Fully closed-loop control method and system for multi-station servo feeding robot

Abstract

The present application provides a full closed-loop control method and system for a multi-station servo feeding robot, wherein the method includes: when the multi-station servo feeding robot performs plate grasping, it first detects the plate thickness in real time and queries the preset thickness-pressure relationship to determine the target pressure value, and then generates a real-time pressure value through a dynamic pressure adjustment mechanism. The system compares the real-time pressure with the target pressure to obtain the deviation, dynamically adjusts the proportional, integral, and differential parameters in combination with the fuzzy rule library, and uses the adjusted parameters to calculate and generate a dynamic pressure compensation amount. The compensation amount is superimposed on the target pressure to form a comprehensive control instruction, which is fed back to the actuator to realize closed-loop adjustment, and ultimately the plate maintains stable pressure and fixed position throughout the entire grasping and transfer process, effectively eliminating slippage. The present application improves the pressure control accuracy and anti-slip stability during the plate transfer process.

Description

Technical Field

[0001] The present application relates to the field of industrial automation technology, and in particular to a full-closed-loop control method and system for a multi-station servo feeding robot. Background Art

[0002] In automated stamping production lines, multi-station servo feeding robots must adapt to the gripping of metal sheets of varying materials and thicknesses. Due to the varying surface characteristics of the sheets and the high-speed transfer conditions, traditional clamping methods struggle to balance gripping reliability and transfer efficiency. Therefore, there is an urgent need to develop an intelligent control system that can automatically optimize gripping force based on the operating conditions.

[0003] Existing technology uses a segmented control strategy based on pressure thresholds. Force sensors monitor clamping pressure in real time and switch to different pressure output modes when the detected value exceeds a preset range. The system sets multiple sets of pressure parameters and automatically selects the corresponding output level based on the detected pressure fluctuations.

[0004] This solution suffers from a response delay in pressure level switching, which can easily cause clamping force fluctuations when the plate thickness changes suddenly. The pressure parameter set must be manually pre-set, preventing adaptive learning of the optimal pressure curve for different operating conditions. The dynamic characteristics of the robot at high speeds are not factored into the control, resulting in both deformation of thin plates and slippage of thick plates. Summary of the Invention

[0005] The present application provides a fully closed-loop control method and system for a multi-station servo feeding robot, which is used to solve the problems of low pressure control accuracy and poor anti-slip stability during plate transfer in the prior art.

[0006] In a first aspect, the present application provides a fully closed-loop control method for a multi-station servo feeding robot, comprising:

[0007] When the multi-station servo feeding robot performs the grabbing action, the material thickness data of the grabbed plate is obtained in real time;

[0008] Searching for a target pressure value corresponding to the material thickness data from a preset thickness-pressure mapping relationship;

[0009] Based on the target pressure value, controlling a dynamic pressure regulation mechanism to generate a real-time dynamic pressure value;

[0010] Based on the real-time dynamic pressure value and the target pressure value, in combination with a preset fuzzy rule library, respectively adjusting the proportional parameter, the integral parameter, and the differential parameter;

[0011] Performing pressure control calculations on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter to generate a dynamic pressure compensation amount;

[0012] The dynamic pressure compensation amount and the target pressure value are combined to form a pressure closed-loop control to prevent the gripped plate from slipping, so as to maintain a constant pressure and a stable position of the gripped plate during the gripping and feeding process.

[0013] Optionally, adjusting the proportional parameter, the integral parameter, and the differential parameter based on the real-time dynamic pressure value and the target pressure value in combination with a preset fuzzy rule library includes:

[0014] Calculating a deviation between the real-time dynamic pressure value and the target pressure value;

[0015] Matching the deviation with a preset fuzzy rule base;

[0016] According to the matching results, the proportional parameter adjustment channel, the integral parameter adjustment channel and the differential parameter adjustment channel are activated;

[0017] The proportional parameter is adjusted in the proportional parameter adjustment channel, the integral parameter is adjusted in the integral parameter adjustment channel, and the differential parameter is adjusted in the differential parameter adjustment channel.

[0018] Optionally, matching the deviation with a preset fuzzy rule base includes:

[0019] Extracting membership function sets and control rule sets from a preset fuzzy rule base;

[0020] Inputting the deviation into the membership function set for fuzzification processing to obtain the membership degree of the deviation corresponding to a plurality of preset linguistic variables;

[0021] Obtain fuzzy output data based on all preset linguistic variables and their corresponding membership degrees;

[0022] The fuzzified output data is matched with pre-stored control rules in the control rule set.

[0023] Optionally, matching the fuzzy output data with pre-stored control rules in the control rule set includes:

[0024] Traversing each pre-stored control rule in the control rule set;

[0025] Determining whether the fuzzy output data satisfies the antecedent condition of the current ergodic control rule;

[0026] When the antecedent condition is met, the control rule is marked as a valid matching rule;

[0027] All the valid matching rules are summarized to generate a parameter adjustment instruction set, which is a matching result.

[0028] Optionally, the dynamic pressure regulation mechanism includes a power mixing output unit, a pressure conversion unit, and a position servo unit;

[0029] The controlling the dynamic pressure regulation mechanism based on the target pressure value to generate a real-time dynamic pressure value includes:

[0030] Inputting the target pressure value into a power hybrid output unit, and generating a first output component and a second output component through the power hybrid output unit;

[0031] converting the first output component into a normal pressure of the contact surface by the pressure conversion unit;

[0032] converting the second output component into a tangential constraint of the contact surface by the position servo unit;

[0033] The normal pressure and the tangential constraint are fused to form a real-time dynamic pressure value.

[0034] Optionally, performing pressure control calculations on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter to generate a dynamic pressure compensation amount includes:

[0035] Multiply the deviation by the adjusted proportional parameter to obtain the proportional compensation component;

[0036] Multiplying the continuously accumulated value of the deviation by the adjusted integral parameter to obtain an integral compensation component;

[0037] Multiplying the unit time change rate of the deviation by the adjusted differential parameter to obtain a differential compensation component;

[0038] The proportional compensation component, the integral compensation component, and the differential compensation component are added together to generate a dynamic pressure compensation amount.

