High-performance brushless motor control system, method and medium based on ROS2

Abstract

The present invention discloses a high-performance brushless motor control system, method and medium based on ROS2, which belongs to the field of motor control technology, wherein the system includes: a task acquisition module for acquiring control target information and configuration target information; a configuration tool module for initializing a configuration parameter set; a control management module for generating control instruction information; a component management module for feedback control response status; wherein the task acquisition module and the configuration tool module are respectively connected to the control management module in communication, and the component management module is connected to the control management module in communication. The present application solves the technical problems of high closedness, poor compatibility and inability to be deeply integrated with modern robot operating systems in traditional motor control systems, and achieves the technical effects of real-time analysis of control tasks, flexible configuration of motor parameters, improved compatibility of motor control systems, and ensuring the accuracy and real-time responsiveness of motor control through modular design.

Description

Technical Field

[0001] The present invention relates to the field of motor control technology, and in particular to a high-performance brushless motor control system, method and medium based on ROS2. Background Art

[0002] Existing brushless motor control systems are widely used in the field of robotics and automation, but their closedness and compatibility issues limit the flexible expansion of the system. Most traditional motor control systems are closed in design and cannot be compatible with software and hardware from different manufacturers. Especially in scenarios where multi-device collaboration is required, the problem of poor compatibility becomes more and more obvious. In addition, early motor control systems have deficiencies in multi-axis collaborative control and real-time performance, making it difficult to achieve high-precision control of motor position, speed and torque. This defect is particularly prominent in complex industrial robots and high-performance mobile robot applications, which cannot guarantee the coordination of multi-axis motion, thereby affecting overall work efficiency and product quality. In response to these deficiencies, although existing control systems have developed, they still have problems with functionality, ease of use, and shallow integration with modern robot operating systems (such as ROS2). Some motor control systems can only provide basic drive support and lack comprehensive control and management capabilities for complex robot systems, especially in high-dynamic and complex load scenarios, making it difficult to meet the real-time and response requirements of the system. In addition, in terms of real-time monitoring and remote debugging, many existing control systems still rely on traditional communication methods and cannot use modern communication technologies to achieve efficient transmission and debugging control of motor status. Summary of the invention

[0003] This application provides a high-performance brushless motor control system, method and medium based on ROS2, aiming to solve the technical problems of traditional motor control systems being highly closed, poorly compatible and unable to be deeply integrated with modern robot operating systems. It achieves the technical effect of real-time analysis of control tasks and flexible configuration of motor parameters through modular design, improving the compatibility of motor control systems, and ensuring the accuracy and real-time responsiveness of motor control.

[0004] The first aspect disclosed in the present application provides a high-performance brushless motor control system based on ROS2, the system comprising: a task acquisition module, the task acquisition module is used to receive and parse control tasks, and obtain control target information and configuration target information; a configuration tool module, the configuration tool module is used to initialize the configuration parameter set of the target motor according to the configuration target information, and the configuration parameter set includes performance configuration parameters and communication configuration parameters; a control management module, the control management module is used to access an adaptive function library to call functional components based on the control target information, and make control decisions for the target motor by calling functional components to generate control instruction information; a component management module, the component management module is used to control the target motor according to the control instruction information, and feedback the control response status; wherein the task acquisition module and the configuration tool module are respectively communicated with the control management module, and the component management module is communicated with the control management module.

[0005] The second aspect disclosed in the present application provides a high-performance brushless motor control method based on ROS2, the method comprising: receiving and parsing a control task, obtaining control target information and configuration target information; initializing a configuration parameter set of a target motor according to the configuration target information, the configuration parameter set comprising performance configuration parameters and communication configuration parameters; based on the control target information, accessing an adaptive function library to call a function component, and making a control decision for the target motor by calling the function component to generate control instruction information; performing a control response of the target motor according to the control instruction information, and feeding back the control response status.

