Integrated motor and guide rail system and method

Abstract

The invention relates to the technical field of linear transmission, in particular to an integrated motor and guide rail system and method, by integrating a linear motor stator and a precision guide rail on the same integral base subjected to finish machining, motion deflection and deformation caused by assembly accumulative errors of a traditional split type structure are fundamentally eliminated, and the service life of the motor and guide rail system is prolonged. And the shortest power transmission path and the maximum rigidity are ensured. The motor rotor directly drives the sliding block through the rigid connection bridge, and back-clearance-free power transmission is achieved. In the software level, the motion control module adopts an advanced algorithm combining feedforward compensation and multivariable feedback, so that system disturbance is effectively inhibited, and the whole system can realize nanoscale positioning precision and excellent dynamic tracking performance under high speed and high acceleration. Multiple technical effects of high sensitivity, strong waterproof performance, operation comfort, electrical connection reliability and the like of the switch are realized.

Description

Technical Field

[0001] This invention relates to the field of linear drive technology, and in particular to an integrated motor and guide rail system and method. Background Technology

[0002] In the field of high-end precision motion control, the performance of linear motor drive systems directly affects the positioning accuracy and dynamic response of key equipment such as semiconductor equipment and precision measuring instruments. Traditional solutions generally adopt a separate design of the motor and guide rail. This structure has inherent mechanical defects: accumulated assembly errors lead to motion sway and deformation; excessively long power transmission paths reduce system rigidity; and vibration and positioning deviations are prone to occur under high-speed and high-acceleration conditions. At the control level, conventional PID algorithms are difficult to effectively compensate for nonlinear disturbances and time-varying parameters, limiting the dynamic performance of the system. In terms of condition monitoring, existing technologies mostly rely on discrete sensors and simple threshold alarms, lacking comprehensive perception and prediction capabilities of equipment operating status. More importantly, the massive amount of operational data generated by the system is not fully utilized, and the life assessment of key components still adopts a periodic maintenance mode, which increases maintenance costs and cannot avoid sudden failures. Although the industry has tried to improve performance by increasing the number of sensors or optimizing local algorithms, the lack of electromechanical co-design and a unified data processing architecture often makes it difficult to fundamentally solve the problem of synergistic optimization of accuracy, reliability, and intelligent operation and maintenance. Especially in scenarios requiring nanometer-level positioning and long-term stable operation, traditional solutions can no longer meet the increasingly stringent technical requirements of modern high-end equipment, necessitating a new solution that integrates innovative mechanical design and intelligent algorithms. Summary of the Invention

[0003] To address the aforementioned issues, this invention, through the organic combination of deep mechatronics integration and intelligent software empowerment, successfully solves the core requirements of high-precision, high-reliability, and intelligent operation and maintenance of motion systems in the high-end equipment field. It is an integrated motor and guide rail system and method with outstanding technical advantages and broad application prospects.

[0004] The technical solution adopted in this invention is: an integrated motor and guide rail system, which relies on an integrated platform for the integrated motor and guide rail. The integrated platform includes an integrated mechanical body that integrates the linear motor stator and the precision guide rail on the same base, a slider driven by the motor mover, and a multi-modal sensor group mounted on the slider.

[0005] It also includes the following core software modules deployed on edge computing devices or embedded industrial computers:

[0006] The system management module provides a graphical human-computer interaction interface, receives user input of guide rail travel, motion parameters, control parameters and safety thresholds, and completes system initialization configuration.

[0007] The real-time acquisition and processing module establishes a high-speed data link with the multimodal sensor group. It is responsible for synchronously acquiring the raw data of all sensors at a fixed sampling period, and performing preprocessing such as filtering, timestamp alignment and unit standardization on the data to generate standardized sensor data packets.

[0008] The status monitoring module has a built-in three-dimensional digital model with the same physical properties as the integrated mechanical body. It receives standardized sensor data packets, drives the digital model to perform real-time synchronous motion, and dynamically displays the system's real-time position, speed, load force, temperature and vibration status on the human-machine interface.

[0009] The motion control module receives motion commands from the upper-layer application, generates a smooth, continuous, and dynamically optimal reference trajectory in real time based on preset motion constraints and optimization objectives, and calculates the real-time control quantity for driving the linear motor by combining the standardized sensor data package with an advanced control algorithm.

[0010] The data maintenance module continuously records all sensor and control data during system operation, analyzes data characteristics through machine learning algorithms, predicts the remaining service life of key components, and issues maintenance warnings in advance.

[0011] A further improvement to the above solution is that the real-time acquisition and processing module is configured as follows:

[0012] Multiple independent data acquisition threads are created, corresponding to the position sensor, inertial measurement unit, torque sensor, and temperature sensor, respectively;

[0013] Hardware interrupts or high-precision timers are used to ensure strict timing synchronization of all data acquisition threads;

[0014] The collected raw data is denoised using digital filters, and the data of different physical quantities are converted to spatial coordinate system and unit, and finally packaged into a standardized data structure with a unified timestamp.

