A driving control method and system for an automatic centering bracket

Abstract

The present invention discloses a driving control method and system for an automatic centering bracket, which relates to the field of automated control technology, including obtaining all factors that cause the deviation of a coater, selecting all factors of the coater deviation to construct a cause set; setting multiple deviation combinations based on the cause set, and determining a deviation coefficient according to each deviation combination; defining a coater deviation scenario by the coater deviation, detecting the coater deviation within a period of time, and thus selecting a coater deviation scenario; formulating different control strategies for the coater deviation scenario and the deviation coefficient, thereby driving the automatic centering bracket to adjust the deviation. This application improves the accuracy and adaptability of the driving control by constructing the cause of the coater deviation and the deviation scenario, and ensures the safe and normal operation of the coater.

Description

Technical Field

[0001] The present application relates to the field of automation control technology, and more specifically, to a drive control method and system for an automatic centering bracket. Background Art

[0002] Several key technologies are applied behind the drive control of the automatic centering bracket. Advanced position sensors are used to monitor the position of the coating material, and control algorithms such as MPC are used to calculate the bracket fine-tuning amount to ensure timely and accurate adjustments. Actuators such as electric motors or hydraulic systems enable bracket movement, while real-time monitoring and feedback systems ensure continuous monitoring and adjustment of the coating process through data transmission and processing. Adaptive control algorithms can adapt to different working conditions, while emergency shutdown systems provide safety protection. The synergy of these technologies ensures the efficient operation of the coater and the consistency of coating quality.

[0003] In the prior art, there are many factors that affect the deviation of the coating machine, the working conditions are relatively complex, and the inherent drive control strategy cannot adapt to the complex and changeable alignment requirements, resulting in low drive control accuracy and poor adaptability.

[0004] Therefore, how to improve the accuracy and adaptability of drive control is a technical problem that needs to be solved. Summary of the invention

[0005] The present invention provides a driving control method of an automatic centering bracket, which is used to solve the technical problems of low precision and poor adaptability of driving control in the prior art. The method is applied to a coating machine, and the method comprises:

[0006] Obtain all factors that cause the coating machine to deviate, and select all factors that cause the coating machine to deviate to construct a cause set;

[0007] Multiple deviation combinations are set based on the cause set, and deviation coefficients are determined based on each deviation combination;

[0008] Defining the coating machine deviation scenario through the coating machine deviation situation, detecting the coating machine deviation situation within a period of time, and thus selecting the coating machine deviation scenario;

[0009] Different control strategies are formulated according to the deviation scenarios and deviation coefficients of the coating machine to drive the automatic centering bracket to adjust the deviation.

[0010] In some embodiments of the present application, the selection of coating machine deviations from all factors to construct a cause set includes:

[0011] Filter out the data of coating machine deviation in the historical data and record it as deviation data;

[0012] Calculate the causal degree between each coating machine deviation factor and the deviation amount in the deviation data, and screen out the coating machine deviation factors whose causal degree exceeds the corresponding threshold;

[0013] Count the first frequency and the second frequency corresponding to each factor among the screened coating machine deviation factors;

[0014] The first frequency is the number of times the factor parameter appears in the standard interval, and the second frequency is the number of times the factor parameter appears in the non-standard interval;

[0015] A cause set is constructed based on the screened coating machine deviation factors, and the first frequency and the second frequency are marked on the factors.

[0016] In some embodiments of the present application, multiple deviation combinations are set based on a cause set, including:

[0017] Obtaining a first probability and a second probability according to a first frequency and a second frequency of each coating machine deviation factor in the cause set;

[0018] Randomly generate parameters of the coating machine deviation factor through the first probability and the second probability of each coating machine deviation factor, and divide the parameters into different intervals;

[0019] The parameter intervals of each coating machine deviation factor are randomly combined, and each combination is a deviation combination.

[0020] In some embodiments of the present application, the deviation coefficient is determined according to each deviation combination, including:

[0021] Determine all types of coating machine deviation factors in each deviation combination, and determine the first serial number corresponding to each other;

[0022] Determine the parameter range of the deviation factor of each coating machine, and determine the commonly corresponding second serial number;

[0023] A deviation coefficient is determined based on the first sequence number and the second sequence number.