[0006] The third aspect of the present application provides a medium having a computer program stored thereon, wherein the computer program implements the following modules when executed by a processor: a task acquisition module, wherein the task acquisition module is used to receive and parse control tasks, and obtain control target information and configuration target information; a configuration tool module, wherein the configuration tool module is used to initialize a configuration parameter set of a target motor according to the configuration target information, wherein the configuration parameter set includes performance configuration parameters and communication configuration parameters; a control management module, wherein the control management module is used to access an adaptive function library to perform function component calls based on the control target information, and perform control decisions on the target motor by calling the function components to generate control instruction information; and a component management module, wherein the component management module is used to perform control responses on the target motor according to the control instruction information, and to feed back the control response status.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The task acquisition module is used to receive and analyze the control task to obtain the control target information and the configuration target information; the configuration tool module is used to initialize the configuration parameter set of the target motor according to the configuration target information, and the configuration parameter set includes performance configuration parameters and communication configuration parameters; the control management module is used to access the adaptive function library based on the control target information to call the function component, and make the control decision of the target motor by calling the function component to generate the control instruction information; the component management module is used to make the control response of the target motor according to the control instruction information, and feedback the control response status; wherein the task acquisition module and the configuration tool module are respectively connected to the control management module in communication, and the component management module is connected to the control management module in communication. The technical problems of high closedness, poor compatibility and inability to deeply integrate with modern robot operating systems of traditional motor control systems are solved, and the technical effects of real-time analysis of control tasks, flexible configuration of motor parameters, improved compatibility of motor control systems, and guaranteed accuracy and real-time responsiveness of motor control are achieved through modular design.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A structural schematic diagram of a high-performance brushless motor control system based on ROS2 is provided for the embodiment of the present application;

[0011] Figure 2 A flow chart of a high-performance brushless motor control method based on ROS2 is provided for an embodiment of the present application.

[0012] Description of the accompanying drawings: task acquisition module 1, configuration tool module 2, control management module 3, component management module 4. DETAILED DESCRIPTION

[0013] This application solves the technical problems of traditional motor control systems, such as high closedness, poor compatibility, and inability to be deeply integrated with modern robot operating systems, by providing a high-performance brushless motor control system, method, and medium based on ROS2. It achieves the technical effect of real-time analysis of control tasks and flexible configuration of motor parameters through modular design, improving the compatibility of the motor control system, and ensuring the accuracy and real-time responsiveness of motor control.

[0014] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0015] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a high-performance brushless motor control system based on ROS2, the system comprising:

[0016] The task acquisition module 1 is used to receive and analyze the control task, and obtain the control target information and the configuration target information.

[0017] Specifically, in the task acquisition module 1, the system terminal receives task instructions from the user, which usually contain specific requirements or goals for motor operation. After receiving the task, the system terminal will parse the key content of the task and extract the control target information (such as the target position) and configuration target information (such as the communication method or performance parameter setting) used to drive the motor. The control target information is directly used to determine the operation mode of the motor, while the configuration target information provides data support for the basic configuration of the motor control. Through this process, the task requirements can be converted into specific operating parameters so that precise control operations can be performed based on this information in the future.

[0018] Furthermore, the task acquisition module 1 includes a human-computer interaction interface, which is used to connect to the user end to receive the control task and feedback the control response state, wherein the control response state is visually fed back through the user end.

[0019] In a preferred embodiment, the task acquisition module 1 includes a human-computer interaction interface, which is responsible for connecting to the user end, receiving the control task sent by the user, and feeding back the control response status of the motor to the user. Through this interface, the user can use instructions to publish the current task requirements, such as industrial production, indoor cleaning, cargo distribution, etc. At the same time, the system terminal will feed back the real-time operating status of the motor (such as position, speed, temperature, etc.) to the user end and display it in a graphical way on the user interface. This visual feedback enables the user to intuitively understand the current status of the motor, facilitate real-time monitoring and adjustment operations, thereby enhancing the operability and user experience of the system.

[0020] Configuration tool module 2, the configuration tool module 2 is used to initialize the configuration parameter set of the target motor according to the configuration target information, the configuration parameter set includes performance configuration parameters and communication configuration parameters.

