Turret motor controller and motor control method thereof

Abstract

The application discloses a rotary table motor controller and a motor control method thereof, and particularly relates to the technical field of rotary table motor control, and comprises a main control module, a driving module, a feedback module, a power module and a communication module, wherein the main control module is electrically connected with the driving module, the feedback module and the communication module respectively, the power module supplies power for the main control module, the driving module, the feedback module and the communication module, the power module adopts an isolated power supply design and has overcurrent, overvoltage, short-circuit protection functions and low-power consumption design. Through a closed-loop system of high-precision sensing, intelligent algorithm decision, self-adaptive execution and predictive maintenance, the application comprehensively solves the problems of insufficient precision, poor load adaptability and poor stability of traditional rotary table motor controllers, and realizes a breakthrough improvement in control precision, response speed and scene adaptability, thereby providing a reliable motor control solution for the field of high-end equipment manufacturing.

Description

Technical Field

[0001] This invention relates to the field of turntable motor control technology, and more specifically, to a turntable motor controller and a motor control method thereof. Background Technology

[0002] A rotary table motor is a core actuator that achieves rotational motion through precise control. Driven by a servo system, it enables precise adjustment of angle and speed. It is widely used in inertial navigation platforms in the aerospace field, rotary tables of precision machine tools, and robot joint drive systems. Taking aerospace as an example, satellite attitude adjustment rotary tables need to operate stably with an accuracy of ±0.01°, while rotary tables in precision machining centers require speed fluctuations of ≤±1% at high speeds (3000rpm) to ensure the machining accuracy of complex curved parts. With the increasing demand for flexible manufacturing in industrial development, rotary table motors are evolving from single rotation control to multi-axis linkage and high-precision dynamic response, which places higher demands on the performance of controllers.

[0003] In existing technologies, traditional turntable motor controllers mostly use a single sensor to collect motor status, which has limited sensing dimensions and is prone to data blind spots. The control algorithm is simple and fixed, and cannot dynamically adjust the strategy according to load changes. It is prone to deviations under high-precision positioning or dynamic working conditions, making it difficult to meet the precision control requirements at the ±0.01° level. Furthermore, it lacks a prediction and compensation mechanism for motor performance degradation. At the same time, the drive module power adjustment speed is slow and the range is narrow. When faced with sudden load changes, the speed fluctuates greatly. The protection mechanism is imperfect, and the response to overcurrent, overvoltage, temperature and vibration abnormalities is lagging, resulting in a high equipment failure rate and a short mean time between failures. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a turntable motor controller and a motor control method thereof to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a turntable motor controller and its motor control method, comprising a main control module, a drive module, a feedback module, a power supply module, and a communication module. The main control module is electrically connected to the drive module, the feedback module, and the communication module respectively. The power supply module supplies power to the main control module, the drive module, the feedback module, and the communication module. The power supply module adopts an isolated power supply design and has overcurrent, overvoltage, and short-circuit protection functions as well as a low-power design.

[0006] The main control module includes a high-performance microprocessor that runs fuzzy PID control algorithm and model predictive control algorithm, and has a built-in deep learning-based dynamic control model. The dynamic control model optimizes the control parameters of the fuzzy PID control algorithm and model predictive control algorithm by combining offline training and online iteration. It is used to receive real-time motor operating status information from the feedback module to dynamically switch between multiple algorithms, and to send control signals to the drive module according to the control algorithm and instructions.

[0007] The feedback module includes an encoder, a current sensor, a laser interferometer, an accelerometer, and a temperature sensor. The encoder is mounted on the output shaft of the turntable motor and is used to detect the motor's speed and position information and convert them into digital signals to be fed back to the main control module. The current sensor is used to monitor the motor's operating current and feed it back to the main control module. The laser interferometer is used to acquire high-precision position information of the motor. The accelerometer is used to detect the motor's vibration data. The temperature sensor is used to monitor the temperature of the motor and key components of the controller.

