Master-slave shaft coupling control method and system, terminal equipment and medium

Abstract

The invention discloses a master-slave shaft coupling control method and system, terminal equipment and a medium, and the method comprises the steps: obtaining the sample data of a master shaft and a slave shaft, and carrying out the preprocessing of the sample data through a filtering method, and obtaining the observation data of the master shaft and the slave shaft; performing parameter identification based on the observation data, and updating system parameters of a control quantity model; and calculating a motion distance control amount based on the control amount model and the observation data, and performing coupling control on a master axis and a slave axis based on the motion distance control amount. According to the method, the sampling data is optimized in real time, and the control strategy is dynamically adjusted in combination with the parameter identification technology, so that gradual optimization and dynamic adjustment are realized in the motion control process, and the problems of high noise sensitivity, insufficient parameter identification and lack of process optimization in the existing profiling control technology are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of master-slave axis coupling control, and in particular to a master-slave axis coupling control method, system, terminal equipment and medium for surface precision machining. Background Art

[0002] In the field of motion control, master-slave axis coupling motion is widely used in precision manufacturing equipment such as CNC machine tools and industrial robots. In order to achieve high-precision master-slave axis motion collaboration, the method of profiling control is usually adopted, that is, the slave axis is driven by the position, speed or acceleration signal of the master axis for synchronous control. However, traditional profiling control methods often find it difficult to ensure the robustness and motion accuracy of the system when facing complex environments (such as signal noise and uncertain parameter changes).

[0003] In the prior art, the profiling control relies on real-time signals. Once the sensor or environment introduces noise, it is easy to cause the following error to increase. Moreover, the traditional method mainly focuses on the matching of the output trajectory, rather than the data optimization of the motion process itself, and fails to effectively improve the overall performance of the system. Furthermore, in a complex system, the kinematic parameters of the master and slave axes may change over time, and the existing methods are difficult to identify the parameters in real time and adjust the control strategy. Finally, the data-driven technology has not yet been organically combined with the filtering algorithm and the parameter identification method, and it is difficult to achieve global optimization of the complex dynamic coupling process. Specifically, in the prior art, the traditional profiling control based on PID realizes the synchronization between the master and slave axes through simple proportional, integral and differential control, but due to the single model, it is difficult to adapt to complex nonlinear motion. The improved method based on feedforward compensation, adding feedforward compensation to the profiling control, can improve the system response speed to a certain extent, but for complex coupled dynamic systems, the compensation effect is limited, and the parameters are difficult to accurately identify. Therefore, the prior art needs to be improved. Summary of the invention

[0004] The technical problem to be solved by the present invention is that, in view of the above-mentioned defects of the prior art, a master-slave axis coupling control method, system, terminal device and medium are provided, aiming to solve the problems of high noise sensitivity, insufficient parameter identification and lack of process optimization in the existing profiling control technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a master-slave axis coupling control method, the method comprising: Acquire sample data of the main axis and the slave axis, and pre-process the sample data using a filtering method to obtain observation data of the main axis and the slave axis; Perform parameter identification based on the observed data and update system parameters of the control quantity model; Based on the control quantity model and the observation data, the motion distance control quantity is calculated, and based on the motion distance control quantity, the master axis and the slave axis are coupled controlled.

[0006] In one implementation, the acquiring of sample data of the master axis and the slave axis includes: Collect encoder position data and sensor data of the master and slave axes during interruption; The encoder position data and the sensor data are stored in a sample buffer in a first-in-first-out manner to obtain sample data of the master axis and the slave axis.

[0007] In one implementation, the preprocessing of the sample data using a filtering method to obtain observation data of the master axis and the slave axis includes: The filtering method is extended Kalman filtering; The extended Kalman filter is used to perform state estimation, error correction and smoothing on the sample data to obtain observation data of the main axis and the slave axis.

[0008] In one implementation, the performing of parameter identification based on the observation data and updating the system parameters of the control quantity model includes: The system parameters include input and output gain coefficients and bias coefficients; The observation data is input into a parameter estimator of a pre-trained deep neural network to update the input-output gain coefficient and bias coefficient of the control quantity model.

