Motor accurate stop control method and system applied to horizontal type inverted combined tool magazine

Abstract

The invention relates to the technical field of motor control, in particular to a motor accurate stop control method and system applied to a horizontal type inverted combined tool magazine, and the method comprises the steps: selecting a zero point position of the horizontal type inverted combined tool magazine, and building a coordinate system; determining a target position of the motor; acquiring operation state parameters and an actual position of the motor; establishing a motor accurate stop control prediction model, and taking the operation state parameters as the input of the motor accurate stop control prediction model to obtain a prediction position; calculating a prediction error between the target position and the prediction position, and formulating a motor operation adjustment strategy to preliminarily adjust motor control parameters; and comparing the actual position with the target position through a feedback control algorithm to obtain an actual error signal, and optimizing motor control parameters. According to the invention, the problem of complex control caused by the influence of inertia and gravity in the horizontal inverted combined tool magazine is effectively solved, the accurate stopping precision of the motor is improved, and meanwhile, the operation efficiency and stability of the whole tool magazine system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of motor control, and in particular to a motor accurate stop control method and system applied to a horizontal inverted combined tool magazine. Background Art

[0002] Horizontal inverted combination tool magazines are widely used in various CNC machine tools, especially in manufacturing environments that require rapid switching of multiple tools to complete complex processing tasks, such as aerospace, automobile manufacturing, mold processing and other fields. In the horizontal inverted combination tool magazine, due to its unique structural design, the spatial layout of the tool magazine and tools is more complex. At the same time, the inertia and gravity effects brought by the inverted structure are more obvious. This complexity puts higher requirements on the precise control of the motor, especially in the process of tool replacement. The motor is required to accurately stop at the specified position during the tool replacement process to ensure the safe locking and rapid exchange of the tool.

[0003] In traditional tool magazine control systems, the parking position of the motor usually relies on a simple feedback control system or a control algorithm based on fixed parameters. This single error compensation mechanism is not accurate enough in complex scenarios, such as gravity and load influences, especially when random errors and system errors coexist, which can easily lead to poor control effects. In the face of complex working conditions, the motor must control the tool magazine to stop accurately in an inverted state. If traditional motor control solutions are used, the effect of gravity may not be effectively offset, resulting in the inability to accurately park the tool, inaccurate tool replacement, increased processing errors, and other problems. Therefore, the development of motor accurate stop control methods for horizontal inverted combined tool magazines has become one of the current research directions. Summary of the invention

[0004] The present invention provides a motor accurate stop control method and system applied to a horizontal inverted combined tool magazine, which can effectively solve the problems in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A motor accurate stop control method applied to a horizontal inverted combined tool magazine, the method comprising: Selecting a zero point position of the horizontal inverted combined tool magazine, and establishing a coordinate system based on the zero point position; determining a target position of the motor according to the coordinate system; Collect the running status parameters and actual position of the motor; Establishing a motor quasi-stop control prediction model, using the operating state parameters as inputs of the motor quasi-stop control prediction model to predict the quasi-stop position of the motor to obtain a predicted position; Calculating a prediction error between the target position and the predicted position, and formulating a motor operation adjustment strategy according to the prediction error to preliminarily adjust the motor control parameters; The actual position is compared with the target position through a feedback control algorithm to obtain an actual error signal, and the motor control parameters are optimized according to the actual error signal.

[0006] Further, determining the target position of the motor according to the coordinate system includes: Defining the coordinates of the working area of ​​the horizontal inverted combined tool magazine according to the coordinate system, and establishing a coordinate database; Numbering the working areas of the horizontal inverted combined tool magazine, and identifying the coordinates of the target working area in the coordinate database according to the numbers; The target position offset of the motor is introduced, and the target position of the motor is generated by combining the target position offset and the target working area coordinates.

[0007] Furthermore, introducing the target position offset of the motor includes: Based on the established coordinate system, defining the direction of the target position offset by the direction of the target position of the motor relative to the working area; Determine the space required for the motor, define the minimum safety distance, and calculate the size of the target offset according to the space required for the motor and the minimum safety distance. The calculation formula is as follows: ; in, is the target offset, is the direction coefficient, is the motor length, is the minimum safe distance, is the clearance distance; The target position of the motor is generated by combining the size and direction of the target position offset and the target working area coordinates.