[0008] The drive module includes an adaptive power adjustment unit, which includes a power amplifier circuit and a drive chip. It is used to adjust the output power in real time according to the changes in motor load. The power amplifier circuit is used to amplify the control signal output by the main control module. The drive chip is used to control the phase sequence and current of the motor according to the instructions of the main control module.

[0009] The communication module supports RS485 and Ethernet communication protocols, and is used to realize data communication and interaction between the controller and the host computer or other devices.

[0010] Preferably, the deep learning-based dynamic control model adopts an LSTM neural network structure. By training with historical operating data, it can predict the performance degradation trend of the motor under different operating conditions and adjust the control strategy in advance.

[0011] Preferably, the switching threshold between the fuzzy PID control algorithm and the model predictive control algorithm is dynamically set by the real-time data of the feedback module. When the motor speed fluctuation exceeds 3% or the position deviation exceeds 0.005°, the algorithm automatically switches from the fuzzy PID control algorithm to the model predictive control algorithm.

[0012] Preferably, the detection accuracy of the laser interferometer reaches ±0.5μm / m, the sampling frequency of the accelerometer is not less than 10kHz, and the measurement range of the temperature sensor is -40℃ to 125℃ with a measurement accuracy of ±0.1℃.

[0013] Preferably, the adaptive power adjustment unit includes a wideband power amplifier and an intelligent power distribution chip, which can adjust the power output within 0.1ms, and the power adjustment range is 10%-120% of the rated power.

[0014] Preferably, the communication module adopts a 5G-MEC edge computing architecture, with a data transmission latency of ≤10ms, supports a real-time data upload rate of over 100Mbps, and is compatible with RS485 and Ethernet communication protocols.

[0015] A turntable motor control method is also provided, based on the aforementioned turntable motor controller, comprising the following steps:

[0016] S1: System initialization, power module starts and completes power supply self-test of each module, confirms stable output of isolated power supply, communication module initializes and establishes communication connection with host computer, feedback module performs zero-point calibration and accuracy verification of encoder, current sensor, laser interferometer, accelerometer and temperature sensor;

[0017] S2: Operating condition perception and model loading. The main control module collects the initial speed, position, current, vibration and temperature data of the motor through the feedback module, inputs them into the dynamic control model based on the LSTM neural network structure, and the dynamic control model combines historical operating data to identify the current load type and operating condition, loads the corresponding preset control parameters, classifies the historical operating data into training sets according to operating conditions, inputs them into the LSTM network for iterative training, and predicts the motor performance degradation trend through the dynamic control model.

[0018] S3: Dynamic switching of control algorithm. The main control module uses the fuzzy PID control algorithm when the motor speed fluctuation is ≤3% and the position deviation is ≤0.005°, based on the working condition perception results and real-time data from the feedback module. When the speed fluctuation exceeds 3% or the position deviation exceeds 0.005°, it automatically switches to the model predictive control algorithm. During the switching process, the dynamic control model synchronously corrects the algorithm parameters.

[0019] S4: Adaptive power regulation and closed-loop control. The adaptive power regulation unit of the drive module amplifies the control signal output by the main control module through the power amplifier circuit. The drive chip adjusts the motor phase sequence and current. At the same time, the adaptive power regulation unit adjusts the output power to 10% to 120% of the rated power within 0.1ms according to the load change. The feedback module collects the motor operation data in real time and transmits it to the main control module to form closed-loop control.

[0020] S5: Abnormal monitoring and protection. When the temperature sensor detects that the temperature exceeds the range of -40℃ to 125℃, the acceleration sensor detects abnormal vibration data, or the current sensor detects abnormal current, the main control module immediately instructs the drive module to cut off the output, the power supply module starts the overcurrent, overvoltage or short circuit protection function, and the communication module transmits the alarm information to the host computer.

[0021] S6: Online iterative optimization of the model. The dynamic control model is used for offline training and online iteration at preset intervals, using the latest running data to update the prediction parameters of the motor performance degradation trend, ensuring that the control strategy matches the real-time state of the motor, and the iteration process does not affect the normal operation of the motor.

