A multi-axis motion control method and system for automated equipment

Abstract

The present invention discloses a multi-axis motion control method and system for automated equipment, which relates to the technical field of multi-axis motion control. By collecting the data of the equipment in real time and based on the real-time error and equipment status, an adaptive compensation strategy is designed and adjusted. This adaptive ability enables the system to flexibly cope with factors such as equipment aging and external environment changes, ensuring high control accuracy and stability under different working conditions. Through the cooperation of the data acquisition and processing module and the error detection and analysis module, the system can accurately obtain the motion error value and change trend. This provides an accurate basis for subsequent compensation adjustment, enabling the compensation amount to be dynamically optimized in each control cycle, avoiding error accumulation, and improving the system accuracy and stability; by cleaning and standardizing the multi-axis data group DW, the system can more accurately identify and evaluate the source of errors, thereby designing a more refined compensation strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-axis motion control, and specifically provides a multi-axis motion control method and system for automated equipment. Background Art

[0002] The multi-axis motion control system of automated equipment belongs to the field of automation control and is an important part of modern industrial automation technology. Automation control systems are widely used in industrial production, machinery manufacturing, robotics, precision instruments and other fields, promoting the improvement of production efficiency and product quality. Especially in production lines with high-precision and high-efficiency requirements, the motion control system plays a core role. In the specific application of automation control systems, multi-axis motion control systems are particularly important. By coordinating multiple actuators to work together, they precisely control the motion path and state of the equipment and are widely used in scenarios such as numerically controlled machine tools, robots, and automated assembly lines.

[0003] Existing control systems usually rely on fixed compensation parameters and control strategies. Although this traditional method can meet some basic control requirements, in actual applications, as the equipment usage time increases, the working state of the equipment changes, resulting in a gradual decline in the control accuracy of the system. In addition, traditional control systems have poor ability to cope with external environmental changes and lack sufficient flexibility and adaptability.

[0004] The deficiencies of the current control system mainly stem from its lack of adaptive ability to the dynamic changes of the equipment. Fixed compensation parameters fail to consider in real time the influence of external factors such as equipment wear and temperature changes, so they cannot adjust the control strategy in time to cope with these changes. This leads to the system's inability to effectively handle error accumulation caused by equipment aging, environmental changes, etc. As the equipment usage time increases, the control accuracy gradually decreases, which may be manifested as an increase in repetitive errors on the production line and an increase in motion trajectory deviation, ultimately affecting the accuracy and production efficiency of the product. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a multi-axis motion control method and system for automated equipment, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A multi-axis motion control system for automated equipment includes a data acquisition and processing module, an error detection and analysis module, an adaptive controller design module, a compensation adjustment and motion control module, a feedback and status update module, and a performance evaluation and optimization module;

[0007] The data acquisition and processing module collects data in the multi-axis control system through sensors, fits it into the original data group AW, and performs preprocessing to obtain the multi-axis data group DW;

[0008] The error detection and analysis module performs real-time error detection based on the acquired multi-axis data set DW to obtain the error value Ex and the error change trend Etr;

[0009] The adaptive controller design module designs an adaptive controller to generate a compensation strategy according to the acquired error value Ex and error change trend Etr, obtains the response compensation amount Udy, and calculates and obtains the final compensation amount Ux(t);

[0010] The compensation adjustment and motion control module adjusts the motion trajectory, bearing speed Vx, and bearing acceleration ax of each bearing according to the acquired final compensation amount Ux(t) for real-time compensation and feeds it back to the compensation strategy;

[0011] After the real-time compensation is completed, the feedback and status update module collects the device feedback data and readjusts the compensation strategy to obtain the new compensation amount nUx;

[0012] The performance evaluation and optimization module evaluates the overall performance of the system based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, obtains the system performance score PF, and adjusts the control strategy to obtain the adjusted compensation amount TnUx.

[0013] Preferably, the data acquisition and processing module includes a data acquisition unit and a data preprocessing unit;

[0014] The data acquisition unit collects data of the bearing through sensors, including the bearing temperature Ta, bearing acceleration ax, bearing load La, and motion deviation Px, and fits them into the original data set AW;

[0015] Among them, the bearing temperature Ta is collected through a temperature sensor, and the temperature sensor can detect the temperature change caused by friction and load factors during the operation of the device;

[0016] The bearing acceleration ax is collected through an acceleration sensor;

[0017] The bearing load La is collected through a load sensor, and the bearing load La reflects the working state of each shaft;

[0018] The motion deviation Px is collected through a position sensor, which reflects the difference between the actual position Xact and the target position Xtar of the bearing;

[0019] The motion deviation Px is obtained through the following formula:

[0020] ;

[0021] The data preprocessing unit cleans and standardizes the acquired original data set AW to obtain a multi-axis data set DW;

[0022] The cleaning includes filtering processing and outlier processing. The filtering processing removes noise and interference in the original data set AW by using a filter; the outlier processing eliminates outliers in the original data set AW by using an outlier detection algorithm;

[0023] The standardization processing uses a standardization method to convert the data into a unified scale to obtain a multi-axis data set DW;

[0024] The multi-axis data set DW is obtained through the following formula:

[0025] ;

[0026] In the formula, DWb represents the b-th item of data in the multi-axis data set DW, AWb represents the b-th item of data in the original data set AW, μAWb represents the mean of the b-th item of data in the original data set AW, and σAWb represents the standard deviation of the b-th item of data in the original data set AW.

[0027] Preferably, the error detection and analysis module includes a data feature extraction unit and an error acquisition unit:

[0028] The data feature extraction unit extracts features from the multi-axis data set DW, extracts features that affect the motion accuracy and control effect, including the temperature change rate ΔTa, the load change rate ΔLa, and the motion deviation change rate ΔPx, and fits them into a change feature set LP;

[0029] The temperature change rate ΔTa is obtained through the following formula:

[0030] ;

[0031] In the formula, Ta(t) represents the bearing temperature at time t, Ta(t - 1) represents the bearing temperature at time t - 1, and Δt represents the time interval;

[0032] The load change rate ΔLa is obtained through the following formula:

[0033] ;

[0034] In the formula, La(t) represents the bearing load at time t, and La(t - 1) represents the bearing load at time t - 1;

[0035] The motion deviation change rate ΔPx is obtained through the following formula:

[0036] ;

[0037] Wherein, Px(t) represents the motion deviation at time t, and Px(t - 1) represents the motion deviation at time t - 1;

[0038] The error acquisition unit calculates and obtains the error value Ex according to the obtained temperature change rate ΔTa, load change rate ΔLa, and motion deviation change rate ΔPx, and dynamically analyzes the error value Ex. By performing time series analysis to smooth the error curve, it identifies the error change trend Etr; this analysis helps to determine whether the error is caused by instantaneous disturbances or long-term changes;

[0039] The error value Ex is obtained through the following formula:

[0040] ;

[0041] ;

[0042] Wherein, respectively represent the preset weight values of the temperature change rate ΔTa, load change rate ΔLa, and motion deviation change rate ΔPx, and , fin represents the interaction term function between features, represents the interaction term coefficient between the temperature change rate ΔTa and the load change rate ΔLa, represents the interaction term coefficient between the load change rate ΔLa and the motion deviation change rate ΔPx, represents the interaction term coefficient between the temperature change rate ΔTa and the motion deviation change rate ΔPx; represents the coupling effect between different features;

[0043] The error change trend Etr is obtained through the following formula:

[0044] ;

[0045] Wherein, Ex(t - i) represents the error value at time t - i, n represents the time window size for calculating the error trend, and i represents the index variable used to traverse the past n time points.

