Displacement error dynamic compensation method and device, equipment and medium

Abstract

The invention relates to the technical field of motor control and sensors, and discloses a displacement error dynamic compensation method, device and equipment and a medium, and the method comprises the steps: obtaining the temperature data and original displacement data of a plurality of temperature measurement points in a moving part and an environment, calling a pre-built nonlinear mapping relation between a temperature gradient and a displacement deviation, and calculating the displacement error of the moving part; based on the nonlinear mapping relation, adaptive filtering is carried out on the temperature data and the original displacement data, a trend component representing temperature drift is separated out, displacement data without the trend component is obtained, and a dynamic compensation instruction is generated based on the trend component and the nonlinear mapping relation; and adjusting the displacement data of which the trend component is removed and outputting compensation displacement data. According to the method, the nonlinear mapping model of the temperature gradient and the displacement deviation is constructed, the drift trend is extracted in combination with multi-point temperature sensing and self-adaptive filtering, the compensation instruction is dynamically generated, accurate real-time correction of the displacement error is achieved, and the responsiveness and accuracy of compensation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of motor control and sensor technology, and particularly relates to a displacement error dynamic compensation method, device, equipment and storage medium. BACKGROUND

[0002] In a high-precision motor control system, the encoder is a key component for displacement detection, and its precision directly affects the overall positioning performance of the system. However, traditional encoders generally lack real-time monitoring capability for the internal temperature state of the motor, and often rely on external sensors to measure the ambient temperature or the shell temperature. Such indirect monitoring means not only cannot reflect the real temperature distribution inside the encoder or at the key positions of the moving parts, but also has a response delay problem, which is difficult to meet the requirements of dynamic control systems with high sensitivity to thermal response.

[0003] In addition, the existing displacement compensation method is mostly based on a static temperature compensation model, which only corrects the error at a constant temperature, and does not fully consider the nonlinear shift caused by the change of temperature over time. Due to uneven distribution of internal heat sources during motor operation, the expansion degree of each structural component is different, causing local thermal deformation. The lack of dynamic compensation mechanism will lead to error accumulation and continuous interference to the positioning accuracy.

[0004] More importantly, the current commonly used temperature compensation strategy fails to establish a systematic correlation between displacement error and temperature change, and cannot adjust the compensation strategy according to the real-time temperature gradient. This non-targeted compensation method is particularly prominent in high-speed or high-load application scenarios, resulting in limited overall compensation accuracy and difficulty in meeting the actual needs of high-precision servo control systems for dynamic elimination of small errors. SUMMARY

[0005] The main purpose of the present application is to provide a displacement error dynamic compensation method, device, equipment and storage medium, which aims to solve the technical problems that the prior art cannot realize nonlinear temperature gradient and displacement deviation correlation modeling and dynamic compensation based on multi-point temperature sensing, resulting in low displacement error compensation accuracy and poor real-time performance in complex thermal field environment.

[0006] To achieve the above purpose, the present application provides a displacement error dynamic compensation method, comprising: obtaining temperature data at multiple temperature measurement points inside the moving part and the environment in which it is located, and obtaining original displacement data of the moving part; calling a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation, the nonlinear mapping relationship being established by calibrating displacement deviation curves at different temperatures; Adaptively filtering the temperature data and the original displacement data based on the nonlinear mapping relationship, separating a trend component representing temperature drift, and obtaining displacement data without the trend component; Generating dynamic compensation instructions based on the trend component and the nonlinear mapping relationship; Adjusting the displacement data without the trend component according to the dynamic compensation instructions, and outputting compensated displacement data.

[0007] Further, to achieve the above object, the present application provides a displacement error dynamic compensation device, comprising: A temperature and displacement acquisition module for acquiring temperature data at multiple temperature measuring points inside a moving component and an environment thereof, and acquiring original displacement data of the moving component; A nonlinear mapping construction module for calling a nonlinear mapping relationship between temperature gradient and displacement deviation pre-constructed, the nonlinear mapping relationship being established by calibrating displacement deviation curves at different temperatures; An adaptive filtering module for adaptively filtering the temperature data and the original displacement data based on the nonlinear mapping relationship, separating a trend component representing temperature drift, and obtaining displacement data without the trend component; A compensation instruction generation module for generating dynamic compensation instructions based on the trend component and the nonlinear mapping relationship; A displacement compensation execution module for adjusting the displacement data without the trend component according to the dynamic compensation instructions, and outputting compensated displacement data.

[0008] Further, to achieve the above object, the present application also provides a computer device, comprising a memory, a processor, and a displacement error dynamic compensation program stored on the memory and executable on the processor, the displacement error dynamic compensation program being executed by the processor to implement steps of the displacement error dynamic compensation method as described above.

[0009] Further, to achieve the above object, the present application also provides a computer readable storage medium, the storage medium storing a displacement error dynamic compensation program, the displacement error dynamic compensation program being executed by a processor to implement steps of the displacement error dynamic compensation method as described above.

[0010] Beneficial effects: The application relates to the field of motor control and sensor technology and discloses a displacement error dynamic compensation method, device, equipment and medium, which comprises the following steps: acquiring temperature data of multiple temperature measuring points in a moving component and an environment where the moving component is located, acquiring original displacement data of the moving component, calling a pre-constructed nonlinear mapping relationship between a temperature gradient and a displacement deviation, adaptively filtering the temperature data and the original displacement data based on the nonlinear mapping relationship to separate a trend component representing temperature drift, and obtaining displacement data from which the trend component is removed, generating a dynamic compensation instruction based on the trend component and the nonlinear mapping relationship, adjusting the displacement data from which the trend component is removed according to the dynamic compensation instruction, and outputting compensated displacement data. The application can dynamically perceive and model the influence of temperature change on displacement error by perceiving multiple temperature points and establishing a nonlinear mapping relationship between a temperature gradient and a displacement deviation, and can further extract a temperature drift trend and generate a dynamic compensation instruction by combining an adaptive filtering method, so that real-time compensation of displacement error can be realized under different thermal load conditions, and the compensation accuracy and response speed are improved. BRIEF DESCRIPTION OF DRAWINGS

[0011] The application will be further described below in combination with the drawings and embodiments, and the drawings are as follows: Figure 1 An application environment diagram of a displacement error dynamic compensation method in an embodiment of the application; Figure 2 A flow diagram of the displacement error dynamic compensation method in an embodiment of the application; Figure 3 A functional module diagram of a preferred embodiment of the displacement error dynamic compensation device of the application; Figure 4 A structure diagram of a computer device in an embodiment of the application; Figure 5 Another structure diagram of a computer device in an embodiment of the application. DETAILED DESCRIPTION

[0012] It should be understood that the specific embodiments described herein are merely intended to explain the application, and are not intended to limit the application.

[0013] The displacement error dynamic compensation method provided by the embodiments of the application can be applied to, for example, Figure 1The application is applied to an application environment in which a user terminal communicates with a service terminal through a network. The service terminal can obtain temperature data at multiple temperature measuring points inside a moving component and an environment in which the moving component is located, obtain original displacement data of the moving component, call a pre-constructed nonlinear mapping relationship between a temperature gradient and a displacement deviation, perform adaptive filtering processing on the temperature data and the original displacement data based on the nonlinear mapping relationship to separate a trend component representing temperature drift, and obtain displacement data from which the trend component is removed. The service terminal generates a dynamic compensation instruction based on the trend component and the nonlinear mapping relationship, adjusts the displacement data from which the trend component is removed according to the dynamic compensation instruction, and outputs compensated displacement data. The application can dynamically perceive and model the influence of temperature change on displacement error by multi-point temperature sensing and establishing a nonlinear mapping relationship between a temperature gradient and a displacement deviation, and can extract a temperature drift trend and generate a dynamic compensation instruction by combining an adaptive filtering method, so that real-time compensation for displacement error is realized under different thermal load conditions, and the accuracy and response speed of compensation are improved. The user terminal can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The service terminal can be implemented by an independent server or a server cluster composed of multiple servers. The application will be described in detail through specific embodiments.

[0014] Please refer to Figure 2 , Figure 2 The flowchart of an embodiment of the displacement error dynamic compensation method provided by the application is shown. It should be noted that although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown.

[0015] As Figure 2 shown, the displacement error dynamic compensation method provided by the application includes the following steps: S10, obtaining temperature data at multiple temperature measuring points inside a moving component and an environment in which the moving component is located, and obtaining original displacement data of the moving component; In this embodiment, in the process of dynamically compensating displacement error, first, temperature data at multiple temperature measuring points inside a moving component and an environment in which the moving component is located is obtained. The temperature data sources include different structural regions of the moving component. In order to more accurately capture changes in heat sources and their effects on the structure, multiple temperature sensors need to be arranged at the stator assembly, the rotor region, the encoder region, and the heat exchange boundary. These sensor types can include thermocouples, thermistors, or integrated digital temperature acquisition chips, and their deployment methods can be adjusted according to installation space and thermal coupling characteristics, such as being closely adhered to the surface of the stator winding, or using a wireless temperature acquisition module to realize dynamic data transmission in the rotor.