[0039] Optionally, combining the dynamic pressure compensation amount with the target pressure value to form a pressure closed-loop control for preventing the gripped plate from slipping includes:

[0040] Using the dynamic pressure compensation amount as a real-time correction amount and the target pressure value as a reference control amount;

[0041] Adding the real-time correction amount to the reference control amount to generate a comprehensive pressure instruction;

[0042] Feeding back the comprehensive pressure instruction to the execution terminal of the dynamic pressure regulation mechanism;

[0043] The real-time dynamic pressure value output by the execution terminal is continuously compared with the target pressure value to form a pressure closed-loop control.

[0044] In a second aspect, the present application provides a fully closed-loop control system for a multi-station servo feeding robot, comprising:

[0045] The acquisition module is used to obtain the material thickness data of the grasped plate in real time when the multi-station servo feeding robot performs the grasping action;

[0046] A search module, configured to search for a target pressure value corresponding to the material thickness data from a preset thickness-pressure mapping relationship;

[0047] a control module, configured to control a dynamic pressure regulation mechanism based on the target pressure value to generate a real-time dynamic pressure value;

[0048] an adjustment module, configured to adjust a proportional parameter, an integral parameter, and a differential parameter based on the real-time dynamic pressure value and the target pressure value in combination with a preset fuzzy rule library;

[0049] a calculation module, for performing pressure control calculations on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter, respectively, to generate a dynamic pressure compensation amount;

[0050] A module is formed, which is used to combine the dynamic pressure compensation amount with the target pressure value to form a pressure closed-loop control to prevent the grasped plate from slipping, so as to maintain constant pressure and stable position of the grasped plate during the grasping and feeding process.

[0051] In a third aspect, the present application provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a full closed-loop control method for a multi-station servo feeding robot as described in any one of the first aspects.

[0052] In a fourth aspect, the present application provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implements a full closed-loop control method for a multi-station servo feeding robot as described in any one of the first aspects.

[0053] In the present application, a full-closed-loop control method for a multi-station servo feeding robot is provided, the method comprising: when the multi-station servo feeding robot performs a gripping action, obtaining material thickness data of the gripped plate in real time; searching for a target pressure value corresponding to the material thickness data from a preset thickness-pressure mapping relationship; based on the target pressure value, controlling a dynamic pressure adjustment mechanism to generate a real-time dynamic pressure value; based on the real-time dynamic pressure value and the target pressure value, adjusting the proportional parameter, integral parameter and differential parameter respectively in combination with a preset fuzzy rule library; performing pressure control calculations on the adjusted proportional parameter, adjusted integral parameter and adjusted differential parameter respectively to generate a dynamic pressure compensation amount; combining the dynamic pressure compensation amount with the target pressure value to form a pressure closed-loop control to prevent the gripped plate from slipping, so as to maintain constant pressure and stable position of the gripped plate during the gripping and feeding process.

[0054] The technical solution provided by this application has the following beneficial effects:

[0055] This application accurately identifies the characteristics of plates of varying thicknesses, providing a benchmark for subsequent pressure control. It quickly determines the optimal clamping pressure for the current plate thickness, eliminating errors associated with manual adjustments. It enables real-time dynamic adjustment of pressure output to accommodate varying operating conditions. It intelligently optimizes control parameters based on actual operating conditions, improving system response speed and stability. It accurately calculates the required pressure compensation, eliminating static errors and dynamic disturbances. This ensures a stable clamping state throughout the entire gripping and transfer process, effectively preventing slippage.

[0056] Furthermore, the present application also calculates the deviation between the real-time pressure and the target pressure, matches it with the fuzzy rule library, and adjusts the proportional, integral and differential parameters according to the matching results to achieve adaptive optimization of the control parameters.

[0057] In addition, this step enables the control system to dynamically adjust parameters according to actual working conditions, improve the response speed and stability of pressure regulation, and ensure reliable gripping and smooth transfer of plates of different thicknesses.

[0058] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0060] Figure 1A flow chart of a fully closed-loop control method for a multi-station servo feeding robot provided in an embodiment of the present application;

[0061] Figure 2 A schematic structural diagram of a fully closed-loop control system for a multi-station servo feeding robot provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0064] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0065] In the pressure control of multi-station servo feeding robots, existing control schemes based on fixed PID parameters or segmented pressure thresholds have limitations: when faced with the gripping requirements of plates of varying thicknesses, fixed parameters are difficult to adapt to dynamic operating conditions, resulting in a delayed pressure regulation response. Thin plates are prone to deformation due to overpressure, while thick plates slip due to underpressure. While the segmented threshold strategy can switch pressure levels, the switching process relies on preset empirical values ​​and cannot optimize parameters in real time. In high-speed feeding scenarios, pressure overshoot and regulation oscillation still exist. The root cause of these shortcomings lies in the static treatment of the plate thickness-pressure mapping relationship in existing schemes, as well as the disconnection between control parameters and real-time operating conditions. A closed-loop control method that integrates material thickness characteristics with dynamic parameter self-tuning is urgently needed.

[0066] In response to the above problems, this application proposes a full-closed-loop control method for a multi-station servo feeding robot, the core of which is to dynamically associate the plate thickness data with the real-time pressure deviation, and intelligently adjust the proportional, integral, and differential parameters through a fuzzy rule library. Specifically, the system first matches the target pressure value according to the real-time material thickness, and then generates normal pressure and tangential constraints through a gas-electric hybrid drive mechanism; at the same time, the fuzzy rule library is used to perform a multi-dimensional analysis of the pressure deviation, dynamically optimize the control parameters and generate dynamic pressure compensation, and finally form a pressure-position bistable closed-loop control. This method breaks through the rigid constraints of the traditional fixed parameter mode. Through the dynamic mapping of thickness-pressure and the adaptive adjustment of parameters, it not only eliminates the risk of thin plate deformation and thick plate slippage, but also improves the control stability under high-speed feeding conditions, fundamentally solving the problems of adjustment lag and insufficient adaptability to working conditions caused by parameter solidification in the existing technology.