[0046] Preferably, the adaptive controller design module includes an error response design unit and a dynamic compensation adjustment unit;

[0047] Based on the obtained error value Ex and error change trend Etr, the error response design unit combines with PID control to design an error response function and obtains the error compensation amount Uex(t);

[0048] The error compensation amount Uex(t) is obtained through the following formula:

[0049] ;

[0050] Where, Ex represents the error value at time t, Kp represents the proportional gain, Ki represents the integral gain, Kd represents the derivative gain, dt represents the integral, represents the rate of change of the error at time t;

[0051] The obtained error compensation amount Uex(t) is combined with the error change trend Etr, and the error change trend Etr is used as a dynamic weight to adjust the compensation strategy to obtain the response compensation amount Udy;

[0052] Among them, the error change trend Etr can affect the amplitude of the compensation response;

[0053] When the error value Ex continues to increase within a fixed period, the controller needs to increase the response;

[0054] When the error value Ex remains stable within a fixed period, the controller reduces the response;

[0055] The response compensation amount Udy is obtained through the following formula:

[0056] ;

[0057] Where, fq represents the trend change function.

[0058] Preferably, the dynamic compensation adjustment unit combines the obtained response compensation amount Udy with the temperature change rate ΔTa and the load change rate ΔLa to obtain the final compensation amount Ux(t);

[0059] The temperature compensation amount Uta is obtained by the influence of the temperature change rate ΔTa on the bearing motion performance;

[0060] The temperature compensation amount Uta is obtained through the following formula:

[0061] ;

[0062] Where, represents the sensitivity coefficient of the temperature change to the compensation amount;

[0063] The load compensation amount Ula is obtained by the influence of the load change rate ΔLa on the bearing stability and motion accuracy;

[0064] The load compensation amount Ula is obtained through the following formula:

[0065] ;

[0066] Where, represents the influence coefficient of the load change on the compensation amount;

[0067] The temperature compensation amount Uta and the load compensation amount Ula are combined to obtain the final compensation amount Ux(t);

[0068] The final compensation amount Ux(t) is obtained through the following formula:

[0069] ;

[0070] In the formula, k1 and k2 represent adjustment coefficients.

[0071] Preferably, the compensation adjustment and motion control module includes a motion trajectory correction unit and a speed and acceleration adjustment unit;

[0072] The motion trajectory correction unit adjusts and corrects the motion trajectory of the bearing in real time according to the obtained final compensation amount Ux(t), corrects the deviation on the trajectory through the compensation amount, so that the device can accurately operate along the predetermined path and obtain the position Xz of the bearing;

[0073] Through coordination with the motion paths of other axes in the system, the adjustment of the correction path is based on the current actual deviation of each axis for feedback adjustment; after each correction, the new path will be fed back to the next control cycle. If the system path correction effect is not ideal, the final compensation amount Ux(t) will be adjusted again to ensure that the final path is as accurate as possible; the control system adjusts the compensation strategy according to the feedback to ensure the continuity and stability of the path correction process;

[0074] The position Xz of the bearing is obtained through the following formula:

[0075] ;

[0076] In the formula, Xz(t) represents the position of the bearing at time t, and Xz(t - 1) represents the position of the bearing at time t - 1;

[0077] The speed and acceleration adjustment unit adjusts the bearing speed Vx and the bearing acceleration ax in real time according to the corrected position Xz of the bearing;

[0078] The bearing speed Vx is obtained through the following formula:

[0079] ;

[0080] In the formula, Vx(t) represents the bearing speed at time t;

[0081] The bearing acceleration ax is obtained through the following formula:

[0082] ;

[0083] In the formula, ax(t) represents the bearing acceleration at time t;

[0084] According to the real-time feedback of the bearing speed Vx and the bearing acceleration ax, dynamically adjust the compensation strategy, regulate the changes in the bearing speed Vx and the bearing acceleration ax, and maintain the normal state of the bearing within a fixed period.

[0085] Preferably, the feedback and state update module includes a device state collection and analysis unit and a compensation strategy adjustment and update unit;

[0086] The device state collection and analysis unit collects the feedback data of the bearing in real time through sensors, including the new motion deviation nPx and the new bearing acceleration nax, processes and evaluates the state of the collected new motion deviation nPx and new bearing acceleration nax, analyzes the deviation between the motion accuracy and acceleration of the bearing and the target position, obtains the state error index ESt, and compares it with the preset error threshold Tes to determine the compensation state of the bearing;

[0087] The state error index ESt is obtained through the following formula:

[0088] ;

[0089] The compensation state of the bearing is obtained by matching in the following way:

[0090] When the state error index ESt ≤ the error threshold Tes, it means that the bearing state is normal and no compensation is required;

[0091] When the state error index ESt > the error threshold Tes, it means that the bearing state is abnormal and compensation is required.

[0092] Preferably, the compensation strategy adjustment and update unit readjusts the compensation strategy according to the obtained state error index ESt; by dynamically adjusting the compensation amount, ensure that the device always maintains the best accuracy and stability throughout the operation process;

[0093] When the state error index ESt > the error threshold Tes, based on the evaluation result of the bearing state, the state error index ESt, re-evaluate the final compensation amount Ux(t) and make corresponding adjustments to obtain the new compensation amount nUx;

[0094] The new compensation amount nUx is obtained through the following formula:

[0095] ;

[0096] In the formula, Kn represents the adjustment factor of the compensation amount.

[0097] Preferably, the performance evaluation and optimization module evaluates the overall performance of the system based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, obtains the system performance score PF, and compares it with the preset performance score threshold Tpf to determine the effectiveness of the current control strategy;

[0098] The system performance score PF is obtained through the following formula:

[0099] ;

[0100] In the formula, Tp represents the evaluation period, respectively represent the preset weight values of the error value Ex, the new compensation amount nUx, and the error change trend Etr, and ;

[0101] The effectiveness of the control strategy is obtained through the following matching method:

[0102] When the system performance score PF ≥ the performance score threshold Tpf, it means that the control strategy has met the requirements and no adjustment is needed;

[0103] When the system performance score PF < the performance score threshold Tpf, it means that the control strategy does not meet the requirements, and the control strategy needs to be adjusted to adjust the new compensation amount nUx to obtain the adjusted compensation amount TnUx;

[0104] The adjusted compensation amount TnUx is obtained through the following formula:

[0105] ;

[0106] In the formula, C represents the compensation factor, which controls the adjustment amplitude of the compensation amount.

[0107] According to the adjusted control strategy, the system will update the compensation amount and enter the next control cycle; this enables the system to continuously adjust and optimize the control strategy according to the real-time evaluation results.