[0015] A further improvement to the above scheme is that the status monitoring module includes a model loading unit, a physics engine unit, a visualization rendering unit, and an alarm logic unit. The model loading unit is used to load the three-dimensional computer-aided design model of the integrated mechanical body. The physics engine unit maps the position, force, and acceleration information in the standardized sensor data package to the kinematic and dynamic states of the corresponding parts in the three-dimensional model. The visualization rendering unit is used to render and display the motion state of the mechanical body in real time in the form of three-dimensional animation on the graphical interface, and overlay key monitoring parameters in the form of a data panel. The alarm logic unit predefines the judgment logic for various abnormal working conditions, and automatically triggers visual and audible alarm signals when the received sensor data exceeds the safety threshold.

[0016] A further improvement to the above scheme is that the motion control module includes a trajectory generation unit, a multivariable state feedback controller, a feedforward compensator, a disturbance observer, and a control synthesis unit. The trajectory generation unit uses a high-order spline curve interpolation algorithm to generate a smooth reference trajectory based on the target position, velocity, acceleration, and acceleration constraints. The multivariable state feedback controller takes as input the error between the reference trajectory and the actual state fed back by the standardized sensor data packet, and calculates the initial control quantity using a linear quadratic regulator or a model predictive control algorithm. The feedforward compensator generates a feedforward control quantity based on the differential signal of the reference trajectory and the inverse model of the system to improve the system's tracking performance. The disturbance observer estimates and compensates for external disturbances in real time based on torque sensor feedback and the system model. The control synthesis unit superimposes the initial control quantity, feedforward control quantity, and disturbance compensation quantity output by the state feedback controller to generate the final digital control command and outputs it to the hardware driver layer.

[0017] A smart control method for an integrated motor and guide rail system, characterized by being executed by a software module deployed on an edge computing device or an embedded industrial computer, comprising the following steps:

[0018] System initialization and parameter configuration steps: The system management module loads the user-preset guide rail travel, motion parameters, control parameters and safety thresholds to complete the initialization configuration and self-test of the hardware platform and software modules;

[0019] Real-time multimodal data synchronous acquisition and fusion steps: The real-time acquisition and processing module synchronously acquires raw data from position, inertia, torque and temperature sensors at a fixed sampling period. After filtering, timestamp alignment and coordinate system unification, a standardized system status data packet is generated.

[0020] Digital twin driving and real-time status monitoring steps: The status monitoring module receives the system status data packet, drives the three-dimensional digital model to move synchronously, and displays the position, speed, load force, temperature and vibration status in real time on the human-machine interface, and performs anomaly judgment and alarm based on safety thresholds.

[0021] Intelligent motion planning and adaptive control steps: The motion control module receives upper-level motion commands, generates a smooth optimal reference trajectory by combining preset constraints, and calculates real-time control quantities based on the system state data packet through multivariate feedback, feedforward compensation and disturbance observation, and outputs them to the motor driver;

[0022] Predictive maintenance and health management steps: The data maintenance module continuously records and stores system operation data in the background, analyzes performance degradation characteristics through machine learning algorithms, evaluates the system health index and predicts the remaining lifespan of key components, and generates maintenance warnings.

[0023] A further improvement to the above scheme is that the real-time multimodal data synchronous acquisition and fusion step specifically includes:

[0024] Parallel data acquisition sub-step: Create multiple independent data acquisition threads corresponding to the sensor type, and use hardware interrupt mechanism to ensure strict synchronization of sampling time;

[0025] Data preprocessing sub-step: Apply low-pass digital filters to the raw data collected by each thread to suppress high-frequency noise;

[0026] Data fusion and standardization sub-steps: Spatial coordinate registration and physical unit unification are performed on the filtered multi-source data, and a unified high-precision timestamp is added. The data is then packaged into a standard data package for use by upper-layer modules.

[0027] The beneficial effects of this invention are:

[0028] Compared to existing motor guide rail systems, this invention integrates the linear motor stator and precision guide rail onto a single, precision-machined integral base, fundamentally eliminating motion sway and deformation caused by accumulated assembly errors in traditional split structures. This ensures the shortest power transmission path and maximized rigidity. The motor mover directly drives the slider via a rigid connecting bridge, achieving backlash-free power transmission. On the software level, the motion control module employs an advanced algorithm combining feedforward compensation and multivariable feedback to effectively suppress system disturbances, enabling the entire system to achieve nanometer-level positioning accuracy and excellent dynamic tracking performance at high speeds and high accelerations. This invention introduces a digital twin-based status monitoring module and multi-sensor data fusion technology. The status monitoring module utilizes a 3D model consistent with the physical entity for real-time synchronous simulation, allowing the operator to intuitively observe the system's all-around operating status, far exceeding the display dimensions of traditional dashboards. By leveraging the machine learning analytics capabilities of the data maintenance module, the system can automatically learn and identify performance degradation patterns from massive amounts of operational data. This enables early warning of faults such as guide rail wear and motor overheating, as well as accurate prediction of remaining lifespan, significantly improving equipment availability and reliability while reducing the risk of unexpected downtime and maintenance costs. The graphical system management module greatly simplifies complex parameter configuration and system debugging processes, lowering the technical barrier for operators. The real-time acquisition and processing module ensures the consistency, accuracy, and real-time performance of massive amounts of multi-source heterogeneous data through strict time synchronization and data standardization, providing a reliable data foundation for higher-level control algorithms. Furthermore, the entire software system adopts a modular architecture with clearly defined responsibilities and interfaces for each core module. This facilitates functional expansion and upgrades and enhances the overall fault tolerance and safety level of the system through centralized anomaly diagnosis and alarm logic. This invention, through the organic combination of deep mechatronics integration and intelligent software empowerment, successfully addresses the core needs of high-end equipment for high-precision, high-reliability, and intelligent operation and maintenance of motion systems, demonstrating significant technical advantages and broad application prospects.