[0024] In some embodiments of the present application, a coating machine deviation scenario is defined by a coating machine deviation condition, including:

[0025] The deviation of the coating machine includes the deviation amount and the rate of change of the deviation amount;

[0026] If the rate of change of the deviation is less than the first threshold, the deviation scenario of the coating machine is defined as the first deviation scenario, and the deviation calculation is performed using a preset algorithm;

[0027] If the rate of change of the deviation is not less than the first threshold, the deviation situation of the coating machine is defined as the second deviation situation, and the deviation calculation is performed through the model predictive control algorithm;

[0028] If the deviation amount and the rate of change of the deviation amount both present periodic deviation, the coating machine deviation scenario is defined as the third deviation scenario, and the periodic fluctuation is identified and suppressed through frequency domain analysis or filtering technology, so as to perform deviation calculation.

[0029] In some embodiments of the present application, a coating machine deviation scenario is defined as a first deviation scenario, and deviation calculation is performed using a preset algorithm, including:

[0030] Set up small deviation scenarios, medium deviation scenarios, and large deviation scenarios with different deviation amounts;

[0031] If the first deviation scenario is a small deviation scenario, the P algorithm is used to calculate the deviation;

[0032] If the first deviation scenario is a medium deviation scenario, the PI algorithm is used for deviation calculation;

[0033] If the first deviation scenario is a large deviation scenario, the PID algorithm is used to perform deviation calculation.

[0034] In some embodiments of the present application, different control strategies are formulated for the deviation scenarios and deviation coefficients of the coating machine, including:

[0035] Calculate the deviation according to the corresponding algorithm in the coating machine deviation scenario to obtain the initial adjustment amount;

[0036] Set the standard range of adjustment amount corresponding to each coating machine deviation scenario;

[0037] Obtaining a target adjustment amount based on the initial adjustment amount and the deviation coefficient;

[0038] Control is performed based on the relationship between the target adjustment amount and the standard range of the adjustment amount.

[0039] In some embodiments of the present application, control is performed according to the relationship between the target adjustment amount and the standard adjustment amount interval, including:

[0040] If the target adjustment amount is within the standard adjustment amount range, the target adjustment amount is output for control;

[0041] Otherwise, the target adjustment amount is adjusted based on the difference between the target adjustment amount and the standard adjustment amount interval, and the adjusted target adjustment amount is input for control.

[0042] Correspondingly, the present application also provides a driving control system for an automatic centering bracket, which is applied to a coating machine and includes:

[0043] The first module is used to obtain all factors that cause the coating machine to deviate, and select all factors that cause the coating machine to deviate to construct a cause set;

[0044] The second module is used to set a plurality of deviation combinations based on the cause set and determine a deviation coefficient according to each deviation combination;

[0045] The third module is used to define the coating machine deviation scenario through the coating machine deviation situation, detect the coating machine deviation situation within a period of time, and thus select the coating machine deviation scenario;

[0046] The fourth module is used to formulate different control strategies according to the deviation scenarios and deviation coefficients of the coating machine, so as to drive the automatic centering bracket to adjust the deviation.

[0047] By applying the above technical solutions, all factors that cause the deviation of the coater are obtained, and all factors of the coater deviation are selected to construct a cause set; multiple deviation combinations are set based on the cause set, and the deviation coefficient is determined according to each deviation combination; the coater deviation scenario is defined by the coater deviation, and the coater deviation within a period of time is detected, so as to select the coater deviation scenario; different control strategies are formulated for the coater deviation scenario and the deviation coefficient, so as to drive the automatic centering bracket to adjust the deviation. This application improves the accuracy and adaptability of the drive control and ensures the safe and normal operation of the coater by constructing the causes of the coater deviation and the deviation scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A schematic flow chart of a driving control method for an automatic centering bracket proposed in an embodiment of the present invention is shown;

[0050] Figure 2 A schematic structural diagram of a driving control system for an automatic centering bracket proposed in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0052] An embodiment of the present application provides a driving control method for an automatic centering bracket, which is applied to a coating machine. The centering bracket generally includes a driving device, a detection device, an adjustment device and a control device.

[0053] Drive device:

[0054] Bracket Movement: Enables precise and timely movement of brackets, adjusted based on the output of the control algorithm.