[0108] A multi-axis motion control method for automated equipment includes the following steps:

[0109] Step 1: The data acquisition and processing module collects data in the multi-axis control system through sensors, fits it into the original data group AW, and performs preprocessing to obtain the multi-axis data group DW;

[0110] Step 2: The error detection and analysis module performs real-time error detection based on the obtained multi-axis data group DW to obtain the error value Ex and the error change trend Etr;

[0111] Step 3: The adaptive controller design module designs an adaptive controller to generate a compensation strategy based on the obtained error value Ex and error change trend Etr, obtains the response compensation amount Udy, and calculates and obtains the final compensation amount Ux(t);

[0112] Step 4: The compensation adjustment and motion control module adjusts the motion trajectories, bearing speeds Vx, and bearing accelerations ax of each bearing according to the obtained final compensation amount Ux(t), performs real-time compensation, and feeds it back into the compensation strategy;

[0113] Step 5: After the real-time compensation is completed, the feedback and status update module collects the device feedback data and readjusts the compensation strategy to obtain the new compensation amount nUx;

[0114] Step 6: The performance evaluation and optimization module evaluates the overall performance of the system based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, obtains the system performance score PF, and adjusts the control strategy to obtain the adjusted compensation amount TnUx.

[0115] The present invention provides a multi-axis motion control method and system for automated equipment, having the following beneficial effects:

[0116] (1) During system operation, by collecting device data in real time and based on real-time errors and device states, an adaptive compensation strategy is designed and adjusted. This adaptive ability enables the system to flexibly cope with factors such as equipment aging and external environment changes, ensuring high control accuracy and stability under different working conditions. Through the cooperation of the data acquisition and processing module and the error detection and analysis module, the system can monitor and analyze the motion state of the device in real time, accurately obtain the motion error value and change trend. This provides an accurate basis for subsequent compensation adjustment, enabling the compensation amount to be dynamically optimized in each control cycle, avoiding error accumulation, and improving system accuracy and stability.

[0117] The adaptive controller design module generates a compensation strategy based on real-time errors and change trends, and incorporates temperature changes and load changes into the compensation adjustment process. This compensation adjustment method based on dynamic changes can better cope with the influence of external factors such as equipment aging and temperature fluctuations on system accuracy, effectively reduce errors, and improve production efficiency and product quality.

[0118] (2) The data preprocessing unit cleans and standardizes the original data set AW, removing noise and interference and making the data of different dimensions comparable. Through filtering and outlier detection, the system can ensure the high quality and accuracy of the data, reducing errors caused by noise or outliers. The standardization process enables sensor data from different sources to be unified in scale, thus improving the precision and effectiveness of subsequent analysis and control strategy design. In a multi-axis motion control system, accurate error detection is the key to ensuring the high-precision operation of the equipment. By cleaning and standardizing the multi-axis data set DW, the system can more accurately identify and evaluate the sources of errors, thus designing a more refined compensation strategy.

[0119] (3) The combination of the error response design unit and the dynamic compensation adjustment unit enables the control system to adjust the compensation strategy based on the error change trend. Within a fixed cycle, when the error value continues to increase, the controller will enhance the response to eliminate the deviation; when the error is stable, the system will reduce the response to avoid overcompensation. This trend-based dynamic compensation method can optimize the control process, avoid overreaction and system oscillation, thus improving the stability and response efficiency of the control system.

[0120] It not only considers the error value and change trend, but also incorporates the temperature change rate ΔTa and the load change rate ΔLa into the compensation adjustment process. This enables the compensation strategy to be adjusted in real time according to the environmental changes during the operation of the equipment. For example, temperature fluctuations may affect the motion performance of the equipment, while load changes will affect the motion accuracy and stability. In this way, the system can more precisely adapt to the changes in the working environment, ensuring high-precision control of the equipment under different working conditions.

[0121] (4) The motion trajectory correction unit adjusts the motion trajectory of the bearing in real time according to the compensation amount Ux(t). This trajectory correction based on the real-time compensation amount ensures that the equipment can always move precisely along the predetermined path during operation, avoiding trajectory deviation caused by external environmental changes or equipment state fluctuations. Compared with the traditional control system that relies on fixed trajectory planning, this adaptive correction method can better handle the dynamic changes of the equipment, improving the accuracy and flexibility of the system. The speed and acceleration adjustment unit dynamically adjusts the speed and acceleration of the bearing according to the corrected bearing position Xz. This enables the system to optimize the operating state of the bearing based on real-time feedback, avoiding the reduction in accuracy caused by unstable motion or uneven speed in the traditional control system. Through continuous speed and acceleration adjustment, the system can maintain the normal state of the bearing within a fixed cycle, ensuring the efficient operation and precision control of the equipment. Description of the Drawings

[0122] Figure 1 It is a schematic block diagram flow chart of a multi-axis motion control system for an automated device according to the present invention;

[0123] Figure 2 Schematic diagram of the steps of a multi-axis motion control method for an automated device according to the present invention;

[0124] Figure 3 System block diagram flow schematic diagram for compensation acquisition according to the present invention;

[0125] Figure 4 Line graph for error value acquisition according to the present invention;

[0126] Figure 5 Bar graph of the final compensation amount according to the present invention. Specific implementation manners

[0127] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0128] Embodiment 1

[0129] The present invention provides a multi-axis motion control system for an automated device. Please refer to Figures 1 to 5 , including a data acquisition and processing module, an error detection and analysis module, an adaptive controller design module, a compensation adjustment and motion control module, a feedback and status update module, and a performance evaluation and optimization module;

[0130] The data acquisition and processing module collects data in the multi-axis control system through sensors, fits it into the original data group AW, and performs preprocessing to obtain the multi-axis data group DW;

[0131] The error detection and analysis module performs real-time error detection based on the obtained multi-axis data group DW to obtain the error value Ex and the error change trend Etr;

[0132] The adaptive controller design module designs an adaptive controller to generate a compensation strategy according to the obtained error value Ex and the error change trend Etr, obtains the response compensation amount Udy, and calculates and obtains the final compensation amount Ux(t);

[0133] The compensation adjustment and motion control module adjusts the motion trajectory, bearing speed Vx, and bearing acceleration ax of each bearing according to the obtained final compensation amount Ux(t), performs real-time compensation, and feeds it back into the compensation strategy;

[0134] The feedback and status update module collects device feedback data after the real-time compensation is completed, and readjusts the compensation strategy to obtain the new compensation amount nUx;

[0135] Based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, the performance evaluation and optimization module evaluates the overall performance of the system, obtains the system performance score PF, and adjusts the control strategy to obtain the adjusted compensation amount TnUx.

[0136] In this embodiment, by collecting multi-axis data of the device in real time and based on real-time errors and device status, an adaptive compensation strategy is designed and adjusted. This adaptive ability enables the system to flexibly cope with factors such as device aging and external environment changes, ensuring high control accuracy and stability under different working conditions. Through the cooperation of the data acquisition and processing module and the error detection and analysis module, the system can monitor and analyze the motion state of the device in real time, accurately obtain the motion error value and change trend. This provides an accurate basis for subsequent compensation adjustment, enabling the compensation amount to be dynamically optimized in each control cycle, avoiding error accumulation, and improving system accuracy and stability.