[0016] In addition to acquiring thermal information within the structure, it is also necessary to obtain the temperature influence of the external environment. Therefore, ambient temperature sensors need to be deployed near the outer shell or cooling interface of moving parts. This temperature information can be sampled in real time and transmitted in parallel to the processing unit to form a timestamped temperature monitoring dataset. The data collected by each sensor needs to be corrected through a unified time synchronization mechanism to avoid misjudgments of the spatial thermal state due to acquisition delays or network jitter.

[0017] The acquisition of displacement data relies on displacement detection encoders on moving parts. These encoders can be incremental photoelectric encoders, absolute encoders, or magnetic encoders. Their installation position must be close to the axis of displacement being observed to ensure accurate sensing of areas sensitive to thermal deformation. The raw position signal generated by the encoder is typically a pulse sequence or digital angle data, which needs to be converted into physical displacement values ​​through decoding logic.

[0018] After the temperature and displacement data are collected, spatial point binding and data alignment operations need to be performed. This involves establishing a mapping relationship between the spatial coordinates of each temperature sampling point and the corresponding mechanical structure area, and aligning the data of each channel according to the time dimension to ensure that temperature changes and displacement changes are synchronized. This provides an accurate input basis for subsequent temperature drift trend extraction and displacement error modeling.

[0019] In practical implementation, thermocouple sensors can be attached to three symmetrical locations in the stator assembly, and analog signals can be transmitted to the main control board in real time through the analog-to-digital conversion module; a wireless temperature sensor can be selected in the rotor to send data through Bluetooth or radio frequency channel; an embedded NTC sensor is used on the encoder housing, which is read synchronously through the I²C interface; the ambient temperature sensor can be a DS18B20 type digital temperature sensor, which is fixed in the air-cooling channel or outside the housing.

[0020] The displacement detection encoder uses a magnetic absolute encoder with a resolution of no less than 0.001mm, and uploads the position signal to the processing platform via the RS485 protocol. The sampling frequency of all sensors and encoders is uniformly set to 10Hz, and a synchronization clock is set for data alignment. If deployed in high-speed rotating environments, a dithering mechanism or filtering buffer needs to be added to handle transient data jumps.

[0021] In the data preprocessing stage, a multi-channel data fusion mechanism is used to perform spatial interpolation calculations on multiple temperature points to form a three-dimensional heat distribution matrix. At the same time, the encoder displacement data is smoothed and abnormal mutation sample points are removed to ensure that the basic data used for nonlinear mapping modeling is accurate and stable.

[0022] Example Description: In a high-precision servo motor application, to compensate for axial displacement errors caused by stator winding temperature rise during prolonged operation, three K-type thermocouple sensors are deployed at the winding slot openings, a wireless infrared temperature module is installed at the rotor center, a digital thermometer is placed in the environmental control box, and an absolute encoder is installed at the output shaft end. Once the equipment starts operating, the system periodically collects data from all sensors and encoders, synchronizing it to the host computer via a data acquisition card. Combined with subsequent mapping models and filtering algorithms, the displacement compensation value can be updated in real time, effectively compressing displacement deviations caused by thermal drift and improving the stability and accuracy of system operation.

[0023] This embodiment simultaneously acquires temperature information and actual displacement data from multiple key structural components, establishing a joint spatial and temporal dataset. This effectively captures the impact of minute deformations caused by local heat sources on displacement errors, avoiding the lag and bias inherent in traditional static corrections using only ambient temperature or single-point temperature. This approach improves the resolution and accuracy of temperature field monitoring, thus providing a more refined input basis for high-precision displacement compensation.

[0024] S20, invoke the pre-built nonlinear mapping relationship between temperature gradient and displacement deviation, which is established by calibrating the displacement deviation curves at different temperatures; In this embodiment, to achieve dynamic displacement compensation based on temperature conditions, an established nonlinear mapping relationship needs to be invoked. This mapping relationship describes the correspondence between temperature gradient and displacement deviation. This nonlinear mapping relationship is not directly assumed to be in the form of a function, but is generated through an experimental calibration process. Its core lies in establishing displacement deviation curves under multiple temperature conditions and extracting the law of influence of temperature field changes on structural displacement.

[0025] The construction of the nonlinear mapping relationship relies on the spatiotemporal coupling characteristics between the temperature gradient and the displacement offset. The temperature gradient refers to the temperature difference between different measurement points, which generates local thermal stress in the structural material, leading to microscopic or macroscopic displacement deviations. This deviation does not exhibit a simple linear response relationship; therefore, it is necessary to establish a nonlinear function mapping between multivariate inputs and the target displacement deviation through experimental fitting or machine learning modeling methods. Typically, this process encompasses the following characteristic inputs: the spatial coordinates of the measurement points, the corresponding temperature value, the rate of temperature change per unit time, and the initial displacement reference value. The target output is the cumulative displacement offset under a specific thermal state.

[0026] This nonlinear mapping relationship can be expressed using neural network models, support vector regression models, radial basis function networks, or multidimensional fitting surfaces based on interpolation. The calibration data acquisition process involves multiple rounds of control experiments. Under constant speed and load conditions, the ambient temperature is gradually increased while displacement deviation values ​​are collected simultaneously. Finally, a set of function parameters or a trained model is obtained using fitting tools for subsequent compensation calculations.

[0027] When invoking this nonlinear mapping relationship, the currently acquired temperature data needs to be input into the corresponding model, which then outputs an estimated displacement error for the current moment. This estimate serves as the numerical basis for the temperature-induced displacement trend, providing crucial data support for subsequent filtering and compensation processes.

[0028] In practical implementation, historical experimental data can be used to calibrate a specific model of motor. The ambient temperature is set to gradually increase from 25°C to 85°C, and the differences between internal temperature measurement data and actual displacement data at different time points are recorded. A functional mapping relationship between the temperature gradient vector and the corresponding displacement error is established through these experimental points. In the modeling stage, a multilayer perceptron neural network is used. The input layer includes the temperature value of each temperature measurement point, the initial displacement state, the current ambient temperature, and the rate of change. The output is the displacement deviation at the corresponding time point.

[0029] After training, the model is deployed to the computing module. In subsequent operation phases, the collected real-time temperature data vector is directly input, and the corresponding displacement deviation prediction value is output. This process achieves low-latency compensation calculation through high-frequency parallel computing, ensuring a balance between real-time performance and accuracy.

[0030] In high-stability applications, fitting functions can be established using mathematical methods such as spline interpolation or Chebyshev polynomial fitting, and stored in the system parameter table in a lookup table format. During the call, the displacement error estimate under the current temperature condition can be quickly obtained through multi-dimensional indexing, simplifying the computational burden.

[0031] Example Description: Taking an automated precision platform as an example, in the initial construction phase, a standard testing platform is used to perform long-term sampling of its axial displacement error under different temperature distributions, obtaining 100 sets of temperature-displacement paired data. Based on this data, a neural network is built using the TensorFlow framework in Python, ultimately training a model with 6 input dimensions, 3 hidden layers, and a single-value deviation prediction output. During subsequent system operation, the temperature values ​​of the current six temperature measurement points are collected in real time and input into the model, obtaining the current estimated displacement deviation once per second. This is used to assist in displacement filtering and dynamic compensation command generation, significantly improving the platform's positioning stability and repeatability accuracy.

[0032] This embodiment effectively models the complex influence of temperature gradients on displacement by constructing and utilizing a nonlinear mapping relationship established based on calibration experiments, overcoming the limitations of traditional linear correction models that cannot adapt to nonlinear errors. This mechanism possesses excellent generalization ability and real-time responsiveness, accurately identifying and predicting displacement error trends under different temperature conditions, thus providing high-precision input for subsequent compensation strategies.

[0033] S30, Based on the nonlinear mapping relationship, the temperature data and the original displacement data are subjected to adaptive filtering to separate the trend component characterizing the temperature drift and obtain the displacement data after removing the trend component. In this embodiment, to further reduce displacement measurement errors caused by changes in the thermal environment, adaptive filtering is performed by combining the original displacement data and temperature data based on the established nonlinear mapping relationship. The core of this processing flow lies in separating the temperature drift trend superimposed on the displacement change signal and extracting the displacement response that does not contain the temperature influence, so as to obtain more realistic structural displacement data.

[0034] Adaptive filtering dynamically adjusts filter parameters based on changes in the external environment and the state of the system under test, thereby extracting and suppressing non-stationary interference components in the signal. The filter used here does not employ fixed weights; instead, it uses the output of the nonlinear mapping relationship as the filter's adjustment factor. This allows the filter to sense changes in the current thermal field, thus specifically enhancing its ability to capture drift components. This process goes beyond simple high-pass or low-pass processing; it introduces a trend modeling mechanism to accurately extract the slow-moving terms in the displacement signal caused by temperature gradient changes.

[0035] The trend component is defined here as a displacement trajectory that changes slowly over time and is highly correlated with the current temperature gradient characteristics. After extraction, the original displacement data is subtracted from this trend component to obtain the structural response value after removing the temperature effect. In this process, the trend component is an estimate of the temperature response displacement output based on a nonlinear mapping relationship, and then a filter is used to identify and separate the corresponding components in the actual data.

