Six-degree-of-freedom motion platform servo control system

Abstract

The invention discloses a six-degree-of-freedom motion platform servo control system, and belongs to the technical field of automation control. The six-degree-of-freedom motion platform servo control system comprises a motion planning unit, a self-adaptive adjusting unit and an intelligent monitoring unit. The problems that the control precision is reduced and potential faults are difficult to find and process in time due to an existing fixed parameter control method are solved, a deep learning algorithm and a real-time dynamic model are adopted, the motion trail can be generated and optimized in real time according to task requirements, the control accuracy and efficiency are improved, and the control cost is reduced. The control parameters of the servo motor can be monitored and adjusted in real time to adapt to the change of the state of the motion platform, the stability and the response speed of the system are further improved, the states of the servo motor and the motion platform can be monitored in real time, potential faults can be found and processed in time, and the system reliability is improved. Meanwhile, potential faults can be predicted through the big data analysis technology, and equipment damage and production accidents are effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control, and specifically to a servo control system for a six-degree-of-freedom motion platform. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing technologies, as a high-precision and multi-degree-of-freedom mechanical device, the six-degree-of-freedom motion platform has been widely used in fields such as aerospace, automotive manufacturing, and simulation training. The six-degree-of-freedom motion platform realizes complex motion trajectories and precise positioning through a servo control system, which is of great significance for improving production efficiency and ensuring product quality.

[0003] However, the traditional fixed-parameter control method cannot adapt to the changes in the state of the motion platform, which may lead to a decrease in control accuracy or even system instability. In addition, it is also crucial to monitor and protect the servo motor and the motion platform in real time for abnormalities or faults. Traditional monitoring methods often rely on manual inspections and experience judgments, making it difficult to detect and handle potential faults in a timely manner, which may lead to equipment damage or production accidents; therefore, it does not meet the existing requirements, and for this reason, we propose a servo control system for a six-degree-of-freedom motion platform. Summary of the Invention

[0004] The purpose of the present invention is to provide a servo control system for a six-degree-of-freedom motion platform. By adopting a deep learning algorithm and a real-time dynamic model, it can generate and optimize motion trajectories in real time according to task requirements, and can also monitor and adjust the control parameters of the servo motor in real time to adapt to the changes in the state of the motion platform. It can also monitor the states of the servo motor and the motion platform in real time, discover and handle potential faults in a timely manner, and at the same time, by combining big data analysis technology, it can predict potential faults, effectively avoiding the occurrence of equipment damage and production accidents, and solving the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A servo control system for a six-degree-of-freedom motion platform, the system includes a motion planning unit, an adaptive adjustment unit, and an intelligent monitoring unit;

[0006] The motion planning unit is configured to, based on a deep learning algorithm and combined with the dynamic characteristics of the motion platform, generate motion trajectory information in real time according to the input task requirements and distribute it to each servo controller, and each servo controller drives the servo motor to perform telescopic motion according to the motion trajectory information;

[0007] The adaptive adjustment unit is configured to collect the motion data of the motion platform in real time and dynamically adjust the control parameters of the servo motor based on the real-time motion data;

[0008] The intelligent monitoring unit is configured to collect the status of the servo motor and the motion range of the motion platform in real time, determine whether there are any abnormalities or faults in the servo motor and the motion platform, and perform in-depth mining on the collected data by combining big data analysis to predict potential faults.

[0009] Further, the motion planning unit includes:

[0010] A task parsing module configured to parse the input task requirements using natural language processing technology and convert the task requirements into recognizable instructions or parameters;

[0011] A model construction module configured to establish a real-time dynamic model based on the physical characteristics and historical motion data of the motion platform using deep learning algorithms, and consider load changes, friction, and inertia factors. Among them, the deep learning algorithms use recurrent neural networks and long short-term memory networks;

[0012] A trajectory planning module configured to analyze the constraint conditions in the trajectory planning and generate motion trajectory information based on the task requirements and the real-time dynamic model;

[0013] A trajectory allocation module configured to allocate the generated motion trajectory information to each servo controller in real time. The servo controller calculates the control parameters from the motion trajectory information, and the servo controller independently drives the servo motor to perform telescopic motion according to the control parameters.