[0137] The adaptive controller design module generates a compensation strategy according to real-time errors and change trends, and incorporates temperature changes and load changes into the compensation adjustment process. This compensation adjustment method based on dynamic changes can better cope with the impact of external factors such as device aging and temperature fluctuations on system accuracy, effectively reduce errors, and improve production efficiency and product quality.

[0138] The feedback and status update module can collect the feedback data of the device after compensation and readjust the compensation strategy according to real-time feedback. This real-time feedback mechanism enables the system to continuously self-optimize throughout the operation process, timely correct possible control deviations, thus avoiding the accuracy decline caused by inflexible parameters in traditional systems. The performance evaluation and optimization module can comprehensively consider the error value, the new compensation amount, and the error change trend, evaluate the overall performance of the system, and optimize the control strategy. By real-time evaluating and adjusting the control strategy, the system can continuously maintain the best performance, thus avoiding the performance decline caused by the control strategy not adapting to system changes.

[0139] Embodiment 2

[0140] This embodiment is an explanatory description based on Embodiment 1, please refer to Figure 1 , specifically: The data acquisition and processing module includes a data acquisition unit and a data preprocessing unit;

[0141] The data acquisition unit collects data of the bearing through sensors, including bearing temperature Ta, bearing acceleration ax, bearing load La, and motion deviation Px, and fits them into the original data group AW;

[0142] Among them, the bearing temperature Ta is acquired by collecting through a temperature sensor, and the temperature sensor can detect the temperature change caused by friction and load factors during the operation of the equipment;

[0143] The bearing acceleration ax is acquired by collecting through an acceleration sensor;

[0144] The bearing load La is acquired by collecting through a load sensor, and the bearing load La reflects the working state of each shaft;

[0145] The motion deviation Px is acquired by collecting through a position sensor, which reflects the difference between the actual position Xact and the target position Xtar of the bearing;

[0146] The motion deviation Px is obtained through the following formula:

[0147] ;

[0148] The data preprocessing unit cleans and standardizes the acquired original data group AW to obtain the multi-axis data group DW;

[0149] Cleaning includes filtering processing and outlier processing. Filtering processing removes noise and interference in the original data group AW by using a filter; outlier processing eliminates outliers in the original data group AW by using an outlier detection algorithm;

[0150] The standardization processing uses a standardization method to convert the data into a unified scale to obtain the multi-axis data group DW;

[0151] The multi-axis data group DW is obtained through the following formula:

[0152] ;

[0153] In the formula, DWb represents the b-th item of data in the multi-axis data group DW, AWb represents the b-th item of data in the original data group AW, μAWb represents the mean of the b-th item of data in the original data group AW, and σAWb represents the standard deviation of the b-th item of data in the original data group AW.

[0154] In this embodiment, multiple sensors are integrated to collect data in real time. This comprehensive real-time data collection ability enables the control system to understand the operating state of the equipment in detail and promptly detect and respond to any abnormalities during equipment operation. Compared with traditional control systems, it can provide more accurate monitoring and control, enhancing the reliability of the system. The data collection unit generates the original data group AW by integrating the data of different sensors, and these data cover multiple aspects of equipment operation. By fusing and analyzing data in multiple dimensions, the system can more comprehensively and accurately identify the state and potential problems of the equipment.

[0155] The data preprocessing unit cleans and standardizes the original data set AW, removing noise and interference and making the data in different dimensions comparable. Through filtering and outlier detection, the system can ensure the high quality and accuracy of the data, reducing errors caused by noise or outliers. The standardization process enables sensor data from different sources to be unified in scale, thereby improving the accuracy and effectiveness of subsequent analysis and control strategy design.

[0156] In a multi-axis motion control system, accurate error detection is the key to ensuring high-precision operation of the device. By cleaning and standardizing the multi-axis data set DW, the system can more accurately identify and evaluate the sources of errors, thereby designing a more refined compensation strategy. This enables the system to dynamically adjust the compensation strategy and compensate in real time for errors caused by equipment aging, external environment, etc., improving the control accuracy. By optimizing the data acquisition and processing process, the system can process a large amount of real-time data more efficiently, reducing control errors caused by low data quality. This not only improves the reliability of the system but also reduces production downtime due to equipment errors and inaccurate control, thereby enhancing production efficiency.

[0157] Embodiment 3

[0158] This embodiment is an explanatory note based on Embodiment 2. Please refer to Figure 3 and Figure 4 , specifically: The error detection and analysis module includes a data feature extraction unit and an error acquisition unit:

[0159] The data feature extraction unit extracts features from the multi-axis data set DW, extracting features that affect motion accuracy and control effect, including the temperature change rate ΔTa, the load change rate ΔLa, and the motion deviation change rate ΔPx, and fitting them into a change feature set LP;

[0160] The temperature change rate ΔTa is obtained through the following formula:

[0161] ;

[0162] In the formula, Ta(t) represents the bearing temperature at time t, Ta(t - 1) represents the bearing temperature at time t - 1, and Δt represents the time interval;

[0163] The load change rate ΔLa is obtained through the following formula:

[0164] ;

[0165] In the formula, La(t) represents the bearing load at time t, and La(t - 1) represents the bearing load at time t - 1;

[0166] The motion deviation change rate ΔPx is obtained through the following formula:

[0167] ;

[0168] Wherein, Px(t) represents the motion deviation at time t, and Px(t - 1) represents the motion deviation at time t - 1;

[0169] The error acquisition unit calculates and obtains the error value Ex according to the obtained temperature change rate ΔTa, load change rate ΔLa, and motion deviation change rate ΔPx, and dynamically analyzes the error value Ex, smooths the error curve through time series analysis, and identifies the error change trend Etr;

[0170] The error value Ex is obtained through the following formula, as shown in Table 1 specifically:

[0171] ;

[0172] ;

[0173] Wherein, respectively represent the preset weight values of the temperature change rate ΔTa, load change rate ΔLa, and motion deviation change rate ΔPx, and , fin represents the interaction term function between features, represents the interaction term coefficient between the temperature change rate ΔTa and the load change rate ΔLa, represents the interaction term coefficient between the load change rate ΔLa and the motion deviation change rate ΔPx, represents the interaction term coefficient between the temperature change rate ΔTa and the motion deviation change rate ΔPx;

[0174] Specific example:

[0175] Set to be 0.4, 0.35, and 0.25 respectively;

[0176] Obtain the temperature change rate ΔTa = 0.20;

[0177] The load change rate ΔLa = 0.50;

[0178] The motion deviation change rate ΔPx = 0.05;

[0179] fin(LP) = 0.038;

[0180] Calculate and obtain the error value Ex;

[0181] ;

[0182] Table 1 Error value calculation and acquisition table;

[0183]

[0184] The error change trend Etr is obtained through the following formula:

[0185] ;

[0186] In the formula, Ex(t - i) represents the error value at time t - i, n represents the time window size for calculating the error trend, and i represents the index variable used to traverse the past n time points.