[0008] Furthermore, in the established coordinate system, the target position offset needs to be introduced for each axial direction, and the target position is calculated separately in each axial direction.

[0009] Furthermore, the establishment of a motor accurate stop control prediction model includes: Collect historical motion state data and position data of the motor, align timestamps, and construct a time series data set; Preprocessing the time series data set; Selecting the structure of the motor quasi-stop control prediction model and compiling the motor quasi-stop control prediction model; Input the preprocessed time series data set to implement the training, evaluation and optimization of the motor accurate stop control prediction model; The quasi-stop position of the motor is predicted by using the optimized motor quasi-stop control prediction model.

[0010] Furthermore, the timestamp alignment includes: Analyze the timestamp frequency of each data and detect inconsistent timestamp intervals; Select an appropriate alignment method to align the data to the same time series; The data after the timestamp alignment is filtered to obtain a time series data set.

[0011] Furthermore, the motor accurate stop control prediction model includes: An input layer, receiving the processed time series dataset; The bidirectional LSTM layer, which consists of a forward LSTM layer, a backward LSTM layer, and a merging layer, is used to process time series data and capture time dependencies and sequence information; A dense layer, composed of several fully connected layers, maps the output of the bidirectional LSTM layer to the required dimension through the fully connected layers; Dropout layer, randomly discarding a certain proportion of neurons during training; The output layer generates the final prediction result of the accurate stop position of the motor.

[0012] Furthermore, an error compensation layer is added after the bidirectional LSTM layer of the motor quasi-stop control prediction model, system error compensation is introduced, and the predicted position is dynamically adjusted. The error compensation formula is: ; in, is the predicted position after error compensation, is the preliminary accurate stop position predicted by the bidirectional LSTM layer, is the mass of the load, is the acceleration due to gravity, is the angle between the motor movement direction and the vertical direction, is the moment of inertia of the motor, is the acceleration of the motor at time t, is the rotation radius of the motor, is the friction coefficient, is the normal force of the motor system, is the stiffness constant of the motor system.

[0013] Furthermore, it includes: the merging layer splices the outputs generated by the forward LSTM layer and the backward LSTM layer in the feature dimension by connecting.

[0014] A motor accurate stop control system applied to a horizontal inverted combined tool magazine, the system comprising: A coordinate system establishment module selects a zero point position of the horizontal inverted combined tool magazine and establishes a coordinate system based on the zero point position; A target position determination module, which determines the target position of the motor according to the coordinate system; Data acquisition module, collecting the running status parameters and actual position of the motor; A prediction model building module is used to build a motor quasi-stop control prediction model, and use the operating state parameters as inputs of the motor quasi-stop control prediction model to predict the quasi-stop position of the motor to obtain a predicted position; A prediction error adjustment module is used to calculate the prediction error between the target position and the predicted position, and formulate a motor operation adjustment strategy according to the prediction error to perform preliminary adjustment on the motor control parameters; The actual error adjustment module compares the actual position with the target position through a feedback control algorithm to obtain an actual error signal, and optimizes the motor control parameters according to the actual error signal.

[0015] The technical solution of the present invention can achieve the following technical effects: It effectively solves the complex control problems caused by the influence of inertia and gravity in the horizontal inverted combination tool magazine, making the tool replacement process more stable and efficient. At the same time, it takes into account the historical status and future movement trend of the motor, thereby improving the motor's precise stopping accuracy and enhancing the system's adaptability under complex working conditions. At the same time, it improves the operating efficiency and stability of the entire tool magazine system.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of a motor accurate stop control method applied to a horizontal inverted combined tool magazine; Figure 2 A schematic diagram of the process of timestamp alignment; Figure 3 It is a structural diagram of the motor quasi-stop control prediction model; Figure 4 It is a structural schematic diagram of the motor accurate stop control system applied to the horizontal inverted combined tool magazine. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0021] Embodiment 1 like Figure 1 As shown, a motor accurate stop control method applied to a horizontal inverted combined tool magazine includes: S1: Select the zero point position of the horizontal inverted combined tool magazine and establish a coordinate system based on the zero point position; Specifically, in order to accurately locate and control the movement of the motor in the system, the zero point position can be determined to establish a coordinate system to uniformly describe the position of the motor and all other components. By clarifying the zero point and establishing the coordinate system, the control system can accurately control the mechanical movement according to the position data in the coordinate system. The established coordinate system can be a Cartesian coordinate system, a polar coordinate system, etc. Selecting an appropriate coordinate system can better achieve precise positioning of the tool magazine motor control.

