the identification. In addition, since the capacity of each candidate storage space is pre-set, the solution of the present application can pre-determine the maximum storage space required.

[0131] Corresponding to the above method embodiment, an embodiment of the present invention further provides a mechanical parameter identification system for a motor, which can be referred to in correspondence with the above.

[0132] See also Figure 5 FIG. 1 is a schematic diagram of a structure of a motor mechanical parameter identification system according to an embodiment of the present invention, comprising:

[0133] The data sampling module 501 is used to determine the real-time motor operation data at the current sampling moment; the real-time motor operation data includes motor acceleration;

[0134] a data classification module 502 for determining a target storage space for storing real-time motor operation data from each candidate storage space based on the motor acceleration; each candidate storage space has a different capacity, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration;

[0135] The identification module 503 is configured to store the real-time motor operation data into the target storage space, and perform motor mechanical parameter identification based on the motor operation data stored in the target storage space and the non-target storage space.

[0136] In a specific embodiment of the present invention, the data classification module 502 is specifically configured to:

[0137] Determine a target acceleration interval to which the motor acceleration belongs; the target acceleration interval is any one of a plurality of preset acceleration intervals;

[0138] Determine a target storage space corresponding to the target acceleration range from each candidate storage space;

[0139] Each candidate storage space corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the middle value of the corresponding preset acceleration interval.

[0140] In a specific embodiment of the present invention, the motor operation data includes the motor speed; the identification module 503 includes:

[0141] a data partitioning unit, configured to store the real-time motor operation data in a target storage space, and determine first operation data and second operation data from the motor operation data stored in the target storage space and the non-target storage space, respectively, based on the motor speed; the motor speed in the first operation data is a positive number, and the motor speed in the second operation data is a negative number;

[0142] a forward mechanical parameter calculation unit, configured to calculate a forward mechanical parameter based on the first operating data;

[0143] a negative mechanical parameter calculation unit, configured to calculate a negative mechanical parameter based on the second operating data;

[0144] The target mechanical parameter determination unit is configured to determine the target mechanical parameter of the motor based on the positive mechanical parameter and the negative mechanical parameter.

[0145] In a specific embodiment of the present invention, the first operating data includes a plurality of operating parameters; the forward mechanical parameter calculation unit is specifically configured to:

[0146] Performing statistics on each operating parameter in the first operating data to obtain a statistical value corresponding to each operating parameter;

[0147] Based on the statistical values ​​corresponding to each operating parameter, the forward mechanical parameters are calculated.

[0148] In a specific embodiment of the present invention, the positive mechanical parameters include the positive moment of inertia, the positive disturbance torque, and the positive viscous friction coefficient; the operating parameters include the motor acceleration ω′, the motor torque T e , motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 The forward mechanical parameter calculation unit calculates the forward mechanical parameters based on the statistical values ​​of the operating parameters, including:

[0149] Substitute the statistical values ​​of each operating parameter into the preset identification formula group to calculate the positive disturbance torque Tp, positive moment of inertia Jp, and positive viscous friction coefficient Bp;

[0150] The preset identification formula group includes:

[0151]

[0152] a + represents the statistical value corresponding to ω′, b + Indicates T e The corresponding statistical value, c + represents the statistical value corresponding to ω, d + Represents ω′T e The corresponding statistical value, e + represents the statistical value corresponding to ω′ω, f + Indicates T e The statistical value corresponding to ω, g + Represents ω′ 2 The corresponding statistical value, h + Represents ω 2 Corresponding statistical value; M represents the total number of the first running data and the second running data.

[0153] In a specific embodiment of the present invention, the identification module 503 is specifically used to:

[0154] After storing the real-time motor operation data in the target storage space and the non-target storage space, determining whether a preset identification condition is met based on the motor operation data stored in the target storage space and the non-target storage space;

[0155] If so, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space respectively.

[0156] In a specific embodiment of the present invention, the preset identification conditions include:

[0157] The total amount of motor operation data stored in the current target storage space and the non-target storage space is greater than a first threshold, and the difference between the largest space number and the smallest space number among the space numbers corresponding to the storage spaces storing the motor operation data exceeds a second threshold;

[0158] The N storage spaces are numbered in sequence from 1 to N.

[0159] Corresponding to the above method and system embodiments, an embodiment of the present invention further provides a mechanical parameter identification device for a motor and a computer-readable storage medium, which can be referred to in correspondence with the above.

[0160] The mechanical parameter identification device of the motor may include:

[0161] memory for storing computer programs;

[0162] The processor is configured to execute a computer program to implement the steps of the above-mentioned method for identifying the mechanical parameters of the motor.

[0163] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the above-described method for identifying the mechanical parameters of a motor. The computer-readable storage medium herein includes random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art.

[0164] It should also be noted that, in this application, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0165] Those skilled in the art may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0166] Specific examples are used in this application to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only intended to help understand the technical solutions and core concepts of the present invention. It should be noted that, for those skilled in the art, various improvements and modifications may be made to the present invention without departing from the principles of the present invention, and such improvements and modifications also fall within the scope of protection of the present invention.