[0036] The key to this process lies in the dynamic update strategy for the filter parameters, which is based on factors including the current rate of temperature change, the magnitude of the temperature gradient, the residuals of the nonlinear model, and the rate of change of displacement data. By setting a sliding window or exponentially weighted moving average mechanism, the adaptive filter can maintain a fast response while possessing strong robustness.

[0037] In practical applications, the system first inputs real-time multi-point temperature data into a nonlinear mapping model, outputting a set of predicted displacement trend values ​​caused by the current temperature. This predicted trend is then used as the desired drift signal and compared with the original displacement data using adaptive filtering.

[0038] A variable-parameter Kalman filter structure can be used to introduce the nonlinear model output into the state transition equation and update the state estimate by combining the measurement noise of the original displacement data. When the nonlinear mapping output changes drastically, the weight of the filter on the drift component is dynamically increased; when the temperature changes slowly, the response trajectory dominated by the original signal is regressed.

[0039] Another approach is a combined filtering method based on empirical mode decomposition (EMD) and nonlinear function adjustment. First, the modal components of each order are decomposed by EMD. Then, based on the nonlinear model, it is determined which mode is related to temperature drift. Component screening and reconstruction are then performed to retain the true response components.

[0040] The final output of the system is displacement data with the trend component removed, which can be used for subsequent dynamic compensation.

[0041] Example Description: Taking a servo positioning system as an example, the original encoder displacement data exhibited a periodic, slow drift during long-term operation, which analysis revealed was consistent with the body's temperature rise trend. In the experiment, a three-layer neural network model was constructed, and data from six temperature measurement points were input to obtain an estimate of the current drift trend. A variable-step-size weighted filter was used to adjust the estimate in real time. The filter was initially set to a 50 ms response window, which was shortened to 20 ms during rapid thermal deformation, and then automatically adjusted back according to the rate of temperature change. The detrended displacement data obtained after filtering had a standard deviation reduced by approximately 60% compared to the original data, providing reliable input for subsequent compensation command generation.

[0042] This embodiment combines nonlinear mapping with an adaptive filtering strategy to achieve real-time identification and separation of temperature-driven displacement error components, making the original displacement signal more accurate and stable after removing the effects of heat. Compared to fixed-parameter filters, dynamically adjusted filtering methods can respond more sensitively to changes in the temperature field, effectively improving the accuracy of pre-compensation processing and reducing cumulative errors.

[0043] S40, Based on the trend component and the nonlinear mapping relationship, generate a dynamic compensation command; In this embodiment, after extracting the trend component, to further improve the immediacy and accuracy of the compensation response, it is necessary to construct a compensation control signal, i.e., a dynamic compensation command, for the trend component. The generation of this command depends on the functional linkage between the trend component and the established nonlinear mapping relationship. The control parameters required for compensation are derived through the mapping model, thereby adapting to the dynamic displacement correction requirements under different thermal states.

[0044] Dynamic compensation commands are a sequence of control parameters used to correct the current displacement response, typically including information such as displacement direction, compensation amplitude, and duration. Essentially, they generate a corresponding inverse correction path based on the nonlinear relationship between thermally induced trends and the original displacement, enabling the system to adjust and counteract thermal drift in the actual response.

[0045] The trend component is considered as an estimate of the displacement shift caused by the current thermal field, while the nonlinear mapping relationship provides a model of the displacement change function driven by temperature. By performing an inverse mapping on the trend component, the range of compensation values ​​that the system should output at present is obtained, and based on the time series characteristics, it is transformed into a continuous compensation command stream.

[0046] The generation of compensation instructions not only considers the amplitude characteristics of the trend component itself, but also introduces the trend change rate as a response adjustment factor to ensure timely updates to the compensation instructions when thermal drift accelerates or reverses. Furthermore, compensation instructions are typically encapsulated in structured data format, containing fields such as timestamp, execution priority, and reset flag to support real-time parsing and scheduling execution by subsequent modules.

[0047] The system can functionalize nonlinear mapping relationships based on multinomial fitting or neural network structures, receiving trend components as input and calculating the reverse correction value required for the current compensation. For example, if the nonlinear mapping model uses a radial basis function network (RBF), the trend component input will activate the corresponding hidden units and output compensation coefficients, which are then used to construct vectorized compensation instructions.

[0048] In implementation, the dynamic compensation command can be expressed in a difference control format: compensation displacement = –F(trend component), where F is a regression function based on a nonlinear mapping relationship. This function structure supports continuous input updates and sliding prediction, meaning that the compensation command is dynamically adjusted as the trend component changes over time.

[0049] Another approach is to construct a pre-trained lookup table, which maps trend components under different amplitude and rate combinations to specific compensation instruction templates. The optimal matching set of compensation parameters is obtained through a fast lookup table and injected into the downstream execution module in real time.

[0050] Optionally, to improve the robustness and fault tolerance of instructions, a dynamic instruction smoothing mechanism can be introduced to perform linear or nonlinear interpolation on the compensation parameters near the time step, making the compensation control process more continuous and stable and preventing mechanical system resonance caused by compensation fluctuations.

[0051] Example Description: Taking a high-precision CNC positioning platform as an example, under the influence of a heat source, its structural thermal deformation leads to a gradual accumulation of displacement, forming a significant trend drift. The system collects the current temperature distribution through deployed multi-point temperature measurement units and extracts the trend component using a filtering module. The system's built-in neural network nonlinear mapping model accepts the trend component as input and outputs the corresponding negative compensation value, generating a dynamic compensation command containing the compensation amplitude and direction. This command is continuously sent to the displacement execution controller with an update cycle of 10 ms, achieving rapid tracking and correction of thermal drift.

[0052] This embodiment generates dynamic compensation commands by combining trend components with nonlinear mapping relationships, achieving both adaptability and precision in compensation control. This mechanism can calculate and output a compensation control sequence that is inversely matched to the offset change trend caused by the thermal field in real time, avoiding cumulative distortion caused by compensation delays or static error estimation, and effectively improving the stability and controllability of high-precision displacement systems under complex thermal environments.

[0053] S50, according to the dynamic compensation instruction, adjust the displacement data after removing the trend component, and output the compensated displacement data.

[0054] In this embodiment, after generating the dynamic compensation command, the system needs to apply the compensation control signal to the displacement data after removing the trend component, thereby outputting the compensated displacement data after thermal drift correction. The key to this process is how to accurately and without delay integrate the dynamic compensation command into the displacement result after filtering out the effects of temperature drift, using methods such as numerical superposition, state control, or response interpolation.

[0055] Displacement data with trend components removed is a numerical sequence that has been filtered to eliminate disturbances strongly correlated with temperature changes, preserving the true displacement response characteristics of the measured object under actual physical driving forces. Using this data as the baseline for compensation processing allows for displacement correction without introducing redundant disturbances.

[0056] The dynamic compensation command, as a control input, typically includes information such as the compensation displacement amplitude, direction, and application time window. The system can perform point-to-point mapping between the command and the displacement data after removing the trend component, or perform sliding weighted fusion on the time axis to finally obtain the compensation displacement data after correcting the offset effect.

[0057] The compensation method can employ a weighted superposition mechanism, whereby at each time sampling point, the dynamic compensation value is directly superimposed with the current displacement value after removing the trend component, constructing a new displacement output sequence. This superposition operation needs to be performed under synchronous timing control to ensure precise alignment of the compensation command and displacement data on the time axis.

[0058] To improve the smoothness and stability of the output results, the system can also be equipped with a buffer module to input multiple consecutive compensation commands and corresponding displacement data into a linear interpolator or a Bézier curve fitter to construct a smooth and continuous compensation displacement trajectory, preventing mechanical oscillations or data anomalies caused by sudden displacement changes.

[0059] During processing, the system can perform a one-to-one compensation operation between the dynamic compensation command and the displacement data after removing the trend component, step by step,. Specifically, the compensation model can be defined as follows: Compensated displacement = Displacement after removing trend component + Compensation command output value The model supports interrupt-based calls in the standard controller. That is, at each sampling point, the displacement value after removing the trend component is read first, then the compensation instruction value at the current moment is read, the sum is executed, and the compensation displacement data cache is updated.

[0060] When the compensation command contains multidimensional quantities (such as direction vectors), a vector superposition method can be introduced to combine the displacement vector and the compensation vector according to their component directions, thereby constructing the compensation response data of a three-dimensional space or multi-axis system. For cases of discontinuous sampling or compensation packet loss, a difference prediction model can be constructed to estimate the compensation parameters of the current frame using the compensation values ​​of previous and subsequent frames, thus enhancing the integrity of the data stream.

[0061] At the output layer, a data formatting and timestamp binding mechanism can be set to encapsulate the compensated displacement data in a structured manner, including fields such as the compensated value, the original displacement value, the compensation amplitude, and the effective window. This facilitates direct parsing and verification by downstream devices and is suitable for various scenarios such as precision motion control and thermal stability monitoring.