[0009] In one implementation, the calculating the motion distance control amount based on the control amount model and the observation data includes: The observation data includes the current positions of the master axis and the slave axis; Inputting the observed data into the control quantity model to calculate the desired positions of the main axis and the slave axis; Based on the deviation between the desired position and the current position, a control amount of the movement distance is calculated.

[0010] In one implementation, the calculating the control amount of the movement distance based on the deviation between the desired position and the current position includes: Calculating sliding surface parameters based on the deviation between the desired position and the current position to obtain an adaptive sliding surface; Based on the adaptive sliding surface, a movement distance control amount is calculated.

[0011] In one implementation, the method further includes: An additional sensor is provided at the actual processing position, and the sensor is used to collect error data after the system input in real time; Performing secondary parameter identification on the error data to obtain input and output gain coefficients and bias coefficients of a compensation model of the motion distance control amount; Calculating a compensation value based on the compensation amount model and the observation data; Based on the compensation value, error compensation is performed on the movement distance control amount.

[0012] In a second aspect, an embodiment of the present invention further provides a master-slave axis coupling control system, the system comprising: An observation data acquisition module is used to acquire sample data of the main axis and the slave axis, and pre-process the sample data using a filtering method to obtain observation data of the main axis and the slave axis; A control quantity model acquisition module, used for performing parameter identification based on the observation data and updating system parameters of the control quantity model; The control quantity acquisition module is used to calculate the motion distance control quantity based on the control quantity model and the observation data, and to perform coupling control on the main axis and the slave axis based on the motion distance control quantity.

[0013] In a third aspect, an embodiment of the present invention further provides a terminal device, comprising a memory, a processor, and a master-slave axis coupling control program stored in the memory and executable on the processor, wherein when the processor executes the master-slave axis coupling control program, the steps of the master-slave axis coupling control method of any one of the above-mentioned schemes are implemented.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a master-slave axis coupling control program is stored. When the master-slave axis coupling control program is executed by a processor, the steps of the master-slave axis coupling control method described in any one of the above schemes are implemented.

[0015] Beneficial effects: The present invention discloses a master-slave axis coupling control method, system, terminal device and medium. Compared with the prior art, the method first obtains sample data of the master axis and the slave axis, and uses a filtering method to preprocess the sample data to obtain the observation data of the master axis and the slave axis; then, parameter identification is performed based on the observation data to update the system parameters of the control quantity model; finally, based on the control quantity model and the observation data, the motion distance control quantity is calculated, and based on the motion distance control quantity, the master-slave axis is coupled and controlled. The present invention optimizes the sampled data in real time, dynamically adjusts the control strategy in combination with parameter identification technology, and uses a first-in-first-out motion controller buffer management algorithm in the motion control process to achieve real-time incremental optimization of sample parameters, thereby achieving coupling control of the master-slave axis motion control quantity and the target state value of the actuator, achieving high-precision contour motion control, and solving the problems of high noise sensitivity, insufficient parameter identification and lack of process optimization in the existing contour control technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a specific implementation of the master-slave axis coupling control method provided in an embodiment of the present invention.

[0017] Figure 2 It is a motion control flow chart provided by an embodiment of the present invention.

[0018] Figure 3 It is a flow chart of sample data processing, adaptive controller generation and control quantity calculation provided by an embodiment of the present invention.

[0019] Figure 4 It is a flow chart of master-slave axis data coupling and first-in-first-out algorithm to achieve real-time incremental optimization provided by an embodiment of the present invention.

[0020] Figure 5 It is a principle block diagram of a master-slave axis coupling control device provided by an embodiment of the present invention.