[0187] In this embodiment, the data feature extraction unit can comprehensively analyze the factors affecting the motion accuracy and control effect of the device by extracting multiple features. By extracting and fitting these changing features, the system can accurately identify and evaluate the changes in the device state, thereby achieving effective positioning of the error source. This comprehensive feature extraction enhances the system's response ability to complex factors and can precisely capture the subtle changes of the device in the dynamic working state.

[0188] By calculating the temperature change rate, load change rate, and motion deviation change rate through the error acquisition unit, the motion error of the device can be evaluated in real time. These calculation results are combined with the dynamic analysis method to smooth the error curve, and then the change trend of the error is identified. This dynamic analysis method can help the system timely identify long-term existing errors and sudden error changes, so as to take appropriate compensation measures.

[0189] In this embodiment, the error value is calculated by assigning weights to different features and combining their interaction effects. This multi-feature joint optimization method can comprehensively consider the influence of multiple factors on the motion accuracy and control effect. Compared with the error detection of a single feature, it can achieve more accurate error identification and correction. By assigning appropriate weights to each feature, the system can more reasonably adjust the compensation strategy according to the actual operating conditions and optimize the overall performance of the device. The calculation of the error change trend Etr provides a long-term error evaluation and prediction mechanism for the system. Through the smoothing process of historical error data, the system can accurately identify the long-term trend of the error, which helps to avoid unnecessary control adjustments caused by short-term error fluctuations. The identification of the error trend enables the compensation strategy to be adjusted more targeted, avoiding overcompensation or lag response, and ensuring that the device always operates at the best state.

[0190] By integrating multi-dimensional real-time data acquisition, feature extraction, error detection, and dynamic analysis, this embodiment can effectively respond to the changes of the device in different working environments and improve the robustness of the system; through more accurate error detection and dynamic compensation strategies, the system can adjust the motion trajectory and control parameters in real time, reducing the error accumulation caused by device aging or external environment changes. Precise control not only improves the motion accuracy of the device but also reduces the downtime and production of unqualified products caused by errors, thereby improving production efficiency and product quality.

[0191] Example 4

[0192] This example is an explanation carried out in Example 3. Please refer to Figure 3 and Figure 5 , specifically: The adaptive controller design module includes an error response design unit and a dynamic compensation adjustment unit;

[0193] Based on the obtained error value Ex and error change trend Etr, the error response design unit combines with PID control to design an error response function and obtain an error compensation amount Uex(t);

[0194] The error compensation amount Uex(t) is obtained through the following formula:

[0195] ;

[0196] In the formula, Ex represents the error value at time t, Kp represents the proportional gain, Ki represents the integral gain, Kd represents the derivative gain, dt represents the integral, represents the change rate of the error at time t;

[0197] The obtained error compensation amount Uex(t) is combined with the error change trend Etr, and the error change trend Etr is used as a dynamic weight adjustment compensation strategy to obtain a response compensation amount Udy;

[0198] Among them, the error change trend Etr can affect the amplitude of the compensation response;

[0199] When the error value Ex continuously increases within a fixed period, the controller needs to increase the response;

[0200] When the error value Ex remains stable within a fixed period, the controller reduces the response;

[0201] The response compensation amount Udy is obtained through the following formula:

[0202] ;

[0203] In the formula, fq represents the trend change function.

[0204] The dynamic compensation adjustment unit combines the obtained response compensation amount Udy with the temperature change rate ΔTa and the load change rate ΔLa to obtain the final compensation amount Ux(t);

[0205] Through the influence of the temperature change rate ΔTa on the bearing motion performance, the temperature compensation amount Uta is obtained;

[0206] The temperature compensation amount Uta is obtained through the following formula:

[0207] ;

[0208] In the formula, represents the sensitivity coefficient of temperature change to the compensation amount;

[0209] Based on the influence of the load change rate ΔLa on the bearing stability and motion accuracy, the load compensation amount Ula is obtained;

[0210] The load compensation amount Ula is obtained through the following formula:

[0211] ;

[0212] In the formula, represents the influence coefficient of load change on the compensation amount;

[0213] The temperature compensation amount Uta and the load compensation amount Ula are combined to obtain the final compensation amount Ux(t);

[0214] The final compensation amount Ux(t) is obtained through the following formula:

[0215] ;

[0216] In the formula, k1 and k2 represent adjustment coefficients.

[0217] Table 2 Final compensation calculation table:

[0218]

[0219] In this embodiment, an adaptive control algorithm based on the error value Ex and the error change trend Etr is introduced, and an error response function is designed by combining PID control. Compared with the traditional fixed compensation method, this adaptive response mechanism can adjust the compensation amount in real time and compensate flexibly according to the actual state of the device. This dynamic adjustment based on the error change trend improves the flexibility of the system, enabling it to respond promptly to the increase or decrease of errors and maintaining the stability and accuracy of the system.

[0220] The combination of the error response design unit and the dynamic compensation adjustment unit enables the control system to adjust the compensation strategy based on the error change trend. Within a fixed period, when the error value continuously increases, the controller will enhance the response to eliminate the deviation; when the error is stable, the system will reduce the response to avoid overcompensation. This dynamic compensation method based on the trend can optimize the control process, avoid overreaction and system oscillation, thereby improving the smoothness and response efficiency of the control system.

[0221] It not only considers the error value and the change trend, but also incorporates the temperature change rate ΔTa and the load change rate ΔLa into the compensation adjustment process. This enables the compensation strategy to be adjusted in real time according to the environmental changes during the operation of the device. For example, temperature fluctuations may affect the motion performance of the device, while load changes will affect the motion accuracy and stability. In this way, the system can more precisely adapt to the changes in the working environment and ensure that the device can maintain high-precision control under different working conditions.

[0222] The dynamic adjustment of the temperature compensation amount Uta and the load compensation amount Ula ensures that the control strategy can adapt to the operating state of the device in real time. By adjusting the sensitivity and influence coefficients of temperature changes and load changes, the system can effectively cope with the impact of external environmental fluctuations on the motion accuracy of the device. This mechanism improves the adaptability of the system to dynamic environmental changes, reduces the errors caused by external factors, and thus improves the stability of the system.

[0223] The final compensation amount Ux(t) combines factors such as error response, temperature change, and load change, ensuring that the compensation amount within each control cycle can be dynamically adjusted to minimize the motion error and optimize the control accuracy. This flexible and intelligent compensation strategy effectively avoids the problem of reduced control accuracy caused by fixed compensation in traditional methods and enhances the stability of the system during long-term operation.

[0224] Embodiment 5

[0225] This embodiment is an explanatory description based on Embodiment 4. Please refer to Figure 1 , specifically: The compensation adjustment and motion control module includes a motion trajectory correction unit and a speed and acceleration adjustment unit;

[0226] The motion trajectory correction unit adjusts and corrects the motion trajectory of the bearing in real time according to the obtained final compensation amount Ux(t) to obtain the position Xz of the bearing;

[0227] The position Xz of the bearing is obtained through the following formula:

[0228] ;

[0229] In the formula, Xz(t) represents the position of the bearing at time t, and Xz(t - 1) represents the position of the bearing at time t - 1;

[0230] The speed and acceleration adjustment unit adjusts the bearing speed Vx and the bearing acceleration ax in real time according to the corrected position Xz of the bearing;

[0231] The bearing speed Vx is obtained through the following formula:

[0232] ;

[0233] Where, Vx(t) represents the bearing speed at time t;

[0234] The bearing acceleration ax is obtained by the following formula:

[0235] ;

[0236] Where, ax(t) represents the bearing acceleration at time t;

[0237] According to the bearing speed Vx and bearing acceleration ax of real-time feedback, dynamically adjust the compensation strategy, regulate the changes of the bearing speed Vx and bearing acceleration ax, and maintain the normal state of the bearing within a fixed period.