[0022] Preferably, the recognition response time of the operating condition identification in S2 is ≤50ms, the load type in S2 includes light load, heavy load and impact load, the operating conditions include steady-state operation, dynamic speed regulation and high-precision positioning, and the prediction of motor performance degradation trend through dynamic control model in S2 includes:

[0023] Real-time calculation of performance degradation factor during online runtime :

[0024]

[0025] in , These are the weighting coefficients. The change in temperature per unit time. The rate of change of rotational speed;

[0026] when > At that time, increase the control margin in advance, the aforementioned This is the preset performance degradation factor threshold.

[0027] Preferably, the online parameter optimization cycle in S4 is 10ms / time, and the parameters optimized each time include the proportional coefficient, integral time, and derivative time.

[0028] Preferably, the online iterative training in S6 adopts an incremental learning method, with the model update time for each iteration ≤1s, and does not affect the normal operation of the motor.

[0029] The technical effects and advantages of this invention are as follows:

[0030] By integrating encoders, laser interferometers, accelerometers, and temperature sensors through the feedback module, comprehensive real-time monitoring of motor speed, position, vibration, and temperature is achieved, providing high-precision data support for closed-loop control and solving the perception blind spot problem caused by traditional controllers relying on only a single sensor. The main control module dynamically switches between fuzzy PID and model predictive control algorithms, based on a 3% speed fluctuation and a 0.005° position deviation threshold, to ensure control stability under steady-state low-load conditions and improve response speed under dynamic high-load or high-precision positioning. Compared with single-algorithm control accuracy, it improves the accuracy and meets the ±0.01° positioning requirements of aerospace, precision machining, and other scenarios. The LSTM neural network model is trained and iterated online using historical data, and predicts the motor performance degradation trend based on the attenuation factor calculation, adjusting the control strategy in advance to avoid accuracy loss due to performance degradation and improve the long-term accuracy retention rate of the motor.

[0031] By enhancing load adaptability and operational stability, the wideband power amplifier of the drive module, in conjunction with the intelligent power distribution chip, can adjust the output power to 10%-120% of the rated power within 0.1ms, quickly adapting to different working conditions such as light load, heavy load, and impact load. This solves the speed fluctuation problem of traditional controllers when the load changes suddenly. The isolated design of the power module and the overcurrent, overvoltage, and short circuit protection functions, combined with the temperature, vibration, and current anomaly monitoring of the feedback module, can cut off the output and alarm within 10ms when an anomaly occurs, reducing the risk of equipment damage and greatly improving the controller's mean time between failures (MTBF).

[0032] With multi-protocol communication and remote control capabilities, the communication module is compatible with RS485, Ethernet and 5G-MEC architecture, enabling seamless integration with host computers and the Industrial Internet. It supports remote parameter setting, status monitoring and fault diagnosis, meeting the remote collaboration needs of flexible industrial manufacturing. The low-power design of the power module reduces system energy consumption. Combined with the incremental learning and iteration of the dynamic control model, it can optimize control parameters without stopping the motor, adapt to the performance changes of the motor during long-term operation, and extend the service life of the equipment.

[0033] In summary, the turntable motor controller and its motor control method provided by this invention comprehensively solve the problems of insufficient accuracy, poor load adaptability, and poor stability of traditional turntable motor controllers through a closed-loop system of high-precision sensing, intelligent algorithm decision-making, adaptive execution, and predictive maintenance. It achieves breakthrough improvements in control accuracy, response speed, and scenario adaptability, providing a reliable motor control solution for the high-end equipment manufacturing field. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the system structure of the present invention.

[0035] Figure 2This is a schematic diagram of the method flow structure of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] As attached Figure 1 The turntable motor controller shown includes a main control module, a drive module, a feedback module, a power supply module, and a communication module. The main control module is electrically connected to the drive module, the feedback module, and the communication module respectively. The power supply module supplies power to the main control module, the drive module, the feedback module, and the communication module. The power supply module adopts an isolated power supply design and has overcurrent, overvoltage, and short circuit protection functions as well as low power consumption design.