[0062] Example Description: In a high-precision linear displacement measurement system, the displacement of the target object is acquired in real time by a magnetic grating sensor. Due to the inconsistent thermal expansion between the sensor body and the structural components, there is a temperature-dependent offset in the acquired displacement. The system removes the trend component through a pre-processing module, obtains the displacement data with the trend component removed, and generates real-time updated dynamic compensation instructions based on the trend component.

[0063] The control module adds the compensation command to the current displacement data and outputs real-time compensated displacement data. The output data is sent to the host controller via serial port, achieving high-precision feedback control under thermal drift conditions. Experimental results show that this processing step reduces the overall displacement error to approximately 20% of the original error, while maintaining high stability and repeatability within an operating range where temperature variations exceed 15°C.

[0064] This embodiment applies dynamic compensation commands to displacement data after removing trend components, enabling reverse correction of thermal drift disturbances and significantly improving the accuracy and usability of displacement data in variable temperature environments. This processing mechanism supports continuous and adaptive adjustment, dynamically offsetting errors caused by thermal changes and avoiding system performance degradation due to miscompensation or delayed response, thus providing an accurate and stable data foundation for subsequent feedback control and state judgment.

[0065] This invention relates to the field of motor control and sensor technology, and discloses a method, device, equipment, and medium for dynamic compensation of displacement errors. The method includes: acquiring temperature data at multiple temperature measurement points inside a moving part and in its environment; acquiring the original displacement data of the moving part; calling a pre-constructed nonlinear mapping relationship between temperature gradient and displacement deviation; performing adaptive filtering on the temperature data and the original displacement data based on this nonlinear mapping relationship to separate the trend component representing temperature drift, and obtaining displacement data with this trend component removed; generating a dynamic compensation command based on the trend component and the nonlinear mapping relationship; adjusting the displacement data with the trend component removed according to the dynamic compensation command; and outputting compensated displacement data. This invention achieves real-time compensation of displacement errors under different thermal load conditions by sensing temperatures at multiple points and establishing a nonlinear mapping relationship between temperature gradient and displacement deviation, enabling dynamic sensing and modeling of the impact of temperature changes on displacement errors. Combined with an adaptive filtering method to extract temperature drift trends and generate dynamic compensation commands, this improves the accuracy and response speed of compensation.

[0066] In one embodiment, step S10 includes: S101, a multi-channel stator temperature sensor is deployed inside the stator assembly of the moving part; S102, deploy multiple rotor temperature sensors inside the rotor of the moving part; S103, a multi-channel encoder temperature sensor is deployed inside the encoder of the moving part; S104, An ambient temperature sensor is deployed at the heat exchange boundary of the moving part; S105, A displacement detection encoder is installed on the rotor of the moving component; S106, stator temperature data is collected through the multi-channel stator temperature sensor; S107, Rotor temperature data is collected through the multi-channel rotor temperature sensor; S108, encoder temperature data is acquired through the multi-channel encoder temperature sensor; S109, Ambient temperature data is collected through the ambient temperature sensor; S110, integrate the stator temperature data, rotor temperature data, encoder temperature data and ambient temperature data to form a temperature monitoring dataset; S111, the rotor position signal is acquired by the displacement detection encoder, and raw displacement data is generated based on the rotor position signal.

[0067] In this embodiment, to effectively compensate for displacement errors caused by thermal drift, a complete temperature sensing and displacement observation channel needs to be constructed first. Multiple temperature measurement points are set up inside the moving parts and in their environment, and various temperature and rotor displacement information are accurately collected, providing a foundation for subsequent data fusion and compensation processing.

[0068] Deploying multiple stator temperature sensors inside the stator assembly allows for the acquisition of localized heat distribution data at locations such as the stator windings and electromagnet core. These locations, due to the combined effects of energized heating and external heat conduction, often become the initiation points of temperature changes, decisively influencing the thermal field distribution. This multi-point deployment helps capture non-uniform heating conditions in space, providing high-resolution temperature profiles for thermal gradient modeling.

[0069] Multiple rotor temperature sensors are deployed inside the rotor to detect the temperature rise distribution of rotating components caused by eddy currents, motor load, and centrifugal effect. The rotor's rotation causes dynamic changes in its heat conduction path; therefore, the collected temperature data reflects its true thermal response characteristics during operation. To ensure real-time performance and accuracy, temperature information can be acquired through wireless temperature acquisition modules or non-contact infrared thermometry.

[0070] As a precision displacement detection unit, the internal temperature change of the encoder directly affects the signal stability and sensitivity. Deploying multiple encoder temperature sensors inside the encoder can accurately obtain the temperature rise around key sensitive components, especially the local temperature near the photoelectric encoder chip, sensing element, and signal amplification circuit, which helps to analyze the causes of temperature deviation in the encoder output signal.

[0071] Deploying ambient temperature sensors at the heat exchange boundary is to obtain the thermal conduction boundary conditions between moving parts and the external system, and to assess the impact of the system's operating conditions on the overall temperature field. Changes in ambient temperature often affect the system's cooling efficiency and thermal stability; therefore, its measurement data is indispensable in dynamic compensation modeling.

[0072] A displacement encoder is installed on the rotor to acquire the rotor's angular position signal. This encoder can be incremental or absolute, ensuring high-precision sensing of displacement changes at different angular velocities. Based on the acquired angular displacement signal, position integration processing can be performed in conjunction with time information to obtain the raw displacement data, which serves as a reference for subsequent filtering and compensation processing.

[0073] The system synchronously collects temperature values ​​from various measurement points through multiple temperature channels and integrates the temperature data from the stator, rotor, encoder, and environment to form a time-corresponding and location-related temperature monitoring dataset. This dataset reflects the evolution characteristics of the thermal field in both spatial and temporal dimensions, which helps in the subsequent construction of a multivariate thermal drift model and the execution of dynamic error compensation.

[0074] The acquisition of raw displacement data is performed simultaneously with temperature monitoring, ensuring that each set of displacement data has a matching temperature observation background, thereby achieving basic data alignment for thermal-displacement coupling modeling. This synchronous acquisition mechanism improves the accuracy and response speed of dynamic compensation, and is particularly suitable for applications with high dynamics and severe thermal disturbances.

[0075] This embodiment deploys multiple types of temperature sensors inside the moving parts and in their surrounding environment, simultaneously acquiring data related to displacement detection. This not only comprehensively perceives the evolution of thermal gradients during system operation but also constructs a temperature monitoring channel with high spatial resolution and strong real-time response. The synchronous acquisition of raw displacement data and temperature monitoring datasets provides stable input for thermal drift analysis and nonlinear error modeling. This acquisition mechanism significantly improves the adaptability and response efficiency of subsequent compensation models, reducing displacement measurement errors caused by local overheating, heat conduction lag, or external environmental disturbances.

[0076] In one embodiment, step S20 above includes: S201, the moving part operates at multiple predetermined temperature points; S202, while maintaining a constant temperature at each predetermined temperature point, record the steady-state displacement deviation data; S203, Generate a cluster of displacement deviation curves based on the recorded steady-state displacement deviation data; S204, Extract feature parameters based on the displacement deviation curve cluster; S205, construct a nonlinear mapping model using the extracted feature parameters; S206, Store the nonlinear mapping model in a relational database; S207, when responding to the displacement compensation command, the nonlinear mapping model in the relational database is invoked.

[0077] In this embodiment, to achieve dynamic compensation for displacement error based on temperature changes, a nonlinear mapping model between temperature gradient and displacement deviation needs to be established. This model must reflect the steady-state error response of the moving parts under different thermal field distributions. The establishment of this mapping model is based on systematic calibration, requiring sampling of the moving parts' operating states in multiple temperature-controlled environments to extract the functional law of temperature affecting displacement accuracy.

[0078] Operating the moving parts at multiple predetermined temperature points is the initial stage of model building. The selection of predetermined temperature points needs to cover typical temperature values ​​within the operating range of the moving parts, including room temperature, steady-state temperature after heating, and extreme high-temperature operating points. By controlling the environmental chamber or built-in heat source to heat and maintain system stability, the key temperature measurement points reach the expected temperature levels and the temperature rise trend remains within a stable range.

[0079] While maintaining a constant temperature at each predetermined temperature point, steady-state displacement deviation data is recorded. This data reflects the static displacement error caused by the combined effects of temperature changes on material thermal expansion, changes in motor magnetic properties, and structural stress response. The purpose of steady-state acquisition is to filter out instantaneous changes caused by dynamic disturbances and extract representative offset values ​​under specific thermal equilibrium conditions. These data are typically obtained by comparing them with standard operating conditions using a high-precision laser interferometer, grating ruler, or encoder.

[0080] A cluster of displacement deviation curves is generated based on the recorded steady-state displacement deviation data. This involves plotting the displacement offset at different temperature points as a function relationship, thereby constructing a surface representation of the thermal-displacement response. Due to the significant nonlinearity of the thermal response of different components, the resulting cluster of curves reflects the trend of non-constant curvature and a nonlinear increase in slope with increasing temperature.