[0067] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0068] Figure 1 This is a flow chart of a fully closed-loop control method for a multi-station servo feeding robot provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0069] Step 101: When the multi-station servo feeding robot performs a grabbing action, the material thickness data of the grabbed plate is obtained in real time.

[0070] In step 101, the multi-station servo feeding robot represents an automated device used to transfer sheets between processing stations, with high-precision positioning and clamping capabilities. Material thickness data refers to the thickness measurement of the gripped sheet, collected in real time by sensors and used in subsequent pressure control calculations.

[0071] In an embodiment of the present application, when the robot grasps the plate, the thickness of the plate is detected in real time by a thickness sensor installed on the clamp. The sensor transmits the collected thickness signal to the control system. The system filters the signal to eliminate noise interference and finally obtains accurate material thickness data for use in subsequent steps.

[0072] For example, when a servo feeding robot grabs a metal sheet, the thickness sensor uses the ultrasonic thickness measurement principle to measure the current sheet thickness as 2.5mm. The data is filtered by the signal conditioning circuit and transmitted to the main control unit as a benchmark parameter for subsequent pressure control.

[0073] Step 102: searching for a target pressure value corresponding to the material thickness data from a preset thickness-pressure mapping relationship.

[0074] In step 102, the thickness-pressure mapping relationship represents a table that corresponds to a preset sheet thickness and the required clamping pressure, ensuring stable gripping of sheets of varying thicknesses. The target pressure value represents the ideal clamping pressure calculated based on the current sheet thickness and is used to guide pressure adjustment.

[0075] In this embodiment of the present application, the control system queries a pre-stored thickness-pressure mapping table based on the material thickness data obtained in step 101 to find the target pressure value that matches the thickness. The mapping table is established based on experimental data to ensure that the appropriate clamping force is obtained for plates of different thicknesses.

[0076] For example, the system queries the preset mapping table and finds that the target pressure value corresponding to a 2.5mm thick plate is 100N. This value will be used as the benchmark for subsequent pressure adjustments.

[0077] Step 103: Based on the target pressure value, controlling the dynamic pressure regulation mechanism to generate a real-time dynamic pressure value.

[0078] In step 103, the dynamic pressure regulation mechanism, which is composed of a gas-electric hybrid drive system, adjusts the output pressure in real time according to the target pressure value. The real-time dynamic pressure value represents the actual clamping pressure acting on the plate, obtained from sensor feedback.

[0079] In an embodiment of the present application, the system inputs the target pressure value into the dynamic pressure regulation mechanism, which outputs normal pressure and tangential constraint force through the coordinated work of the pneumatic unit and the electric servo unit. The pressure sensor monitors the actual pressure value in real time and feeds back to the control system to form a preliminary closed loop.

[0080] For example, after the target pressure value of 100N is input into the adjustment mechanism, the pneumatic cylinder outputs 60N normal pressure, the servo motor provides 40N tangential constraint force, and the pressure sensor detects that the current actual composite pressure is sqrt(60²+40²)=72N. This value is fed back to the system as the real-time dynamic pressure value.

[0081] Step 104: Based on the real-time dynamic pressure value and the target pressure value, in combination with a preset fuzzy rule library, respectively adjust the proportional parameter, the integral parameter, and the differential parameter.

[0082] In step 104, the fuzzy rule base stores expert experience rules, which are used to dynamically adjust PID parameters to adapt to different operating conditions. The proportional, integral, and differential parameters are the control parameters in the servo feeding robot's pressure control system. The proportional parameter determines the system's response to current pressure deviations, the integral parameter eliminates steady-state errors caused by accumulated historical deviations, and the differential parameter reflects pressure trends and suppresses system oscillations. These three parameters work together to control the robot's pressure regulation mechanism, achieving precise pressure control by dynamically adjusting the output characteristics of the gas-electric hybrid drive system.

[0083] In an embodiment of the present application, the system calculates the deviation between the real-time dynamic pressure value and the target pressure value, inputs the deviation into the fuzzy rule library for matching, and dynamically adjusts the proportional, integral, and differential parameters according to the matching results to make the control system more adaptable to actual working conditions.

[0084] For example, the current deviation is 28N. After matching the fuzzy rule library, it is decided to increase the proportional parameter from 1.0 to 1.2, reduce the integral parameter from 0.8 to 0.7, and keep the differential parameter unchanged at 0.5. The optimized parameters are used to generate the pressure compensation amount.

[0085] Step 105: performing pressure control calculations on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter to generate a dynamic pressure compensation amount.

[0086] In step 105 , the dynamic pressure compensation amount represents a correction value calculated based on the optimized PID parameters, and is used to eliminate pressure deviation.

[0087] In an embodiment of the present application, the system multiplies the current deviation, the historical deviation accumulation value and the deviation change rate with the adjusted proportional, integral and differential parameters respectively to obtain three compensation components, which are superimposed to generate the total dynamic pressure compensation.

[0088] For example, proportional compensation = 28N × 1.2 = 33.6N, integral compensation = 50N·s × 0.7 = 35N·s, differential compensation = 10N / s × 0.5 = 5N·s, and total compensation = 33.6 + 35 + 5 = 73.6N.

[0089] Step 106: Combining the dynamic pressure compensation amount with the target pressure value, a pressure closed-loop control is formed to prevent the gripped plate from slipping, so as to maintain a constant pressure and a stable position of the gripped plate during the gripping and feeding process.

[0090] In step 106 , the pressure closed-loop control means stabilizing the actual pressure around the target value through real-time compensation to ensure gripping reliability.

[0091] In an embodiment of the present application, the system superimposes the dynamic pressure compensation amount with the target pressure value, generates a comprehensive control instruction and feeds it back to the actuator, continuously compares the actual pressure with the target value, dynamically adjusts the output, and forms a closed-loop control.

[0092] For example, the comprehensive instruction = 100N + 73.6N = 173.6N. The actuator adjusts the output according to the instruction, so that the actual pressure gradually approaches 100N and finally stabilizes in the range of 98-102N to ensure that there is no slippage of the plate.