[0038] The main control module includes a high-performance microprocessor that runs fuzzy PID control algorithm and model predictive control algorithm, and has a built-in deep learning-based dynamic control model. The dynamic control model optimizes the control parameters of the fuzzy PID control algorithm and model predictive control algorithm through a combination of offline training and online iteration. It is used to receive real-time motor operating status information from the feedback module to dynamically switch between multiple algorithms, and to send control signals to the drive module according to the control algorithm and instructions.

[0039] The feedback module includes an encoder, a current sensor, a laser interferometer, an accelerometer, and a temperature sensor. The encoder is mounted on the output shaft of the turntable motor and is used to detect the motor's speed and position information and convert it into a digital signal to be fed back to the main control module. The current sensor is used to monitor the motor's operating current and feed it back to the main control module. The laser interferometer is used to obtain high-precision position information of the motor. The accelerometer is used to detect the motor's vibration data. The temperature sensor is used to monitor the temperature of the motor and key components of the controller.

[0040] The drive module includes an adaptive power adjustment unit, which includes a power amplifier circuit and a drive chip. It is used to adjust the output power in real time according to the changes in motor load. The power amplifier circuit is used to amplify the control signal output by the main control module. The drive chip is used to control the phase sequence and current of the motor according to the instructions of the main control module.

[0041] The communication module supports RS485 and Ethernet communication protocols, which are used to realize data communication and interaction between the controller and the host computer or other devices.

[0042] The deep learning-based dynamic control model uses an LSTM neural network structure and is trained using historical operating data. It can predict the performance degradation trend of the motor under different operating conditions and adjust the control strategy in advance.

[0043] The switching threshold between the fuzzy PID control algorithm and the model predictive control algorithm is dynamically set by the real-time data from the feedback module. When the motor speed fluctuation exceeds 3% or the position deviation exceeds 0.005°, the algorithm automatically switches from the fuzzy PID control algorithm to the model predictive control algorithm.

[0044] The laser interferometer has a detection accuracy of ±0.5μm / m, the accelerometer has a sampling frequency of no less than 10kHz, and the temperature sensor has a measurement range of -40℃ to 125℃ with a measurement accuracy of ±0.1℃.

[0045] The adaptive power adjustment unit includes a wideband power amplifier and an intelligent power distribution chip, which can adjust the power output within 0.1ms, with a power adjustment range of 10%-120% of the rated power.

[0046] The communication module adopts a 5G-MEC edge computing architecture with a data transmission latency of ≤10ms, supports real-time data upload rates of over 100Mbps, and is compatible with RS485 and Ethernet communication protocols.

[0047] A turntable motor control method is also provided, based on the aforementioned turntable motor controller, comprising the following steps:

[0048] S1: System initialization, power module starts and completes power supply self-test of each module, confirms stable output of isolated power supply, communication module initializes and establishes communication connection with host computer, feedback module performs zero-point calibration and accuracy verification of encoder, current sensor, laser interferometer, accelerometer and temperature sensor;

[0049] S2: Operating condition perception and model loading. The main control module collects the initial speed, position, current, vibration and temperature data of the motor through the feedback module, inputs them into the dynamic control model based on the LSTM neural network structure, and the dynamic control model combines historical operating data to identify the current load type and operating condition, loads the corresponding preset control parameters, classifies the historical operating data into training sets according to operating conditions, inputs them into the LSTM network for iterative training, and predicts the motor performance degradation trend through the dynamic control model.

[0050] The response time for operating condition identification in S2 is ≤50ms. Load types in S2 include light load, heavy load, and impact load. Operating conditions include steady-state operation, dynamic speed regulation, and high-precision positioning. S2 predicts motor performance degradation trends through a dynamic control model, including:

[0051] Real-time calculation of performance degradation factor during online runtime :

[0052]

[0053] in , These are the weighting coefficients. The change in temperature per unit time. The rate of change of rotational speed;

[0054] when > In this case, increase the control margin in advance. The preset performance degradation factor threshold;

[0055] S3: Dynamic switching of control algorithm. The main control module uses the fuzzy PID control algorithm when the motor speed fluctuation is ≤3% and the position deviation is ≤0.005°, based on the working condition perception results and real-time data from the feedback module. When the speed fluctuation exceeds 3% or the position deviation exceeds 0.005°, it automatically switches to the model predictive control algorithm. During the switching process, the dynamic control model synchronously corrects the algorithm parameters.