[0014] Further, the model construction module performs the following processes:

[0015] Collect the physical characteristics and historical motion data of the motion platform, and perform preprocessing operations of cleaning and normalization. Among them, the physical characteristics of the motion platform include mass and inertia;

[0016] Use the deep learning algorithms of recurrent neural networks and long short-term memory networks, combine the preprocessed data, and train the real-time dynamic model;

[0017] Verify and optimize the real-time dynamic model through cross-validation and grid search techniques;

[0018] Complete training and optimization, and deploy the real-time dynamic model to actual applications.

[0019] Further, the trajectory planning module performs the following processes:

[0020] Based on the task requirements and the real-time dynamic model, analyze the constraint conditions in the trajectory planning, including speed limits, acceleration limits, and obstacle avoidance requirements;

[0021] Under the constraint conditions, use optimization algorithms to generate motion trajectory information that meets the task requirements. Among them, the optimization algorithms include genetic algorithms or particle swarm algorithms.

[0022] Further, the trajectory allocation module includes:

[0023] A trajectory segmentation module configured to segment the generated motion trajectory information into multiple sub-trajectories, with each sub-trajectory corresponding to the control range of a servo controller;

[0024] A real-time allocation module configured to allocate the segmented sub-trajectories to each servo controller in real time, and during the trajectory allocation process, detect whether there are conflict problems, where the conflict problem is whether there is a situation where multiple servo controllers control the same area simultaneously.

[0025] Further, the adaptive adjustment unit includes:

[0026] A data acquisition module configured to collect the motion data of the motion platform in real time through sensors, including position, speed, acceleration, and load parameters, and perform data cleaning, denoising, and normalization operations on the collected motion data;

[0027] A control strategy module configured to select a suitable control mode according to the task requirements, including position control, speed control, torque control, or mixed control mode, and adopt PID control, fuzzy control, or adaptive control strategies to calculate the required control amount based on the real-time monitored motion data;

[0028] A parameter adjustment module configured to dynamically adjust the control parameters of the servo motor, including current, voltage, and frequency, according to the real-time monitored state of the motion platform and the preset control strategy.

[0029] Further, the parameter adjustment module includes:

[0030] An adjustment monitoring module configured to apply the adjusted control parameters to the servo motor and monitor the adjusted motion effect in real time;

[0031] A comparison and evaluation module configured to evaluate the effectiveness of the control strategy by comparing the adjusted motion effect with the expected target, and determine whether to further optimize and adjust the control strategy according to the evaluation result.

[0032] Further, the intelligent monitoring unit includes:

[0033] A real-time acquisition module configured to collect the state data of the servo motor in real time, including current, voltage, temperature, and speed, and collect the motion range data of the motion platform in real time, including position, speed, and acceleration;

[0034] The data analysis module is configured to analyze the real-time collected status data and motion range data, determine whether there are abnormalities or faults in the servo motor and the motion platform. At the same time, through big data analysis, it deeply mines the status data and motion range data to predict potential faults;

[0035] The abnormal warning module is configured to, based on the analysis results and prediction results of the abnormal analysis module, if there are abnormal situations, immediately issue a warning notice.

[0036] Furthermore, the data analysis module includes:

[0037] The abnormal analysis module is configured to set thresholds according to the operating parameters and historical data of the servo motor and the motion platform. If the real-time collected status data and motion range data exceed the thresholds, it is determined that there are abnormal situations in the servo motor and the motion platform;

[0038] The abnormal prediction module is configured to, based on big data analysis technology, use data mining algorithms and combine historical data to deeply analyze the real-time collected status data and motion range data, and identify potential faults in the servo motor and the motion platform.

[0039] Furthermore, the abnormal analysis module is configured with an emergency stop mechanism. Once it is determined that there are abnormal situations in the servo motor and the motion platform, the emergency stop mechanism is immediately activated. The emergency stop mechanism is used to cut off the power supply of the motion platform.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] 1. Based on the deep learning algorithm and combined with the dynamic characteristics of the motion platform, the present invention can generate motion trajectory information that matches the task requirements in real time, ensuring the accuracy and real-time nature of the motion. The generated motion trajectory information is efficiently allocated to each servo controller, and the servo controller drives the servo motor to perform telescopic motion, improving the overall operating efficiency of the system. And the motion data of the motion platform is collected in real time, and the control parameters of the servo motor are dynamically adjusted according to these data. Through adaptive adjustment, the control effect of the servo motor can be optimized, improving the stability and response speed of the system, and reducing energy consumption and wear.