[0055] Speed ​​Control: Control the speed at which the bracket moves to ensure a smooth adjustment process and avoid moving too fast or too slow.

[0056] Detection device:

[0057] Position monitoring: monitor the position of the coating material in real time and detect the offset.

[0058] Sensor selection: Select the appropriate sensor type, such as photoelectric sensor, laser sensor, etc., to meet the specific coating material and environmental requirements.

[0059] Data transfer: The monitored position information is transferred to the control device for further processing.

[0060] Adjustment device:

[0061] Fine-tune the bracket: adjust the position of the bracket in real time according to the detected offset of the coating material.

[0062] Precision: Provide sufficient precision to ensure the accuracy of bracket adjustment to meet coating requirements.

[0063] Real-time: Rapidly respond to detected offsets to achieve timely adjustments and avoid accumulated errors.

[0064] Controls:

[0065] Control Algorithm: Develop and implement a control algorithm that calculates the amount of fine-tuning the bracket needs to make based on the detected deflection.

[0066] Output generation: Generates control signals (adjustments) and transmits them to the drive device to achieve movement of the bracket.

[0067] System stability: Ensure the stability of the control system to prevent oscillation or unstable operation.

[0068] like Figure 1 As shown, the method comprises the following steps:

[0069] Step S101, obtaining all factors causing the coating machine to deviate, and selecting all factors causing the coating machine to deviate to construct a cause set.

[0070] In this embodiment, the deviation factors of the coating machine include uneven materials, equipment wear, mechanical vibration, etc. In order to improve the accuracy of subsequent control, factors that meet the requirements are selected and important factors are constructed.

[0071] In some embodiments of the present application, the selection of coating machine deviations from all factors to construct a cause set includes:

[0072] Filter out the data of coating machine deviation in the historical data and record it as deviation data;

[0073] Calculate the causal degree between each coating machine deviation factor and the deviation amount in the deviation data, and screen out the coating machine deviation factors whose causal degree exceeds the corresponding threshold;

[0074] Count the first frequency and the second frequency corresponding to each factor among the screened coating machine deviation factors;

[0075] The first frequency is the number of times the factor parameter appears in the standard interval, and the second frequency is the number of times the factor parameter appears in the non-standard interval;

[0076] A cause set is constructed based on the screened coating machine deviation factors, and the first frequency and the second frequency are marked on the factors.

[0077] In this embodiment, the causal degree refers to the causal index of the two, which is a method based on state space reconstruction and nearest neighbor distance, used to determine the direction and magnitude of the causal relationship. For two independent systems or factors X and Y, the state spaces of the two systems are established according to the state space reconstruction theory.

[0078] For a sample point x in the state space X n , x rn，1 , ..., x rn，k . represents x n k nearest neighbors in the state space X, calculate x n The average Euclidean distance to k nearest neighbors;

[0079] ;

[0080] For a sample point y in the state space Y n ,y sn，1 , ..., y sn，k Represents y n The k nearest points in the state space Y are mapped to the state space X and x is calculated. n and k nearest neighbor points x sn，1 , ..., x sn，k The average Euclidean distance of

[0081] ;

[0082] In order to simplify the calculation, we can use x n The average distance to all N sample points;

[0083] ;

[0084] The nonlinear interdependence indicator is the state space method, which determines the causal relationship between systems based on the mapping relationship of the state space and is defined as:

[0085] ;

[0086] According to the definition, 0< ≤1, when When it approaches 0, systems X and Y are independent of each other. When it is significantly greater than 0, there is a causal relationship from system X to Y, and the closer it is to 1, the stronger the causality.

[0087] It should be noted that other indicators that can characterize causal relationships are also possible, and this application only provides a specific method.

[0088] Step S102: multiple deviation combinations are set based on the cause set, and a deviation coefficient is determined according to each deviation combination.

[0089] In this embodiment, the deviation combination is a combination of different causes, so as to determine a deviation coefficient, which describes the joint influence of multiple causes.

