Robot welding seam polishing accurate force control method and system based on fuzzy PID (Proportion Integration Differentiation)

Abstract

The invention relates to a fuzzy PID (Proportion Integration Differentiation)-based precise force control method and system for welding seam polishing of a robot. The method comprises the following steps: scanning three-dimensional characteristics of an actual welding seam by using a laser sensor, and obtaining first point set data corresponding to a sensor coordinate system; converting the three-dimensional features of the theoretically expected weld joint to the sensor coordinate system to obtain second point set data; welding seam deviation information is obtained according to the first point set data and the second point set data, and the welding seam deviation information comprises welding seam deviation and the change rate of the welding seam deviation; according to the weld joint deviation information, PID parameters of a PID controller are subjected to online adjustment through fuzzy logic; and according to the adjusted PID parameters and the weld joint deviation information, the grinding force of the robot is controlled. By monitoring the position deviation of the welding seam in real time and dynamically adjusting PID parameters, precise control over the grinding force of the robot is achieved, and high-efficiency and high-quality welding seam grinding is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot weld grinding, and in particular to a robot weld grinding precision force control method and system based on fuzzy PID. Background Art

[0002] In the traditional robot weld grinding process, the lack of effective force control and position deviation adjustment mechanism often leads to unstable grinding quality and low efficiency. On the one hand, the traditional grinding system relies only on fixed parameters or other simple control strategies, and cannot be flexibly adjusted according to the dynamic changes of the weld surface position deviation; on the other hand, the thermal deformation, shape and size differences of the weld during the welding process will cause the robot to accumulate errors during the grinding process, making it difficult to ensure the consistency and accuracy of the weld grinding. In addition, if the grinding force is not properly controlled, excessive force will cause the grinding head to damage or collapse the base material, and too little force will make it impossible to effectively remove the weld excess, thereby failing to meet subsequent process requirements. Summary of the invention

[0003] To this end, the present invention provides a robot weld grinding precision force control method and system based on fuzzy PID, which realizes precise control of the robot grinding force by real-time monitoring of weld position deviation and dynamically adjusting PID parameters, thereby achieving efficient and high-quality weld grinding.

[0004] In order to solve the above technical problems, the present invention provides a robot weld grinding precision force control method based on fuzzy PID, comprising:

[0005] Scanning the three-dimensional features of the actual weld using a laser sensor, and obtaining the first point set data corresponding to the sensor coordinate system;

[0006] Converting the three-dimensional features of the theoretically expected weld to the sensor coordinate system to obtain second point set data;

[0007] Obtaining weld deviation information according to the first point set data and the second point set data, wherein the weld deviation information includes weld deviation and a change rate of weld deviation;

[0008] According to the weld deviation information, PID parameters of the PID controller are adjusted online using fuzzy logic;

[0009] The robot grinding force is controlled according to the adjusted PID parameters and the weld deviation information.

[0010] In one embodiment of the present invention, the three-dimensional characteristics of the actual weld and / or the three-dimensional characteristics of the theoretically expected weld include the position, shape and curvature of the weld.

[0011] In one embodiment of the present invention, the three-dimensional features of the theoretically expected weld are obtained from a design drawing or a three-dimensional model of the workpiece.

[0012] In one embodiment of the present invention, converting the three-dimensional features of the theoretically expected weld to the sensor coordinate system includes: converting the three-dimensional features of the theoretically expected weld to the sensor coordinate system according to the following conversion formula:

[0013]

[0014] in, is the position of the laser sensor relative to the end of the robot, is the robot’s position in the world coordinate system, The position of the laser sensor in the world coordinate system.

[0015] In one embodiment of the present invention, obtaining weld deviation information according to the first point set data and the second point set data includes:

[0016] Calculate the best match between the first point set data and the second point set data, and for each pair of matching points, calculate the weld deviation according to the formula ΔP=(Δx, Δy, Δz); obtain the change rate of the weld deviation according to the weld deviation data at two consecutive moments;

[0017] Among them, ΔP is the deviation vector; (Δx, Δy, Δz) represents the offset of the weld in the three-dimensional coordinate system.

[0018] In one embodiment of the present invention, obtaining weld deviation information according to the first point set data and the second point set data further includes:

[0019] The deviation value is output in digital or graphical form, a comparison of the three-dimensional features between the actual weld and the theoretically expected weld is displayed, and the deviation value is marked.