[0022] S2: Determine the target position of the motor according to the coordinate system; S3: Collect the running state parameters and actual position of the motor; Specifically, in order to perform precise motor control, the operating status parameters and actual position of the motor can be collected, including position, speed, voltage, current and other parameters. To collect these parameters, suitable sensors or detection equipment can be selected, installed and calibrated, and the data acquisition system can be prepared. The data acquisition system needs to have sufficient input channels and be able to support sensors with high sampling rates; sensor signals may be affected by noise such as electromagnetic interference and mechanical vibration. Therefore, when collecting data, low-pass filters or other signal processing technologies can be used to eliminate noise and signal interference, ensure the accuracy of the collected data, obtain high-precision motor operating parameters, and provide strong support for the control system.

[0023] S4: establishing a motor quasi-stop control prediction model, using the operating state parameters as inputs of the motor quasi-stop control prediction model to predict the motor quasi-stop position, and obtaining a predicted position; On the basis of the above embodiments, in order to predict the exact stop position of the motor in advance and avoid overshoot or undershoot, a suitable model can be selected as the motor exact stop control prediction model, including long short-term memory network, support vector regression, multi-layer perceptron, etc., to predict the exact stop position of the motor through the operating state parameters of the motor; and step S3 continuously collects the real-time operating state parameters of the motor, these parameters can reflect the current operating state of the motor, and if you want to use the collected operating state parameters as the input of the prediction model, you can pre-process the collected operating state parameters, including denoising, normalization and other operations, to ensure the quality and consistency of the data, in line with the input requirements of the prediction model.

[0024] S5: Calculate the prediction error between the target position and the predicted position, formulate a motor operation adjustment strategy based on the prediction error, and make preliminary adjustments to the motor control parameters; S6: Compare the actual position with the target position through a feedback control algorithm to obtain an actual error signal, and optimize the motor control parameters according to the actual error signal.

[0025] In this embodiment, the motor control parameters can be adjusted in stages. The predicted error adjustment is performed in the early and middle stages of the motor movement, mainly to reduce large-scale errors, while the actual error adjustment is performed when the motor approaches the target position, mainly for precise correction; in step S5, the target position of the motor and the quasi-stop position predicted by the prediction model are compared, and the error between the two, namely the predicted error, is calculated, which is mainly used to formulate a preliminary adjustment strategy to adjust the operating state of the motor in advance to reduce the deviation when the motor actually stops. For example, if the predicted error is large and the motor is close to the target position, the motor speed can be gradually reduced to slowly approach the target.

[0026] In step S6, based on the actual error signal and the difference between the current position and the target position obtained through actual measurement, the control system will adjust the operating parameters of the motor to further optimize the operation of the motor. This stage usually uses classic feedback control algorithms, such as PID control, to ensure that the motor can be quickly and stably adjusted to the target position. During the movement of the motor, the system usually transitions from a rough adjustment based on the predicted error to a fine adjustment based on the actual error. The two complement each other to ensure that the motor can stop accurately at the target position.

[0027] The present invention effectively solves the complex control problems caused by the influence of inertia and gravity in the horizontal inverted combined tool magazine, making the tool replacement process more stable and efficient. At the same time, it takes into account the historical state and future movement trend of the motor, thereby improving the motor's precise stopping accuracy, and also enhances the system's adaptability under complex working conditions, while improving the operating efficiency and stability of the entire tool magazine system.

[0028] Based on the above embodiment, determining the target position of the motor according to the coordinate system includes: A1: Define the working area coordinates of the horizontal inverted combined tool magazine according to the coordinate system and establish a coordinate database; A2: Number the working areas of the horizontal inverted combined tool magazine, and identify the coordinates of the target working area in the coordinate database according to the numbers; Specifically, in order to ensure that the motor can access each tool accurately and systematically, the spatial position of each tool or workstation in the horizontal inverted combination tool magazine can be defined. The coordinates of each tool position should be relatively defined based on the zero position, and should be accurately calibrated according to the geometric structure of the tool magazine design and managed by establishing a database; each work area will be assigned a unique number, which is associated with the corresponding coordinates in the coordinate database. When a specific tool needs to be found, the system can query the coordinate database by number to find the corresponding work area coordinates.