[0029] This intelligent control method for integrated motor and guide rail systems ensures the spatiotemporal consistency of multi-source information such as position, inertia, torque, and temperature through rigorous real-time multimodal data synchronous acquisition and fusion, providing a highly reliable data foundation for control decisions. Based on this, the intelligent motion planning and adaptive control steps employ a strategy combining feedforward compensation and multivariable feedback, proactively predicting and compensating for system dynamic lag and external disturbances, rather than simply relying on error feedback. This model- and data-driven control approach enables smooth and accurate motion trajectory tracking even under high-speed and variable load conditions, effectively improving the system's response speed and steady-state accuracy. Through digital twin-driven and real-time status monitoring steps, this method maps the actual operating state of the physical system onto a virtual 3D model in real time, achieving panoramic and visual monitoring of the system's internal operation. Operators can not only obtain traditional numerical information but also intuitively perceive the overall behavior of the system. Combined with preset safety thresholds, this method can make millisecond-level judgments and trigger alarms for conditions such as exceeding limits and abnormal vibration, enhancing the safety and reliability of system operation and providing strong protection for critical tasks. Predictive maintenance and health management procedures overcome the limitations of traditional periodic maintenance or post-failure repair. By continuously recording operational data and using machine learning algorithms to analyze performance degradation characteristics, the system can accurately assess the current health status and scientifically predict the remaining lifespan of critical components. This allows maintenance activities to be precisely scheduled based on the actual health condition of the equipment, avoiding resource waste caused by over-maintenance and preventing unplanned downtime due to sudden failures, thereby significantly improving the overall utilization rate of equipment and reducing the total lifecycle maintenance cost. This invention, through a process-oriented and intelligent step design, tightly couples perception, decision-making, execution, and maintenance processes, forming a highly efficient, reliable, and adaptive closed-loop control system. It provides an advanced solution for the field of high-end precision motion control, possessing outstanding technical advantages and broad application prospects. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the connection between the integrated motor and guide rail system of the present invention;

[0031] Figure 2 This is a connection diagram of the real-time acquisition and processing module of the present invention;

[0032] Figure 3 This is a connection diagram of the status monitoring module of the present invention;

[0033] Figure 4 This is a connection diagram of the motion control module of the present invention. Detailed Implementation

[0034] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0035] It should be noted that when a component is said to be "fixed to" another component, it can be directly attached to the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Figures 1-4 As shown, in one embodiment of the present invention, an integrated motor and guide rail system is provided, which relies on an integrated platform for the integrated motor and guide rail. The integrated platform includes an integrated mechanical body that integrates the linear motor stator and the precision guide rail on the same base, a slider driven by the motor mover, and a multimodal sensor group mounted on the slider.

[0037] It also includes the following core software modules deployed on edge computing devices or embedded industrial computers:

[0038] The system management module provides a graphical human-computer interaction interface, receives user input of guide rail travel, motion parameters, control parameters and safety thresholds, and completes system initialization configuration.

[0039] The real-time acquisition and processing module establishes a high-speed data link with the multimodal sensor group. It is responsible for synchronously acquiring the raw data of all sensors at a fixed sampling period, and performing preprocessing such as filtering, timestamp alignment and unit standardization on the data to generate standardized sensor data packets.

[0040] The status monitoring module has a built-in three-dimensional digital model with the same physical properties as the integrated mechanical body. It receives standardized sensor data packets, drives the digital model to perform real-time synchronous motion, and dynamically displays the system's real-time position, speed, load force, temperature and vibration status on the human-machine interface.

[0041] The motion control module receives motion commands from the upper-layer application, generates a smooth, continuous, and dynamically optimal reference trajectory in real time based on preset motion constraints and optimization objectives, and calculates the real-time control quantity for driving the linear motor by combining the standardized sensor data package with an advanced control algorithm.

[0042] The data maintenance module continuously records all sensor and control data during system operation, analyzes data characteristics through machine learning algorithms, predicts the remaining service life of key components, and issues maintenance warnings in advance.