[0020] In one embodiment of the present invention, according to the weld deviation information, fuzzy logic is used to adjust the PID parameters of the PID controller online, including:

[0021] The fuzzy logic controller adjusts the proportional, integral and differential parameters of the PID controller according to the weld deviation information, including:

[0022] The fuzzy logic controller converts the weld deviation and the change rate of the weld deviation into a fuzzy set;

[0023] Based on experience and experiments, a fuzzy rule base is established, and the rules form a knowledge base in the form of "if-then";

[0024] Mapping input variables, namely weld deviation and rate of change of weld deviation, to the fuzzy set;

[0025] Through the membership function, the degree to which the input variable belongs to a fuzzy set is determined, and the corresponding activation degree of each fuzzy set is obtained;

[0026] Calculate the activation strength of the antecedent part, i.e. the "if" part, of each rule in the fuzzy rule base;

[0027] The fuzzy outputs of all rules are combined into a comprehensive fuzzy set through the fuzzy synthesis method, the comprehensive fuzzy set is defuzzified, and the proportional, integral and differential parameters of the PID controller are updated.

[0028] In one embodiment of the present invention, the robot grinding force is controlled according to the adjusted PID parameters and the weld deviation information, including controlling the robot grinding force according to the following formula:

[0029]

[0030] Where F is the control force; e is the weld deviation; ∫edt is the integral of the deviation; is the rate of change of weld deviation; Kp, Ki and Kd are the proportional, integral and differential parameters of the PID controller after adjustment.

[0031] The present invention also provides a robot weld grinding precision force control system based on fuzzy PID, comprising:

[0032] A first point set data acquisition module is used to scan the three-dimensional features of the actual weld using a laser sensor and obtain first point set data corresponding to the sensor coordinate system;

[0033] A second point set data acquisition module is used to convert the three-dimensional features of the theoretically expected weld into the sensor coordinate system to obtain second point set data;

[0034] A weld deviation information acquisition module, used to obtain weld deviation information according to the first point set data and the second point set data, wherein the weld deviation information includes the weld deviation and the change rate of the weld deviation;

[0035] An online adjustment module, used for adjusting the PID parameters of the PID controller online using fuzzy logic according to the weld deviation information;

[0036] The grinding force control module is used to control the grinding force of the robot according to the adjusted PID parameters and the weld deviation information.

[0037] The above technical solution of the present invention has the following advantages compared with the prior art:

[0038] The present invention discloses a robot weld grinding precision force control method and system based on fuzzy PID. When the robot performs grinding operation, the laser sensor is used to detect the weld position in real time to obtain weld deviation information, and the PID parameters are adjusted online through the fuzzy logic controller, thereby improving the stability and accuracy of the weld grinding process, effectively reducing human intervention, and achieving high-efficiency and high-quality weld grinding. The PID parameters are dynamically adjusted by the fuzzy logic controller to adapt to the changes in the weld position deviation and improve the accuracy and efficiency of weld grinding. At the same time, the closed-loop control of the force feedback device and the tool changing mechanism further ensures the stability and consistency of the grinding quality. The nonlinear processing capability of fuzzy logic can better handle those complex and nonlinear problems that are difficult to solve with traditional PID control strategies. Especially when the weld position deviation is large or changes rapidly, the system can accurately detect the real-time position of the weld through the high-precision measurement of the laser sensor, and provide accurate input data for the fuzzy logic controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0040] Figure 1 It is a flow chart of the robot weld grinding precision force control method based on fuzzy PID of the present invention. DETAILED DESCRIPTION

[0041] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0042] In the present invention, if directions (up, down, left, right, front and back) are described, it is only for the convenience of describing the technical solution of the present invention, and does not indicate or imply that the technical features referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, it cannot be understood as a limitation of the present invention.

[0043] In the present invention, "several" means one or more, "multiple" means more than two, "greater than", "less than", "exceed" and the like are understood to exclude the number itself; "above", "below", "within" and the like are understood to include the number itself. In the description of the present invention, if there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0044] In the present invention, unless otherwise clearly defined, the words "set", "install", "connect" and the like should be understood in a broad sense, for example, they can be directly connected or indirectly connected through an intermediate medium; they can be fixedly connected or detachably connected or integrally formed; they can be mechanically connected or electrically connected or able to communicate with each other; they can be the internal connection of two elements or the interaction relationship between two elements. Those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0045] Example 1

[0046] Reference Figure 1 As shown, a robot weld grinding precision force control method based on fuzzy PID of the present invention comprises:

[0047] S1. Use a laser sensor to scan the three-dimensional features of the actual weld and obtain the first point set data corresponding to the sensor coordinate system. The three-dimensional features of the actual weld include the position, shape and curvature of the weld. The laser sensor is scanned multiple times, and the output result is a point set of the weld in the sensor coordinate system. During multiple scans, the laser sensor performs repetitive measurements along the weld area (such as line scanning at different positions along the weld direction, or superimposing multiple frames of the same area in the motion trajectory of the robot end), so as to obtain more complete and dense point cloud data (first point set).