[0056] S4: Adaptive power regulation and closed-loop control. The adaptive power regulation unit of the drive module amplifies the control signal output by the main control module through the power amplifier circuit. The drive chip adjusts the motor phase sequence and current. At the same time, the adaptive power regulation unit adjusts the output power to 10% to 120% of the rated power within 0.1ms according to the load change. The feedback module collects the motor operation data in real time and transmits it to the main control module to form closed-loop control.

[0057] In S4, the online parameter optimization cycle is 10ms / time, and the parameters optimized each time include the proportional coefficient, integral time, and derivative time.

[0058] S5: Abnormal monitoring and protection. When the temperature sensor detects that the temperature exceeds the range of -40℃ to 125℃, the acceleration sensor detects abnormal vibration data, or the current sensor detects abnormal current, the main control module immediately instructs the drive module to cut off the output, the power supply module starts the overcurrent, overvoltage or short circuit protection function, and the communication module transmits the alarm information to the host computer.

[0059] S6: Online model iterative optimization. The dynamic control model is used for offline training and online iteration at preset intervals, using the latest running data to update the prediction parameters of motor performance degradation trend, ensuring that the control strategy matches the real-time state of the motor, and the iteration process does not affect the normal operation of the motor.

[0060] In S6, online iterative training adopts an incremental learning approach, with each iteration's model update time ≤ 1 second, without affecting the normal operation of the motor.

[0061] Finally, it should be noted that the accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0062] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A turntable motor controller, comprising a main control module, a drive module, a feedback module, a power supply module, and a communication module, characterized in that: The main control module is electrically connected to the drive module, feedback module and communication module respectively. The power supply module supplies power to the main control module, drive module, feedback module and communication module. The power supply module adopts an isolated power supply design and has overcurrent, overvoltage and short circuit protection functions and low power consumption design. The main control module includes a high-performance microprocessor that runs fuzzy PID control algorithm and model predictive control algorithm, and has a built-in deep learning-based dynamic control model. The dynamic control model optimizes the control parameters of the fuzzy PID control algorithm and model predictive control algorithm by combining offline training and online iteration. It is used to receive real-time motor operating status information from the feedback module to dynamically switch between multiple algorithms, and to send control signals to the drive module according to the control algorithm and instructions. The feedback module includes an encoder, a current sensor, a laser interferometer, an accelerometer, and a temperature sensor. The encoder is mounted on the output shaft of the turntable motor and is used to detect the motor's speed and position information and convert them into digital signals to be fed back to the main control module. The current sensor is used to monitor the motor's operating current and feed it back to the main control module. The laser interferometer is used to acquire high-precision position information of the motor. The accelerometer is used to detect the motor's vibration data. The temperature sensor is used to monitor the temperature of the motor and key components of the controller. The drive module includes an adaptive power adjustment unit, which includes a power amplifier circuit and a drive chip. It is used to adjust the output power in real time according to the changes in motor load. The power amplifier circuit is used to amplify the control signal output by the main control module. The drive chip is used to control the phase sequence and current of the motor according to the instructions of the main control module. The communication module supports RS485 and Ethernet communication protocols, and is used to realize data communication and interaction between the controller and the host computer or other devices.

2. The turntable motor controller according to claim 1, characterized in that: The deep learning-based dynamic control model adopts an LSTM neural network structure. It is trained using historical operating data and can predict the performance degradation trend of the motor under different operating conditions, and adjust the control strategy in advance.

3. The turntable motor controller according to claim 1, characterized in that: The switching threshold between the fuzzy PID control algorithm and the model predictive control algorithm is dynamically set by the real-time data of the feedback module. When the motor speed fluctuation exceeds 3% or the position deviation exceeds 0.005°, the algorithm automatically switches from the fuzzy PID control algorithm to the model predictive control algorithm.