[0043] This embodiment integrates the linear motor stator and precision guide rail onto a single, precision-machined base, fundamentally eliminating motion sway and deformation caused by accumulated assembly errors in traditional split structures. This ensures the shortest power transmission path and maximized rigidity. The motor mover directly drives the slider via a rigid connecting bridge, achieving backlash-free power transmission. On the software level, the motion control module employs an advanced algorithm combining feedforward compensation and multivariable feedback to effectively suppress system disturbances, enabling the entire system to achieve nanometer-level positioning accuracy and excellent dynamic tracking performance at high speeds and high accelerations. This invention introduces a digital twin-based status monitoring module and multi-sensor data fusion technology. The status monitoring module uses a 3D model consistent with the physical entity for real-time synchronous simulation, allowing operators to intuitively observe the system's all-around operating status, far exceeding the display dimensions of traditional dashboards. Combined with the machine learning analysis capabilities of the data maintenance module, the system can automatically learn and identify performance degradation patterns from massive amounts of operating data, achieving early warning of faults such as guide rail wear and motor overheating, and accurate prediction of remaining lifespan. This greatly improves equipment availability and reliability, and reduces the risk of unexpected downtime and maintenance costs. The graphical system management module greatly simplifies the complex parameter configuration and system debugging process, lowering the technical threshold for operators. The real-time acquisition and processing module, through strict timing synchronization and data standardization, ensures the consistency, accuracy, and real-time performance of massive amounts of multi-source heterogeneous data, providing a reliable data foundation for higher-level control algorithms. Furthermore, the entire software system adopts a modular architecture, with clear responsibilities and interfaces for each core module. This not only facilitates functional expansion and upgrades but also enhances the overall fault tolerance and safety level of the system through centralized anomaly diagnosis and alarm logic. This embodiment, through the organic combination of deep mechatronics integration and intelligent software empowerment, successfully addresses the core needs of high-end equipment for high-precision, high-reliability, and intelligent operation and maintenance of motion systems, demonstrating significant technical advantages and broad application prospects.

[0044] The real-time acquisition and processing module is configured to: create multiple independent data acquisition threads, corresponding to the position sensor, inertial measurement unit, torque sensor, and temperature sensor respectively; employ hardware interrupts or high-precision timer mechanisms to ensure strict timing synchronization of all data acquisition threads; apply digital filters to the acquired raw data for noise reduction; perform spatial coordinate system unification and unit conversion on the data of different physical quantities; and finally package the data into a standardized data structure with a unified timestamp.

[0045] In this embodiment, the real-time acquisition and processing module effectively solves the fusion error problem caused by inconsistent sampling times of multi-source heterogeneous sensor data by creating an independent sensor data acquisition thread and employing a hardware interrupt mechanism to ensure strict synchronization. Applying digital filtering and coordinate system unification processing to the raw data significantly improves the signal-to-noise ratio and spatial consistency of state information such as position, inertia, and torque. The resulting standardized data packet with a unified timestamp provides a highly reliable, spatiotemporally unified data foundation for subsequent digital twin-driven, high-precision control, and health prediction, serving as a key guarantee for the entire system to achieve intelligent closed-loop control.

[0046] The status monitoring module includes a model loading unit, a physics engine unit, a visualization rendering unit, and an alarm logic unit. The model loading unit loads the 3D computer-aided design model of the integrated mechanical body. The physics engine unit maps the position, force, and acceleration information from the standardized sensor data package to the kinematic and dynamic states of the corresponding components in the 3D model. The visualization rendering unit renders and displays the motion state of the mechanical body in real-time in a 3D animation format on a graphical interface, and overlays key monitoring parameters in a data panel format. The alarm logic unit predefines judgment logic for various abnormal operating conditions, automatically triggering visual and audible alarm signals when the received sensor data exceeds a safety threshold. In this embodiment, the status monitoring module, through the collaborative work of model loading, the physics engine, visualization rendering, and the alarm logic unit, constructs a high-fidelity digital twin system. This module maps abstract sensor data (such as position, force, and acceleration) in real-time and drives the 3D model to move synchronously, enabling the operator to intuitively and panoramically monitor the actual operating state inside the system, achieving a seamless connection between the physical entity and the virtual model. This visualization monitoring method far exceeds the display dimensions of traditional instruments, improving the intuitiveness and depth of status perception. Meanwhile, the integrated alarm logic unit can perform millisecond-level judgment and multi-modal alarms for abnormal operating conditions, significantly enhancing the system's proactive safety protection capabilities.

[0047] The motion control module includes a trajectory generation unit, a multivariable state feedback controller, a feedforward compensator, a disturbance observer, and a control synthesis unit. The trajectory generation unit uses a high-order spline curve interpolation algorithm to generate a smooth reference trajectory based on the target position, velocity, acceleration, and acceleration constraints. The multivariable state feedback controller takes as input the error between the reference trajectory and the actual state fed back by the standardized sensor data packet, and calculates the initial control quantity using a linear quadratic regulator or model predictive control algorithm. The feedforward compensator generates a feedforward control quantity based on the differential signal of the reference trajectory and the system inverse model to improve the system's tracking performance. The disturbance observer estimates and compensates for external disturbances in real time based on torque sensor feedback and the system model. The control synthesis unit superimposes the initial control quantity, feedforward control quantity, and disturbance compensation quantity output by the state feedback controller to generate the final digital control command and outputs it to the hardware driver layer. In this embodiment, the motion control module, through the collaborative work of trajectory generation, state feedback, feedforward compensation, disturbance observation, and control synthesis unit, constructs a high-precision, robust composite control architecture. This module employs a high-order spline algorithm to generate smooth reference trajectories that satisfy multiple constraints, fundamentally avoiding shocks and vibrations caused by abrupt trajectory changes. By organically integrating state feedback control based on model prediction or linear quadratic regulators, feedforward compensation based on the system inverse model, and disturbance suppression based on real-time torque observation, this control method not only responds quickly to command changes but also proactively predicts and mitigates the effects of multiple factors such as system dynamic lag, model uncertainty, and external load disturbances. This strategy significantly improves the trajectory tracking accuracy and dynamic response stability of the system under complex operating conditions such as high speed and variable load.