[0021] Figure 6 It is a block diagram of the internal structure principle of the intelligent terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations or steps, nor must they be executed in the order described. For example, some operations or steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0024] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms. It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish between identical or similar items with substantially identical functions and effects. For example, the first control information and the second control information are only used to distinguish different control information, and their order is not limited. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not necessarily limit the differences. It should be further understood that the term “and / or” used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] In the prior art, the profiling control relies on real-time signals. Once the sensor or environment introduces noise, it is easy to cause the following error to increase. Moreover, the traditional method mainly focuses on the matching of the output trajectory, rather than the data optimization of the motion process itself, and fails to effectively improve the overall performance of the system. Furthermore, in a complex system, the kinematic parameters of the master and slave axes may change over time, and the existing methods are difficult to identify the parameters in real time and adjust the control strategy. Finally, the data-driven technology has not yet been organically combined with the filtering algorithm and the parameter identification method, making it difficult to achieve global optimization of the complex dynamic coupling process. Specifically, in the prior art, the traditional profiling control based on PID achieves synchronization between the master and slave axes through simple proportional, integral and differential control, but due to the single model, it is difficult to adapt to complex nonlinear motion. The improved method based on feedforward compensation adds feedforward compensation to the profiling control, which can improve the system response speed to a certain extent, but for complex coupled dynamic systems, the compensation effect is limited, and the parameters are difficult to accurately identify.

[0026] In order to solve the problems of the prior art, the present invention provides a master-slave axis coupling control method, which optimizes the sampled data in real time, dynamically adjusts the control strategy in combination with parameter identification technology, and uses a first-in-first-out motion controller buffer management algorithm in the motion control process to achieve real-time incremental optimization of sample parameters, realize the coupling control of the master-slave axis motion control quantity and the target state value of the actuator, and realize high-precision contour motion control, solving the problems of high noise sensitivity, insufficient parameter identification and lack of process optimization in the existing contour control technology. In other words, the present invention can significantly improve the coordinated motion accuracy of the master-slave axis and the robustness of the system, especially in high-noise and complex dynamic environments. Specifically, the enhancement of robustness can effectively suppress the influence of sensor noise on motion accuracy; through real-time parameter identification, the control strategy is dynamically adjusted to adapt to a variety of operating scenarios, so that the system obtains parameter adaptive capabilities; by combining data drive and filtering technology, the observation data is optimized while reading to ensure the continuity and smoothness of the motion process.

[0027] In the technical solution of the present invention, the specific motion control process is as follows Figure 2As shown. First, the sample data is processed, then the adaptive sliding mode controller is generated, and then the adaptive controller parameters are calculated in real time. If the conditions are not met, it returns to recalculate. After the conditions are met, the adaptive controller calculates the control amount. If the conditions are not met, it also needs to return to recalculate. After that, the master-slave axis data is coupled, and the control amount is stored in the first-in-first-out buffer. In this process, it is determined whether the buffer is full. If it is not full, it will continue to store. If it is full, the cam motion mode is started. Finally, when the cam moves, it is determined whether the movement is in place. If it is not in place, the cam motion mode is restarted. If it is in place, the motion control process ends.

[0028] This embodiment provides a master-slave axis coupling control method, such as Figure 1 As shown, the specific steps include: Step S100: Acquire sample data of the master axis and the slave axis, and pre-process the sample data using a filtering method to obtain observation data of the master axis and the slave axis.

[0029] In this embodiment, the relevant operation data of the main axis and the slave axis in the profiling control system are obtained by sampling. The operation data obtained by sampling is sample data, which may include position data, speed data, acceleration data, force or torque data, etc. Specifically, the position data represents the real-time position information of the main axis and the slave axis during the movement process, which is used to determine its current state and movement trajectory; the speed data represents the movement speed of the main axis and the slave axis, which is used to analyze the dynamic characteristics of the movement; the acceleration data represents the acceleration information of the main axis and the slave axis, which helps to evaluate the smoothness and response speed of the movement; the force or torque data represents the force or torque borne by the main axis and the slave axis, which is used to analyze the load condition and control effect. After obtaining the sample data, the above data is preprocessed using a filtering method to achieve data optimization and smooth control. The filtering method may include low-pass filtering, high-pass filtering, band-pass filtering, Kalman filtering, moving average filtering, etc., and may also include advanced filtering methods, such as particle filtering or nonlinear observers. The filtering method aims to remove high-frequency, low-frequency, random noise and interference and smooth the data. After the sample data is preprocessed, the observation data of the main axis and the slave axis are obtained, and the observation data is the preprocessed sample data.