[0093] This method achieves precise pressure control during the plate grasping process through real-time material thickness detection and target pressure matching, combined with fuzzy PID self-tuning and dynamic compensation, effectively solving the adjustment lag and working condition adaptability problems caused by parameter solidification in traditional methods, and improving grasping stability and transfer efficiency.

[0094] In order to solve the problem that the pressure control parameters of the multi-station servo feeding robot are difficult to adaptively adjust during the sheet material grabbing process, in some embodiments, step 104: adjusting the proportional parameter, the integral parameter, and the differential parameter based on the real-time dynamic pressure value and the target pressure value in combination with a preset fuzzy rule library includes:

[0095] Step 201: Calculate the deviation between the real-time dynamic pressure value and the target pressure value.

[0096] In step 201 , the deviation refers to the difference between the real-time dynamic pressure value and the target pressure value, and is used to characterize the accuracy of the current pressure control.

[0097] In an embodiment of the present application, the system obtains the actual pressure value currently applied to the plate through a pressure sensor, subtracts it from the preset target pressure value, and obtains the pressure deviation at the current moment. This deviation will serve as the basis for subsequent parameter adjustments.

[0098] Step 202: Match the deviation with a preset fuzzy rule library.

[0099] In the embodiment of the present application, the system compares the calculated deviation with the condition part in the fuzzy rule base, determines the most matching rule group through membership calculation, and provides a decision basis for subsequent parameter adjustment.

[0100] Step 203: activating the proportional parameter adjustment channel, the integral parameter adjustment channel, and the differential parameter adjustment channel according to the matching result.

[0101] In step 203, the parameter adjustment channel refers to a logic processing path within the system specifically used to modify specific control parameters.

[0102] In the embodiment of the present application, based on the fuzzy rule matching results, the system simultaneously opens three independent parameter adjustment channels, corresponding to the modification paths of the proportional, integral and differential parameters, respectively, to ensure that various parameters can be independently adjusted according to actual needs.

[0103] Step 204: respectively adjusting the proportional parameter in the proportional parameter adjustment channel, adjusting the integral parameter in the integral parameter adjustment channel, and adjusting the differential parameter in the differential parameter adjustment channel.

[0104] In step 204, parameter adjustment refers to modifying the numerical values ​​of control parameters in respective channels according to the instructions of the fuzzy rule base.

[0105] In an embodiment of the present application, the system increases or decreases the proportional coefficient according to the rule requirements in the proportional parameter adjustment channel, modifies the integral time constant in the integral parameter adjustment channel, adjusts the differential action intensity in the differential parameter adjustment channel, and finally outputs a set of optimized control parameters.

[0106] Here's a specific example:

[0107] During the operation of a multi-station servo feeding robot on an automated production line, when the robot grasps a 3.0mm thick aluminum alloy sheet, the thickness sensor measures the actual sheet thickness. After signal processing, the precise thickness value of 3.0mm is obtained. The system then consults a preset thickness-pressure table to determine the target pressure corresponding to this thickness as 120N. The control system inputs the target value of 120N into the pressure regulation system, where the pneumatic actuator outputs a vertical pressure of 75N and the servo drive unit generates a horizontal restraining force of 45N. The resulting pressure is calculated as sqrt(75²+45²)=87N to obtain the current real-time dynamic pressure value. The system calculates the deviation between the target pressure of 120N and the actual pressure of 87N to be 33N. This deviation is then input into a preset fuzzy rule library for matching analysis. The fuzzy rule library contains multiple empirically defined adjustment rules. For example, when the deviation is within the range of 30-40N, the corresponding rule requires adjusting the proportional parameter from 1.0 to 1.3, the integral parameter from 0.8 to 0.6, and the differential parameter to remain unchanged at 0.5.

[0108] In the embodiment of the present application, the method realizes dynamic optimization of control parameters through real-time deviation analysis and fuzzy rule matching, so that the robot arm can automatically adapt to the grasping requirements of plates of different thicknesses, effectively solves the adjustment lag problem of fixed parameter control method under variable working conditions, and improves the stability and adaptability of pressure control.

[0109] In order to solve the problem of insufficient matching accuracy between the deviation and the control rule in the manipulator pressure control, in some embodiments, step 202: matching the deviation with a preset fuzzy rule base includes:

[0110] Step 301: extracting a membership function set and a control rule set from a preset fuzzy rule base.

[0111] In step 301, the membership function set is a set of mathematical functions used to convert precise values ​​into fuzzy concepts. Each function corresponds to a linguistic variable, such as "pressure is too low." The control rule set stores empirical rules for parameter adjustment under different operating conditions.

[0112] In an embodiment of the present application, the system calls a preset membership function group and rule group from a storage unit to provide a calculation basis for subsequent fuzzy processing.

[0113] Step 302: Input the deviation into the membership function set for fuzzification processing to obtain the membership degree of the deviation corresponding to a plurality of preset linguistic variables.

[0114] In step 302, fuzzification processing involves converting specific numerical values ​​into membership degrees of multiple linguistic variables. The membership degree of a preset linguistic variable refers to the degree to which the deviation belongs to a specific linguistic description (e.g., "small," "moderate," or "large"). Its value ranges from 0 to 1, where 0 indicates a state that does not belong to the description at all, and 1 indicates a state that completely belongs to the description. The specific value reflects the degree to which the deviation belongs to the linguistic description.

[0115] In an embodiment of the present application, the system inputs the deviation into a set of membership functions, and calculates the degree to which the deviation belongs to language variables such as "too small", "moderate", and "too large" through each function, obtaining a set of membership values ​​between 0 and 1.

[0116] Step 303: Obtain fuzzy output data based on all preset linguistic variables and corresponding membership degrees.

[0117] In step 303, the fuzzified output data refers to structured information including linguistic variables and their corresponding membership degrees.

[0118] In the embodiment of the present application, the system organizes all the language variables and their memberships obtained in step 302 into a data packet in a unified format for use in rule matching.

[0119] Step 304: Match the fuzzy output data with pre-stored control rules in the control rule set.