[0029] A3: Introduce the target position offset of the motor, and combine the target position offset and the target working area coordinates to generate the target position of the motor.

[0030] The introduction of the offset in this embodiment is to cope with different operational requirements and ensure that the motor can adapt to factors such as tool type, workstation characteristics or mechanical structure when stopping at a precise position. For example, for larger or longer tools, the motor may need to stop at a slightly farther position to avoid collision, while for smaller tools, the motor can be closer to the center; by introducing the offset, the system can flexibly adjust the position of the motor so that it can be accurately docked to a specific tool position, ensuring that the motor has a more suitable and optimized final target position before the movement begins.

[0031] In order to obtain the optimal motor target position, the motor target position offset is introduced, including: A31: Based on the established coordinate system, the direction of the target position offset is defined by the direction of the motor's target position relative to the working area; A32: Determine the space required for the motor, define the minimum safety distance, and calculate the target offset based on the space required for the motor and the minimum safety distance. The calculation formula is as follows: ; in, is the target offset, is the direction coefficient, is the motor length, is the minimum safe distance, is the clearance distance; Specifically, in order to ensure that the motor and the tool do not collide, a certain safety distance should be left when the motor approaches the tool. In order to align the center of the motor with the tool, the target position of the motor needs to take into account the length of the motor itself, and it should be ensured that the motor does not hit other obstacles or equipment during movement, especially in complex environments, so a clearance distance can be left. In order to ensure that the final motion path of the motor is reasonable and the motor can move in the correct direction, a directional coefficient can be introduced. The directional coefficient can be used to determine whether the motor moves in the positive or reverse direction on each coordinate axis, thereby ensuring that the motor can dock with the tool in the correct direction.

[0032] A33: Generate the target position of the motor by combining the size and direction of the target position offset and the target working area coordinates.

[0033] This step adjusts the standard target working area coordinates by combining the size and direction of the offset to generate the final target position of the motor. The target position is calculated based on the standard coordinates plus the offset. The calculation formula is: Motor target position = target working area coordinates + offset size × offset direction.

[0034] As a preference of this embodiment, in the established coordinate system, a target position offset needs to be introduced for each axial direction, and the target position is calculated separately in each axial direction.

[0035] Specifically, since the motor runs in multiple axes, the target position of each axis needs to be calculated separately. Therefore, the offset of each axis can be added to the standard target position to finally obtain the actual target position of the motor in each axis. This method of calculating the target position axis by axis can ensure that the motor is precisely controlled in each direction in a complex three-dimensional space, ensuring that each axis of the motor in the three-dimensional space can achieve the best motion effect and ensuring that the offsets of different axes will not affect each other.

[0036] On the basis of the above-mentioned embodiment, a motor accurate stop control prediction model is established, including: B1: Collect the historical motion state data and position data of the motor, align the timestamps, and build a time series dataset; B2: Preprocess the time series dataset; The data collected in this step comes from multiple sensors or devices and may be collected at different times. In order to ensure that all status and position data can accurately reflect the status of the motor at the same time, timestamp alignment can be performed to ensure that each data point can match other related data points; and the collected data is used as training data for the prediction model. In order to ensure the quality and consistency of the input data, data preprocessing can be performed, including data cleaning, normalization or standardization, smoothing or noise reduction, etc., so that the model can effectively learn on a cleaner and standardized data set.

[0037] B3: Select the structure of the motor quasi-stop control prediction model and compile the motor quasi-stop control prediction model; Specifically, in order to enable efficient training and optimization of the model, you can select a suitable model structure and configure the various components of the model for compilation. Compiling the model is to convert the selected model structure into an executable training framework and define the model's optimization objectives, loss functions, optimizer and other parameters so that the model can be trained and evaluated correctly.