[0048] The data maintenance module is configured to execute the following process:

[0049] The data warehousing step stores sensor data, control commands, and alarm events throughout the entire system lifecycle in the form of a time-series database;

[0050] The feature extraction step periodically extracts feature vectors related to mechanical wear and performance degradation from historical data, such as the changing trend of guide rail friction coefficient, motor temperature rise rate, and changes in vibration energy spectrum.

[0051] The health assessment step uses a trained machine learning model to analyze the current feature vector and outputs the system's current health index.

[0052] The lifespan prediction step uses a regression prediction algorithm based on the decline trend of the health index to predict the remaining lifespan of the slider, guide rail, or motor coil, and generates a maintenance suggestion report on the human-machine interface.

[0053] In this embodiment, the data maintenance module achieves in-depth management and intelligent operation and maintenance of the system's entire lifecycle through a closed-loop process of data warehousing, feature extraction, health assessment, and lifespan prediction. This module systematically stores massive amounts of operational data, constructing a valuable digital asset library for the equipment. By automatically extracting key performance degradation features such as friction coefficient trends and temperature rise rates, and combining this with machine learning models for quantitative health assessment, the module can accurately detect early signs of performance degradation that are difficult to perceive with the human eye. Crucially, its lifespan prediction function, based on a data-driven model, enables a scientific estimation of the remaining lifespan of core components.

[0054] A smart control method for an integrated motor and guide rail system, executed by a software module deployed on an edge computing device or an embedded industrial computer, includes the following steps:

[0055] System initialization and parameter configuration steps: The system management module loads the user-preset guide rail travel, motion parameters, control parameters and safety thresholds to complete the initialization configuration and self-test of the hardware platform and software modules;

[0056] Real-time multimodal data synchronous acquisition and fusion steps: The real-time acquisition and processing module synchronously acquires raw data from position, inertia, torque and temperature sensors at a fixed sampling period. After filtering, timestamp alignment and coordinate system unification, a standardized system status data packet is generated.

[0057] Digital twin driving and real-time status monitoring steps: The status monitoring module receives the system status data packet, drives the three-dimensional digital model to move synchronously, and displays the position, speed, load force, temperature and vibration status in real time on the human-machine interface, and performs anomaly judgment and alarm based on safety thresholds.

[0058] Intelligent motion planning and adaptive control steps: The motion control module receives upper-level motion commands, generates a smooth optimal reference trajectory by combining preset constraints, and calculates real-time control quantities based on the system state data packet through multivariate feedback, feedforward compensation and disturbance observation, and outputs them to the motor driver;

[0059] Predictive maintenance and health management steps: The data maintenance module continuously records and stores system operation data in the background, analyzes performance degradation characteristics through machine learning algorithms, evaluates the system health index and predicts the remaining lifespan of key components, and generates maintenance warnings.

[0060] This method ensures the spatiotemporal consistency of multi-source information such as position, inertia, torque, and temperature through rigorous real-time multimodal data synchronization and fusion, providing a highly reliable data foundation for control decisions. Based on this, the intelligent motion planning and adaptive control steps employ a strategy combining feedforward compensation and multivariable feedback, proactively predicting and compensating for system dynamic lag and external disturbances, rather than simply relying on error feedback. This model- and data-driven control approach enables smooth and accurate motion trajectory tracking even under high-speed and variable load conditions, effectively improving the system's response speed and steady-state accuracy. Through digital twin-driven and real-time status monitoring steps, this method maps the actual operating state of the physical system onto a virtual 3D model in real time, achieving panoramic and visual monitoring of the system's internal operation. Operators can not only obtain traditional numerical information but also intuitively perceive the overall behavior of the system. Combined with preset safety thresholds, this method can make millisecond-level judgments and trigger alarms for conditions such as exceeding limits and abnormal vibration, enhancing the safety and reliability of system operation and providing strong protection for critical tasks. Predictive maintenance and health management steps overcome the limitations of traditional periodic maintenance or post-failure repair. By continuously recording operational data and using machine learning algorithms to analyze performance degradation characteristics, the system can accurately assess the current health status and scientifically predict the remaining lifespan of key components. This allows maintenance activities to be precisely scheduled based on the actual health condition of the equipment, avoiding resource waste caused by over-maintenance and preventing unplanned downtime due to sudden failures, thereby significantly improving the overall utilization rate of the equipment and reducing the total lifecycle maintenance cost. This invention, through a process-oriented and intelligent step design, tightly couples perception, decision-making, execution, and maintenance processes, forming a highly efficient, reliable, and adaptive closed-loop control system. It provides an advanced solution for the field of high-end precision motion control, possessing outstanding technical advantages and broad application prospects.