[0238] The feedback and state update module includes a device state collection and analysis unit and a compensation strategy adjustment and update unit;

[0239] The device state collection and analysis unit collects the feedback data of the bearing in real time through sensors, including the new motion deviation nPx and the new bearing acceleration nax, processes and evaluates the state of the collected new motion deviation nPx and new bearing acceleration nax, analyzes the deviation between the motion accuracy and acceleration of the bearing and the target position, obtains the state error index ESt, and compares it with the preset error threshold Tes to judge the compensation state of the bearing;

[0240] The state error index ESt is obtained by the following formula:

[0241] ;

[0242] The compensation state of the bearing is obtained by matching in the following way:

[0243] When the state error index ESt ≤ the error threshold Tes, it means that the bearing state is normal and no compensation is required;

[0244] When the state error index ESt > the error threshold Tes, it means that the bearing state is abnormal and compensation is required.

[0245] In this embodiment, the motion trajectory correction unit adjusts the motion trajectory of the bearing in real time according to the compensation amount Ux(t). This trajectory correction based on real-time compensation ensures that the device can always move precisely along the predetermined path during operation, avoiding trajectory deviations caused by changes in the external environment or fluctuations in the device state. Compared with the traditional control system that relies on fixed trajectory planning, this adaptive correction method can better cope with the dynamic changes of the device, improving the accuracy and flexibility of the system. The speed and acceleration adjustment unit dynamically adjusts the speed and acceleration of the bearing according to the corrected bearing position Xz. This enables the system to optimize the operating state of the bearing based on real-time feedback, avoiding the reduction in accuracy caused by unstable motion or uneven speed in the traditional control system. Through continuous speed and acceleration adjustment, the system can maintain the normal state of the bearing within a fixed cycle, ensuring the efficient operation and precision control of the device.

[0246] The dynamic compensation mechanism in this embodiment combines the real-time feedback of the bearing speed Vx and the bearing acceleration ax, continuously optimizing the compensation strategy during the operation of the device. By continuously adjusting the changes in speed and acceleration, the system can compensate according to the actual motion state of the device, maintaining the device in the optimal operating state all the time. This flexible feedback mechanism enables the device to respond to environmental changes and unstable factors during operation in real time, avoiding the degradation of system performance due to device aging or external changes. The device state collection and analysis unit evaluates the motion accuracy of the device by collecting the motion deviation and acceleration data of the bearing in real time and calculating the state error index ESt. When the state error index ESt exceeds the preset error threshold, the system can automatically identify device abnormalities and perform necessary compensations. This accurate state evaluation and judgment mechanism ensures that the device can be adjusted in time when the state deviation is large, reducing unnecessary compensations or overlooking potential control problems, and improving the response speed and stability of the system.

[0247] By comparing the state error index ESt with the error threshold Tes, the system can make a timely response when the bearing state is abnormal, thus avoiding the further accumulation of system errors or device damage. This fault warning and recovery ability can reduce the downtime and production losses caused by device deviations, improving the reliability and production efficiency of the device. The automated compensation mechanism enables the device to self-adjust when deviations occur, ensuring continuous high-precision operation.

[0248] Embodiment 6

[0249] This embodiment is an explanatory note based on Embodiment 5. Please refer to Figure 1 , specifically: The compensation strategy adjustment and update unit readjusts the compensation strategy according to the obtained state error index ESt;

[0250] When the state error index ESt > the error threshold Tes, based on the evaluation result of the bearing state, the state error index ESt, re-evaluate the final compensation amount Ux(t), and make corresponding adjustments to obtain the new compensation amount nUx;

[0251] The new compensation amount nUx is obtained through the following formula:

[0252] ;

[0253] In the formula, Kn represents the adjustment factor of the compensation amount.

[0254] The performance evaluation and optimization module evaluates the overall performance of the system based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, obtains the system performance score PF, and compares it with the preset performance score threshold Tpf to judge the effectiveness of the current control strategy;

[0255] The system performance score PF is obtained through the following formula:

[0256] ;

[0257] In the formula, Tp represents the evaluation period, respectively represent the preset weight values of the error value Ex, the new compensation amount nUx, and the error change trend Etr, and ;

[0258] The effectiveness of the control strategy is obtained through the following matching method:

[0259] When the system performance score PF ≥ the performance score threshold Tpf, it means that the control strategy has met the requirements and no adjustment is needed;

[0260] When the system performance score PF < the performance score threshold Tpf, it means that the control strategy does not meet the requirements, and the control strategy needs to be adjusted to adjust the new compensation amount nUx to obtain the adjusted compensation amount TnUx;

[0261] The adjusted compensation amount TnUx is obtained through the following formula:

[0262] ;

[0263] In the formula, C represents the compensation factor.

[0264] In this embodiment, the compensation strategy is dynamically adjusted through the state error index ESt. When the state error index ESt exceeds the preset threshold Tes, the system will re-evaluate and adjust the final compensation amount Ux(t) based on the real-time state of the device. This mechanism enables the system to adjust the compensation amount in a timely manner according to the current operating state of the device, so as to respond quickly when the device has an abnormality, improving the adaptive ability and accuracy of the system.

[0265] The introduction of the system performance score PF makes the optimization of the compensation strategy more intelligent. By comprehensively evaluating the error value Ex, the new compensation amount nUx, and the error change trend Etr, the system can quantitatively judge the effectiveness of the current control strategy. If the current performance score is lower than the preset threshold, the system will automatically optimize the control strategy and adjust the compensation amount nUx to improve the overall performance of the system. This adaptive adjustment process avoids the problems of inaccurate control or low efficiency that may be caused by static adjustment in traditional systems. The performance evaluation and optimization module evaluates the effectiveness of the control strategy based on real-time data and makes optimization adjustments as needed. This real-time feedback and adjustment mechanism ensures that the system can maintain the best state at any time. Regardless of how the device state changes, the system can adjust the compensation strategy according to the performance score to ensure that the device operates efficiently and precisely in a complex environment.

[0266] In this embodiment, by setting the threshold for control strategy adjustment, the performance score threshold Tpf, to judge the effectiveness of the control strategy, overcompensation or overly drastic control adjustments are avoided. The system will dynamically adjust the compensation strategy according to the change of the error score to ensure that unnecessary overreactions will not occur during the control process, thereby reducing the occurrence of unstable situations. This not only improves the stability of the system but also enhances the control precision. By optimizing the control strategy and dynamically adjusting the compensation amount, the system can maintain high efficiency and reliability under different operating conditions. For example, when the device shows a deviation, the compensation strategy can correct it in a timely manner, reducing the risk of error accumulation and avoiding the occurrence of device failures. The continuous optimization and adjustment of the compensation strategy improve the overall operating efficiency of the device and ensure stability during long-term operation.