Claims

1. A method for identifying mechanical parameters of a motor, characterized in that: include: Determine the real-time motor operation data at the current sampling moment; The real-time motor operation data includes motor acceleration; determining, from each candidate storage space according to the motor acceleration, a target storage space for storing the real-time motor operation data; The capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration; The real-time motor operation data is stored in the target storage space, and the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space.

2. The method for identifying the mechanical parameters of a motor according to claim 1, wherein: The step of determining a target storage space for storing the real-time motor operation data from candidate storage spaces according to the motor acceleration includes: Determining a target acceleration interval to which the motor acceleration belongs; the target acceleration interval being any one of a plurality of preset acceleration intervals; Determining a target storage space corresponding to the target acceleration interval from each of the candidate storage spaces; Each of the candidate storage spaces corresponds to a preset acceleration interval, and the capacity of the candidate storage space is positively correlated with the absolute value of the middle value of the corresponding preset acceleration interval.

3. The method for identifying the mechanical parameters of a motor according to claim 1, wherein: The motor operation data includes a motor speed; and the motor mechanical parameter identification based on the motor operation data stored in the target storage space and the non-target storage space includes: determining first operating data and second operating data from the motor operating data stored in the target storage space and the non-target storage space, respectively, based on the motor speed; the motor speed in the first operating data is a positive number, and the motor speed in the second operating data is a negative number; Calculating forward mechanical parameters based on the first operating data; calculating a negative mechanical parameter based on the second operating data; A target mechanical parameter of the motor is determined based on the positive mechanical parameter and the negative mechanical parameter.

4. The method for identifying the mechanical parameters of a motor according to claim 3, wherein: The first operating data includes a plurality of operating parameters; the forward mechanical parameters are calculated based on the first operating data, including: Performing statistics on each operating parameter in the first operating data to obtain a statistical value corresponding to each operating parameter; Based on the statistical values ​​corresponding to the operating parameters, the forward mechanical parameters are calculated.

5. The method for identifying the mechanical parameters of a motor according to claim 4, wherein: The positive mechanical parameters include positive moment of inertia, positive disturbance torque, and positive viscous friction coefficient; the operating parameters include motor acceleration ω′, motor torque T e , motor speed ω, ω′T e ,ω′ω,T e ω, ω′ 2 and ω 2 ; The forward mechanical parameters are calculated based on the statistical values ​​of the operating parameters, including: Substituting the statistical values ​​of the operating parameters into a preset identification formula group to calculate the positive disturbance torque Tp, the positive moment of inertia Jp, and the positive viscous friction coefficient Bp; The preset identification formula group includes: a + represents the statistical value corresponding to ω′, b + Indicates T e The corresponding statistical value, c + represents the statistical value corresponding to ω, d + represents ω′T e The corresponding statistical value, e + represents the statistical value corresponding to ω′ω, f + Indicates T e The statistical value corresponding to ω, g + Represents ω′ 2 The corresponding statistical value, h + Represents ω 2 Corresponding statistical value; M represents the total number of the first running data and the second running data.

6. The method for identifying the mechanical parameters of a motor according to claim 1, wherein: The step of storing the real-time motor operation data in the target storage space and identifying the mechanical parameters of the motor based on the motor operation data stored in the target storage space and the non-target storage space respectively includes: After storing the real-time motor operation data in the target storage space, determining whether a preset identification condition is met based on the motor operation data stored in the target storage space and the non-target storage space; If so, the mechanical parameters of the motor are identified based on the motor operation data stored in the target storage space and the non-target storage space respectively.

7. The method for identifying the mechanical parameters of a motor according to claim 6, wherein: The preset identification conditions include: The total amount of motor operation data currently stored in the target storage space and the non-target storage space is greater than a first threshold, and the difference between the largest space number and the smallest space number among the space numbers corresponding to the storage spaces storing the motor operation data is greater than a second threshold; The N storage spaces are numbered in sequence from 1 to N.

8. A motor mechanical parameter identification system, characterized in that: include: Data sampling module, used to determine the real-time motor operation data at the current sampling moment; The real-time motor operation data includes motor acceleration; a data classification module, configured to determine a target storage space for storing the real-time motor operation data from each candidate storage space according to the motor acceleration; The capacity of each candidate storage space is different, and the capacity of the target storage space is positively correlated with the absolute value of the motor acceleration; The identification module is used to store the real-time motor operation data in the target storage space, and perform mechanical parameter identification of the motor based on the motor operation data stored in the target storage space and the non-target storage space.

9. A device for identifying the mechanical parameters of a motor, characterized in that: include: memory for storing computer programs; A processor is configured to execute the computer program to implement the steps of the method for identifying the mechanical parameters of a motor according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for identifying the mechanical parameters of a motor according to any one of claims 1 to 7 are implemented.

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