[0081] Feature parameters are extracted based on the aforementioned curve clusters, typically including higher-order descriptive variables such as slope change rate, inflection point location, local maximum offset value, inflection point spacing, and fluctuation amplitude. These parameters are used to quantify the intensity, sensitive range, and nonlinearity of the effect of temperature on displacement, providing structured input for subsequent modeling.

[0082] A nonlinear mapping model is constructed using extracted feature parameters, employing various data fitting methods, including radial basis function networks, support vector regression, multinomial fitting, or deep neural networks. The model's input is the temperature values ​​of each measurement point in the temperature monitoring dataset, and its output is the predicted displacement offset under the current temperature distribution. Regularization mechanisms are introduced during the modeling process to prevent overfitting, and cross-validation is used to ensure generalization ability.

[0083] Storing this nonlinear mapping model in a relational database ensures that the model can be used in different runtime tasks. The database contains information such as model structure, parameter set, version number, and applicable scope, and supports fast indexing and scheduling by time tag or thermal condition tag.

[0084] During the compensation execution phase, when the system responds to a displacement compensation command, it automatically invokes the corresponding nonlinear mapping model in the relational database and performs offset prediction and compensation control based on the current real-time temperature input data. This invoke-based design enhances the flexibility and configurability of model usage, allowing different tasks or devices to load different calibrated versions of the model.

[0085] This embodiment systematically calibrates the steady-state displacement deviation of moving parts at multiple predetermined temperature points and constructs a nonlinear mapping model based on the characteristic parameters of a cluster of curves. This model accurately captures the nonlinear influence of temperature gradient on displacement error. The model is stored in a database and uses a calling mechanism to achieve rapid response to compensation tasks. Even in application scenarios with drastic temperature rises and frequent dynamic loads, it still maintains good real-time performance and accuracy adaptability, thereby significantly improving the robustness and precision control capability of the overall displacement compensation system.

[0086] In one embodiment, step S30 above includes: S301, The temperature data is input into the reference signal channel to generate a reference signal; S302, input the original displacement data into the main signal channel to generate a mixed displacement signal; S303, Initialize the adaptive filter coefficients according to the nonlinear mapping relationship; S304, The hybrid displacement signal is processed using the adaptive filter coefficients to generate an initial filtered output signal; S305, calculate the error signal based on the reference signal and the initial filtered output signal; S306, Iteratively update the adaptive filter coefficients based on the error signal to generate an optimized adaptive filter; S307, The mixed displacement signal is processed using an optimized adaptive filter to generate a temperature drift trend component; S308, subtract the temperature drift trend component from the mixed displacement signal to obtain displacement data with the trend component removed.

[0087] In this embodiment, to extract and eliminate the interference of temperature drift on displacement measurement from the systematic shift caused by temperature effects, a filtering mechanism with dynamic self-learning capability is required, enabling it to adaptively adjust the filtering strategy according to different thermal field inputs. This processing flow applies structured constraints to the filter based on nonlinear mapping relationships to ensure that the filtering results accurately reflect the drift trend driven by temperature.

[0088] First, temperature data is input into the reference signal channel to generate a baseline reference signal. The temperature data includes time-series information collected from various monitoring points in the stator, rotor, encoder, and environment. After preprocessing, such as unit normalization, time alignment, and missing data interpolation, this temperature data is converted into an estimated sequence of theoretical displacement response using a nonlinear mapping model, which serves as the reference signal. This baseline signal reflects the potential response trend of displacement to temperature changes and is used as a guiding signal for filter adjustment direction.

[0089] Next, the raw displacement data is input into the main signal channel to generate a hybrid displacement signal. This raw displacement data comes from the displacement changes recorded by the encoder at the current moment, and is superimposed with the actual motion state, including a slowly varying offset component caused by thermal drift, thus containing interference information that needs to be removed. The hybrid displacement signal is synchronized with the reference signal in a time-series manner and used as the filter training input.

[0090] The adaptive filter coefficients are initialized based on a nonlinear mapping relationship. These initial coefficients are not arbitrarily chosen, but rather set based on prior information from the temperature-displacement mapping model, which assigns weights to the influence of each heat source point on the total displacement. This initial value selection based on a nonlinear model helps improve the convergence speed and directional accuracy of the initial filter output.

[0091] The mixed displacement signal is processed using adaptive filter coefficients to generate an initial filtered output signal. This initial output reflects an initial estimate of the thermal drift trend in the displacement under the current filter configuration. This output provides a comparative reference for subsequent error calculations.

[0092] An error signal is calculated by comparing the initial filtered output signal with a reference signal. This error measures the deviation between the current filtered output and the theoretical temperature response. The error signal serves as the basis for feedback adjustment, controlling the direction and magnitude of the adaptive filter coefficient updates.

[0093] Subsequently, the adaptive filter coefficients are iteratively updated based on the error signal to generate an optimized adaptive filter. This process can employ LMS (Least Mean Square) or RLS (Recursive Least Squares) algorithms to adjust the weights of each channel in real time, minimizing the prediction error of the output. Through multiple iterations, the filter continuously enhances its ability to detect temperature-driven drift.

[0094] Finally, an optimized adaptive filter is used to process the mixed displacement signal to extract the trend component characterizing the system's drift tendency under temperature-driven conditions. This trend component is a low-frequency, slowly varying drift, which corresponds to the thermal inertia of mechanical structures in most industrial scenarios.

[0095] By removing the trend component from the mixed displacement signal, high-fidelity displacement data after drift removal is obtained. This data more accurately reflects the actual dynamic response state of the mechanical system after eliminating thermal field interference, providing a reliable basis for subsequent position control or precision closed-loop control.

[0096] For example, the root mean square evaluation formula for temperature drift error:

[0097] This formula is used to quantitatively evaluate the accuracy of adaptive filters in extracting thermal drift trend components. It measures whether the current filter configuration can effectively capture and extract temperature-driven systematic offset trends by calculating the root mean square error (RMSE) between the reference signal and the filter output. The smaller this error, the closer the filter output is to the theoretical temperature response signal, i.e., the more accurate the temperature drift modeling.

[0098] Where, e(n) = d(n) - y(n); N is the total number of sampling points, representing the number of samples used for evaluation, usually the number of data points within a time window; e(n) is the error signal at time n, representing the difference between the reference signal d(n) and the filter output y(n), i.e., the current modeling error; d(n) is the theoretical drift response (reference signal) at time n, an estimated value generated by nonlinear mapping of temperature data, representing the theoretical trend of temperature influence on displacement; y(n) is the filter output signal at time n, the temperature drift trend component estimated by the adaptive filter based on the original displacement signal; The sum of squared errors measures the overall deviation between the output and the reference at all time points.

[0099] This embodiment constructs an adaptive filtering mechanism guided by a nonlinear mapping model, which can not only accurately separate the systematic displacement drift trend caused by temperature, but also maintain the ability to continuously optimize filter parameters in dynamic thermal change environments. This results in obtaining purified displacement data with higher accuracy, lower latency, and stronger anti-drift capability, significantly improving the dynamic adaptability and environmental robustness of the displacement measurement system.

[0100] In one embodiment, step S40 above includes: S401, Input the trend component into the compensation decision module; S402, the current parameters of the nonlinear mapping relationship are called in the compensation decision module; S403, calculate the axial displacement compensation and radial displacement compensation using the current parameters and the trend components respectively; S404, The axial displacement compensation amount and radial displacement compensation amount are integrated in the compensation decision module to generate a multi-dimensional compensation vector; S405, the multidimensional compensation vector is converted into a dynamic compensation instruction by the compensation decision module.

[0101] In this embodiment, to compensate for multidimensional displacement drift caused by temperature, it is necessary to combine trend components with a pre-constructed nonlinear mapping model to quantify and dynamically correct the actual error, thereby outputting a compensation command with physical meaning. This process considers not only the absolute value change of displacement but also its directional differences in space, thus ensuring the pertinence and accuracy of the compensation.

[0102] First, the trend component is input into the compensation decision module. The trend component is a low-frequency drift term separated from the raw displacement data under temperature-driven conditions, reflecting the current thermal response state of the system. The compensation decision module, acting as the execution center, receives this data and triggers the compensation process. This module is typically deployed in the controller firmware or works in conjunction with the temperature monitoring system as an embedded signal processing unit.

[0103] The compensation decision module calls upon the current parameters of the nonlinear mapping relationship. These parameters are not static constants, but rather a set of thermally coupled model weights retrieved from the database on demand and loaded in real time based on historical calibration results. The parameters contain the correspondence between how the temperature gradient affects axial and radial displacements under different thermal field distributions, and may exist in the form of polynomial coefficients, neural network weights, or lookup table functions.

[0104] Subsequently, the axial and radial displacement compensation amounts are calculated using the current parameters and trend components, respectively. Here, the trend components need to be decomposed into responses in different structural directions and mapped to the axial and radial compensation channels respectively. Axial compensation mainly addresses position drift caused by the longitudinal thermal expansion of the motor, while radial compensation involves lateral effects such as bearing displacement and armature skew. During the mapping process, response prediction is performed through interpolation fitting, nonlinear functions, or neural network-based inference to ensure the dynamic accuracy of the compensation results.