[0267] Embodiment 7

[0268] A multi-axis motion control method for an automated device, please refer to Figure 2 , specifically: including the following steps:

[0269] Step 1: The data acquisition and processing module collects data in the multi-axis control system through sensors, fits it into the original data group AW, and performs preprocessing to obtain the multi-axis data group DW;

[0270] Step 2: The error detection and analysis module performs real-time error detection based on the obtained multi-axis data group DW to obtain the error value Ex and the error change trend Etr;

[0271] Step 3: The adaptive controller design module designs an adaptive controller to generate a compensation strategy according to the obtained error value Ex and the error change trend Etr, obtains the response compensation amount Udy, and calculates and obtains the final compensation amount Ux(t);

[0272] Step 4: The compensation adjustment and motion control module adjusts the motion trajectories, bearing speeds Vx, and bearing accelerations ax of each bearing according to the obtained final compensation amount Ux(t), performs real-time compensation, and feeds it back into the compensation strategy;

[0273] Step 5: After the real-time compensation is completed, the feedback and status update module collects the device feedback data and readjusts the compensation strategy to obtain a new compensation amount nUx;

[0274] Step 6: The performance evaluation and optimization module evaluates the overall performance of the system based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, obtains the system performance score PF, and adjusts the control strategy to obtain the adjusted compensation amount TnUx.

[0275] In this embodiment, through the data acquisition and processing module, various types of data in the multi-axis control system are collected in real time using sensors, and are converted into a multi-axis data set DW through preprocessing and standardization, ensuring that the data used by the system has high quality, accuracy, and timeliness. By removing noise, outliers, and standardizing the data, the system can reduce data interference, improve the reliability of subsequent analysis and control, and thus ensure the accuracy and effectiveness of the compensation strategy.

[0276] The error detection and analysis module can analyze the error value Ex and the error change trend Etr in the motion system based on real-time data and perform dynamic detection. This method can accurately evaluate the real-time operating state of the device, timely detect error accumulation or deviation, and ensure that the system can make timely corrections when errors occur. This accurate error analysis greatly improves the stability and control accuracy of the device motion. The compensation adjustment and motion control module adjusts the motion trajectories, speeds, and accelerations of each bearing based on the final compensation amount, enabling the system to respond in real time to changes in the motion state of the device. This adjustment not only reduces errors caused by changes in the device state but also enables the device to maintain the optimal motion trajectory, reducing instability and irregularity during motion, thereby improving the smoothness and accuracy of the system.

[0277] The performance evaluation and optimization module regularly evaluates the overall performance of the system based on the error value, the new compensation amount, and the error change trend, and calculates the system performance score PF. If the system performance score is lower than the preset threshold, the module will automatically adjust the compensation strategy. This performance evaluation and optimization ability based on real-time feedback enables the system to continuously improve its performance during the entire operation process, reduce system errors, and ensure that the device can operate efficiently and stably under any operating conditions.

[0278] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-axis motion control system for an automated device, characterized in that: It includes a data acquisition and processing module, an error detection and analysis module, an adaptive controller design module, a compensation adjustment and motion control module, a feedback and status update module, and a performance evaluation and optimization module; The data acquisition and processing module collects data in the multi-axis control system through sensors, fits it into the original data set AW, and performs preprocessing to obtain the multi-axis data set DW; Based on the obtained multi-axis data set DW, the error detection and analysis module performs real-time error detection to obtain the error value Ex and the error change trend Etr; The error detection and analysis module includes a data feature extraction unit and an error acquisition unit: The data feature extraction unit extracts features from the multi-axis data set DW, extracts the features that affect the motion accuracy and control effect, including the temperature change rate ΔTa, the load change rate ΔLa, and the motion deviation change rate ΔPx, and fits them into the change feature set LP; The temperature change rate ΔTa is obtained through the following formula: ； where Ta(t) represents the bearing temperature at time t, Ta(t - 1) represents the bearing temperature at time t - 1, and Δt represents the time interval; The load change rate ΔLa is obtained through the following formula: ； where La(t) represents the bearing load at time t, La(t - 1) represents the bearing load at time t - 1; The motion deviation change rate ΔPx is obtained through the following formula: ； where Px(t) represents the motion deviation at time t, Px(t - 1) represents the motion deviation at time t - 1; The error acquisition unit calculates the error value Ex based on the obtained temperature change rate ΔTa, load change rate ΔLa, and motion deviation change rate ΔPx, and dynamically analyzes the error value Ex, smooths the error curve through time series analysis, and identifies the error change trend Etr; The error value Ex is obtained through the following formula: ； ； In the formula, respectively represent the preset weight values of the temperature change rate ΔTa, the load change rate ΔLa, and the motion deviation change rate ΔPx, and , fin(LP) represents the interaction term function between the features in the change feature set LP, represents the interaction term coefficient of the temperature change rate ΔTa and the load change rate ΔLa, represents the interaction term coefficient of the load change rate ΔLa and the motion deviation change rate ΔPx, represents the interaction term coefficient of the temperature change rate ΔTa and the motion deviation change rate ΔPx; The error change trend Etr is obtained through the following formula: ； where Ex(t - i) represents the error value at time t - i, n represents the time window size for calculating the error trend, and i represents the index variable used to traverse the past n time points; The adaptive controller design module designs an adaptive controller to generate a compensation strategy based on the obtained error value Ex and error change trend Etr, obtains the response compensation amount Udy, and calculates the final compensation amount Ux(t); The compensation adjustment and motion control module adjusts the motion trajectory, bearing speed Vx, and bearing acceleration ax of each bearing according to the obtained final compensation amount Ux(t), performs real-time compensation, and feeds it back into the compensation strategy; After the real-time compensation is completed, the feedback and status update module collects the device feedback data and re-adjusts the compensation strategy to obtain the new compensation amount nUx; Based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, the performance evaluation and optimization module evaluates the overall performance of the system, obtains the system performance score PF, and adjusts the control strategy to obtain the adjusted compensation amount TnUx.

2. The multi-axis motion control system for an automated device according to claim 1, characterized in that: The data acquisition and processing module includes a data acquisition unit and a data preprocessing unit; The data acquisition unit collects data of the bearing through sensors, including the bearing temperature Ta, bearing acceleration ax, bearing load La, and motion deviation Px, and fits them into the original data set AW; Among them, the bearing temperature Ta is collected through a temperature sensor, which can detect the temperature changes caused by friction and load factors during the operation of the equipment; The bearing acceleration ax is collected through an acceleration sensor; The bearing load La is collected through a load sensor, and the bearing load La reflects the working state of each shaft; The motion deviation Px is collected through a position sensor, which reflects the difference between the actual position Xact and the target position Xtar of the bearing; The motion deviation Px is obtained through the following formula: ； The data preprocessing unit cleans and standardizes the obtained original data set AW to obtain the multi-axis data set DW; The cleaning includes filtering processing and outlier processing. The filtering processing removes noise and interference in the original data set AW by using a filter; the outlier processing eliminates outliers in the original data set AW by using an outlier detection algorithm; The standardization processing uses a standardization method to convert the data into a unified scale to obtain the multi-axis data set DW; The multi-axis data set DW is obtained through the following formula: ； In the formula, DWb represents the b-th item of data in the multi-axis data set DW, AWb represents the b-th item of data in the original data set AW, μAWb represents the mean value of the b-th item of data in the original data set AW, and σAWb represents the standard deviation of the b-th item of data in the original data set AW.