[0090] In some embodiments of the present application, multiple deviation combinations are set based on a cause set, including:

[0091] Obtaining a first probability and a second probability according to a first frequency and a second frequency of each coating machine deviation factor in the cause set;

[0092] Randomly generate parameters of the coating machine deviation factor through the first probability and the second probability of each coating machine deviation factor, and divide the parameters into different intervals;

[0093] The parameter intervals of each coating machine deviation factor are randomly combined, and each combination is a deviation combination.

[0094] In this embodiment, the first probability and the second probability are respectively the probability of the parameter appearing within the standard interval and the probability of the parameter appearing outside the standard interval.

[0095] In some embodiments of the present application, the deviation coefficient is determined according to each deviation combination, including:

[0096] Determine all types of coating machine deviation factors in each deviation combination, and determine the first serial number corresponding to each other;

[0097] Determine the parameter range of the deviation factor of each coating machine, and determine the commonly corresponding second serial number;

[0098] A deviation coefficient is determined based on the first sequence number and the second sequence number.

[0099] In this embodiment, the category of the coating machine deviation factor corresponds to a serial number, the parameter interval corresponds to another serial number, and the two serial numbers together correspond to a deviation coefficient.

[0100] It should be noted that the corresponding relationships in the context are all obtained from historical experience or mathematical models and are common knowledge in this field.

[0101] Step S103, defining a coating machine deviation scenario through the coating machine deviation situation, detecting the coating machine deviation situation within a period of time, and thus selecting a coating machine deviation scenario.

[0102] In this embodiment, different deviation scenarios are set for different deviation situations, so as to improve the accuracy of the control strategy and provide a reasonable and appropriate driving control strategy for the deviation situation.

[0103] In some embodiments of the present application, a coating machine deviation scenario is defined by a coating machine deviation condition, including:

[0104] The deviation of the coating machine includes the deviation amount and the rate of change of the deviation amount;

[0105] If the rate of change of the deviation is less than the first threshold, the deviation scenario of the coating machine is defined as the first deviation scenario, and the deviation calculation is performed using a preset algorithm;

[0106] If the rate of change of the deviation is not less than the first threshold, the deviation situation of the coating machine is defined as the second deviation situation, and the deviation calculation is performed through the model predictive control algorithm;

[0107] If the deviation amount and the rate of change of the deviation amount both present periodic deviation, the coating machine deviation scenario is defined as the third deviation scenario, and the periodic fluctuation is identified and suppressed through frequency domain analysis or filtering technology, so as to perform deviation calculation.

[0108] In this embodiment, the deviation calculation is the calculated adjustment amount.

[0109] In some embodiments of the present application, a coating machine deviation scenario is defined as a first deviation scenario, and deviation calculation is performed using a preset algorithm, including:

[0110] Set up small deviation scenarios, medium deviation scenarios, and large deviation scenarios with different deviation amounts;

[0111] If the first deviation scenario is a small deviation scenario, the P algorithm is used to calculate the deviation;

[0112] If the first deviation scenario is a medium deviation scenario, the PI algorithm is used for deviation calculation;

[0113] If the first deviation scenario is a large deviation scenario, the PID algorithm is used to perform deviation calculation.

[0114] In this embodiment, the specific situations of the three deviations are as follows:

[0115] Small deviation situation (first deviation situation):

[0116] Definition: The coating material slightly deviates from the target position.

[0117] Control strategy: Use the proportional control (P control) algorithm to directly calculate the fine-tuning amount based on the deviation. The fine-tuning amount is proportional to the deviation, but does not consider past historical information.

[0118] Moderate deviation situation (first deviation situation):

[0119] Definition: The coating material deviates from the target position to a large extent.

[0120] Control strategy: Using the proportional-integral (PI control) algorithm, while taking into account the deviation, the integral term can help correct the accumulated error of the system and improve the stability of the system.

[0121] Big deviation situation (first deviation situation):

[0122] Definition: The coating material is seriously deviated from the target position.

[0123] Control strategy: Use the proportional-integral-derivative (PID control) algorithm, which not only considers the deviation and integral terms, but also adds the differential term to suppress system oscillation and improve response speed.

[0124] Rapidly changing deviation situation (second deviation situation):

[0125] Definition: Rapid change in the position of the coating material.

[0126] Control strategy: Use model predictive control (MPC) algorithms to better adapt to rapidly changing conditions by building models to predict future deviations and factoring those predictions into control.