[0021] Specifically, in the configuration tool module 2, the system terminal initializes the configuration parameter set of the target motor according to the configuration target information to ensure that the motor can efficiently and accurately perform the control task under preset conditions. The configuration parameter set includes performance configuration parameters and communication configuration parameters. Among them, the performance configuration parameters include setting key performance parameters and control algorithm parameters. The key performance parameters include the speed, acceleration, torque limit, etc. of the motor, and the control algorithm parameters include PID control parameters, adaptive control and other algorithm parameters. By setting the maximum speed and acceleration of the motor, the maximum operating speed and acceleration performance that the motor can reach during the task execution can be determined to meet the speed requirements of different working scenarios. By setting the torque limit of the motor, that is, the maximum torque that the motor can output, it can be ensured that the motor will not be overloaded when the load is too large, thereby protecting the stability of the motor and the driver. By setting the control algorithm parameters, it can be ensured that the motor can perform speed and position control that meets the current needs. The communication configuration parameters include parameters related to ROS2 communication, such as setting ROS2 topics, services, and action communication parameters, so that the status information of the motor can be published to the specified topic in the ROS2 system in real time, and control instructions from other ROS2 nodes can be received. These communication parameters allow the motor to seamlessly interact with other robot components, allowing the control system to flexibly respond to the communication requirements of different tasks. Through the initialization of the above performance and communication configuration parameters, the system terminal creates a configuration environment for the target motor that adapts to the task requirements, ensuring that the motor can efficiently perform control tasks in specific operations and maintain real-time communication with the ROS2 system to achieve intelligent and flexible control effects.

[0022] The control management module 3 is used to access the adaptive function library to call the function component based on the control target information, and make a control decision for the target motor by calling the function component to generate control instruction information.

[0023] Specifically, in the control management module 3, the system terminal accesses the adaptive function library to call the corresponding functional components according to the control target information, so as to formulate the most suitable control scheme for the current task for the motor. Specifically, the system terminal analyzes the control target information (for example, the speed, position or action target currently required), and based on these requirements, searches for control components suitable for the current task requirements in the adaptive function library. This adaptive function library is established through adaptive control components, contains the calling constraints of functional components, and can adapt to different load conditions and dynamically changing task requirements. After screening out the control components that need to be called, the system terminal will make control decisions for these called control components and configure corresponding state control instructions or state acquisition instructions for each called control component. Subsequently, all configured instructions are added to the control instruction information to guide how the motor should adjust the operating details such as speed, direction or torque, so as to ensure that the motor maintains accurate and flexible control effects in a complex and changing operating environment.

[0024] Furthermore, the control management module 3 includes.

[0025] An adaptive control component, the adaptive control component is used to establish calling constraints of multiple function components in an adaptive function library, and select the corresponding calling function component to make a control decision based on the control target information and the calling constraints, and output the control instruction information; wherein the adaptive function library includes multiple state control function components and multiple state acquisition function components, and the control instruction information includes state control instructions and state acquisition instructions.

[0026] In an optional embodiment, the control management module 3 has an adaptive control component inside, which is used to select and call appropriate functional components from the adaptive function library to achieve dynamic control of the motor. Specifically, the adaptive control component will first set the calling conditions and restrictions (i.e., calling constraints) of the functional components in the adaptive function library. These constraints are defined according to different control objectives and task requirements to ensure that the system can flexibly select the most suitable control method when executing the task. During the task execution process, the adaptive control component will compare the calling constraints of each functional component in the adaptive function library with the control target information, select the corresponding control and acquisition functional components and make decisions. For example, if the current task requires precise speed control, a state control functional component will be selected to maintain a stable speed, such as a speed control functional component containing an adaptive control algorithm. At the same time, state acquisition functional components, such as position monitoring components and current acquisition components, will also be called to track the position and current state of the motor in real time to prevent deviation from the target path and overheating of the motor due to excessive load. Finally, the adaptive control component will generate specific control instruction information, including state control instructions (instructing how the motor runs) and state acquisition instructions (instructing how the acquisition system feeds back real-time status). Through this process, the control strategy can be automatically adjusted during task execution to adapt to various working conditions and requirements, ensuring precise control and stable operation of the motor.

[0027] Furthermore, the control management module 3 also includes a model prediction control component, which is used to predict the state of the target motor and optimize the control instruction information according to the state prediction result. The execution steps of the model prediction control component include:

[0028] A dynamic mathematical model between a target motor and a load is established, and the dynamic mathematical model is initialized in combination with the control task and the working state information; the control instruction information is input into the dynamic mathematical model for control simulation, and the predicted state information of the target motor is obtained according to the control simulation result; the control instruction information is used as a state baseline, and the predicted state information is combined to perform super-line identification, and the control instruction information is optimized.