[0042] 2. By collecting the status of the servo motor and the motion range data of the motion platform in real time, the present invention can timely detect abnormalities and faults, avoid equipment damage and production accidents. And combined with big data analysis technology, deeply mining the collected data can predict potential faults of the equipment, providing strong support for the maintenance and repair of the equipment, and further improving the safety of the equipment. Description of the Drawings

[0043] Figure 1This is the overall structure diagram of the six-degree-of-freedom motion platform servo control system of the present invention. Specific embodiments

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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 of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] To solve the technical problems that the existing fixed-parameter control method cannot adapt to the changes in the state of the motion platform, which may lead to a decrease in control accuracy or even system instability. In addition, it is also crucial to monitor and protect the servo motor and the motion platform in real time for abnormalities or faults. Traditional monitoring methods often rely on manual inspections and experience judgments, making it difficult to detect and handle potential faults in a timely manner. Please refer to Figure 1 This embodiment provides the following technical solutions:

[0046] A six-degree-of-freedom motion platform servo control system, the system includes a motion planning unit, an adaptive adjustment unit, and an intelligent monitoring unit;

[0047] The motion planning unit is configured to, based on a deep learning algorithm and in combination with the dynamic characteristics of the motion platform, generate motion trajectory information in real time according to the input task requirements and distribute it to each servo controller, and each servo controller drives the servo motor to perform telescopic motion according to the motion trajectory information;

[0048] The adaptive adjustment unit is configured to collect the motion data of the motion platform in real time and dynamically adjust the control parameters of the servo motor based on the real-time motion data;

[0049] The intelligent monitoring unit is configured to collect the state of the servo motor and the motion range of the motion platform in real time, determine whether there are abnormalities or faults in the servo motor and the motion platform, and perform in-depth mining on the collected data in combination with big data analysis to predict potential faults.

[0050] The technical effects of the above content are as follows: Based on the deep learning algorithm combined with the dynamic characteristics of the motion platform, the motion planning unit can generate accurate motion trajectory information in real time according to the task requirements, thereby significantly improving the trajectory accuracy and motion efficiency of the motion platform. Moreover, the motion trajectory information can be reasonably distributed to each servo controller to ensure that each servo motor can perform telescopic motion according to the planned trajectory, thus realizing the efficient execution of complex motion tasks. By collecting the motion data of the motion platform in real time, the adaptive adjustment unit can dynamically adjust the control parameters of the servo motor. This adjustment mechanism can effectively cope with the dynamic changes during the motion process and improve the stability and adaptability of the system. The intelligent monitoring unit can collect the status of the servo motor and the motion range of the motion platform in real time, detect abnormalities or faults in a timely manner, thereby effectively avoiding shutdown or damage caused by faults. At the same time, by combining big data analysis to deeply mine the collected data, potential faults can be predicted, and measures can be taken in advance to reduce the maintenance cost.

[0051] The motion planning unit includes:

[0052] A task parsing module configured to parse the input task requirements using natural language processing technology and convert the task requirements into recognizable instructions or parameters;

[0053] A model construction module configured to establish a real-time dynamic model based on the physical characteristics and historical motion data of the motion platform using deep learning algorithms, and consider factors such as load changes, friction, and inertia, so that the model can better adapt to complex working conditions and improve the robustness and adaptability of the system. Among them, the deep learning algorithm uses a recurrent neural network and a long short-term memory network, and specifically executes the following processes:

[0054] Collect the physical characteristics and historical motion data of the motion platform, and perform preprocessing operations such as cleaning and normalization, which can improve the efficiency and quality of model training. Among them, the physical characteristics of the motion platform include mass and inertia;

[0055] Use the deep learning algorithms of recurrent neural network and long short-term memory network, combined with the preprocessed data, to train the real-time dynamic model;

[0056] Verify and optimize the real-time dynamic model through cross-validation and grid search techniques;

[0057] Complete training and optimization, and deploy the real-time dynamic model to actual applications;