[0030] In one implementation, the step of obtaining sample data of the master axis and the slave axis specifically includes the following steps: Step S110, collecting encoder position data and sensor data of the main shaft and the slave shaft during the interruption; Step S120: storing the encoder position data and the sensor data into a sample buffer in a first-in-first-out manner to obtain sample data of the master axis and the slave axis.

[0031] In this embodiment, if Figure 3As shown, firstly, the position data of the main shaft and the slave shaft are obtained through the encoder, and the position data may include absolute position, relative position, and position increment. Then, the speed data, acceleration data, force or torque data are obtained through the sensor, and the above data can be obtained specifically through the actual values ​​of the voltage analog value of the pressure value or the voltage analog value of the distance read by the sensor. Next, the above encoder position data and sensor data are stored in the buffer in a first-in-first-out manner. The purpose of storing the above data in the buffer in a first-in-first-out manner is to ensure the timeliness and data consistency of the sampled data. The above data stored in the buffer is used as sample data to prepare for subsequent data processing. Further, it is determined whether the sample data meets the restrictions of the profiling control. If so, the next step of data processing is entered. If it does not meet the restrictions and there is abnormal data, the encoder and sensor data are re-read.

[0032] In one implementation, the method of preprocessing the sample data using a filtering method to obtain observation data of the master axis and the slave axis specifically includes the following steps: Step S130, the filtering method is extended Kalman filtering; Step S140: Use the extended Kalman filter to perform state estimation, error correction and smoothing on the sample data to obtain observation data of the main axis and the slave axis.

[0033] In this embodiment, the filtering method is specifically selected as an extended Kalman filter. Figure 3As shown in the figure, the extended Kalman filter performs state estimation, error correction and smoothing on the sample data. The extended Kalman filter processes the sample data with large noise in real time to ensure the smooth transition of the sample data and avoid the motion jump caused by noise interference. Compared with the traditional simple filtering method, the extended Kalman filter has more advantages in noise suppression and data continuity guarantee. Among them, in the actual master-slave axis coupling control scenario, the system is often nonlinear. For example, the movement of the master and slave axes may be affected by a variety of nonlinear factors, such as friction, load changes, etc. The extended Kalman filter can effectively estimate the state of the nonlinear system. It estimates the state of the system by linearizing the nonlinear system and using the framework of the Kalman filter. Furthermore, the sample data of the master and slave axes include multi-source data such as encoder position data and sensor data. The extended Kalman filter can effectively fuse these different types of data. Comprehensively consider the data from different sensors, perform weighted processing on these data, and obtain accurate observation data. At the same time, during the movement of the master and slave axes, due to the existence of various interference factors, the collected data will have certain errors. The extended Kalman filter can correct these errors in real time. Similarly, the sample data of the master and slave axes collected may contain noise, resulting in large data fluctuations. The extended Kalman filter can smooth these data. Through its filtering mechanism, it can reduce the impact of noise on the data while retaining the main features of the data. For example, for the high-frequency noise generated by mechanical vibration and other reasons in the encoder position data, it can effectively filter out, making the obtained observation data smoother.

[0034] In the process of sampling the above data, traditional embedded systems rely solely on general-purpose processors to perform sampling tasks. Limited by their clock frequency and architecture characteristics, the sampling frequency is often difficult to meet high-frequency requirements. Therefore, in order to further improve system performance, dedicated hardware acceleration units such as digital signal processors (DSPs) and field programmable gate arrays (FPGAs) are added to embedded systems to accelerate the computational efficiency of data sampling and filtering optimization processes.

[0035] Step S200: performing parameter identification based on the observation data, and updating system parameters of the control quantity model.

[0036] In this embodiment, the control quantity model is used to obtain the expected position of the master and slave axes. Specifically, the input of the model is the observation data, that is, the position data of the master and slave axes, etc., and the output is the expected position of the master and slave axes. The model also includes system parameters. The system parameters are updated in real time through parameter identification, that is, the control quantity model is updated through parameter identification.