[0048] It can be understood that the weld position is the coordinate distribution of the weld in three-dimensional space, which determines the direction and spatial position of the weld; the weld shape is the cross-sectional profile of the weld and the external geometric shape, such as the weld width and height; the weld curvature is the degree of curvature in the direction of the weld and the cross-sectional direction. The weld point cloud data output by scanning is finally recorded in the sensor coordinate system in the form of "(x, y, z)".

[0049] Laser sensors can detect the weld position in real time and provide high-precision measurement. Laser sensors can provide measurement accuracy at the micron level and are suitable for accurate detection of weld position.

[0050] S2. Convert the three-dimensional features of the theoretically expected weld to the sensor coordinate system to obtain second point set data.

[0051] The three-dimensional features of the theoretically expected weld can be obtained from a design drawing or a three-dimensional model of the workpiece. The three-dimensional features of the theoretically expected weld also include the position, shape and curvature of the weld.

[0052] Specifically, the three-dimensional features of the theoretically expected weld are converted to the sensor coordinate system according to the following conversion formula:

[0053]

[0054] in, is the position of the laser sensor relative to the end of the robot, is the robot’s position in the world coordinate system, The position of the laser sensor in the world coordinate system.

[0055] S3. Obtain weld deviation information based on the first point set data and the second point set data, wherein the weld deviation information includes the weld deviation and the rate of change of the weld deviation. Specifically including:

[0056] Calculate the best match between the first point set data and the second point set data, and for each pair of matching points, calculate the weld deviation according to the formula ΔP=(Δx, Δy, Δz); obtain the change rate of the weld deviation according to the weld deviation data at two consecutive moments;

[0057] Among them, ΔP is the deviation vector; (Δx, Δy, Δz) represents the offset of the weld in the three-dimensional coordinate system.

[0058] The deviation value is output in digital or graphical form, showing the comparison of the three-dimensional features between the actual weld and the theoretical expected weld, and the deviation value is marked, which can help the operator to intuitively understand the gap between the actual weld and the theoretical expectation, and make necessary manual adjustments.

[0059] S4, according to the weld deviation information, using fuzzy logic to adjust the PID parameters of the PID controller online. Specifically, the fuzzy logic controller is used to adjust the proportional, integral and differential parameters of the PID controller according to the weld deviation information, including the following steps:

[0060] The fuzzy logic controller converts the weld deviation and the rate of change of the weld deviation into a fuzzy set; the data can be fuzzified according to the deviation range and the range of the rate of change of the weld deviation. For example: negative large (NB), negative small (NS), zero (Z), positive small (PS), positive large (PB).

[0061] Based on experience and experiments, a fuzzy rule base is established, and the rules form a knowledge base in the form of "if-then". For example, if the weld deviation is "positive and large" and the change rate is "positive and large", Kp increases; if the weld deviation E is "zero" and the change rate is "negative and small", Ki decreases, etc. These rules need to fully cover the responses of different deviation and change rate combinations to ensure that the controller can properly adjust the PID parameters under various circumstances.

[0062] Mapping input variables, namely weld deviation and rate of change of weld deviation, to the fuzzy set;

[0063] Through the membership function (such as trigonometric function, trapezoidal function or Gaussian function, etc.), the degree to which the input variable belongs to a fuzzy set (i.e., the membership value) is determined, and the corresponding activation degree of each fuzzy set is obtained for subsequent rule calculation;

[0064] Calculate the activation strength of the antecedent part, i.e., the "if" part, of each rule in the fuzzy rule base; the activation strength of the rule can be obtained by performing intersection or union operations (usually minimum and maximum operations). The higher the activation strength, the higher the matching degree of the rule to the current deviation and change rate combination.

[0065] The fuzzy outputs of all rules are combined into a comprehensive fuzzy set through fuzzy synthesis methods (common methods include maximization synthesis (Max) or weighted average method).

[0066] Defuzzify the comprehensive fuzzy set (such as the centroid method, maximum membership method, etc.), and update the proportional, integral and differential parameters of the PID controller. Output the adjusted PID parameters to the PID controller so that it can control the weld grinding force in real time according to the new Kp, Ki and Kd; in the next sampling cycle, if the deviation continues to change, repeat the above steps, continuously and adaptively adjust the PID parameters to achieve precise control of the grinding force.

[0067] S5. Controlling the grinding force of the robot according to the adjusted PID parameters and the weld deviation information.