[0061] The specific steps of real-time multimodal data synchronous acquisition and fusion include:

[0062] Parallel data acquisition sub-step: Create multiple independent data acquisition threads corresponding to the sensor type, and use hardware interrupt mechanism to ensure strict synchronization of sampling time;

[0063] Data preprocessing sub-step: Apply low-pass digital filters to the raw data collected by each thread to suppress high-frequency noise;

[0064] Data fusion and standardization sub-steps: Spatial coordinate registration and physical unit unification are performed on the filtered multi-source data, and a unified high-precision timestamp is added. The data is then packaged into a standard data package for use by upper-layer modules.

[0065] In this embodiment, the real-time multimodal data synchronous acquisition and fusion step establishes a high-precision, low-latency data sensing foundation through a collaborative mechanism of parallel acquisition, preprocessing, and standardized fusion. This step utilizes multithreading and hardware interrupt technology to ensure strict time synchronization of data from heterogeneous sensors such as position, torque, and vibration sensors from the source, effectively eliminating the data phase difference problem caused by traditional time-division sampling. Targeted digital filtering significantly suppresses interference such as high-frequency electromagnetic noise in the field, improving the signal-to-noise ratio. Finally, the standardized data packet formed after spatial registration, unit unification, and timestamp alignment provides highly consistent, spatiotemporally aligned multidimensional data input for upper-level status monitoring, motion control, and other modules.

[0066] The specific steps of intelligent motion planning and adaptive control include:

[0067] Trajectory planning sub-step: Using a quintic spline curve interpolation algorithm, a continuous and smooth position, velocity, and acceleration reference trajectory is generated based on the target point command and acceleration constraints;

[0068] The calculation sub-steps of the composite control law are as follows: Based on the error between the reference trajectory and the actual state, the feedback control quantity is calculated through a linear quadratic regulator; at the same time, the feedforward control quantity is calculated based on the acceleration feedforward of the reference trajectory and the inverse model of the system; and based on the feedback from the torque sensor, the external disturbance is estimated in real time through the disturbance observer and the compensation quantity is generated.

[0069] Control quantity synthesis and output sub-step: The above feedback control quantity, feedforward control quantity and disturbance compensation quantity are vector superimposed to generate the final digital control command, which drives the linear motor after digital-to-analog conversion.

[0070] In this embodiment, the intelligent motion planning and adaptive control steps achieve high-precision and high-dynamic tracking of complex commands through the organic combination of trajectory planning, composite control law calculation, and control synthesis. This step employs a quintic spline interpolation algorithm to generate a high-order smooth reference trajectory, fundamentally avoiding abrupt acceleration changes and significantly reducing mechanical shock and vibration. By vector-fusing closed-loop feedback control based on a linear quadratic regulator, feedforward control based on a system model, and disturbance compensation based on real-time observation, this control strategy not only quickly eliminates tracking errors but also actively predicts and counteracts the effects of system inertia, friction, and external load disturbances. This composite control architecture greatly improves the system's dynamic response speed, trajectory tracking accuracy, and adaptive robustness under varying load conditions, ensuring stable and reliable operation of the integrated motor and guide rail system in high-speed, high-precision applications.

[0071] The composite control law calculation sub-step further includes an online parameter identification and adaptive update process:

[0072] Real-time acquisition of input control commands and output motion response data from the system;

[0073] Based on the recursive least squares method, the current equivalent mass and damping coefficient parameters of the online identification system are determined.

[0074] The newly identified parameters are updated in real time to the weight matrix of the linear quadratic regulator and the inverse model of the feedforward compensator, enabling the control system to adapt to load changes and mechanical characteristic drift.

[0075] In this embodiment, the online parameter identification and adaptive update process endows the control system with dynamic self-optimization capabilities, significantly improving its control accuracy and robustness throughout its entire lifecycle. This process, using recursive least squares, identifies key dynamic parameters such as the system's equivalent mass and damping in real-time and online, enabling it to keenly capture system characteristic drift caused by load changes, mechanical wear, or temperature variations. Subsequently, the system automatically updates the identified new parameters into the weight matrix and feedforward compensation model of the linear quadratic regulator, achieving real-time closed-loop optimization of the controller parameters. Its technical advantages lie in effectively overcoming model mismatch problems caused by long-term operation, significantly suppressing the impact of load disturbances on motion accuracy, ensuring that the system maintains high-precision and high-stability motion performance even when facing varying workpiece weights or performance characteristics, reducing reliance on external precise modeling, and enhancing the system's applicability and long-term reliability.