4. The turntable motor controller according to claim 1, characterized in that: The laser interferometer has a detection accuracy of ±0.5μm / m, the accelerometer has a sampling frequency of not less than 10kHz, and the temperature sensor has a measurement range of -40℃ to 125℃ with a measurement accuracy of ±0.1℃.

5. The turntable motor controller according to claim 1, characterized in that: The adaptive power adjustment unit includes a wideband power amplifier and an intelligent power distribution chip, which can adjust the power output within 0.1ms, and the power adjustment range is 10%-120% of the rated power.

6. The turntable motor controller according to claim 1, characterized in that: The communication module adopts a 5G-MEC edge computing architecture, with a data transmission latency of ≤10ms, supports real-time data upload rates of over 100Mbps, and is compatible with RS485 and Ethernet communication protocols.

7. A turntable motor control method, applied to the turntable motor controller according to any one of claims 1-6, characterized in that: Includes the following steps: S1: System initialization, power module starts and completes power supply self-test of each module, confirms stable output of isolated power supply, communication module initializes and establishes communication connection with host computer, feedback module performs zero-point calibration and accuracy verification of encoder, current sensor, laser interferometer, accelerometer and temperature sensor; S2: Operating condition perception and model loading. The main control module collects the initial speed, position, current, vibration and temperature data of the motor through the feedback module, inputs them into the dynamic control model based on the LSTM neural network structure, and the dynamic control model combines historical operating data to identify the current load type and operating condition, loads the corresponding preset control parameters, classifies the historical operating data into training sets according to operating conditions, inputs them into the LSTM network for iterative training, and predicts the motor performance degradation trend through the dynamic control model. S3: Dynamic switching of control algorithm. The main control module uses the fuzzy PID control algorithm when the motor speed fluctuation is ≤3% and the position deviation is ≤0.005°, based on the working condition perception results and real-time data from the feedback module. When the speed fluctuation exceeds 3% or the position deviation exceeds 0.005°, it automatically switches to the model predictive control algorithm. During the switching process, the dynamic control model synchronously corrects the algorithm parameters. S4: Adaptive power regulation and closed-loop control. The adaptive power regulation unit of the drive module amplifies the control signal output by the main control module through the power amplifier circuit. The drive chip adjusts the motor phase sequence and current. At the same time, the adaptive power regulation unit adjusts the output power to 10% to 120% of the rated power within 0.1ms according to the load change. The feedback module collects the motor operation data in real time and transmits it to the main control module to form closed-loop control. S5: Abnormal monitoring and protection. When the temperature sensor detects that the temperature exceeds the range of -40℃ to 125℃, the acceleration sensor detects abnormal vibration data, or the current sensor detects abnormal current, the main control module immediately instructs the drive module to cut off the output, the power supply module starts the overcurrent, overvoltage or short circuit protection function, and the communication module transmits the alarm information to the host computer. S6: Online iterative optimization of the model. The dynamic control model is used for offline training and online iteration at preset intervals, using the latest running data to update the prediction parameters of the motor performance degradation trend, ensuring that the control strategy matches the real-time state of the motor, and the iteration process does not affect the normal operation of the motor.

8. The turntable motor control method according to claim 7, characterized in that: The identification response time for the operating condition in S2 is ≤50ms. The load types in S2 include light load, heavy load, and impact load. The operating conditions include steady-state operation, dynamic speed regulation, and high-precision positioning. The prediction of motor performance degradation trend through the dynamic control model in S2 includes: Real-time calculation of performance degradation factor during online runtime : in , These are the weighting coefficients. The change in temperature per unit time. The rate of change of rotational speed; when > At that time, increase the control margin in advance, the aforementioned This is the preset performance degradation factor threshold.

9. The turntable motor control method according to claim 7, characterized in that: The online parameter optimization cycle in S4 is 10ms / time, and the parameters optimized each time include the proportional coefficient, integral time, and derivative time.

10. The turntable motor control method according to claim 7, characterized in that: The online iterative training in S6 adopts an incremental learning method, with the model update time for each iteration being ≤1s, and it does not affect the normal operation of the motor.

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