3. The multi-axis motion control system for an automated device according to claim 1, wherein: The adaptive controller design module includes an error response design unit and a dynamic compensation adjustment unit; The error response design unit combines with PID control based on the obtained error value Ex and error change trend Etr, designs an error response function, and obtains the error compensation amount Uex(t); The error compensation amount Uex(t) is obtained through the following formula: ； Where, Ex(t) represents the error value at time t, Kp represents the proportional gain, Ki represents the integral gain, Kd represents the derivative gain, dt represents the integral, represents the rate of change of the error at time t; The obtained error compensation amount Uex(t) is combined with the error change trend Etr, and the error change trend Etr is used as a dynamic weight adjustment compensation strategy to obtain the response compensation amount Udy; Among them, the error change trend Etr can affect the amplitude of the compensation response; When the error value Ex continuously increases within a fixed period, the controller needs to increase the response; When the error value Ex remains stable within a fixed period, the controller reduces the response; The response compensation amount Udy is obtained through the following formula: ； In the formula, fq represents the trend change function.

4. A multi-axis motion control system for an automated device according to claim 3, characterized in that: The dynamic compensation adjustment unit combines the obtained response compensation amount Udy with the temperature change rate ΔTa and the load change rate ΔLa to obtain the final compensation amount Ux(t); The temperature compensation amount Uta is obtained through the influence of the temperature change rate ΔTa on the bearing motion performance; The temperature compensation amount Uta is obtained through the following formula: ； In the formula, represents the sensitivity coefficient of the temperature change to the compensation amount; The load compensation amount Ula is obtained through the influence of the load change rate ΔLa on the bearing stability and motion accuracy; The load compensation amount Ula is obtained through the following formula: ； In the formula, represents the influence coefficient of load change on the compensation amount; The temperature compensation amount Uta and the load compensation amount Ula are combined to obtain the final compensation amount Ux(t); The final compensation amount Ux(t) is obtained through the following formula: ； Wherein, k1 and k2 represent adjustment coefficients.

5. A multi-axis motion control system for an automated device according to claim 4, characterized in that: The compensation adjustment and motion control module includes a motion trajectory correction unit and a speed and acceleration adjustment unit; The motion trajectory correction unit adjusts and corrects the motion trajectory of the bearing in real time according to the obtained final compensation amount Ux(t), and obtains the position Xz of the bearing; The position Xz of the bearing is obtained by the following formula: ； Wherein, Xz(t) represents the position of the bearing at time t, and Xz(t - 1) represents the position of the bearing at time t - 1; The speed and acceleration adjustment unit adjusts the bearing speed Vx and the bearing acceleration ax in real time according to the corrected position Xz of the bearing; The bearing speed Vx is obtained by the following formula: ； Wherein, Vx(t) represents the bearing speed at time t; The bearing acceleration ax is obtained by the following formula: ； Wherein, ax(t) represents the bearing acceleration at time t; According to the real-time feedback of the bearing speed Vx and the bearing acceleration ax, the compensation strategy is dynamically adjusted to regulate the changes of the bearing speed Vx and the bearing acceleration ax, and maintain the normal state of the bearing within a fixed period.

6. The multi-axis motion control system for an automated device according to claim 1, wherein: The feedback and state update module includes a device state collection and analysis unit and a compensation strategy adjustment and update unit; The device state collection and analysis unit collects the feedback data of the bearing in real time through sensors, including the new motion deviation nPx and the new bearing acceleration nax, processes and evaluates the state of the collected new motion deviation nPx and the new bearing acceleration nax, analyzes the motion accuracy of the bearing and the deviation between the acceleration and the target position, obtains the state error index ESt, and compares it with the preset error threshold Tes to judge the compensation state of the bearing; The state error index ESt is obtained by the following formula: ； The compensation state of the bearing is obtained by the following matching method: When the state error index ESt ≤ the error threshold Tes, it means that the bearing state is normal and no compensation is required; When the state error index ESt > the error threshold Tes, it means that the bearing state is abnormal and compensation is required.

7. A multi-axis motion control system for an automated device according to claim 6, characterized in that: The compensation strategy adjustment and update unit readjusts the compensation strategy according to the obtained state error index ESt; When the state error index ESt > the error threshold Tes, based on the evaluation result of the bearing state, the state error index ESt, the final compensation amount Ux(t) is re-evaluated and adjusted accordingly to obtain the new compensation amount nUx; The new compensation amount nUx is obtained by the following formula: ； Wherein, Kn represents the adjustment factor of the compensation amount.

8. The multi-axis motion control system for an automated device according to claim 7, wherein: The performance evaluation and optimization module evaluates the overall performance of the system based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, obtains the system performance score PF, and compares it with the preset performance score threshold Tpf to judge the effectiveness of the current control strategy; The system performance score PF is obtained by the following formula: ； where Tp represents the evaluation period, respectively represent the preset weight values of the error value Ex, the new compensation amount nUx, and the error change trend Etr, and ; The effectiveness of the control strategy is obtained by the following matching method: When the system performance score PF ≥ the performance score threshold Tpf, it means that the control strategy has met the requirements and no adjustment is required; When the system performance score PF < the performance score threshold Tpf, it indicates that the control strategy does not meet the requirements, and the control strategy needs to be adjusted. The new compensation amount nUx is adjusted to obtain the adjusted compensation amount TnUx; The adjusted compensation amount TnUx is obtained through the following formula: ； In the formula, C represents the compensation factor.

9. A multi-axis motion control method for an automated device, applied to a multi-axis motion control system for an automated device according to any one of claims 1 to 8, characterized in that: It includes the following steps: Step 1: The data acquisition and processing module collects data in the multi-axis control system through sensors, fits it into the original data group AW, and performs preprocessing to obtain the multi-axis data group DW; Step 2: The error detection and analysis module performs real-time error detection based on the obtained multi-axis data group DW to obtain the error value Ex and the error change trend Etr; Step 3: The adaptive controller design module designs an adaptive controller to generate a compensation strategy according to the obtained error value Ex and the error change trend Etr, obtains the response compensation amount Udy, and calculates and obtains the final compensation amount Ux(t); Step 4: The compensation adjustment and motion control module adjusts the motion trajectory, bearing speed Vx, and bearing acceleration ax of each bearing according to the obtained final compensation amount Ux(t), performs real-time compensation, and feeds it back to the compensation strategy; Step 5: After the real-time compensation is completed, the feedback and status update module collects the device feedback data and readjusts the compensation strategy to obtain the new compensation amount nUx; Step 6: The performance evaluation and optimization module evaluates the overall performance of the system based on the error value Ex, the new compensation amount nUx, and the error change trend Etr, obtains the system performance score PF, and adjusts the control strategy to obtain the adjusted compensation amount TnUx.

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