[0037] In one implementation, the step of performing parameter identification based on the observation data and updating the system parameters of the control quantity model specifically includes the following steps: Step S210, the system parameters include input and output gain coefficients and bias coefficients; Step S220: input the observed data into a parameter estimator of a pre-trained deep neural network to update the input-output gain coefficient and bias coefficient of the control quantity model.

[0038] In this embodiment, if Figure 3 As shown, parameter identification is performed based on the observed data. Parameter identification is used to obtain system parameters, and the system parameters include input and output gain coefficients and bias coefficients. Furthermore, the system parameters may also include parameters such as inertia, damping coefficient, and delay. Among them, the output gain coefficient reflects the relationship between the amplification factor of the system input signal and the output signal. The gain coefficient quantifies the degree to which the output signal is enhanced or weakened when the input signal changes. If the input is a small position deviation signal of the main shaft, after the control system, the output is a larger displacement adjustment corresponding to the slave shaft. The bias coefficient is the output offset of the system itself when there is no input signal. Even if the position deviation of the encoder feedback is zero and the sensor does not detect additional load, the motor or actuator may still have an initial output state due to inherent factors such as mechanical installation errors and electromagnetic characteristics. The quantized value corresponding to this initial output state is the bias coefficient.

[0039] In order to obtain the above-mentioned system parameters, a parameter identification method can be used, or a pre-trained deep neural network parameter estimator can be used. If a parameter estimator is used, the historical data of the master and slave axes under different working conditions are used as training data by setting a loss function, and the parameter estimator of the pre-trained deep neural network is obtained by training. The loss function can use a mean square error function to minimize the error between the predicted gain coefficient, bias coefficient and the actual value. The iterative training method can use a stochastic gradient descent algorithm. In each round of training, a batch of data is input into the network, the loss is calculated, the network weights are updated by back propagation, and the network parameters are continuously optimized until the loss on the validation set no longer decreases significantly, and a convergence state is reached to obtain a pre-trained deep neural network parameter estimator. After obtaining the pre-trained deep neural network parameter estimator, the observation data of the master and slave axes are input into the parameter estimator, and real-time system parameters, that is, real-time input and output gain coefficients and bias coefficients, can be obtained. Further, the system parameters are updated to the control quantity model.

[0040] Step S300: Calculate the motion distance control amount based on the control amount model and the observation data, and perform coupling control on the master axis and the slave axis based on the motion distance control amount.

[0041] In this embodiment, after the system parameters of the control quantity model are updated in real time through the above steps, the control quantity model can be used to calculate the motion distance control quantity. The control quantity is used to design the parameters of the PD controller (Proportional-Derivative Controller) in real time, that is, the parameters of the actuator, to achieve the coupling control of the master-slave axis motion control quantity and the target state value of the actuator, and to achieve high-precision contour motion control. The target state value of the actuator includes the voltage analog quantity of the pressure sensor or the voltage analog quantity of the distance sensor.

[0042] In one implementation, the step of calculating the motion distance control amount based on the control amount model and the observation data specifically includes the following steps: Step S310, the observation data includes the current positions of the main axis and the slave axis; Step S320, inputting the observed data into the control quantity model to calculate the expected positions of the main axis and the slave axis; Step S330: Calculate the control amount of the movement distance based on the deviation between the desired position and the current position.

[0043] In this embodiment, the observation data includes the current position of the master and slave axes. Figure 3 As shown, the current position is input into the control quantity model to predict and calculate the expected position of the master and slave axes. The control quantity of the movement distance is calculated according to the deviation between the expected position and the current position. The control quantity is used for the coupling control of the subsequent actuator.

[0044] In one implementation, the step of calculating the control amount of the movement distance based on the deviation between the expected position and the current position of the master and slave axes specifically includes the following steps: Step S331, calculating sliding surface parameters based on the deviation between the desired position and the current position to obtain an adaptive sliding surface; Step S332: Calculate the motion distance control amount based on the adaptive sliding surface.