[0120] In an embodiment of the present application, the system traverses the control rule set, compares the linguistic variables and membership in the fuzzified output data with the condition part of each rule, and finds the rule group that meets the conditions.

[0121] Here's a specific example: In a multi-station servo feeding robot system in an automated production workshop, when the robot grasps a 3.0mm-thick aluminum alloy sheet, the system detects an actual pressure of 87N, a deviation of 33N from the target pressure of 120N. The system first retrieves a preset set of membership functions from a fuzzy rule library. This set contains calculation functions corresponding to three linguistic variables: a membership function for "small" pressure deviation linearly decreases from 1 to 0 in the 0-20N range, a membership function for "moderate" pressure deviation follows a triangular distribution in the 15-35N range, and a membership function for "large" pressure deviation linearly increases from 0 to 1 in the 30-50N range. Substituting a deviation of 33N into these three functions, the resulting membership values ​​are 0 for "small," 0.8 for "moderate," and 0.3 for "large," respectively. These three values ​​constitute the fuzzy output data. The system then matches the call control rule set. Rule R005 stipulates that when the "moderate" membership is ≥ 0.7 and the "large" membership is ≤ 0.4, an adjustment should be applied: the proportional parameter is increased by 0.3 and the integral parameter is decreased by 0.2. The membership calculation formula is: small membership = max(0,1-deviation / 20), moderate membership = max(0,1-|deviation-25| / 10), and large membership = max(0,(deviation-30) / 20). These calculations convert the specific deviation into a fuzzy language description, achieving accurate rule matching.

[0122] In the embodiment of the present application, the method converts the precise deviation into a fuzzy language description and then intelligently matches it with empirical rules, so that the control system can more reasonably select the adjustment strategy, effectively solving the problem of inaccurate rule selection under critical working conditions in traditional methods, and improving the adaptability and stability of pressure control.

[0123] To further improve the accuracy and completeness of fuzzy rule matching, in some embodiments, step 304: matching the fuzzified output data with pre-stored control rules in the control rule set includes:

[0124] Step 401: traverse each pre-stored control rule in the control rule set.

[0125] In step 401, the control rule set refers to a database storing a plurality of pressure control experience rules, and each rule includes two parts: antecedent condition and consequent action.

[0126] In the embodiment of the present application, the system reads each rule in the rule base in sequence to prepare for subsequent condition judgment.

[0127] Step 402: Determine whether the fuzzy output data satisfies the antecedent condition of the current traversal control rule.

[0128] In step 402, the antecedent condition of the control rule refers to the prerequisite in the pre-stored control rules used to determine whether the rule is triggered. It is pre-defined manually and stored in the control rule set. Its content is a logical combination of a linguistic variable and an intensity threshold (for example, "if the pressure deviation is positive and the intensity value is ≥ 0.6"). The rule is determined to be effective by comparing the current fuzzy output data with this condition.

[0129] In an embodiment of the present application, the system compares the linguistic variables and their membership in the fuzzified output data with the conditional part of the current rule item by item to check whether all conditional requirements are met.

[0130] Step 403: When the antecedent condition is met, the control rule is marked as a valid matching rule.

[0131] In step 403, the effective matching rule refers to a control rule whose antecedent conditions are completely consistent with the current working conditions.

[0132] In the embodiment of the present application, when all conditions of a rule are met, the system marks the rule as valid, indicating that the rule can be adopted.

[0133] Step 404: Summarize all the valid matching rules and generate a parameter adjustment instruction set, which is the matching result.

[0134] In step 404, the parameter adjustment instruction set refers to a collection of action instructions corresponding to all valid rules.

[0135] In the embodiment of the present application, the system collects the consequent actions of all marked rules and organizes them into a unified parameter adjustment solution output.

[0136] Here's a specific example: In a certain intelligent production line, when a multi-station servo feeding robot handles a 3.0mm aluminum alloy sheet, the system obtains fuzzy output data calculated from a deviation of 33N. The pressure deviation has a membership of 0 for "small," 0.8 for "moderate," and 0.3 for "large." The system then runs through ten pre-stored control rules, first checking rule R003, which requires "small" ≥ 0.5. Since the actual membership of "small" is 0, it is skipped. Next, the system checks rule R005, which requires "moderate" ≥ 0.7 and "large" ≤ 0.4. Since the current data fully meets these two conditions, the system marks rule R005 as valid. The system then checks rule R008, which requires "large" ≥ 0.5. Since the actual membership of "large" is 0.3, it is not marked. After traversing all rules, the system summarizes and finds that there is only one valid matching rule, R005. Based on this rule, it generates an adjustment instruction set including "increase the proportional parameter by 0.3 and decrease the integral parameter by 0.2." The proportional parameter adjustment amount of 0.3 is an empirical value preset based on the degree of deviation, and the integral parameter adjustment amount of 0.2 is a correction value set to prevent overshoot.

[0137] In the embodiment of the present application, the method ensures that the control strategy selection is comprehensive and accurate through systematic rule traversal and condition judgment, effectively avoids the omission or mismatch of important rules, and makes the adjustment of pressure control parameters more scientific and reasonable.

[0138] In order to more accurately achieve dynamic pressure regulation of the multi-station servo feeding robot, in some embodiments, step 103: the dynamic pressure regulation mechanism includes a power mixing output unit, a pressure conversion unit and a position servo unit.

[0139] The method of controlling the dynamic pressure regulation mechanism based on the target pressure value to generate a real-time dynamic pressure value includes: Step 501: inputting the target pressure value into a power hybrid output unit, and generating a first output component and a second output component through the power hybrid output unit.

[0140] In step 501, the hybrid power output unit is an intelligent distribution module that decomposes the target pressure value into components in different directions. The first output component is the control variable generated by the hybrid power output unit based on the target pressure value to generate the vertical clamping force. This is converted into normal pressure by the pressure conversion unit to ensure the sheet is securely grasped. The second output component is the control variable generated by the same unit to generate the horizontal restraining force. This is converted into a tangential constraint by the position servo unit to prevent sheet slippage during transfer. These two components control the mechanical effects in different directions respectively, but together they constitute a complete pressure control system.