[0038] B4: Input the preprocessed time series data set to implement the training, evaluation and optimization of the motor accurate stop control prediction model; In the time series data set, the original time series data is divided into multiple subsequences, and sample-label pairs are constructed. Each subsequence is the input of the model, and the label is the quasi-stop position of the motor. After the construction, the time series data set can be divided into training set, validation set and test set to train the model; during the model training process, the model parameters can be adjusted through the back propagation algorithm to reduce the loss value and improve the prediction ability of the model. After each round of training, the validation set data can be used to evaluate the model, calculate the loss and indicators on the validation set, monitor the generalization ability of the model, and prevent overfitting or underfitting. After the model training is completed, the test set can be used to evaluate the final performance of the model. The test set should be data that the model has never seen during the training process; after training, verification and evaluation, the prediction model can gradually learn the relationship between the motor motion state and the quasi-stop position, and provide accurate quasi-stop position prediction in the final application.

[0039] B5: The motor’s quasi-stop position is predicted through the optimized motor quasi-stop control prediction model.

[0040] Based on the above embodiments, Figure 2 As shown, timestamp alignment includes: B11: Analyze the timestamp frequency of each data and detect inconsistent timestamp intervals; B12: Select a suitable alignment method to align the data to the same time series; B13: Filter the data after timestamp alignment to obtain a time series data set.

[0041] As a preferred embodiment of this invention, in order to keep all data in the same time series framework, the timestamp frequency of each data source can be analyzed to determine whether there are inconsistent time intervals, find out the inconsistent time intervals in the data set, determine which data points need to be adjusted, and mark those points with too large or too small intervals, and prepare to take corresponding alignment measures, including: (1) Upsampling: If the interval between data points is too large, more data points can be generated through interpolation to adjust the data to a higher frequency sampling interval.

[0042] (2) Downsampling: If the interval between data points is too small or the amount of data is too large, the data points can be reduced by averaging or taking the middle value to adjust the data to a lower frequency sampling interval.

[0043] After upsampling or downsampling all data sets, all data points can be aligned to a unified time point, such as every second, every minute, etc., to ensure that all data sets are aligned with this time series.

[0044] As a preferred embodiment of this embodiment, Figure 3 As shown in Figure 1, the motor quasi-stop control prediction model includes: The input layer accepts the processed time series dataset; The input layer is the first layer of the neural network, which is responsible for accepting the processed time series data set and inputting the motor's motion state and position data into the subsequent layers of the model for processing and prediction.

[0045] The bidirectional LSTM layer, which consists of a forward LSTM layer, a backward LSTM layer, and a merging layer, is used to process time series data and capture time dependencies and sequence information; Bidirectional LSTM layer can use two LSTM layers to process time series data and obtain information from past and future time steps at the same time to improve the accuracy of prediction. LSTM is able to capture long-term dependencies, while the bidirectional structure can extract useful features from bidirectional time series.

[0046] The bidirectional LSTM layer consists of a forward LSTM layer, a backward LSTM layer and a merging layer. The forward LSTM layer processes the input time series data, gradually processes the data from the starting point to the end point of the time series, and captures the dependency from the past to the future in the time series; the backward LSTM layer corresponds to the forward LSTM layer, and it processes the data from the end point to the starting point of the time series to capture the dependency from the future to the past; the merging layer combines the outputs of the forward LSTM layer and the backward LSTM layer to form a comprehensive representation based on forward and backward information.

[0047] The dense layer consists of several fully connected layers, which map the output of the bidirectional LSTM layer to the required dimension through the fully connected layers; The dense layer is composed of several fully connected layers. The neurons in each layer are connected to all the neurons in the previous layer. The output of the bidirectional LSTM layer is usually a high-dimensional time series representation. The dense layer can map the output of the bidirectional LSTM layer to the specified dimension in a fully connected manner and further process the data output by the LSTM layer. In order to enhance the expressive power of the model, multiple fully connected layers can be stacked so that the model can gradually extract high-order features from the data. Each fully connected layer further processes the output of the previous layer and gradually compresses the high-dimensional data into the required dimension.

[0048] Dropout layer, randomly discarding a certain proportion of neurons during training; The dropout layer can randomly drop a certain proportion of neurons during the training process to prevent the model from overfitting. In addition, the dropped neurons are different during each training process, so different parts of the model are forced to learn independent feature representations, which can effectively improve the generalization ability of the model and improve the robustness and adaptability of the model.

[0049] The output layer generates the final prediction result of the motor’s accurate stop position.