[0076] Predictive maintenance and health management steps specifically include:

[0077] Data archiving sub-step: Continuously store sensor readings, control commands, and alarm event logs throughout the entire lifecycle of the system in the form of a time-series database;

[0078] Feature extraction and degradation assessment sub-steps: periodically calculate feature vectors from historical data, including trends in guide rail friction coefficients, motor winding temperature rise curves, and vibration spectrum characteristic energy values;

[0079] Intelligent prediction sub-step: Input the current feature vector into a pre-trained support vector machine or neural network model, output the current health index of the system, and predict the remaining service life of key components based on the time series prediction model.

[0080] In this embodiment, the predictive maintenance and health management steps transform the traditional reactive or periodic maintenance model into state-based predictive maintenance through data-driven intelligent analysis methods, significantly improving the operational reliability, availability, and lifespan of the integrated motor and guide rail system. This step comprehensively records the system's full-dimensional operational data using a time-series database, providing a solid data foundation for health assessment. The feature extraction process accurately captures key indicators of performance degradation from massive amounts of data, such as gradual changes in friction coefficient, abnormal temperature rise, and vibration energy evolution. Furthermore, intelligent models such as support vector machines or neural networks are used to accurately quantify and assess the system's health status, and time-series prediction technology is used to proactively forecast the remaining service life of key components. The technical advantage lies in enabling early warning of potential faults, allowing users to plan maintenance in the early stages of performance degradation or before a fault occurs, effectively avoiding production interruptions caused by unplanned downtime. Simultaneously, precise timing of maintenance avoids resource waste caused by over-maintenance.

[0081] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. An integrated motor and guide rail system, characterized in that: Based on an integrated platform for motor and guide rail, the integrated platform includes an integrated mechanical body that integrates the linear motor stator and precision guide rail on the same base, a slider driven by the motor mover, and a multi-modal sensor group mounted on the slider; It also includes the following core software modules deployed on edge computing devices or embedded industrial computers: The system management module provides a graphical human-computer interaction interface, receives user input of guide rail travel, motion parameters, control parameters and safety thresholds, and completes system initialization configuration. The real-time acquisition and processing module establishes a high-speed data link with the multimodal sensor group. It is responsible for synchronously acquiring the raw data of all sensors at a fixed sampling period, and performing preprocessing such as filtering, timestamp alignment and unit standardization on the data to generate standardized sensor data packets. The status monitoring module has a built-in three-dimensional digital model with the same physical properties as the integrated mechanical body. It receives standardized sensor data packets, drives the digital model to perform real-time synchronous motion, and dynamically displays the system's real-time position, speed, load force, temperature and vibration status on the human-machine interface. The motion control module receives motion commands from the upper-layer application, generates a smooth, continuous, and dynamically optimal reference trajectory in real time based on preset motion constraints and optimization objectives, and calculates the real-time control quantity for driving the linear motor by combining the standardized sensor data package with an advanced control algorithm. The data maintenance module continuously records all sensor and control data during system operation, analyzes data characteristics through machine learning algorithms, predicts the remaining service life of key components, and issues maintenance warnings in advance.

2. The integrated motor and guide rail system according to claim 1, characterized in that: The real-time acquisition and processing module is configured as follows: Multiple independent data acquisition threads are created, corresponding to the position sensor, inertial measurement unit, torque sensor, and temperature sensor, respectively; Hardware interrupts or high-precision timers are used to ensure strict timing synchronization of all data acquisition threads; The collected raw data is denoised using digital filters, and the data of different physical quantities are converted to spatial coordinate system and unit, and finally packaged into a standardized data structure with a unified timestamp.

3. The integrated motor and guide rail system according to claim 1, characterized in that: The status monitoring module includes a model loading unit, a physics engine unit, a visualization rendering unit, and an alarm logic unit. The model loading unit is used to load the three-dimensional computer-aided design model of the integrated mechanical body. The physics engine unit maps the position, force, and acceleration information in the standardized sensor data package to the kinematic and dynamic states of the corresponding components in the three-dimensional model. The visualization rendering unit is used to render and display the motion state of the mechanical body in real time in the form of three-dimensional animation on the graphical interface, and overlay key monitoring parameters in the form of a data panel. The alarm logic unit predefines the judgment logic for various abnormal working conditions. When the received sensor data exceeds the safety threshold, it automatically triggers visual and audible alarm signals.

4. The integrated motor and guide rail system according to claim 1, characterized in that: The motion control module includes a trajectory generation unit, a multivariable state feedback controller, a feedforward compensator, a disturbance observer, and a control synthesis unit. The trajectory generation unit uses a high-order spline curve interpolation algorithm to generate a smooth reference trajectory based on the target position, velocity, acceleration, and acceleration constraints. The multivariable state feedback controller takes as input the error between the reference trajectory and the actual state fed back by the standardized sensor data packet, and calculates the initial control quantity using a linear quadratic regulator or model predictive control algorithm. The feedforward compensator generates a feedforward control quantity based on the differential signal of the reference trajectory and the inverse model of the system to improve the system's tracking performance. The disturbance observer estimates and compensates for external disturbances in real time based on torque sensor feedback and the system model. The control synthesis unit superimposes the initial control quantity, feedforward control quantity, and disturbance compensation quantity output by the state feedback controller to generate the final digital control command and outputs it to the hardware driver layer.