[0058] A trajectory planning module configured to analyze the constraint conditions in trajectory planning and generate motion trajectory information based on the task requirements and the real-time dynamic model, and specifically execute the following processes:

[0059] Analyze the constraint conditions in trajectory planning based on task requirements and real-time dynamic models, including speed limits, acceleration limits, and obstacle avoidance requirements;

[0060] Under the constraint conditions, an optimization algorithm is used to generate motion trajectory information that meets the task requirements. Among them, the optimization algorithm includes genetic algorithms or particle swarm algorithms;

[0061] The trajectory allocation module is configured to allocate the generated motion trajectory information to each servo controller in real time. The servo controller calculates the control parameters from the motion trajectory information, and the servo controller independently drives the servo motor to perform telescopic motion according to the control parameters.

[0062] The technical effects of the above content are as follows: The task parsing module uses natural language processing (NLP) technology to quickly parse the input task requirements and convert them into recognizable instructions or parameters. By automatically parsing the task requirements, errors caused by manual conversion of task descriptions are avoided, improving the accuracy and reliability of task execution. The model construction module uses a recurrent neural network (RNN) and a long short-term memory network (LSTM) to establish a real-time dynamic model based on the physical characteristics and historical motion data of the motion platform, which can effectively capture the dynamic characteristics of the motion platform, thus providing a more accurate reference for motion planning. The trajectory planning module analyzes the constraint conditions in trajectory planning (such as speed limits, acceleration limits, and obstacle avoidance requirements) based on task requirements and real-time dynamic models, and uses optimization algorithms (such as genetic algorithms or particle swarm algorithms) to generate motion trajectory information that meets the task requirements, which can effectively solve the trajectory planning problem under complex constraint conditions and improve the accuracy and feasibility of the trajectory. Finally, the trajectory allocation module allocates the generated motion trajectory information to each servo controller in real time, so that the servo controller independently drives the servo motor to perform telescopic motion according to the calculated control parameters, thereby improving the response speed and execution efficiency of the system. Each servo controller independently drives the servo motor, and at the same time realizes coordinated control through the real-time dynamic model to ensure that the overall motion effect of the motion platform meets the task requirements.

[0063] In summary, the motion planning unit significantly improves the motion planning ability and control performance of the six-degree-of-freedom motion platform through efficient natural language processing, accurate dynamic modeling, optimized trajectory planning, and efficient task allocation, and can better meet complex task requirements.

[0064] The trajectory allocation module includes:

[0065] The trajectory segmentation module is configured to segment the generated motion trajectory information into multiple sub-trajectories, and each sub-trajectory corresponds to the control range of a servo controller;

[0066] A real-time allocation module, configured to allocate the segmented sub-trajectories to each servo controller in real time, and during the trajectory allocation process, detect whether there are conflict problems, where the conflict problem is whether there are multiple servo controllers simultaneously controlling the same area.

[0067] The technical effects of the above content are as follows: The trajectory segmentation module decomposes complex motion trajectories into multiple simple sub-trajectories, reducing the control difficulty and at the same time reducing the error accumulation caused by complex trajectories, further improving the control accuracy of the system. The real-time allocation module allocates the segmented sub-trajectories to each servo controller in real time, ensuring that each servo controller can receive task instructions in a timely manner and execute them, improving the system's response speed and task execution efficiency. During the trajectory allocation process, it is detected in real time whether there are conflict problems, such as the situation where multiple servo controllers simultaneously control the same area. Through the conflict detection mechanism, potential control conflicts can be discovered and resolved in a timely manner, avoiding motion errors or failures caused by conflicts.

[0068] An adaptive adjustment unit, including:

[0069] A data acquisition module, configured to collect the motion data of the motion platform in real time through sensors, including position, speed, acceleration, and load parameters, and perform data cleaning, denoising, and normalization operations on the collected motion data, which can effectively remove interference and outliers in the data, improve the accuracy and consistency of the data, and thus provide a high-quality data basis for the implementation of control strategies;

[0070] A control strategy module, configured to select a suitable control mode according to task requirements, including position control, speed control, torque control, or hybrid control mode, and adopt PID control, fuzzy control, or adaptive control strategies, and calculate the required control amount according to the real-time monitored motion data. Among them, the control strategies have different advantages, for example:

[0071] PID control: Suitable for linear systems, capable of rapid response and stable control;

[0072] Fuzzy control: Suitable for non-linear or complex systems, capable of handling fuzziness and uncertainty;

[0073] Adaptive control: Can automatically adjust control parameters according to the dynamic characteristics of the system to adapt to system changes;

[0074] A parameter adjustment module, configured to dynamically adjust the control parameters of the servo motor according to the real-time monitored state of the motion platform and the preset control strategy, including current, voltage, and frequency.