[0050] In order to further improve the accuracy of the motor quasi-stop control prediction model, an error compensation layer is added after the bidirectional LSTM layer of the motor quasi-stop control prediction model, and system error compensation is introduced to dynamically adjust the predicted position. The error compensation formula is: ; in, is the predicted position after error compensation, is the initial accurate stop position predicted by the bidirectional LSTM layer, is the mass of the load, is the acceleration due to gravity, is the angle between the motor movement direction and the vertical direction, is the moment of inertia of the motor, is the acceleration of the motor at time t, is the rotation radius of the motor, is the friction coefficient, is the normal force of the motor system, is the stiffness constant of the motor system.

[0051] In this preferred solution, the error compensation layer is a functional layer added after the bidirectional LSTM layer. Its main function is to correct the quasi-stop position predicted by the bidirectional LSTM model by compensating for the system error. Due to the influence of gravity, inertia and friction on the quasi-stop position of the motor, the prediction results of the bidirectional LSTM may have deviations. The error compensation layer detects these errors and makes dynamic adjustments to ensure that the final output quasi-stop position is more accurate.

[0052] As a preferred embodiment of this embodiment, it includes: the merging layer splices the outputs generated by the forward LSTM layer and the backward LSTM layer in the feature dimension by connecting.

[0053] Specifically, in order to completely retain all the information generated by the forward LSTM and backward LSTM layers, while enhancing the expressiveness of the model and maintaining the independence of the forward and backward features, the output feature vectors of the forward LSTM layer and the backward LSTM layer at the same time step can be concatenated together in the feature dimension to form a new feature vector. The model can simultaneously obtain past and future feature representations at each time step to avoid information loss. If the concatenation is performed by addition or averaging, the forward and backward features will be compressed together, which may result in information loss.

[0054] Embodiment 2: like Figure 4 As shown in the figure, the motor accurate stop control system applied to the horizontal inverted combined tool magazine includes: A coordinate system establishment module selects the zero point position of the horizontal inverted combined tool magazine and establishes a coordinate system based on the zero point position; A target position determination module determines the target position of the motor according to the coordinate system; Data acquisition module, collecting the running status parameters and actual position of the motor; A prediction model building module is used to build a motor quasi-stop control prediction model, and the running state parameters are used as the input of the motor quasi-stop control prediction model to predict the motor's quasi-stop position and obtain the predicted position; The prediction error adjustment module calculates the prediction error between the target position and the predicted position, formulates the motor operation adjustment strategy according to the prediction error, and makes preliminary adjustments to the motor control parameters; The actual error adjustment module compares the actual position with the target position through a feedback control algorithm to obtain an actual error signal, and optimizes the motor control parameters according to the actual error signal.

[0055] The above-mentioned adjustment system in the present invention can effectively realize the motor accurate stop control method of the horizontal inverted combined tool magazine, and the technical effect that can be achieved is as described in the above-mentioned embodiment, which will not be repeated here.

[0056] Although the present application has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present application as defined herein, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A motor accurate stop control method applied to a horizontal inverted combined tool magazine, characterized in that: The method comprises: Selecting a zero point position of the horizontal inverted combined tool magazine, and establishing a coordinate system based on the zero point position; determining a target position of the motor according to the coordinate system; Collect the running status parameters and actual position of the motor; Establishing a motor quasi-stop control prediction model, using the operating state parameters as inputs of the motor quasi-stop control prediction model to predict the quasi-stop position of the motor to obtain a predicted position; Calculating a prediction error between the target position and the predicted position, and formulating a motor operation adjustment strategy according to the prediction error to preliminarily adjust the motor control parameters; The actual position is compared with the target position through a feedback control algorithm to obtain an actual error signal, and the motor control parameters are optimized according to the actual error signal.

2. The motor accurate stop control method applied to the horizontal inverted combined tool magazine according to claim 1 is characterized in that: Determining the target position of the motor according to the coordinate system includes: Defining the coordinates of the working area of ​​the horizontal inverted combined tool magazine according to the coordinate system, and establishing a coordinate database; Numbering the working areas of the horizontal inverted combined tool magazine, and identifying the coordinates of the target working area in the coordinate database according to the numbers; The target position offset of the motor is introduced, and the target position of the motor is generated by combining the target position offset and the target working area coordinates.