[0141] In the embodiment of the present application, after receiving the target pressure value, the system decomposes it into two independent component outputs according to a preset ratio through a built-in distribution algorithm.

[0142] Step 502: Convert the first output component into a normal pressure of the contact surface through the pressure conversion unit.

[0143] In step 502, the pressure conversion unit is an actuator that converts the electrical control signal into actual mechanical pressure. The contact surface refers to the physical contact area between the gripping device of the multi-station servo feeding robot and the gripped sheet. Within this area, the normal pressure and tangential constraints combine to achieve stable gripping and anti-slip control of the sheet. Normal pressure refers to the clamping force perpendicular to the sheet contact surface.

[0144] In the embodiment of the present application, the first output component is sent to the air pressure regulating system, and a corresponding vertical force is generated through the displacement change of the cylinder piston.

[0145] Step 503: Convert the second output component into a tangential constraint of the contact surface through the position servo unit.

[0146] In step 503, the position servo unit refers to the drive system that controls the position accuracy of the manipulator. The tangential constraint refers to the friction force parallel to the contact surface of the plate to prevent slippage.

[0147] In the embodiment of the present application, the second output component is converted into a control signal of the servo motor, and the required horizontal restraining force is generated by adjusting the position of the clamping jaws.

[0148] Step 504: Fusing the normal pressure and the tangential constraint to form a real-time dynamic pressure value.

[0149] In step 504 , fusion refers to a calculation process of synthesizing the forces in two directions into an actual effective pressure.

[0150] In an embodiment of the present application, the system mathematically synthesizes the normal pressure and the tangential constraint through a vector synthesis algorithm to calculate the total pressure value currently actually acting on the plate.

[0151] The following is a specific example: On an automated stamping production line, a multi-station servo feeding robot grips a 2.5mm-thick stainless steel sheet. The system determines a target pressure of 120N based on thickness detection results. This target value is input into the power hybrid output unit and decomposed into a first output component of 75N and a second output component of 45N according to a preset 5:3 distribution ratio. The first component is converted into a 75N vertical clamping force by the pneumatic pressure conversion unit. This conversion is achieved by adjusting the cylinder air pressure. The relationship between the air pressure value P and the output force F satisfies F=P×A, where A represents the effective piston area. The second component is converted into a 45N horizontal anti-slip force by the position servo unit. The servo motor drives the gripper to generate a precise friction constraint. The system monitors the actual forces in both directions in real time, measuring a normal pressure of 73N and a tangential constraint of 44N. The current real-time dynamic pressure value is calculated using the formula sqrt(73²+44²)≈85N. In this formula, 73 represents the measured vertical force, and 44 represents the measured horizontal force.

[0152] In the embodiment of the present application, the method realizes precise regulation of the pressure during the plate grasping process through intelligent decomposition and coordinated control of the vertical clamping force and the horizontal friction force, effectively solving the problem that the traditional single pressure control cannot take into account both the clamping stability and the anti-slip requirements, and improving the operational reliability of the manipulator.

[0153] To more accurately calculate the pressure compensation amount, in some embodiments, step 105 : performing pressure control calculations on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter to generate a dynamic pressure compensation amount, includes:

[0154] Step 601: Multiply the deviation by the adjusted proportional parameter to obtain a proportional compensation component.

[0155] In step 601 , the proportional compensation component refers to an immediate correction value calculated based on the current pressure deviation, reflecting the system's ability to quickly respond to the current error.

[0156] In an embodiment of the present application, the system multiplies the pressure deviation detected in real time by the adjusted proportional parameter to obtain a compensation component for quickly correcting the current deviation.

[0157] Step 602: Multiply the continuously accumulated value of the deviation by the adjusted integral parameter to obtain an integral compensation component.

[0158] In step 602, the cumulative deviation value is the chronological sum of all historical deviation data since the start of deviation detection. This is generated by recording the deviation at each sampling moment in real time and adding them up. The integral compensation component is a correction calculated based on the cumulative historical deviation value and is used to eliminate persistent steady-state errors.

[0159] In the embodiment of the present application, the system accumulates all deviations over a period of time, multiplies the accumulated deviations by the adjusted integral parameter, and obtains a compensation component for eliminating long-term deviations.

[0160] Step 603: Multiply the unit time change rate of the deviation by the adjusted differential parameter to obtain a differential compensation component.

[0161] In step 603, the rate of change of the deviation per unit time is the instantaneous rate of change, calculated by dividing the difference between the deviation at two adjacent sampling moments by the sampling interval. This is calculated by taking the difference between the current deviation and the previous deviation and dividing it by the sampling time. The differential compensation component is a correction calculated based on the rate of change of the deviation and is used to predict and suppress system oscillations.

[0162] In an embodiment of the present application, the system calculates the rate of change of the deviation between the two most recent samples, multiplies it by the adjusted differential parameter, and obtains a compensation component for improving system stability.

[0163] Step 604: Add the proportional compensation component, the integral compensation component, and the differential compensation component to generate a dynamic pressure compensation amount.

[0164] In the embodiment of the present application, the system adds the calculated proportional, integral, and differential compensation components to generate a total compensation amount for overall pressure regulation.

[0165] Here's a specific example: In an automated stamping shop, when a servo feeding robot grabs a 2.5mm thick stainless steel sheet, the system detects that the current actual combined pressure is 80N, which deviates by 40N from the target pressure of 120N. Based on the matching results of the fuzzy rule library, the system adjusts the proportional parameter to 1.3, the integral parameter to 0.6, and the differential parameter remains unchanged at 0.4. When calculating the compensation amount, the current deviation of 40N is first multiplied by the proportional parameter of 1.3 to obtain a proportional compensation component of 52N. The calculation process is 40×1.3=52. The system then accumulates the total deviation over the past 10 seconds to 80N·s, multiplying it by the integral parameter of 0.6 to obtain an integral compensation component of 48N·s. The calculation formula is 80×0.6=48. The system also measured a deviation change of 6 N in the last 0.1 second, calculating the unit time rate to be 60 N / s. Multiplying this by the differential parameter of 0.4 yields a differential compensation component of 24 N·s, calculated as 60 × 0.4 = 24. Finally, the three compensation components are summed to yield 52 + 48 + 24 = 124 N, yielding the total dynamic pressure compensation. The proportional parameter of 1.3 strengthens the correction of the current deviation, the integral parameter of 0.6 moderately mitigates the impact of historical deviations to prevent overshoot, and the differential parameter of 0.4 effectively suppresses pressure fluctuations. The synergistic effect of these three parameters ensures optimal regulation performance.