[0075] The technical effects of the above content are as follows: The data acquisition module can comprehensively and real-time grasp the operating state of the motion platform by collecting the position, speed, acceleration, and load parameters of the motion platform through sensors in real time, providing accurate data support for subsequent control adjustments. The control strategy module can select appropriate position control, speed control, torque control, or hybrid control modes according to task requirements, better adapting to different application scenarios and task requirements, improving the versatility and adaptability of the system. By using PID control, fuzzy control, or adaptive control strategies, it can calculate the required control quantity based on the real-time monitored motion data. By selecting appropriate control strategies and modes, it can improve the response speed, control accuracy, and stability of the system, reduce overshoot and steady-state error. The parameter adjustment module dynamically adjusts the control parameters of the servo motor according to the real-time monitored state of the motion platform and the preset control strategy. By dynamically adjusting the control parameters, it can effectively cope with dynamic factors such as load changes and friction characteristic changes, improving the robustness and adaptability of the system.

[0076] The parameter adjustment module includes:

[0077] The adjustment monitoring module is configured to apply the adjusted control parameters to the servo motor and monitor the adjusted motion effect in real time;

[0078] The comparison and evaluation module is configured to evaluate the effectiveness of the control strategy by comparing the adjusted motion effect with the expected target, and determine whether to further optimize and adjust the control strategy according to the evaluation result.

[0079] The technical effects of the above content are as follows: By monitoring the motion effect in real time, the adjustment monitoring module can timely detect possible problems after parameter adjustment, such as performance not meeting expectations or new abnormalities, providing a basis for subsequent optimization. The comparison and evaluation module evaluates the effectiveness of the control strategy by comparing the adjusted motion effect with the expected target. This comparison and evaluation mechanism can quantify the performance changes after adjustment, providing a clear direction for further optimization of the control strategy. According to the evaluation result, the comparison and evaluation module can determine whether to further optimize and adjust the control strategy. Through the automated evaluation and optimization mechanism, it reduces the dependence on manual experience, reduces optimization errors caused by inaccurate human judgment, and improves the intelligent level of the system.

[0080] The intelligent monitoring unit includes:

[0081] The real-time acquisition module is configured to real-time collect the state data of the servo motor, including current, voltage, temperature, and speed, and real-time collect the motion range data of the motion platform, including position, speed, and acceleration. The collected data covers multiple key parameters of the motor operating state and the platform motion range, ensuring the integrity and comprehensiveness of the data, and providing rich information for subsequent in-depth analysis;

[0082] A data analysis module, configured to analyze the real-time collected status data and motion range data, determine whether there are abnormalities or faults in the servo motor and the motion platform. At the same time, through big data analysis, deeply mine the status data and motion range data to predict potential faults;

[0083] An abnormal warning module, configured to, based on the analysis results and prediction results of the abnormal analysis module, if there is an abnormal situation, immediately issue a warning notice.

[0084] The technical effects of the above content are as follows: The real-time acquisition module can comprehensively master the real-time status of the system operation by real-time collecting data such as the current, voltage, temperature and speed of the servo motor, and the position, speed and acceleration of the motion platform, providing an accurate data basis for subsequent analysis and warning. The data analysis module performs real-time analysis on the collected status data and motion range data, can quickly determine whether there are abnormalities or faults in the servo motor and the motion platform. At the same time, through big data analysis technology, deeply mine the collected data to predict potential faults, so that measures can be taken in advance to avoid downtime or damage caused by sudden faults. Through real-time analysis and in-depth prediction, the data analysis module can discover and handle potential problems in advance, reduce the probability of faults, and thus improve the overall reliability of the system. And the abnormal warning module can immediately issue a warning notice based on the analysis results and prediction results of the data analysis module. Through timely warning, preventive measures can be taken in time for faults, reducing safety risks and economic losses caused by faults.