3. The motor accurate stop control method applied to the horizontal inverted combined tool magazine according to claim 2 is characterized in that: The target position offset of the motor is introduced, including: Based on the established coordinate system, defining the direction of the target position offset by the direction of the target position of the motor relative to the working area; Determine the space required for the motor, define the minimum safety distance, and calculate the size of the target offset according to the space required for the motor and the minimum safety distance. The calculation formula is as follows: ； in, is the target offset, is the direction coefficient, is the motor length, is the minimum safe distance, is the clearance distance; The target position of the motor is generated by combining the size and direction of the target position offset and the target working area coordinates.

4. The motor accurate stop control method applied to the horizontal inverted combined tool magazine according to claim 3 is characterized in that: Including in the established coordinate system, the target position offset needs to be introduced for each axial direction, and the target position is calculated separately in each axial direction.

5. The motor accurate stop control method applied to the horizontal inverted combined tool magazine according to claim 1 is characterized in that: The step of establishing a motor accurate stop control prediction model comprises: Collect historical motion state data and position data of the motor, align timestamps, and construct a time series data set; Preprocessing the time series data set; Selecting the structure of the motor quasi-stop control prediction model and compiling the motor quasi-stop control prediction model; Input the preprocessed time series data set to implement the training, evaluation and optimization of the motor accurate stop control prediction model; The quasi-stop position of the motor is predicted by using the optimized motor quasi-stop control prediction model.

6. The motor accurate stop control method applied to the horizontal inverted combined tool magazine according to claim 5 is characterized in that: The timestamp alignment comprises: Analyze the timestamp frequency of each data and detect inconsistent timestamp intervals; Select an appropriate alignment method to align the data to the same time series; The data after the timestamp alignment is filtered to obtain a time series data set.

7. The motor accurate stop control method applied to the horizontal inverted combined tool magazine according to claim 5 is characterized in that: The motor accurate stop control prediction model includes: An input layer, receiving the processed time series dataset; The bidirectional LSTM layer, which consists of a forward LSTM layer, a backward LSTM layer, and a merging layer, is used to process time series data and capture time dependencies and sequence information; A dense layer, composed of several fully connected layers, maps the output of the bidirectional LSTM layer to the required dimension through the fully connected layers; Dropout layer, randomly discarding a certain proportion of neurons during training; The output layer generates the final prediction result of the accurate stop position of the motor.

8. The motor accurate stop control method applied to the horizontal inverted combined tool magazine according to claim 7 is characterized in that: An error compensation layer is added after the bidirectional LSTM layer of the motor accurate stop control prediction model, system error compensation is introduced, and the predicted position is dynamically adjusted. The error compensation formula is: ； in, is the predicted position after error compensation, is the preliminary accurate stop position predicted by the bidirectional LSTM layer, is the mass of the load, is the acceleration due to gravity, is the angle between the motor movement direction and the vertical direction, is the moment of inertia of the motor, is the acceleration of the motor at time t, is the rotation radius of the motor, is the friction coefficient, is the normal force of the motor system, is the stiffness constant of the motor system.

9. The motor accurate stop control method applied to the horizontal inverted combined tool magazine according to claim 7, characterized in that: include: The merging layer concatenates the outputs generated by the forward LSTM layer and the backward LSTM layer in the feature dimension by means of connection.

10. The motor accurate stop control system applied to the horizontal inverted combined tool magazine is characterized by: The system comprises: A coordinate system establishment module selects a zero point position of the horizontal inverted combined tool magazine and establishes a coordinate system based on the zero point position; A target position determination module, which determines the target position of the motor according to the coordinate system; Data acquisition module, collecting the running status parameters and actual position of the motor; A prediction model building module is used to build a motor quasi-stop control prediction model, and use the operating state parameters as inputs of the motor quasi-stop control prediction model to predict the quasi-stop position of the motor to obtain a predicted position; A prediction error adjustment module is used to calculate the prediction error between the target position and the predicted position, and formulate a motor operation adjustment strategy according to the prediction error to perform preliminary adjustment on the motor control parameters; The actual error adjustment module compares the actual position with the target position through a feedback control algorithm to obtain an actual error signal, and optimizes the motor control parameters according to the actual error signal.

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