[0029] In an optional embodiment, the control management module 3 also includes a model prediction control component for optimizing control instructions by predicting the future state of the motor. In this model prediction control component, the system terminal pre-establishes a dynamic mathematical model, which is initialized by the control task and the sample working state information, and can accurately describe the running behavior of the motor under different load conditions. For the dynamic mathematical model, the system terminal divides the sample control instruction information set and the corresponding sample working state information in the control task into a training set and a verification set, trains the constructed initial dynamic mathematical model through the training set, and verifies it with the verification set after the training to evaluate the prediction performance of the model. The initial dynamic mathematical model is constructed based on a neural network structure, including an input layer, a hidden layer, and an output layer. First, the weights of the model are initialized with random numbers, and the training set is input into the initialized neural network model for forward propagation. The input layer of the model receives the control instruction information, and after passing through the hidden layer, the output layer generates the predicted state of the motor. In each forward propagation, the model uses the sample working state information as the target output to compare the actual prediction results with the real working state data. The error between the predicted result and the true value is calculated using the mean square error loss function (MSE), and the gradient of the loss to the weight of each layer is calculated layer by layer through back propagation. The weight parameters of the model are optimized using the Adam optimizer to minimize the value of the loss function and gradually improve the prediction accuracy of the model. The above training process will be repeated many times until the maximum number of iterations is reached or the loss value converges. After the training is completed, the model is tested for performance using the validation set to evaluate its state prediction accuracy in different control tasks. If the prediction accuracy meets the expected requirements, the current model is used as the final dynamic mathematical model for real-time control task prediction; if the accuracy does not meet the standard, the learning rate, batch size and other hyperparameters can be adjusted to further optimize the prediction effect of the model. The dynamic mathematical model after training can accept real-time instructions in the control task and predict the future operating state of the motor, so as to optimize the control instructions in the future and realize precise control of the motor. After the dynamic mathematical model is established, the system terminal inputs the current control instruction information into the dynamic mathematical model. The dynamic mathematical model simulates these control instructions through the learned mapping relationship to obtain the predicted state information of the motor in the future, such as future speed, position, torque, etc. Subsequently, the current control command information is used as the state baseline to define the ideal operating state that the motor is expected to achieve, that is, the set target speed, position, torque, etc. The state baseline is used to measure the deviation between the actual operating state of the motor and the target state. The system terminal compares the obtained predicted state information with the state baseline to identify whether any state deviation exceeds the preset tolerance range, that is, the over-line situation.The process of over-line identification includes calculating the difference between the predicted state and the state baseline. If the difference exceeds the set tolerance range, for example, the speed is lower than the lower limit of the target speed or the position deviation exceeds the allowable error value, it is determined to be over-line. If the over-line situation is identified, the system terminal will adjust the current control instruction to ensure that the operating state of the motor conforms to the state baseline again. For example, in the case of insufficient speed when carrying goods of different weights, the torque output can be increased or the PID control parameters can be adjusted to improve the response speed of the motor. The optimized control instruction information will be fed back to the component management module 4 to ensure that the motor can be closer to the desired operating state.

[0030] A component management module 4, wherein the component management module 4 is used to perform a control response of the target motor according to the control instruction information and to feed back a control response status; wherein the task acquisition module 1 and the configuration tool module 2 are respectively communicated with the control management module 3, and the component management module 4 is communicated with the control management module 3.

[0031] Specifically, in the component management module 4, the system terminal drives the motor according to the generated control instruction information to realize the real-time response of the motor to the specific control task. The control instruction information contains the operation details that the motor needs to perform, such as the set speed, position or torque parameters. The system terminal transmits these instruction information to the motor driver to control the motor to operate according to the set parameters. In the process of the motor responding to the control instruction, the actual operating state of the motor is continuously monitored, such as the current speed, position, temperature, etc., and these real-time states are collected as feedback information. These feedback state information reflects the actual response of the motor to the control instruction, helping the system terminal to determine whether the motor is running according to the expected instruction, or whether the control instruction needs to be further adjusted. The feedback control response state will be transmitted back to the control management module 3 as the basis for subsequent control decisions. Through this real-time feedback, any deviation or instability can be identified, and adjustments can be made in time to ensure that the motor can accurately complete the control task and meet the task requirements.