[0085] The data analysis module includes:

[0086] An abnormal analysis module, configured to set thresholds according to the operating parameters of the servo motor and the motion platform and historical data. If the real-time collected status data and motion range data exceed the thresholds, it is determined that there are abnormal situations in the servo motor and the motion platform. Among them, the abnormal analysis module is configured with an emergency stop mechanism. Once it is determined that there are abnormal situations in the servo motor and the motion platform, the emergency stop mechanism is immediately activated. The emergency stop mechanism is used to cut off the power supply of the motion platform;

[0087] An abnormal prediction module, configured to, based on big data analysis technology, use data mining algorithms and combine historical data to deeply analyze the real-time collected status data and motion range data to identify potential faults in the servo motor and the motion platform.

[0088] The technical effects of the above content are as follows: By setting thresholds and monitoring the operating parameters of the servo motor and the motion platform in real time, the anomaly analysis module can quickly detect status data and motion range data that exceed the normal range, thus promptly discovering abnormal situations. At the same time, the configured emergency stop mechanism can cut off the power supply of the motion platform immediately after determining an abnormal situation, ensuring the safe operation of the system, preventing equipment damage or personal injury caused by failures. By promptly discovering and handling abnormal situations, the anomaly analysis module can avoid the further expansion of failures and reduce economic losses and equipment damage caused by sudden failures. The anomaly prediction module, based on big data analysis technology, uses data mining algorithms to deeply analyze the real-time collected data, can identify potential failures in the servo motor and the motion platform. By predicting potential failures, the system can arrange maintenance plans in advance and avoid downtime caused by sudden failures.

[0089] Working principle: Based on deep learning algorithms and combined with the dynamic characteristics of the motion platform, the motion planning unit can generate accurate motion trajectory information in real time according to task requirements, thereby significantly improving the trajectory accuracy and motion efficiency of the motion platform. By collecting the motion data of the motion platform in real time, the adaptive adjustment unit can dynamically adjust the control parameters of the servo motor, effectively coping with the dynamic changes during the motion process, thus improving the stability and adaptability of the system. The intelligent monitoring unit can collect the status of the servo motor and the motion range of the motion platform in real time, promptly discover abnormalities or failures, thus effectively avoiding downtime or damage caused by failures. At the same time, by combining big data analysis to deeply mine the collected data, it can predict potential failures, thereby arranging maintenance plans in advance and avoiding downtime caused by sudden failures.

[0090] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0091] Although the 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 variations can be made in these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A six-degree-of-freedom motion platform servo control system, characterized in that, The system includes a motion planning unit, an adaptive adjustment unit, and an intelligent monitoring unit; The motion planning unit is configured to generate motion trajectory information in real time based on a deep learning algorithm and combined with the dynamic characteristics of the motion platform, and allocate it to each servo controller according to the input task requirements. Each servo controller drives the servo motor to perform telescopic motion according to the motion trajectory information; The adaptive adjustment unit is configured to collect the motion data of the motion platform in real time and dynamically adjust the control parameters of the servo motor based on the real-time motion data; The intelligent monitoring unit is configured to collect the status of the servo motor and the motion range of the motion platform in real time, determine whether there are any abnormalities or faults in the servo motor and the motion platform, and perform in-depth mining on the collected data by combining big data analysis to predict potential faults.

2. The six-degree-of-freedom motion platform servo control system according to claim 1, wherein: The motion planning unit includes: A task parsing module configured to parse the input task requirements using natural language processing technology and convert the task requirements into recognizable instructions or parameters; A model construction module configured to establish a real-time dynamic model using a deep learning algorithm based on the physical characteristics and historical motion data of the motion platform, and consider load changes, friction, and inertia factors. Among them, the deep learning algorithm uses a recurrent neural network and a long short-term memory network; A trajectory planning module configured to analyze the constraint conditions in the trajectory planning and generate motion trajectory information based on the task requirements and the real-time dynamic model; A trajectory allocation module configured to allocate the generated motion trajectory information to each servo controller in real time. The servo controller calculates the control parameters from the motion trajectory information, and the servo controller independently drives the servo motor to perform telescopic motion according to the control parameters.