[0127] Periodic deviation situation (third deviation situation):

[0128] Definition: The coating material fluctuates periodically.

[0129] Control strategy: Use frequency domain analysis or filtering technology to identify and suppress periodic fluctuations to maintain coating stability.

[0130] Step S104, formulating different control strategies according to the deviation situation and deviation coefficient of the coating machine, so as to drive the automatic centering bracket to adjust the deviation.

[0131] In some embodiments of the present application, different control strategies are formulated for the deviation scenarios and deviation coefficients of the coating machine, including:

[0132] Calculate the deviation according to the corresponding algorithm in the coating machine deviation scenario to obtain the initial adjustment amount;

[0133] Set the standard range of adjustment amount corresponding to each coating machine deviation scenario;

[0134] Obtaining a target adjustment amount based on the initial adjustment amount and the deviation coefficient;

[0135] Control is performed based on the relationship between the target adjustment amount and the standard range of the adjustment amount.

[0136] In this embodiment, the target adjustment amount is obtained based on the initial adjustment amount and the deviation coefficient. The target adjustment amount is the result of the multiplication. The target adjustment amount is the target of the required adjustment and may be changed later.

[0137] In some embodiments of the present application, control is performed according to the relationship between the target adjustment amount and the standard adjustment amount interval, including:

[0138] If the target adjustment amount is within the standard adjustment amount range, the target adjustment amount is output for control;

[0139] Otherwise, the target adjustment amount is adjusted based on the difference between the target adjustment amount and the standard adjustment amount interval, and the adjusted target adjustment amount is input for control.

[0140] In this embodiment, the target adjustment amount is adjusted based on the difference between the target adjustment amount and the standard adjustment amount interval, which means that the target adjustment amount is the difference between the target adjustment amount and the standard adjustment amount interval near the end point, and different differences correspond to different correction coefficients, and the target adjustment amount (product) is corrected by the correction coefficient.

[0141] By applying the above technical solutions, all factors that cause the deviation of the coater are obtained, and all factors of the coater deviation are selected to construct a cause set; multiple deviation combinations are set based on the cause set, and the deviation coefficient is determined according to each deviation combination; the coater deviation scenario is defined by the coater deviation, and the coater deviation within a period of time is detected, so as to select the coater deviation scenario; different control strategies are formulated for the coater deviation scenario and the deviation coefficient, so as to drive the automatic centering bracket to adjust the deviation. This application improves the accuracy and adaptability of the drive control and ensures the safe and normal operation of the coater by constructing the causes of the coater deviation and the deviation scenario.

[0142] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0143] In order to further explain the technical idea of ​​the present invention, the technical solution of the present invention is now described in combination with specific application scenarios.

[0144] Correspondingly, the present application also provides a driving control system for an automatic centering bracket, which is applied to a coating machine, such as Figure 2 As shown, including:

[0145] The first module 201 is used to obtain all factors that cause the coating machine to deviate, and select all factors that cause the coating machine to deviate to construct a cause set;

[0146] The second module 202 is used to set a plurality of deviation combinations based on the cause set, and determine a deviation coefficient according to each deviation combination;

[0147] The third module 203 is used to define a coating machine deviation scenario through a coating machine deviation situation, detect the coating machine deviation situation within a period of time, and thus select a coating machine deviation scenario;

[0148] The fourth module 204 is used to formulate different control strategies according to the deviation situation and deviation coefficient of the coating machine, so as to drive the automatic centering bracket to adjust the deviation.