[0045] In this embodiment, if Figure 3As shown, adaptive sliding mode control is introduced when calculating the motion distance control amount. Specifically, a sliding surface, i.e., a sliding surface, is first constructed. The sliding surface is a function of the system state variables, which plans the ideal trajectory of the system from any initial state to the desired steady state. First, through the previous steps, the deviation between the desired position and the current position of the master and slave axes is obtained. The deviation is a vector or scalar value, reflecting the distance between the actual operating state of the system and the ideal target. In a second-order system, the first-order derivative (speed deviation) and the second-order derivative (acceleration deviation) of the deviation are combined with the preset weight coefficient to construct a formula for calculating the sliding surface parameters, and the deviation and the sliding surface parameters are associated through the formula. The sliding surface parameters calculated are substituted into the sliding surface function expression to obtain an adaptive sliding surface. The motion distance control amount is obtained through the adaptive sliding surface. According to the slave axis state target value to be maintained (such as the voltage analog value of the pressure value or the voltage analog value of the distance), the sliding surface parameters of the sliding mode control are updated in real time through a first-in-first-out sequential processing method to achieve a gradual approach to the target value.

[0046] In one implementation, the method further includes the following steps: Step S410, additionally setting a sensor at the actual processing position, the sensor is used to collect error data after the system input in real time; Step S420, performing secondary parameter identification on the error data to obtain input and output gain coefficients and bias coefficients of the compensation model of the motion distance control amount; Step S430: calculating a compensation value based on the compensation model and the observation data; Step S440: Based on the compensation value, error compensation is performed on the movement distance control amount.

[0047] In this embodiment, an additional sensor is deployed at the actual processing position, and the sensor can be a high-precision laser displacement sensor. The sensor captures the deviation information between the processing site and the ideal model in real time, and uses it as the error data after the system input. After the error data is collected, the system parameters of the compensation model of the control quantity are obtained by using the parameter identification method. The parameter identification method can use algorithms such as the least squares method. The system parameters of the compensation model specifically include input and output gain coefficients and bias coefficients. The observation data includes the current speed, acceleration and position information of the master and slave axes. These data are multiplied and accumulated with the system parameters in the compensation model to calculate the compensation value. Specifically, the gain coefficient is multiplied by the error data, and then added to the bias coefficient to obtain the compensation value. Error compensation is performed on the motion distance control quantity based on the calculated compensation value.

[0048] When faced with large-scale multi-axis control scenarios, the traditional centralized master-slave axis control architecture has limitations. Therefore, distributing the master-slave axis control logic in multiple control nodes and using network communication to achieve data synchronization and distributed optimization can adapt to larger-scale multi-axis control scenarios.

[0049] In the technical solution of the present invention, the specific process of implementing real-time incremental optimization based on the first-in-first-out algorithm is as follows: Figure 4 As shown. In which, the adaptive controller first calculates the control quantity, and then reads the spindle encoder position data. Then the master-slave axis two-dimensional coordinate system is established, and the master-slave axis two-dimensional array is generated on this basis. The generated two-dimensional array is stored in the first-in-first-out buffer, and then it is determined whether the buffer is full. If it is not full, the spindle encoder position is continued to be read, and the two-dimensional array is continued to be generated and stored in the buffer. When the buffer is full, the cam movement is executed, and the spindle encoder position is read again to determine whether it moves to the next sampling position. If it has not arrived, the cam movement is continued and the position is read; if it has arrived, the used array is deleted, and the sensor sampling value is re-read and the buffer sample data is processed, and then the adaptive controller returns to the step of calculating the control quantity, and the cycle is repeated to achieve real-time incremental optimization. The whole process ensures effective data processing and stable operation of the system through master-slave axis data coupling and first-in-first-out algorithm.

[0050] In summary, under the technical solution of the above embodiment, by optimizing the sampled data in real time and combining the parameter identification technology to dynamically adjust the control strategy, gradual optimization and dynamic adjustment can be achieved in the motion control process, thereby solving the problems of high noise sensitivity, insufficient parameter identification and lack of process optimization in the existing profiling control technology.

[0051] like Figure 5 As shown in , an embodiment of the present invention provides a master-slave axis coupling control system, which includes: an observation data acquisition module 10, a control quantity model acquisition module 20, and a control quantity acquisition module 30.