[0068] Specifically, the robot grinding force is controlled according to the following formula:

[0069]

[0070] Where F is the control force; e is the weld deviation; ∫edt is the integral of the deviation; is the rate of change of weld deviation; Kp, Ki and Kd are the proportional, integral and differential parameters of the PID controller after adjustment.

[0071] It is understandable that the robot can grind the weld according to the calculated control force through the robot actuator. The robot actuator includes components such as grinding motor (grinding tool), cylinder (elastic mechanism), servo motor, etc. The grinding motor (grinding tool) provides rotational grinding or grinding; cylinder / elastic mechanism: provides flexibility or floating function for the grinding head to prevent excessive wear; servo motor: controls the posture or movement of the grinding head to ensure that the force always acts in the required direction and position. It can realize floating constant force grinding of the workpiece by the grinding head, effectively preventing the collapse of the edge or damage to the parent material caused by excessive grinding force. In addition, the robot actuator also includes a tool changing mechanism and a force feedback device. Since long-term grinding will cause wear of the mold, the grinding efficiency and surface accuracy will decrease, the tool changing mechanism can automatically replace the grinding head according to the set tool changing frequency to achieve closed-loop control of the grinding process; the force feedback device is used to monitor the force feedback during the grinding process in real time and adjust the grinding force. The force feedback device includes a force feedback sensor / torque sensor) which can measure the actual force during the grinding process in real time and compare it with the expected force in the PID instruction. If a deviation occurs, dynamic correction will continue to be performed through fuzzy PID.

[0072] When in use, start the laser sensor, the laser sensor is activated, and starts to scan the welding area in real time. The laser sensor emits a laser beam to detect the actual position of the weld. The weld position data collected by the sensor is transmitted to the control system. The control system compares the real-time detected weld position with the expected weld position, calculates the weld position deviation information, and then inputs the previously obtained weld position deviation and its rate of change into the fuzzy logic controller. The fuzzy logic controller converts the precise deviation data into a fuzzy set, and establishes a fuzzy rule base based on experience and experiments. The rules are expressed as a knowledge base composed of "if-then" statements, which comprehensively covers the responses of different deviation and change rate combinations. The input variables (weld deviation e and the rate of change of the weld deviation) are converted into the fuzzy set. ) is mapped to a fuzzy set, and the degree to which a variable belongs to a fuzzy set is expressed by a membership function. For each rule, the activation strength of the rule antecedent is calculated. The fuzzy outputs of all rules are combined into a comprehensive fuzzy set using a fuzzy synthesis method. Thus, new PID parameters (Kp, Ki, and Kd) are obtained, and the adjusted PID parameters are output to the PID controller. The PID controller calculates the corresponding control force according to the preset formula, and the calculated control force is converted into a control signal and sent to the robot actuator. The overall operation is simple and easy.

[0073] This method dynamically adjusts PID parameters through fuzzy logic controller, which can adapt to the changes of weld position deviation and improve the accuracy and efficiency of weld grinding. Fuzzy logic controller can be used to realize real-time optimization and adjustment of PID parameters through fuzzy rule base and fuzzy reasoning mechanism. At the same time, the stability and consistency of grinding quality are further guaranteed through closed-loop control of force feedback device and tool changing mechanism. Through the nonlinear processing capability of fuzzy logic, complex and nonlinear problems that are difficult to solve with traditional PID control strategy can be better handled.

[0074] Example 2

[0075] Based on the same inventive concept, this embodiment provides a robot weld grinding precision force control system based on fuzzy PID. The principle of solving the problem is similar to the robot weld grinding precision force control method based on fuzzy PID, and the repeated parts will not be repeated.

[0076] This embodiment provides a robot weld grinding precision force control system based on fuzzy PID, including:

[0077] The present invention also provides a robot weld grinding precision force control system based on fuzzy PID, comprising:

[0078] A first point set data acquisition module is used to scan the three-dimensional features of the actual weld using a laser sensor and obtain first point set data corresponding to the sensor coordinate system;

[0079] A second point set data acquisition module is used to convert the three-dimensional features of the theoretically expected weld into the sensor coordinate system to obtain second point set data;

[0080] A weld deviation information acquisition module, used to obtain weld deviation information according to the first point set data and the second point set data, wherein the weld deviation information includes the weld deviation and the change rate of the weld deviation;

[0081] An online adjustment module, used for adjusting the PID parameters of the PID controller online using fuzzy logic according to the weld deviation information;

[0082] The grinding force control module is used to control the grinding force of the robot according to the adjusted PID parameters and the weld deviation information.