[0032] Specifically, the task acquisition module 1 and the configuration tool module 2 are connected to the control management module 3 through the communication interface, so that the control management module 3 can receive the task instruction information from the task acquisition module 1 and obtain the motor configuration parameters initialized by the configuration tool module 2. At the same time, the component management module 4 is also connected to the control management module 3, responsible for receiving the control instructions sent by it, and feeding back the actual response state of the motor to the control management module 3. This inter-module communication connection ensures that the task information, configuration parameters, control instructions and feedback status can be smoothly transmitted between the modules, thereby realizing efficient control and real-time adjustment of the motor, and ensuring the coordination and stable operation of the system.

[0033] Furthermore, the component management module 4 is communicatively connected with the driver component, and is control-connected with the target motor through the driver component, wherein the communication connection mode includes USB, UART, and CAN.

[0034] In an optional embodiment, the component management module 4 maintains a communication connection with the driver component and directly controls the operation of the target motor through the driver component. The component management module 4 sends a control instruction to the driver to instruct the motor to perform a specific action or adjust parameters. At the same time, the driver component also feeds back the running status information of the motor to the component management module 4 to ensure closed-loop feedback of the control process. The communication connection can be implemented in a variety of ways, including interfaces such as USB, UART or CAN, to adapt to different hardware configuration requirements, ensure the stability and real-time performance of data transmission, and thus achieve precise control of the motor.

[0035] Furthermore, the system further comprises a sensor acquisition module, which is in communication with the component management module 4, and the execution steps of the sensor acquisition module include:

[0036] A sensor monitoring network composed of multiple sensors is connected; the control instruction information is parsed to obtain monitoring sensor targets, wherein the monitoring sensor targets include dimensional targets and accuracy targets; through the monitoring sensor targets, the sensor monitoring network is controlled to obtain the working status information of the target motor in real time, wherein the working status information includes environmental status information and motor status information; the working status information is used as the control response status, fed back to the component management module 4, and transmitted to the control management module 3 through the component management module 4 to make iterative control decisions.

[0037] In an optional embodiment, the system terminal also includes a sensor acquisition module, which is connected to the component management module 4 through a communication interface to build a monitoring network composed of multiple sensors for real-time acquisition of the running state of the motor. The sensor acquisition module will first parse the control instruction information to determine the sensor target to be monitored, including specific monitoring dimensions (such as temperature, speed, position, etc.) and the required accuracy to ensure that the collected data meets the control requirements. Subsequently, according to these monitoring targets, the sensor acquisition module controls the sensor network to monitor the working state of the motor in real time. The collected data includes both the running state of the motor (such as motor temperature, torque, speed, etc.) and the surrounding environment state (such as ambient temperature, humidity, etc.). These working state information is used as the control response state of the motor and fed back to the component management module 4, and the component management module 4 then transmits these data to the control management module 3. The control management module 3 uses these feedback information to perform continuous control adjustments, so that the system terminal can dynamically respond to the real-time state changes of the motor, optimize the control decision, and ensure that the motor maintains stable and efficient operation under complex working conditions.

[0038] Further, according to the control instruction information, a control response of the target motor is performed, and a control response state is fed back, and the execution step also includes:

[0039] The control response state is transmitted to the control management module 3; the control management module 3 compares the control response state with the control instruction information to obtain the state difference characteristics of the control response state; using the state difference characteristics as call constraints, accessing the adaptive function library to extract the correction function component, and performing state correction on the target motor according to the state difference characteristics.

[0040] In a preferred embodiment, the system terminal transmits the control response state of the motor to the control management module 3 so as to analyze the actual operation of the motor. After receiving these feedbacks, the control management module 3 will compare the current control response state with the current control instruction information, identify the state difference between the two, that is, find the deviation characteristics between the actual operating state and the expected state through difference calculation. These difference characteristics may include information such as speed deviation, position error or temperature overrun. Subsequently, the control management module 3 uses these state difference characteristics as call constraints, accesses the adaptive function library, and extracts suitable correction function components from it. The correction function component adjusts the control parameter instructions according to the specific difference characteristics to ensure that the motor state is gradually corrected to the expected target. Through this feedback adjustment mechanism, the system terminal can dynamically correct the motor state during operation, improve the control accuracy and response accuracy, and thus adapt to the control requirements under different working conditions.