[0052] Specifically, the observation data acquisition module 10 is used to acquire sample data of the main axis and the slave axis, and use a filtering method to preprocess the sample data to obtain the observation data of the main axis and the slave axis; the control quantity model acquisition module 20 is used to perform parameter identification based on the observation data and update the system parameters of the control quantity model; the control quantity acquisition module 30 is used to calculate the motion distance control quantity based on the control quantity model and the observation data, and perform coupling control on the main axis and the slave axis based on the motion distance control quantity.

[0053] In one implementation, the observation data acquisition module 10 includes: The encoder and sensor data acquisition unit is used to collect the encoder position data and sensor data of the main axis and the slave axis during the interruption; A sample data buffer unit, used to store the encoder position data and sensor data into a sample buffer in a first-in-first-out manner to obtain sample data of the main axis and the slave axis; The sample data filtering unit is used to use the extended Kalman filter to perform state estimation, error correction and smoothing on the sample data to obtain observation data of the main axis and the slave axis, and the filtering method is the extended Kalman filter.

[0054] In one implementation, the control quantity model acquisition module 20 includes: A system parameter acquisition unit is used to input the observation data into a parameter estimator of a pre-trained deep neural network, and update the input and output gain coefficients and bias coefficients of the control quantity model, wherein the system parameters include the input and output gain coefficients and the bias coefficients.

[0055] In one implementation, the control amount acquisition module 30 includes: An expected position calculation unit, used for inputting the observation data into the control quantity model to calculate the expected positions of the main axis and the slave axis, wherein the observation data includes the current positions of the main axis and the slave axis; A control amount calculation unit is used to calculate the control amount of the movement distance based on the deviation between the expected position and the current position.

[0056] In one implementation, the control amount calculation unit includes: An adaptive sliding surface acquisition subunit, configured to calculate sliding surface parameters based on a deviation between the desired position and the current position, to obtain an adaptive sliding surface; The control amount calculation subunit is used to calculate the motion distance control amount based on the adaptive sliding surface.

[0057] In one implementation, the system further includes: An error data acquisition unit is used to additionally set a sensor at the actual processing position, and the sensor is used to collect error data after the system input in real time; A compensation amount model system parameter acquisition unit is used to perform secondary parameter identification on the error data to obtain input and output gain coefficients and bias coefficients of the compensation amount model of the motion distance control amount; A compensation value acquisition unit, configured to calculate a compensation value based on the compensation amount model and the observation data; An error compensation unit is used to perform error compensation on the movement distance control amount based on the compensation value.

[0058] Based on the above embodiments, the present invention further provides an intelligent terminal, whose principle block diagram can be shown as follows: Figure 6 As shown. The intelligent terminal includes a processor, a memory, a network interface, a display screen, and a temperature sensor connected through a system bus. Among them, the processor of the intelligent terminal is used to provide computing and control capabilities. The memory of the intelligent terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the intelligent terminal is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a master-slave axis coupling control method is implemented. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen, and the temperature sensor of the intelligent terminal is pre-set inside the intelligent terminal to detect the operating temperature of the internal device.

[0059] Those skilled in the art will understand that Figure 6 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the smart terminal to which the scheme of the present invention is applied. The specific smart terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0060] In one embodiment, a smart terminal is provided, comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by one or more processors, and the one or more programs include instructions for performing the following operations: Acquire sample data of the main axis and the slave axis, and pre-process the sample data using a filtering method to obtain observation data of the main axis and the slave axis; Perform parameter identification based on the observed data and update system parameters of the control quantity model; Based on the control quantity model and the observation data, the motion distance control quantity is calculated, and based on the motion distance control quantity, the master and slave axes are coupled controlled.