[0105] The compensation decision module integrates axial and radial displacement compensation quantities to generate a multi-dimensional compensation vector. This compensation vector is typically a vector quantity in two- or three-dimensional space, possessing directionality and amplitude, representing the geometric displacement deviation that the system should actively compensate for under the current thermal state. The fusion strategy can employ weighted synthesis, vector superposition, or polar coordinate transformation to make the compensation more aligned with the system's structural mechanical constraints and sensor configuration characteristics.

[0106] Finally, the multi-dimensional compensation vector is converted into dynamic compensation instructions by the compensation decision module. These instructions can be used to update position estimates in the control system or directly drive hardware to perform offset corrections, such as adjusting the servo system zero point, modifying feedback loop reference values, or reconfiguring the reference point of the displacement calculation module. The instruction format is determined based on the system bus protocol and control system architecture, and may be a PWM parameter correction instruction, an incremental encoder compensation factor, or a digital displacement calibration command.

[0107] For example, instantaneous axial (or radial) error compensation: Δ(n) = α·e(n) Δ(n) represents the instantaneous compensation component that needs to be added to the multidimensional compensation vector at the current time n, with units consistent with displacement (μm or arc-sec). α represents the dynamic gain coefficient, derived from the "current parameter" retrieval result of the nonlinear mapping relationship by the compensation decision module. It self-tunes in real time according to the motor's thermal state, typically ranging from 0.0 to 2.0. The larger the gain, the more sensitive the compensation is to the latest error. e(n) = d(n) - y(n) is the real-time error signal, where d(n) is the mixed displacement signal (main channel), and y(n) is the output of the optimized adaptive filter (reference channel).

[0108] When calculating the axial displacement compensation and radial displacement compensation using the current parameters and trend components respectively, Δ(n) can be obtained independently in each direction, forming the first part of the "axial displacement compensation" or "radial displacement compensation", which is used to characterize the direct correction amount of the instantaneous error. The compensation amount is written first and then enters the fusion stage.

[0109] Cumulative axial (or radial) error compensation:

[0110] This represents the cumulative compensation amount for the errors over the past m sampling periods, used to correct residuals caused by slow drift or long-term temperature rise; β represents the integral coefficient, which also comes from the "current parameters", and its magnitude reflects the model's tolerance for long-term errors and the strength of compensation. This represents the summation of errors within a sliding window of length m, where the selection of m corresponds to the thermal inertia constant of the temperature field and the sampling period.

[0111] The cumulative axial (or radial) error compensation amount, together with the instantaneous axial (or radial) error compensation amount, constitutes the second part of the "axial displacement compensation amount" or "radial displacement compensation amount". The compensation decision module, during the fusion phase, combines Δ(n) with... The values ​​are superimposed to form the total compensation value in each direction, and then combined with the value in another direction to form a multidimensional compensation vector.

[0112] This embodiment introduces a compensation decision module and, based on the joint reasoning of trend components and nonlinear mapping parameters, can transform the multi-directional displacement drift state caused by heat into a spatial compensation vector and output it in the form of compensation commands that the control system can recognize. This significantly improves the accuracy, real-time performance, and multi-dimensional adaptability of displacement compensation, and solves the problems of difficult decoupling of axial-radial compensation and large response delay in traditional methods. It is suitable for high-precision operating environments with multiple heat source distributions of moving parts.

[0113] In one embodiment, step S405 includes: S4051, acquire historical temperature change data; S4052, Based on the historical temperature change data, a sliding window mechanism is applied to predict the temperature change trend; S4053 generates a pre-compensation amount based on the predicted temperature change trend; S4054, The pre-compensation amount is superimposed on the multi-dimensional compensation vector to generate an updated multi-dimensional compensation vector; S4055, the updated multidimensional compensation vector is converted into a dynamic compensation instruction through the compensation decision module.

[0114] In this embodiment, in order to further enhance the forward-looking response capability of the compensation process to dynamic temperature changes, before generating dynamic compensation instructions, historical temperature change data is introduced and a short-term prediction model is constructed to evaluate the upcoming temperature fluctuation trend, thereby realizing the feedforward correction and dynamic adjustment of the compensation vector.

[0115] First, the compensation decision module acquires historical temperature change data. This historical data typically originates from continuous sampling records of various temperature sensors during previous operating cycles, including multiple temperature measurement points such as the stator, rotor, encoder, and environment. This data can be cached in real-time in a circular queue structure or stored in low-latency local memory to support high-frequency time-series analysis and retrieval.

[0116] After acquiring historical temperature data, a sliding window mechanism is applied to predict temperature change trends. This mechanism divides historical data into multiple overlapping or non-overlapping time periods and performs gradient analysis and fluctuation amplitude modeling on temperature changes within each period to extract the rate of temperature increase or decrease. The prediction mechanism can employ simple linear regression models, exponential smoothing, or time series analysis methods such as the ARIMA model for short-term trend extrapolation to assess the temperature evolution path over a future period.

[0117] Based on the predicted temperature change trend, a pre-compensation quantity is generated. This pre-compensation quantity represents the forward response to the displacement deviation caused by the impending temperature change. Its calculation method is similar to that of trend component compensation, but its input is the estimated result of the future temperature gradient. During the generation process, the predicted temperature slope or difference value is input into the nonlinear mapping model to obtain the corresponding displacement influence estimate, and the pre-compensation sub-quantities are output separately for the axial and radial directions.

[0118] Then, the aforementioned pre-compensation amount is superimposed onto the current multidimensional compensation vector to generate an updated multidimensional compensation vector. This process is accomplished through vector addition or weighted fusion, jointly expressing trend-driven and prediction-driven compensation to ensure that the compensation command not only targets the current temperature response but also anticipates impending changes, thus improving the system's feedforward adaptability. The weighting factor can be dynamically adjusted based on prediction confidence, temperature change rate, or the magnitude of the compensation impact.

[0119] Finally, the compensation decision module converts the updated multidimensional compensation vector into a dynamic compensation command. This command format is consistent with the previously generated basic compensation command, possessing a structured format and system compatibility. It is used to correct the current displacement data or as a feedback correction amount for the controller, achieving more robust multi-source thermal drift elimination. Output methods can employ strategies such as resetting encoded parameters, sending displacement offset commands, or triggering controller interrupts to ensure the compensation command's executability and timely transmission.

[0120] This embodiment introduces a trend prediction mechanism based on historical temperature data to estimate the impact of potential future temperature fluctuations on displacement and adds this estimate to the current compensation vector. This allows for pre-compensation before actual temperature changes occur, effectively reducing accumulated errors caused by compensation lag. While considering current thermal response compensation, it enhances the feedforward correction capability for future disturbances, achieving dynamic and robust control of a high-precision displacement measurement system under complex thermal fields.

[0121] In one embodiment, step S50 above includes: S501, input the dynamic compensation command into the execution controller; S502, the execution controller parses the dynamic compensation command to generate a current control signal and displacement correction parameters; S503, adjust the motor drive current based on the current control signal to generate the actual displacement change; S504, quantify the actual displacement change into a displacement compensation increment; S505, use the displacement correction parameters to correct the original displacement data and generate corrected original displacement data; S506, the corrected original displacement data, the displacement data after removing the trend component, and the displacement compensation increment are fused in three dimensions to generate a three-dimensional fusion result; S507, Based on the three-dimensional fusion result, generate compensation displacement data and output the compensation displacement data.

[0122] In this embodiment, after the dynamic compensation command is generated, it is first input to the execution controller for parsing. The execution controller can be a dedicated processing module integrated into the encoder control unit, servo driver, or main control module. Its main task is to parse the input structured compensation command, extract the multi-dimensional compensation vector parameters contained therein, and convert them into electrical and logic control signals. The compensation command generally includes axial and radial displacement compensation values, control response delay tolerance, pre-compensation time window, etc.

[0123] During the analysis process, the execution controller maps the compensation vector elements in the dynamic compensation command into two types of control quantities: one is the current control signal at the drive end, used to control the motor drive system to produce subtle displacement adjustments; the other is the displacement correction parameter inside the encoder or controller, used to directly correct the measurement deviation generated in the sensing path. These two types of control quantities act on the execution layer and the data layer respectively in the system, ensuring that the compensation process has a dual-channel path of closed-loop regulation and original signal correction.

[0124] The current control signal is sent through the interface with the motor drive module and used to adjust the actual drive current of the motor, generating continuous, minute rotational corrections within the motor to achieve fine-grained spatial displacement changes. The resulting actual displacement changes are collected and quantified in real time by the position feedback system, forming a displacement compensation increment. This increment represents the mechanical motion response directly caused by the current compensation command and plays a fundamental role in further constructing the fusion model.

[0125] Meanwhile, displacement correction parameters are used to digitally correct the original displacement data. The original displacement data is unfiltered and uncompensated, and contains temperature drift residuals and system nonlinear response errors. The correction parameters generate corrected original displacement data by performing an arithmetic transformation or gain adjustment on the original displacement data. Correction methods can include weighted translation, proportional amplification, zero-bias adjustment, etc., depending on the sensor model and error calibration method in the system.