[0166] In the embodiment of the present application, the method comprehensively considers the current deviation, historical deviation and change trend in multiple dimensions, so that the generated compensation amount can not only quickly respond to the current error, but also effectively eliminate the accumulated deviation, while suppressing system oscillation, thereby improving the accuracy and stability of pressure control.

[0167] In order to more perfectly implement pressure closed-loop control, in some embodiments, step 106: combining the dynamic pressure compensation amount and the target pressure value to form a pressure closed-loop control to prevent the grasped plate from slipping, includes: step 701: using the dynamic pressure compensation amount as a real-time correction amount and the target pressure value as a reference control amount.

[0168] In step 701, the real-time correction value refers to the pressure adjustment value dynamically calculated according to the current working conditions. The reference control value refers to the standard pressure value determined according to the thickness of the plate.

[0169] In the embodiment of the present application, the system uses the calculated dynamic pressure compensation amount as a basis for real-time adjustment, and together with the preset target pressure value, it constitutes a control benchmark.

[0170] Step 702: Add the real-time correction amount to the reference control amount to generate a comprehensive pressure instruction.

[0171] In step 702 , the integrated pressure command refers to the final control signal used to instruct the action of the actuator.

[0172] In an embodiment of the present application, the system adds the real-time correction amount to the reference control amount to generate a complete control instruction including the target value and the compensation amount.

[0173] Step 703: Feedback the comprehensive pressure instruction to the execution terminal of the dynamic pressure regulation mechanism.

[0174] In step 703, the execution terminal refers to a pneumatic-electric hybrid actuator that directly drives the manipulator to move.

[0175] In an embodiment of the present application, the system transmits the integrated pressure command to the pneumatic cylinder and servo motor to adjust their output force and position.

[0176] Step 704: Continuously compare the real-time dynamic pressure value output by the execution terminal with the target pressure value to form a pressure closed-loop control.

[0177] In step 704, continuous comparison means that the system cyclically executes the following process at fixed time intervals: real-time acquisition of the actual pressure value output by the execution terminal, calculation of the difference between the actual pressure value and the preset target pressure value, and dynamic adjustment of the control instructions based on the deviation result. This cycle is repeated to form an uninterrupted feedback adjustment loop to ensure that the pressure control always follows the target value changes.

[0178] In the embodiment of the present application, the system continuously detects the actual output pressure and compares it with the target value, and performs the next round of adjustment based on the newly generated deviation, forming a cyclic control.

[0179] Here's a specific example: On an automated stamping production line, when a multi-station servo feeding robot grasps a 2.5mm-thick aluminum alloy sheet, the system sets a target pressure of 110N based on the thickness detection results. When the actual combined pressure is detected to be sqrt(65²+40²)≈76N, a deviation of 34N is calculated. After matching the fuzzy rule base, the system adjusts the proportional parameter to 1.3, the integral parameter to 0.6, and the differential parameter to 0.5. The system then calculates the dynamic pressure compensation as 34×1.3+55×0.6+12×0.5=44.2+33+6=83.2N. Adding this compensation to the target pressure of 110N yields a comprehensive control instruction of 193.2N, where 193.2=110+83.2. According to the instruction, the actuator adjusts the pneumatic cylinder to output 85N normal pressure and the servo motor to output 55N tangential constraint force, so that the actual composite pressure reaches sqrt(85²+55²)≈101N. The system continuously compares the deviation between the measured value and the target value and performs the next round of adjustment. After 3 control cycles, the actual pressure stabilizes in the range of 108-112N, of which the pneumatic cylinder finally outputs 75N and the servo motor outputs 45N. The composite pressure sqrt(75²+45²)≈87N and the deviation from the target value of 110N falls within the allowable range, ensuring that the aluminum alloy sheet remains stably clamped during high-speed transfer. During the entire closed-loop adjustment process, the calculation of the pressure compensation amount fully considers the current deviation, historical deviation accumulation and change trend. The proportional term 44.2N is used to quickly correct the current deviation, the integral term 33N eliminates the accumulated error, and the differential term 6N suppresses pressure fluctuations. The synergistic effect of the three achieves precise pressure control.

[0180] In the embodiment of the present application, this method enables the system to dynamically track the target pressure value through the organic combination of real-time compensation and continuous feedback, effectively solving the problem that traditional open-loop control is difficult to adapt to changes in working conditions, and realizing precise and stable control of the clamping force during the plate transfer process.

[0181] Figure 2 A schematic diagram of the structure of a fully closed-loop control system of a multi-station servo feeding robot provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the system includes:

[0182] The acquisition module 21 is used to obtain the material thickness data of the grasped plate in real time when the multi-station servo feeding robot performs the grasping action.

[0183] The search module 22 is used to search for the target pressure value corresponding to the material thickness data from a preset thickness-pressure mapping relationship.

[0184] The control module 23 is configured to control the dynamic pressure regulation mechanism based on the target pressure value to generate a real-time dynamic pressure value.

[0185] The adjustment module 24 is configured to adjust the proportional parameter, the integral parameter, and the differential parameter based on the real-time dynamic pressure value and the target pressure value in combination with a preset fuzzy rule library.

[0186] The calculation module 25 is used to perform pressure control calculations on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter to generate a dynamic pressure compensation amount.

[0187] The module 26 is formed to combine the dynamic pressure compensation amount with the target pressure value to form a pressure closed-loop control to prevent the gripped plate from slipping, so as to maintain a constant pressure and a stable position of the gripped plate during the gripping and feeding process.