5. The integrated motor and guide rail system according to claim 1, characterized in that: The data maintenance module is configured to execute the following process: The data warehousing step stores sensor data, control commands, and alarm events throughout the entire system lifecycle in the form of a time-series database; The feature extraction step periodically extracts feature vectors related to mechanical wear and performance degradation from historical data, such as the changing trend of guide rail friction coefficient, motor temperature rise rate, and changes in vibration energy spectrum. The health assessment step uses a trained machine learning model to analyze the current feature vector and outputs the system's current health index. The lifespan prediction step uses a regression prediction algorithm based on the decline trend of the health index to predict the remaining lifespan of the slider, guide rail, or motor coil, and generates a maintenance suggestion report on the human-machine interface.

6. An intelligent control method for an integrated motor and guide rail system as described in any one of claims 1 to 5, characterized in that: Performed by a software module deployed on an edge computing device or embedded industrial computer, the steps include: System initialization and parameter configuration steps: The system management module loads the user-preset guide rail travel, motion parameters, control parameters and safety thresholds to complete the initialization configuration and self-test of the hardware platform and software modules; Real-time multimodal data synchronous acquisition and fusion steps: The real-time acquisition and processing module synchronously acquires raw data from position, inertia, torque and temperature sensors at a fixed sampling period. After filtering, timestamp alignment and coordinate system unification, a standardized system status data packet is generated. Digital twin driving and real-time status monitoring steps: The status monitoring module receives the system status data packet, drives the three-dimensional digital model to move synchronously, and displays the position, speed, load force, temperature and vibration status in real time on the human-machine interface, and performs anomaly judgment and alarm based on safety thresholds. Intelligent motion planning and adaptive control steps: The motion control module receives upper-level motion commands, generates a smooth optimal reference trajectory by combining preset constraints, and calculates real-time control quantities based on the system state data packet through multivariate feedback, feedforward compensation and disturbance observation, and outputs them to the motor driver; Predictive maintenance and health management steps: The data maintenance module continuously records and stores system operation data in the background, analyzes performance degradation characteristics through machine learning algorithms, evaluates the system health index and predicts the remaining lifespan of key components, and generates maintenance warnings.

7. The intelligent control method for the integrated motor and guide rail system according to claim 6, characterized in that: The real-time multimodal data synchronous acquisition and fusion steps specifically include: Parallel data acquisition sub-step: Create multiple independent data acquisition threads corresponding to the sensor type, and use hardware interrupt mechanism to ensure strict synchronization of sampling time; Data preprocessing sub-step: Apply low-pass digital filters to the raw data collected by each thread to suppress high-frequency noise; Data fusion and standardization sub-steps: Spatial coordinate registration and physical unit unification are performed on the filtered multi-source data, and a unified high-precision timestamp is added. The data is then packaged into a standard data package for use by upper-layer modules.

8. The intelligent control method for the integrated motor and guide rail system according to claim 7, characterized in that: The intelligent motion planning and adaptive control steps specifically include: Trajectory planning sub-step: Using a quintic spline curve interpolation algorithm, a continuous and smooth position, velocity, and acceleration reference trajectory is generated based on the target point command and acceleration constraints; The calculation sub-steps of the composite control law are as follows: Based on the error between the reference trajectory and the actual state, the feedback control quantity is calculated through a linear quadratic regulator; at the same time, the feedforward control quantity is calculated based on the acceleration feedforward of the reference trajectory and the inverse model of the system; and based on the feedback from the torque sensor, the external disturbance is estimated in real time through the disturbance observer and the compensation quantity is generated. Control quantity synthesis and output sub-step: The above feedback control quantity, feedforward control quantity and disturbance compensation quantity are vector superimposed to generate the final digital control command, which drives the linear motor after digital-to-analog conversion.

9. The intelligent control method for the integrated motor and guide rail system according to claim 8, characterized in that: The composite control law calculation sub-step further includes an online parameter identification and adaptive update process: Real-time acquisition of input control commands and output motion response data from the system; Based on the recursive least squares method, the current equivalent mass and damping coefficient parameters of the online identification system are determined. The newly identified parameters are updated in real time to the weight matrix of the linear quadratic regulator and the inverse model of the feedforward compensator, enabling the control system to adapt to load changes and mechanical characteristic drift.

10. The intelligent control method for the integrated motor and guide rail system according to claim 6, characterized in that: The predictive maintenance and health management steps specifically include: Data archiving sub-step: Continuously store sensor readings, control commands, and alarm event logs throughout the entire lifecycle of the system in the form of a time-series database; Feature extraction and degradation assessment sub-steps: periodically calculate feature vectors from historical data, including trends in guide rail friction coefficients, motor winding temperature rise curves, and vibration spectrum characteristic energy values; Intelligent prediction sub-step: Input the current feature vector into a pre-trained support vector machine or neural network model, output the current health index of the system, and predict the remaining service life of key components based on the time series prediction model.

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