[0126] After generating the compensation increment and correction data, the corrected original displacement data, the displacement data after removing trend components, and the displacement compensation increment are fused in three dimensions. This fusion process is accomplished using a spatial feature weighting method. By constructing a fusion space containing three directions or three sources, feature information from each data source is extracted and jointly expressed. The fusion strategy can be implemented based on priority weighting, principal component analysis, or Bayesian fusion algorithms, with the key focus on suppressing redundant components in the data from each path and retaining the most representative stable compensation information.

[0127] Finally, compensated displacement data is generated based on the 3D fusion results. This data is a consistent result integrating current dynamic compensation, trend removal, and sensor correction, possessing comprehensive characteristics such as thermal drift elimination, nonlinear response suppression, and unified control correction. The compensated displacement data, as the final output, can be used for feedback control, precise positioning, error recording, or high-precision path planning, ensuring that the motion control system maintains high consistency and high precision response capabilities under high temperature gradient and multi-source disturbance conditions.

[0128] Example Description: In a high-precision motor drive system, to eliminate displacement measurement errors caused by thermal drift and ensure precise positioning performance under high dynamic load environments, the following specific implementation process is adopted.

[0129] First, sensors are deployed at multiple key locations on the motor under test. Multiple stator temperature sensors are arranged inside the stator assembly to detect heat buildup in the coils; rotor temperature sensors are arranged inside the rotor to monitor the temperature rise of the rotating body; a dedicated temperature sensor is installed inside the encoder to collect temperature changes within the reading mechanism; an ambient temperature sensor is installed on the motor casing or in the air-cooled area to create thermal boundary conditions; in addition, a high-precision displacement detection encoder is installed at the end of the rotor shaft to collect angular displacement signals. After the system is powered on and enters a stable operating state, all temperature sensors begin continuous sampling and upload real-time temperature data of the stator, rotor, encoder, and environment to the data aggregation module. Simultaneously, the displacement detection encoder synchronously acquires raw position signals and generates raw displacement data.

[0130] Then, the temperature dataset and the original displacement data are input into the dynamic compensation module. This module calls the nonlinear mapping relationship built in the early calibration stage. This mapping relationship is based on running the motor at multiple constant temperature points and recording its steady-state displacement deviation curves. Feature parameters are extracted from the curve set, and a nonlinear mapping model is established using a regression algorithm. During operation, the module inputs the temperature data into the reference signal channel to generate a reference signal, and simultaneously inputs the original displacement data into the main signal channel to generate a mixed signal. The adaptive filter loads the nonlinear mapping model parameters during initialization and performs filtering on the mixed signal. This filter updates its weights based on the LMS or RLS algorithm, continuously optimizing the filter coefficients through the error signal, and finally separating the temperature drift trend component from the mixed signal. This trend component is subtracted from the original displacement signal to obtain the drift-free displacement data.

[0131] Next, the trend component is fed into the compensation decision module as input, along with the current temperature mapping model parameters. The displacement compensation amounts in the axial and radial directions of the current system are calculated and fused into a multi-dimensional compensation vector. Based on this, a sliding window temperature prediction mechanism is introduced, using historical temperature change data to predict future temperature change trends and generate a pre-compensation amount. The predicted amount is then superimposed with the current compensation vector to form an updated compensation vector, which is further converted into structured dynamic compensation instructions.

[0132] Subsequently, the controller parses the compensation command and generates two control quantities: one is a current control signal for motor drive, which is sent to the driver in real time to guide the motor to make a very small displacement adjustment; the other is a displacement correction parameter to correct the original displacement data, which is applied to the encoder feedback data within the signal processing path. The displacement change generated by the motor is quantified as a compensation increment, and together with the corrected original displacement data and the data after removing the trend component, it is input into the three-dimensional fusion unit. The fusion unit integrates the three data based on multi-channel weight calculation or principal component analysis methods to generate the final compensated displacement data with correction, compensation, and drift suppression capabilities, and outputs it to the control system or display terminal for precision control and monitoring.

[0133] This embodiment constructs a dual-path control link of electronic drive and measurement correction by parsing dynamic compensation commands into current control signals and displacement correction parameters. Furthermore, it improves the accuracy and stability of the compensation data by introducing a data fusion mechanism from three sources. This mechanism can effectively eliminate the cumulative effect of displacement errors under thermal dynamic changes, achieving coordinated response of the compensation strategy from the sensing layer to the control layer, ensuring that the system maintains high-precision displacement measurement and stable execution control in complex thermal fields.

[0134] In one embodiment, a dynamic displacement error compensation device is provided, which corresponds one-to-one with the dynamic displacement error compensation method described in the above embodiments. (Refer to...) Figure 3 , Figure 3 This is a schematic diagram of the functional modules of a preferred embodiment of the dynamic displacement error compensation device of the present invention. The modules include a temperature and displacement acquisition module 10, a nonlinear mapping construction module 20, an adaptive filtering module 30, a compensation command generation module 40, and a displacement compensation execution module 50. Detailed descriptions of each functional module are as follows: The temperature and displacement acquisition module 10 is used to acquire temperature data at multiple temperature measurement points inside the moving part and in its environment, and to acquire the original displacement data of the moving part. The nonlinear mapping construction module 20 is used to call a pre-built nonlinear mapping relationship between temperature gradient and displacement deviation, which is established by calibrating displacement deviation curves at different temperatures; The adaptive filtering module 30 is used to perform adaptive filtering on the temperature data and the original displacement data based on the nonlinear mapping relationship, separate the trend component that characterizes the temperature drift, and obtain the displacement data after removing the trend component. The compensation instruction generation module 40 is used to generate dynamic compensation instructions based on the trend component and the nonlinear mapping relationship; The displacement compensation execution module 50 is used to adjust the displacement data after removing the trend component according to the dynamic compensation instruction, and output the compensated displacement data.

[0135] In one embodiment, the temperature and displacement acquisition module 10 is specifically used for: Multiple stator temperature sensors are deployed inside the stator assembly of the moving parts; Multiple rotor temperature sensors are deployed inside the rotor of the moving component; A multi-channel encoder temperature sensor is deployed inside the encoder of the moving part; An ambient temperature sensor is deployed at the heat exchange boundary of the moving part. A displacement detection encoder is installed on the rotor of the moving component; Stator temperature data is collected using the multi-channel stator temperature sensor; Rotor temperature data is acquired using the multi-channel rotor temperature sensor; Encoder temperature data is acquired through the multi-channel encoder temperature sensor; Ambient temperature data is collected using the ambient temperature sensor. The stator temperature data, rotor temperature data, encoder temperature data, and ambient temperature data are integrated to form a temperature monitoring dataset; The rotor position signal is acquired by the displacement detection encoder, and raw displacement data is generated based on the rotor position signal.

[0136] In one embodiment, the nonlinear mapping construction module 20 is specifically used for: The moving parts operate at multiple predetermined temperature points; While maintaining a constant temperature at each predetermined temperature point, record the steady-state displacement deviation data; A cluster of displacement deviation curves is generated based on the recorded steady-state displacement deviation data; Feature parameters are extracted based on the displacement deviation curve cluster; Construct a nonlinear mapping model using the extracted feature parameters; Store the nonlinear mapping model in a relational database; When responding to a displacement compensation command, the nonlinear mapping model in the relational database is invoked.

[0137] In one embodiment, the adaptive filtering module 30 is specifically used for: The temperature data is input into the reference signal channel to generate a reference signal. The original displacement data is input into the main signal channel to generate a mixed displacement signal; Initialize the adaptive filter coefficients according to the nonlinear mapping relationship; The hybrid displacement signal is processed using the adaptive filter coefficients to generate an initial filtered output signal; The error signal is calculated based on the reference signal and the initial filtered output signal. The adaptive filter coefficients are iteratively updated based on the error signal to generate an optimized adaptive filter; The mixed displacement signal is processed using an optimized adaptive filter to generate a temperature drift trend component; The temperature drift trend component is subtracted from the mixed displacement signal to obtain displacement data with the trend component removed.

[0138] In one embodiment, the compensation instruction generation module 40 is specifically used for: The trend component is input into the compensation decision module; The current parameters of the nonlinear mapping relationship are called in the compensation decision module; The axial displacement compensation and radial displacement compensation are calculated using the current parameters and the trend components, respectively. The axial displacement compensation amount and the radial displacement compensation amount are integrated in the compensation decision module to generate a multi-dimensional compensation vector. The compensation decision module converts the multidimensional compensation vector into dynamic compensation instructions.

[0139] In one embodiment, the compensation instruction generation module 40 is specifically used for: Obtain historical temperature change data; Based on the historical temperature change data, a sliding window mechanism is applied to predict the temperature change trend. Generate a pre-compensation amount based on the predicted temperature change trend; The pre-compensation amount is superimposed on the multi-dimensional compensation vector to generate an updated multi-dimensional compensation vector. The updated multidimensional compensation vector is converted into dynamic compensation instructions through the compensation decision module.