[0188] Figure 2 The fully closed-loop control system of the multi-station servo feeding robot can execute Figure 1 The implementation principle and technical effects of the fully closed-loop control method for a multi-station servo feeding robot described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the fully closed-loop control system for a multi-station servo feeding robot in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0189] In one possible design, Figure 2 The fully closed-loop control system of a multi-station servo feeding robot in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0190] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0191] The processing component 32 is used to perform the above Figure 1 The embodiment provides a full closed-loop control method for a multi-station servo feeding robot.

[0192] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0193] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random-access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0194] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0195] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0196] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0197] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0198] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a full closed-loop control method for a multi-station servo feeding robot.

[0199] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0200] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0201] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0202] 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 them. 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 deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A fully closed-loop control method for a multi-station servo feeding robot, characterized in that: include: When the multi-station servo feeding robot performs the grabbing action, the material thickness data of the grabbed plate is obtained in real time; Searching for a target pressure value corresponding to the material thickness data from a preset thickness-pressure mapping relationship; Based on the target pressure value, controlling a dynamic pressure regulation mechanism to generate a real-time dynamic pressure value; Based on the real-time dynamic pressure value and the target pressure value, in combination with a preset fuzzy rule library, respectively adjusting the proportional parameter, the integral parameter, and the differential parameter; Performing pressure control calculations on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter to generate a dynamic pressure compensation amount; The dynamic pressure compensation amount and the target pressure value are combined to form a pressure closed-loop control to prevent the gripped plate from slipping, so as to maintain a constant pressure and a stable position of the gripped plate during the gripping and feeding process.

2. The method according to claim 1, characterized in that The adjusting of proportional parameters, integral parameters, and differential parameters based on the real-time dynamic pressure value and the target pressure value in combination with a preset fuzzy rule library includes: Calculating a deviation between the real-time dynamic pressure value and the target pressure value; Matching the deviation with a preset fuzzy rule base; According to the matching results, the proportional parameter adjustment channel, the integral parameter adjustment channel and the differential parameter adjustment channel are activated; The proportional parameter is adjusted in the proportional parameter adjustment channel, the integral parameter is adjusted in the integral parameter adjustment channel, and the differential parameter is adjusted in the differential parameter adjustment channel.

3. The method according to claim 2, characterized in that The matching of the deviation amount with a preset fuzzy rule base includes: Extracting membership function sets and control rule sets from a preset fuzzy rule base; Inputting the deviation into the membership function set for fuzzification processing to obtain the membership degree of the deviation corresponding to a plurality of preset linguistic variables; Obtain fuzzy output data based on all preset linguistic variables and their corresponding membership degrees; The fuzzified output data is matched with pre-stored control rules in the control rule set.

4. The method according to claim 3, characterized in that The matching of the fuzzy output data with the pre-stored control rules in the control rule set includes: Traversing each pre-stored control rule in the control rule set; Determining whether the fuzzy output data satisfies the antecedent condition of the current ergodic control rule; When the antecedent condition is met, the control rule is marked as a valid matching rule; All the valid matching rules are summarized to generate a parameter adjustment instruction set, which is a matching result.

5. The method according to claim 1, wherein The dynamic pressure regulation mechanism includes a power mixing output unit, a pressure conversion unit and a position servo unit; The controlling the dynamic pressure regulation mechanism based on the target pressure value to generate a real-time dynamic pressure value includes: Inputting the target pressure value into a power hybrid output unit, and generating a first output component and a second output component through the power hybrid output unit; converting the first output component into a normal pressure of the contact surface by the pressure conversion unit; converting the second output component into a tangential constraint of the contact surface by the position servo unit; The normal pressure and the tangential constraint are fused to form a real-time dynamic pressure value.

6. The method according to claim 1, characterized in that The pressure control calculation is performed on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter to generate a dynamic pressure compensation amount, including: Multiply the deviation by the adjusted proportional parameter to obtain the proportional compensation component; Multiplying the continuously accumulated value of the deviation by the adjusted integral parameter to obtain an integral compensation component; Multiplying the unit time change rate of the deviation by the adjusted differential parameter to obtain a differential compensation component; The proportional compensation component, the integral compensation component, and the differential compensation component are added together to generate a dynamic pressure compensation amount.

7. The method according to claim 1, characterized in that The combination of the dynamic pressure compensation amount and the target pressure value to form a pressure closed-loop control to prevent the gripped plate from slipping includes: Using the dynamic pressure compensation amount as a real-time correction amount and the target pressure value as a reference control amount; Adding the real-time correction amount to the reference control amount to generate a comprehensive pressure instruction; Feeding back the comprehensive pressure instruction to the execution terminal of the dynamic pressure regulation mechanism; The real-time dynamic pressure value output by the execution terminal is continuously compared with the target pressure value to form a pressure closed-loop control.

8. A fully closed-loop control system for a multi-station servo feeding robot, characterized in that: include: The acquisition module is used to obtain the material thickness data of the grasped plate in real time when the multi-station servo feeding robot performs the grasping action; A search module, configured to search for a target pressure value corresponding to the material thickness data from a preset thickness-pressure mapping relationship; a control module, configured to control a dynamic pressure regulation mechanism based on the target pressure value to generate a real-time dynamic pressure value; an adjustment module, configured to adjust a proportional parameter, an integral parameter, and a differential parameter based on the real-time dynamic pressure value and the target pressure value in combination with a preset fuzzy rule library; a calculation module, for performing pressure control calculations on the adjusted proportional parameter, the adjusted integral parameter, and the adjusted differential parameter, respectively, to generate a dynamic pressure compensation amount; A module is formed, which is used to combine the dynamic pressure compensation amount with the target pressure value to form a pressure closed-loop control to prevent the grasped plate from slipping, so as to maintain constant pressure and stable position of the grasped plate during the grasping and feeding process.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a full-closed-loop control method for a multi-station servo feeding robot as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, a full-closed-loop control method for a multi-station servo feeding robot as described in any one of claims 1 to 7 is implemented.

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