[0041] Furthermore, the control management module 3 compares the control response state with the control instruction information to obtain the state difference feature of the control response state, and then further includes:

[0042] Taking the state difference feature as the calling constraint, traverse the adaptive function library to match the function components and obtain the corrected function component; determine the consistency between the corrected function component and the called function component. If the corrected function component is consistent with the called function component, make a corrected control decision based on the corrected function component and the state difference feature; if the corrected function component is inconsistent with the called function component, trigger the over-limit accumulator of the called function component accordingly. When the accumulated value of the over-limit accumulator is greater than or equal to the preset over-limit constraint, trigger the function correction of the corresponding called function component.

[0043] In an optional implementation, when the system terminal detects that there is a deviation between the control response state and the control instruction information, the calculated deviation feature will be used as a call constraint. According to this call constraint, the adaptive control component traverses all functional components in the adaptive function library, searches for functional components related to the deviation feature, and compares the call constraint with the standard error range of these related functional components, and finds the functional component with a standard error range smaller than the call constraint as a correction functional component to deal with the current state deviation. Once a qualified correction functional component is found, the system terminal will check whether the correction functional component is consistent with the currently called functional component. For example, the current call functional component may be a component for speed control, and the correction functional component also provides the same control strategy. If the two are consistent, it means that the currently called control strategy is appropriate, so the system terminal makes a direct correction control decision based on the correction functional component and the state difference feature, and calculates a specific correction amount through the deviation feature, such as increasing a certain speed or reducing a certain torque. In order to ensure the stability of control, the correction amount is usually adaptively adjusted to avoid fluctuations caused by excessive correction and ensure that the motor returns to the target state as soon as possible. If it is detected that the correction function component is inconsistent with the currently called function component, the system terminal will trigger the over-limit accumulator of the called function component. The over-limit accumulator will begin to accumulate the number or degree of deviations, and the value of the accumulator will increase every time the deviation is detected to be beyond the preset range. When the accumulated value of the over-limit accumulator reaches or exceeds the preset over-limit constraint, the system terminal will trigger the function correction of the called function component. The specific method of function correction includes adjusting the control strategy so that it can gradually reduce the correction amount or correction frequency. For example, if the system detects that the speed deviation occurs frequently, it will adaptively reduce the amplitude of the speed correction or reduce the frequency of the correction through the function correction strategy, that is, by dynamically reducing the adjustment amplitude of the control parameters, the motor will gradually tend to the target state, and by reducing the correction frequency, the interference with the control stability is reduced to avoid the instability caused by over-adjustment. In this way, according to different state difference characteristics and call constraints, the appropriate correction strategy can be intelligently selected to achieve stable control and optimization of the motor state.

[0044] In summary, the high-performance brushless motor control system based on ROS2 provided in the embodiment of the present application has the following technical effects:

[0045] Task acquisition module 1, the task acquisition module 1 is used to receive and analyze the control task, obtain the control target information and the configuration target information; the configuration tool module 2 is used to initialize the configuration parameter set of the target motor according to the configuration target information, and the configuration parameter set includes performance configuration parameters and communication configuration parameters; control management module 3, the control management module 3 is used to access the adaptive function library to call the function component based on the control target information, and make the control decision of the target motor by calling the function component, and generate control instruction information; component management module 4, the component management module 4 is used to make the control response of the target motor according to the control instruction information, and feedback the control response state; wherein, the task acquisition module 1 and the configuration tool module 2 are respectively connected to the control management module 3 in communication, and the component management module 4 is connected to the control management module 3 in communication. Through the above modules, the technical problems of high closedness, poor compatibility and inability to be deeply integrated with the modern robot operating system of the traditional motor control system are solved, and the technical effect of real-time analysis of control tasks, flexible configuration of motor parameters, improvement of the compatibility of the motor control system, and ensuring the accuracy and real-time responsiveness of motor control is achieved through modular design.