[0140] In one embodiment, the displacement compensation execution module 50 is specifically used for: The dynamic compensation command is input into the execution controller; The execution controller parses the dynamic compensation command to generate current control signals and displacement correction parameters. The motor drive current is adjusted based on the current control signal to generate the actual displacement change. The actual displacement change is quantified into a displacement compensation increment; The original displacement data is corrected using the displacement correction parameters to generate corrected original displacement data. The corrected original displacement data, the displacement data after removing the trend component, and the displacement compensation increment are fused in three dimensions to generate a three-dimensional fusion result; Compensated displacement data is generated based on the three-dimensional fusion result, and the compensated displacement data is output.

[0141] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external user terminals via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a dynamic displacement error compensation method on the server side.

[0142] In one embodiment, a computer device is provided, which may be a user terminal, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a dynamic displacement error compensation method on the user side.

[0143] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Temperature data are acquired at multiple temperature measurement points inside the moving part and in its environment, and the original displacement data of the moving part is also acquired. The pre-built nonlinear mapping relationship between temperature gradient and displacement deviation is invoked, and the nonlinear mapping relationship is established by calibrating the displacement deviation curves at different temperatures; Based on the nonlinear mapping relationship, the temperature data and the original displacement data are subjected to adaptive filtering to separate the trend component characterizing the temperature drift and obtain the displacement data after removing the trend component. Based on the trend component and the nonlinear mapping relationship, a dynamic compensation instruction is generated; According to the dynamic compensation instruction, the displacement data after removing the trend component is adjusted, and the compensated displacement data is output.

[0144] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Temperature data are acquired at multiple temperature measurement points inside the moving part and in its environment, and the original displacement data of the moving part is also acquired. The pre-built nonlinear mapping relationship between temperature gradient and displacement deviation is invoked, and the nonlinear mapping relationship is established by calibrating the displacement deviation curves at different temperatures; Based on the nonlinear mapping relationship, the temperature data and the original displacement data are subjected to adaptive filtering to separate the trend component characterizing the temperature drift and obtain the displacement data after removing the trend component. Based on the trend component and the nonlinear mapping relationship, a dynamic compensation instruction is generated; According to the dynamic compensation instruction, the displacement data after removing the trend component is adjusted, and the compensated displacement data is output.

[0145] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and user side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0146] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0147] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0148] It should be noted that if any software tools or components not belonging to this company appear in the embodiments of this application, they are merely illustrative examples and do not represent actual use. The embodiments described above are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A dynamic compensation method for displacement error, characterized in that, Includes the following steps: Temperature data are acquired at multiple temperature measurement points inside the moving part and in its environment, and the original displacement data of the moving part is also acquired. The pre-built nonlinear mapping relationship between temperature gradient and displacement deviation is invoked, and the nonlinear mapping relationship is established by calibrating the displacement deviation curves at different temperatures; Based on the nonlinear mapping relationship, the temperature data and the original displacement data are subjected to adaptive filtering to separate the trend component characterizing the temperature drift and obtain the displacement data after removing the trend component. Based on the trend component and the nonlinear mapping relationship, a dynamic compensation instruction is generated; According to the dynamic compensation instruction, the displacement data after removing the trend component is adjusted, and the compensated displacement data is output.

2. The dynamic compensation method for displacement error as described in claim 1, characterized in that, Acquire temperature data at multiple temperature measurement points inside the moving part and in its surrounding environment, and acquire the original displacement data of the moving part, including: Multiple stator temperature sensors are deployed inside the stator assembly of the moving parts; Multiple rotor temperature sensors are deployed inside the rotor of the moving component; A multi-channel encoder temperature sensor is deployed inside the encoder of the moving part; An ambient temperature sensor is deployed at the heat exchange boundary of the moving part. A displacement detection encoder is installed on the rotor of the moving component; Stator temperature data is collected using the multi-channel stator temperature sensor; Rotor temperature data is acquired using the multi-channel rotor temperature sensor; Encoder temperature data is acquired through the multi-channel encoder temperature sensor; Ambient temperature data is collected using the ambient temperature sensor. The stator temperature data, rotor temperature data, encoder temperature data, and ambient temperature data are integrated to form a temperature monitoring dataset; The rotor position signal is acquired by the displacement detection encoder, and raw displacement data is generated based on the rotor position signal.

3. The dynamic compensation method for displacement error as described in claim 1, characterized in that, The pre-built nonlinear mapping relationship between temperature gradient and displacement deviation is invoked. This nonlinear mapping relationship is established by calibrating displacement deviation curves at different temperatures, including: The moving parts operate at multiple predetermined temperature points; While maintaining a constant temperature at each predetermined temperature point, record the steady-state displacement deviation data; A cluster of displacement deviation curves is generated based on the recorded steady-state displacement deviation data; Feature parameters are extracted based on the displacement deviation curve cluster; Construct a nonlinear mapping model using the extracted feature parameters; Store the nonlinear mapping model in a relational database; When responding to a displacement compensation command, the nonlinear mapping model in the relational database is invoked.

4. The dynamic compensation method for displacement error as described in claim 1, characterized in that, Based on the aforementioned nonlinear mapping relationship, adaptive filtering is performed on the temperature data and the original displacement data to separate the trend component characterizing temperature drift, and displacement data after removing the trend component is obtained, including: The temperature data is input into the reference signal channel to generate a reference signal. The original displacement data is input into the main signal channel to generate a mixed displacement signal; Initialize the adaptive filter coefficients according to the nonlinear mapping relationship; The hybrid displacement signal is processed using the adaptive filter coefficients to generate an initial filtered output signal; The error signal is calculated based on the reference signal and the initial filtered output signal. The adaptive filter coefficients are iteratively updated based on the error signal to generate an optimized adaptive filter; The mixed displacement signal is processed using an optimized adaptive filter to generate a temperature drift trend component; The temperature drift trend component is subtracted from the mixed displacement signal to obtain displacement data with the trend component removed.

5. The dynamic compensation method for displacement error as described in claim 1, characterized in that, Based on the trend component and the nonlinear mapping relationship, a dynamic compensation instruction is generated, including: The trend component is input into the compensation decision module; The current parameters of the nonlinear mapping relationship are called in the compensation decision module; The axial displacement compensation and radial displacement compensation are calculated using the current parameters and the trend components, respectively. The axial displacement compensation amount and the radial displacement compensation amount are integrated in the compensation decision module to generate a multi-dimensional compensation vector. The compensation decision module converts the multidimensional compensation vector into dynamic compensation instructions.

6. The dynamic compensation method for displacement error as described in claim 5, characterized in that, The compensation decision module converts the multidimensional compensation vector into dynamic compensation instructions, including: Obtain historical temperature change data; Based on the historical temperature change data, a sliding window mechanism is applied to predict the temperature change trend. Generate a pre-compensation amount based on the predicted temperature change trend; The pre-compensation amount is superimposed on the multi-dimensional compensation vector to generate an updated multi-dimensional compensation vector. The updated multidimensional compensation vector is converted into dynamic compensation instructions through the compensation decision module.

7. The dynamic compensation method for displacement error as described in claim 1, characterized in that, According to the dynamic compensation instruction, adjust the displacement data after removing the trend component, and output compensated displacement data, including: The dynamic compensation command is input into the execution controller; The execution controller parses the dynamic compensation command to generate current control signals and displacement correction parameters. The motor drive current is adjusted based on the current control signal to generate the actual displacement change. The actual displacement change is quantified into a displacement compensation increment; The original displacement data is corrected using the displacement correction parameters to generate corrected original displacement data. The corrected original displacement data, the displacement data after removing the trend component, and the displacement compensation increment are fused in three dimensions to generate a three-dimensional fusion result; Compensated displacement data is generated based on the three-dimensional fusion result, and the compensated displacement data is output.

8. A dynamic displacement error compensation device, characterized in that, The displacement error dynamic compensation device includes: The temperature and displacement acquisition module is used to acquire temperature data at multiple temperature measurement points inside the moving part and in its environment, and to acquire the original displacement data of the moving part. The nonlinear mapping construction module is used to call a pre-built nonlinear mapping relationship between temperature gradient and displacement deviation, which is established by calibrating displacement deviation curves at different temperatures; An adaptive filtering module is used to perform adaptive filtering on the temperature data and the original displacement data based on the nonlinear mapping relationship, separate out the trend component characterizing the temperature drift, and obtain the displacement data after removing the trend component. The compensation instruction generation module is used to generate dynamic compensation instructions based on the trend component and the nonlinear mapping relationship; The displacement compensation execution module is used to adjust the displacement data after removing the trend component according to the dynamic compensation instruction, and output the compensated displacement data.

9. A computer device, characterized in that, The computer device includes a memory, a processor, and a displacement error dynamic compensation program stored in the memory and executable on the processor. When the displacement error dynamic compensation program is executed by the processor, it implements the steps of the displacement error dynamic compensation method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a dynamic displacement error compensation program, which, when executed by the processor, implements the steps of the dynamic displacement error compensation method as described in any one of claims 1-7.

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