[0149] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A driving control method for an automatic centering bracket, applied to a coating machine, characterized in that: include: Obtain all factors that cause the coating machine to deviate, and select all factors that cause the coating machine to deviate to construct a cause set; Multiple deviation combinations are set based on the cause set, and deviation coefficients are determined based on each deviation combination; Defining the coating machine deviation scenario through the coating machine deviation situation, detecting the coating machine deviation situation within a period of time, and thus selecting the coating machine deviation scenario; Different control strategies are formulated according to the deviation scenarios and deviation coefficients of the coating machine to drive the automatic centering bracket to adjust the deviation; Select all factors that caused the coater to deviate to construct a cause set, including: Filter out the data of coating machine deviation in the historical data and record it as deviation data; Calculate the causal degree between each coating machine deviation factor and the deviation amount in the deviation data, and screen out the coating machine deviation factors whose causal degree exceeds the corresponding threshold; Count the first frequency and the second frequency corresponding to each factor among the screened coating machine deviation factors; The first frequency is the number of times the factor parameter appears in the standard interval, and the second frequency is the number of times the factor parameter appears in the non-standard interval; Construct a cause set based on the screened coating machine deviation factors, and mark the first frequency and the second frequency on the factor; Set multiple deviation combinations based on cause sets, including: Obtaining a first probability and a second probability according to a first frequency and a second frequency of each coating machine deviation factor in the cause set; Randomly generate parameters of the coating machine deviation factor through the first probability and the second probability of each coating machine deviation factor, and divide the parameters into different intervals; The parameter intervals of the deviation factors of each coating machine are randomly combined, and each combination is a deviation combination; And determine the deviation coefficient based on each deviation combination, including: Determine all types of coating machine deviation factors in each deviation combination, and determine the first serial number corresponding to each other; Determine the parameter range of the deviation factor of each coating machine, and determine the commonly corresponding second serial number; A deviation coefficient is determined based on the first sequence number and the second sequence number.

2. The driving control method of the automatic centering bracket according to claim 1, characterized in that: Define the coater deviation scenario through coater deviation conditions, including: The deviation of the coating machine includes the deviation amount and the rate of change of the deviation amount; If the rate of change of the deviation is less than the first threshold, the deviation scenario of the coating machine is defined as the first deviation scenario, and the deviation calculation is performed using a preset algorithm; If the rate of change of the deviation is not less than the first threshold, the deviation situation of the coating machine is defined as the second deviation situation, and the deviation calculation is performed through the model predictive control algorithm; If the deviation amount and the rate of change of the deviation amount both present periodic deviation, the coating machine deviation scenario is defined as the third deviation scenario, and the periodic fluctuation is identified and suppressed through frequency domain analysis or filtering technology, so as to perform deviation calculation.

3. The driving control method of the automatic centering bracket according to claim 2, characterized in that: Define the coating machine deviation scenario as the first deviation scenario, and perform deviation calculations using a preset algorithm, including: Set up small deviation scenarios, medium deviation scenarios, and large deviation scenarios with different deviation amounts; If the first deviation scenario is a small deviation scenario, the P algorithm is used to calculate the deviation; If the first deviation scenario is a medium deviation scenario, the PI algorithm is used for deviation calculation; If the first deviation scenario is a large deviation scenario, the PID algorithm is used to perform deviation calculation.

4. The driving control method of the automatic centering support according to claim 2, characterized in that: Regarding the deviation of the coating machine Different control strategies are formulated based on the environment and deviation coefficient, including: Calculate the deviation according to the corresponding algorithm in the coating machine deviation scenario to obtain the initial adjustment amount; Set the standard range of adjustment amount corresponding to each coating machine deviation scenario; Obtaining a target adjustment amount based on the initial adjustment amount and the deviation coefficient; Control is performed based on the relationship between the target adjustment amount and the standard range of the adjustment amount.

5. The driving control method of the automatic centering support according to claim 4, characterized in that: Control is performed based on the relationship between the target adjustment amount and the standard adjustment amount range, including: If the target adjustment amount is within the standard adjustment amount range, the target adjustment amount is output for control; Otherwise, the target adjustment amount is adjusted based on the difference between the target adjustment amount and the standard adjustment amount interval, and the adjusted target adjustment amount is input for control.

6. A driving control system for an automatic centering bracket, applied to a coating machine, and implementing the driving control method for the automatic centering bracket according to any one of claims 1 to 5, characterized in that: include: The first module is used to obtain all factors that cause the coating machine to deviate, and select all factors that cause the coating machine to deviate to construct a cause set; The second module is used to set a plurality of deviation combinations based on the cause set and determine a deviation coefficient according to each deviation combination; The third module is used to define the coating machine deviation scenario through the coating machine deviation situation, detect the coating machine deviation situation within a period of time, and thus select the coating machine deviation scenario; The fourth module is used to formulate different control strategies according to the deviation scenarios and deviation coefficients of the coating machine, so as to drive the automatic centering bracket to adjust the deviation.

Images