[0046] Embodiment 2 is based on the same inventive concept as the high-performance brushless motor control system based on ROS2 in the above embodiment. Figure 2 As shown, the embodiment of the present application provides a high-performance brushless motor control method based on ROS2, the method comprising:

[0047] Receive and parse the control task, obtain control target information and configuration target information; initialize the configuration parameter set of the target motor according to the configuration target information, and the configuration parameter set includes performance configuration parameters and communication configuration parameters; based on the control target information, access the adaptive function library to call the function component, and make control decisions for the target motor by calling the function component to generate control instruction information; according to the control instruction information, perform control response of the target motor and feedback the control response status.

[0048] Furthermore, the task acquisition module 1 includes:

[0049] A user terminal is connected to receive the control task and provide feedback on the control response status, wherein the control response status is visually fed back through the user terminal.

[0050] Furthermore, the control management module 3 includes:

[0051] Establish calling constraints for multiple functional components in the adaptive function library, and select the corresponding calling functional components to make control decisions based on the control target information and the calling constraints, and output the control instruction information; wherein the adaptive function library includes multiple state control functional components and multiple state acquisition functional components, and the control instruction information includes state control instructions and state acquisition instructions.

[0052] Furthermore, the control management module 3 includes:

[0053] A dynamic mathematical model between a target motor and a load is established, and the dynamic mathematical model is initialized in combination with the control task and the working state information; the control instruction information is input into the dynamic mathematical model for control simulation, and the predicted state information of the target motor is obtained according to the control simulation result; the control instruction information is used as a state baseline, and the predicted state information is combined to perform super-line identification, and the control instruction information is optimized.

[0054] Furthermore, the component management module 4 includes:

[0055] The device is communicatively connected to a driver component and is control-connected to a target motor through the driver component, wherein the communication connection mode includes USB, UART, and CAN.

[0056] Furthermore, the component management module 4 includes:

[0057] The control response state is transmitted to the control management module 3; the control management module 3 compares the control response state with the control instruction information to obtain the state difference characteristics of the control response state; using the state difference characteristics as call constraints, accessing the adaptive function library to extract the correction function component, and performing state correction on the target motor according to the state difference characteristics.

[0058] Furthermore, the component management module 4 includes:

[0059] Taking the state difference feature as the calling constraint, traverse the adaptive function library to match the function components and obtain the corrected function component; determine the consistency between the corrected function component and the called function component. If the corrected function component is consistent with the called function component, make a corrected control decision based on the corrected function component and the state difference feature; if the corrected function component is inconsistent with the called function component, trigger the over-limit accumulator of the called function component accordingly. When the accumulated value of the over-limit accumulator is greater than or equal to the preset over-limit constraint, trigger the function correction of the corresponding called function component.

[0060] Furthermore, it also includes:

[0061] A sensor monitoring network composed of multiple sensors is connected; the control instruction information is parsed to obtain monitoring sensor targets, wherein the monitoring sensor targets include dimensional targets and accuracy targets; through the monitoring sensor targets, the sensor monitoring network is controlled to obtain the working status information of the target motor in real time, wherein the working status information includes environmental status information and motor status information; the working status information is used as the control response status, fed back to the component management module 4, and transmitted to the control management module 3 through the component management module 4 to make iterative control decisions.

[0062] Embodiment three provides a medium in which a computer program is stored. When a processor executes the computer program, the following modules are implemented: a task acquisition module 1, wherein the task acquisition module 1 is used to receive and parse a control task, and obtain control target information and configuration target information; a configuration tool module 2, wherein the configuration tool module 2 is used to initialize a configuration parameter set of a target motor according to the configuration target information, wherein the configuration parameter set includes performance configuration parameters and communication configuration parameters; a control management module 3, wherein the control management module 3 is used to access an adaptive function library to perform function component calls based on the control target information, and perform control decisions on the target motor by calling the function components to generate control instruction information; and a component management module 4, wherein the component management module 4 is used to perform control responses on the target motor according to the control instruction information, and to feed back the control response status.

[0063] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0064] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application intends to include these modifications and variations.