3. The six-degree-of-freedom motion platform servo control system according to claim 2, characterized in that: The model construction module performs the following process: Collect the physical characteristics and historical motion data of the motion platform, and perform preprocessing operations of cleaning and normalization. Among them, the physical characteristics of the motion platform include mass and inertia; Use the deep learning algorithms of recurrent neural network and long short-term memory network, combined with the preprocessed data, to train the real-time dynamic model; Verify and optimize the real-time dynamic model through cross-validation and grid search techniques; Complete training and optimization, and deploy the real-time dynamic model to actual applications.

4. The six-degree-of-freedom motion platform servo control system according to claim 2, wherein: The trajectory planning module performs the following process: Based on the task requirements and the real-time dynamic model, analyze the constraint conditions in the trajectory planning, including speed limit, acceleration limit, and obstacle avoidance requirements; Under the constraint conditions, use an optimization algorithm to generate motion trajectory information that meets the task requirements. Among them, the optimization algorithm includes a genetic algorithm or a particle swarm algorithm.

5. The six-degree-of-freedom motion platform servo control system according to claim 2, wherein: The trajectory allocation module includes: A trajectory segmentation module configured to segment the generated motion trajectory information into multiple sub-trajectories, and each sub-trajectory corresponds to the control range of a servo controller; A real-time allocation module configured to allocate the segmented sub-trajectories to each servo controller in real time, and detect whether there is a conflict problem during the trajectory allocation process. The conflict problem is whether there is a situation where multiple servo controllers control the same area at the same time.

6. The six-degree-of-freedom motion platform servo control system according to claim 1, wherein: The adaptive adjustment unit includes: The data acquisition module is configured to collect the motion data of the motion platform in real time through sensors, including position, speed, acceleration, and load parameters, and perform data cleaning, denoising, and normalization operations on the collected motion data; The control strategy module is configured to select an appropriate control mode according to the task requirements, including position control, speed control, torque control, or hybrid control mode, and adopt PID control, fuzzy control, or adaptive control strategy to calculate the required control quantity based on the real-time monitored motion data; The parameter adjustment module is configured to dynamically adjust the control parameters of the servo motor, including current, voltage, and frequency, according to the real-time monitored state of the motion platform and the preset control strategy.

7. The six-degree-of-freedom motion platform servo control system according to claim 6, wherein: The parameter adjustment module includes: The adjustment monitoring module is configured to apply the adjusted control parameters to the servo motor and monitor the adjusted motion effect in real time; The comparison and evaluation module is configured to evaluate the effectiveness of the control strategy by comparing the adjusted motion effect with the expected target, and determine whether to further optimize and adjust the control strategy according to the evaluation result.

8. The six-degree-of-freedom motion platform servo control system according to claim 1, characterized in that: The intelligent monitoring unit includes: The real-time acquisition module is configured to collect the state data of the servo motor in real time, including current, voltage, temperature, and speed, and collect the motion range data of the motion platform in real time, including position, speed, and acceleration; The data analysis module is configured to analyze the real-time collected state data and motion range data to determine whether there are any abnormalities or faults in the servo motor and the motion platform. At the same time, through big data analysis, the state data and motion range data are deeply mined to predict potential faults; The abnormal warning module is configured to issue a warning notice immediately if there is an abnormal situation based on the analysis results and prediction results of the abnormal analysis module.

9. The six-degree-of-freedom motion platform servo control system according to claim 8, wherein: The data analysis module includes: The abnormal analysis module is configured to set thresholds according to the operating parameters and historical data of the servo motor and the motion platform. If the real-time collected state data and motion range data exceed the thresholds, it is determined that there are abnormal situations in the servo motor and the motion platform; The abnormal prediction module is configured to deeply analyze the real-time collected state data and motion range data based on big data analysis technology, use data mining algorithms and combine historical data to identify potential faults in the servo motor and the motion platform.

10. The six-degree-of-freedom motion platform servo control system according to claim 9, characterized in that: The abnormal analysis module is configured with an emergency stop mechanism. Once it is determined that there are abnormal situations in the servo motor and the motion platform, the emergency stop mechanism is immediately activated. The emergency stop mechanism is used to cut off the power supply of the motion platform.

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