[0061] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0062] In summary, the present invention provides a method, system, terminal device and medium for coupling control of master and slave axes. Compared with the prior art, the method first obtains sample data of the master and slave axes, and pre-processes the sample data using a filtering method to obtain observation data of the master and slave axes; then, parameter identification is performed based on the observation data to update the system parameters of the control quantity model; finally, based on the control quantity model and the observation data, the motion distance control quantity is calculated, and based on the motion distance control quantity, the master and slave axes are coupled and controlled. The present invention optimizes the sampled data in real time, dynamically adjusts the control strategy in combination with parameter identification technology, and uses a first-in-first-out motion controller buffer management algorithm in the motion control process to achieve real-time incremental optimization of sample parameters, thereby achieving coupling control of the master and slave axis motion control quantity and the target state value of the actuator, achieving high-precision contour motion control, and solving the problems of high noise sensitivity, insufficient parameter identification and lack of process optimization in the existing contour control technology.

[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-described 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 present application. It should be pointed out that, for a person of ordinary skill in the art, several variations 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, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A master-slave axis coupling control method, characterized in that: Applied in profiling control, the method comprises: Acquire sample data of the main axis and the slave axis, and pre-process the sample data using a filtering method to obtain observation data of the main axis and the slave axis; Perform parameter identification based on the observed data and update system parameters of the control quantity model; Based on the control quantity model and the observation data, the motion distance control quantity is calculated, and based on the motion distance control quantity, the master axis and the slave axis are coupled controlled.

2. The master-slave axis coupling control method according to claim 1, characterized in that: The step of obtaining sample data of the master axis and the slave axis includes: Collect encoder position data and sensor data of the master and slave axes during interruption; The encoder position data and the sensor data are stored in a sample buffer in a first-in-first-out manner to obtain sample data of the master axis and the slave axis.

3. The master-slave axis coupling control method according to claim 1, characterized in that: The method of preprocessing the sample data using a filtering method to obtain observation data of the main axis and the slave axis includes: The filtering method is extended Kalman filtering; The extended Kalman filter is used to perform state estimation, error correction and smoothing on the sample data to obtain observation data of the main axis and the slave axis.

4. The master-slave axis coupling control method according to claim 1, characterized in that: The performing parameter identification based on the observed data and updating the system parameters of the control quantity model includes: The system parameters include input and output gain coefficients and bias coefficients; The observation data is input into a parameter estimator of a pre-trained deep neural network to update the input-output gain coefficient and bias coefficient of the control quantity model.

5. The master-slave axis coupling control method according to claim 1, characterized in that: The step of calculating the motion distance control amount based on the control amount model and the observation data includes: The observation data includes the current positions of the master axis and the slave axis; Inputting the observed data into the control quantity model to calculate the desired positions of the main axis and the slave axis; Based on the deviation between the desired position and the current position, a control amount of the movement distance is calculated.

6. The master-slave axis coupling control method according to claim 5, characterized in that: The step of calculating the control amount of the movement distance based on the deviation between the desired position and the current position includes: Calculating sliding surface parameters based on the deviation between the desired position and the current position to obtain an adaptive sliding surface; Based on the adaptive sliding surface, a movement distance control amount is calculated.

7. The master-slave axis coupling control method according to claim 1, characterized in that: The method further comprises: An additional sensor is provided at the actual processing position, and the sensor is used to collect error data after the system input in real time; Performing secondary parameter identification on the error data to obtain input and output gain coefficients and bias coefficients of a compensation model of the motion distance control amount; Calculating a compensation value based on the compensation amount model and the observation data; Based on the compensation value, error compensation is performed on the movement distance control amount.

8. A master-slave axis coupling control system, characterized in that: The system comprises: An observation data acquisition module is used to acquire sample data of the main axis and the slave axis, and pre-process the sample data using a filtering method to obtain observation data of the main axis and the slave axis; A control quantity model acquisition module, used for performing parameter identification based on the observation data and updating system parameters of the control quantity model; The control quantity acquisition module is used to calculate the motion distance control quantity based on the control quantity model and the observation data, and to perform coupling control on the main axis and the slave axis based on the motion distance control quantity.

9. A terminal device, characterized in that: The terminal device includes a memory, a processor, and a master-slave axis coupling control program stored in the memory and executable on the processor. When the processor executes the master-slave axis coupling control program, the steps of the master-slave axis coupling control method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a master-slave axis coupling control program, and when the master-slave axis coupling control program is executed by the processor, the steps of the master-slave axis coupling control method according to any one of claims 1 to 7 are implemented.

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