Claims

1. A high-performance brushless motor control system based on ROS2, characterized by: The system comprises: A task acquisition module, which is used to receive and analyze control tasks, and acquire control target information and configuration target information; A configuration tool module, wherein the configuration tool module is used to initialize a configuration parameter set of a target motor according to the configuration target information, wherein the configuration parameter set includes performance configuration parameters and communication configuration parameters; A control management module, the control management module is used to access the adaptive function library to call the function component based on the control target information, and make a control decision for the target motor by calling the function component to generate control instruction information; A component management module, wherein the component management module is used to perform a control response of the target motor according to the control instruction information and to feedback a control response state, wherein the execution steps of feedbacking the control response state include: Transmitting the control response status to the control management module; The control management module compares the control response state with the control instruction information to obtain a state difference feature of the control response state; Using the state difference feature as a call constraint, accessing an adaptive function library to extract a correction function component, and performing state correction on the target motor according to the state difference feature; Taking the state difference feature as the calling constraint, traversing the adaptive function library to match the function components, obtaining the modified function component, and determining the consistency between the modified function component and the calling function component; If the correction function component is consistent with the calling function component, a correction control decision is made based on the correction function component and the state difference feature; if the correction function component is inconsistent with the calling function component, an over-limit accumulator of the calling function component is triggered accordingly, and when the accumulated value of the over-limit accumulator is greater than or equal to the preset over-limit constraint, the function correction of the corresponding calling function component is triggered; Wherein, the task acquisition module and the configuration tool module are respectively connected in communication with the control management module, and the component management module is connected in communication with the control management module.

2. The high-performance brushless motor control system based on ROS2 as claimed in claim 1, characterized in that: The task acquisition module includes a human-computer interaction interface, and the human-computer interaction interface is used to connect the user end to receive the control task and feedback the control response state, wherein the control response state is visually fed back through the user end.

3. The high-performance brushless motor control system based on ROS2 as claimed in claim 2, characterized in that: The control management module includes: An adaptive control component, the adaptive control component is used to establish call constraints of multiple function components in the adaptive function library, and select the corresponding call function component to make a control decision according to the control target information and the call constraints, and output the control instruction information; The adaptive function library includes a plurality of state control function components and a plurality of state acquisition function components, and the control instruction information includes state control instructions and state acquisition instructions.

4. The high-performance brushless motor control system based on ROS2 as claimed in claim 3, characterized in that: The component management module is communicatively connected with the driver component, and is control-connected with the target motor through the driver component, wherein the communication connection mode includes USB, UART, and CAN.

5. The high-performance brushless motor control system based on ROS2 as claimed in claim 4, characterized in that: The system further includes a sensor acquisition module, which is in communication with the component management module. The execution steps of the sensor acquisition module include: Connect a sensor monitoring network consisting of multiple sensors; Parsing the control instruction information to obtain monitoring sensing targets, wherein the monitoring sensing targets include dimension targets and accuracy targets; By means of the monitoring sensing target, the sensor monitoring network is controlled to obtain the working state information of the target motor in real time, wherein the working state information includes the environmental state information and the motor state information; The working status information is used as the control response status, fed back to the component management module, and transmitted to the control management module through the component management module to perform iterative control decision-making.

6. The high-performance brushless motor control system based on ROS2 as claimed in claim 5, characterized in that: The control management module also includes a model prediction control component, which is used to predict the state of the target motor and optimize the control instruction information according to the state prediction result. The execution steps of the model prediction control component include: Establishing a dynamic mathematical model between the target motor and the load, and initializing the dynamic mathematical model in combination with the control task and the working state information; Inputting the control instruction information into the dynamic mathematical model to perform control simulation, and acquiring predicted state information of the target motor according to the control simulation result; The control instruction information is used as a state baseline, and the predicted state information is combined to perform super line identification, and the control instruction information is optimized.

7. A high-performance brushless motor control method based on ROS2, characterized in that: The method is performed by a high-performance brushless motor control system based on ROS2 according to any one of claims 1 to 6, comprising: Receive and parse control tasks, obtain control target information and configuration target information; Initializing a configuration parameter set of a target motor according to the configuration target information, wherein the configuration parameter set includes performance configuration parameters and communication configuration parameters; Based on the control target information, access the adaptive function library to call the function component, and make a control decision for the target motor by calling the function component to generate control instruction information; According to the control instruction information, a control response of the target motor is performed, and a control response state is fed back.

8. A medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the module of the high-performance brushless motor control system based on ROS2 as claimed in any one of claims 1 to 6 is implemented.

Images