[0083] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0087] Finally, it should be noted that the above specific implementation methods are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A robot weld grinding precision force control method based on fuzzy PID, characterized in that: include: Scanning the three-dimensional features of the actual weld using a laser sensor, and obtaining the first point set data corresponding to the sensor coordinate system; Converting the three-dimensional features of the theoretically expected weld to the sensor coordinate system to obtain second point set data; Obtaining weld deviation information according to the first point set data and the second point set data, wherein the weld deviation information includes weld deviation and a change rate of weld deviation; According to the weld deviation information, PID parameters of the PID controller are adjusted online using fuzzy logic; The robot grinding force is controlled according to the adjusted PID parameters and the weld deviation information.

2. According to the fuzzy PID-based robot weld grinding precision control method of claim 1, it is characterized in that: The three-dimensional characteristics of the actual weld and / or the three-dimensional characteristics of the theoretically expected weld include the position, shape and curvature of the weld.

3. According to the fuzzy PID based robot weld grinding precision control method of claim 1, it is characterized in that: The theory predicts that the three-dimensional characteristics of the weld are obtained from the design drawings or three-dimensional models of the workpiece.

4. According to the fuzzy PID-based robot weld grinding precision force control method of claim 1, it is characterized in that: The three-dimensional features of the theoretically expected weld are converted into the sensor coordinate system, including: converting the three-dimensional features of the theoretically expected weld into the sensor coordinate system according to the following conversion formula: in, is the position of the laser sensor relative to the end of the robot, is the robot’s position in the world coordinate system, The position of the laser sensor in the world coordinate system.

5. The robot weld grinding precision force control method based on fuzzy PID according to claim 1 is characterized in that: Obtaining weld deviation information according to the first point set data and the second point set data, including: Calculate the best match between the first point set data and the second point set data, and for each pair of matching points, calculate the weld deviation according to the formula ΔP=(Δx, Δy, Δz); obtain the change rate of the weld deviation according to the weld deviation data at two consecutive moments; Among them, ΔP is the deviation vector; (Δx, Δy, Δz) represents the offset of the weld in the three-dimensional coordinate system.

6. The robot weld grinding precision force control method based on fuzzy PID according to claim 5 is characterized in that: Obtaining weld deviation information according to the first point set data and the second point set data, further comprising: The deviation value is output in digital or graphical form, a comparison of the three-dimensional features between the actual weld and the theoretically expected weld is displayed, and the deviation value is marked.

7. The robot weld grinding precision force control method based on fuzzy PID according to claim 1 is characterized in that: According to the weld deviation information, the PID parameters of the PID controller are adjusted online using fuzzy logic, including: The fuzzy logic controller adjusts the proportional, integral and differential parameters of the PID controller according to the weld deviation information, including: The fuzzy logic controller converts the weld deviation and the change rate of the weld deviation into a fuzzy set; Based on experience and experiments, a fuzzy rule base is established, and the rules form a knowledge base in the form of "if-then"; Mapping input variables, namely weld deviation and the rate of change of weld deviation, to the fuzzy set; Through the membership function, the degree to which the input variable belongs to a fuzzy set is determined, and the corresponding activation degree of each fuzzy set is obtained; Calculate the activation strength of the antecedent part, i.e. the "if" part, of each rule in the fuzzy rule base; The fuzzy outputs of all rules are combined into a comprehensive fuzzy set through the fuzzy synthesis method, the comprehensive fuzzy set is defuzzified, and the proportional, integral and differential parameters of the PID controller are updated.

8. The robot weld grinding precision force control method based on fuzzy PID according to claim 1 is characterized in that: According to the adjusted PID parameters and the weld deviation information, the robot grinding force is controlled, including controlling the robot grinding force according to the following formula: Where F is the control force; e is the weld deviation; ∫edt is the integral of the deviation; is the rate of change of weld deviation; Kp, Ki and Kd are the proportional, integral and differential parameters of the PID controller after adjustment.

9. A robot weld grinding precision control system based on fuzzy PID, characterized in that: include: A first point set data acquisition module is used to scan the three-dimensional features of the actual weld using a laser sensor and obtain first point set data corresponding to the sensor coordinate system; A second point set data acquisition module is used to convert the three-dimensional features of the theoretically expected weld into the sensor coordinate system to obtain second point set data; A weld deviation information acquisition module, used to obtain weld deviation information according to the first point set data and the second point set data, wherein the weld deviation information includes the weld deviation and the change rate of the weld deviation; An online adjustment module, used for adjusting the PID parameters of the PID controller online using fuzzy logic according to the weld deviation information; The grinding force control module is used to control the grinding force of the robot according to the adjusted PID parameters and the weld deviation information.

Images