A method and system for identifying the friction of a joint of a mechanical arm for ship bending plate forming

Abstract

The application provides a kind of mechanical arm joint friction identification method and system for ship bending plate forming, comprising the following steps: obtaining the speed and friction state parameters of the mechanical arm joint;Obtain the actual curvature of the bending plate;Establish LuGre friction model, and the joint friction force F is obtained according to the speed and friction state parameters of the mechanical arm by LuGre friction model;Establish disturbance observer, and the estimated friction disturbance is obtained by using the disturbance observer and the joint friction force F;Determine the friction compensation control strategy according to the estimated friction disturbance.The application improves the automation level of shipbuilding, and the mechanical arm joint friction identification in the process of ship bending plate forming can be more accurate, which improves the overall automation degree, not only reduces the dependence on manual operation, but also improves the production efficiency and quality control.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control and shipbuilding, and in particular to a method and system for identifying friction of a robot arm joint used in a ship bending forming process. Background Art

[0002] Plate bending is a critical step in shipbuilding. Traditional plate bending methods primarily include machine cold bending and linear hot-water bending. Hot-water bending is widely used due to its superior control over curved surface shape. The degree of automation in this process directly impacts the efficiency and quality of shipbuilding. However, automating this process is challenging due to the complex curved surface shape of steel plates and the thermal deformation characteristics during heating.

[0003] To improve the automation level of hydraulic and thermal sheet metal bending, industrial robotic arms have been introduced. Bending complex curved surfaces requires high-precision motion control. However, the friction characteristics of robotic arm joints are complex and difficult to predict, significantly impacting motion control accuracy. Therefore, identifying and compensating for friction in robotic arm joints has become a key technology for improving operational precision.

[0004] In the existing technology, research on ship bending processing and robotic arms has achieved certain results at home and abroad. The development of water-fire bending technology abroad is relatively mature, and a variety of heating algorithms and technologies have been proposed. For example, Shin et al. proposed an integrated line heating algorithm for automatically forming curved panels. China is also actively researching the automation of water-fire bending technology. Dalian University of Technology has carried out research on cantilevered water-fire processing robots to improve processing accuracy and efficiency. The kinematics and dynamics of the robotic arm are the basis for achieving its precise control. Studies have shown that the operating accuracy of the robotic arm can be improved through reasonable trajectory planning and impedance control. Existing research mainly focuses on friction modeling and identification methods of mechanical systems. For example, when studying dynamic tasks of robots, Bauml et al. proposed some friction compensation methods to improve the operational stability of the robotic arm.

[0005] In existing technologies, the identification and compensation of friction in the manipulator's joints is not precise enough, resulting in large errors during operation. The curved steel plates involved in surface forming are not flat, and the manipulator's posture changes significantly as the motion space changes during processing. This can lead to control errors caused by friction and other nonlinear factors. Therefore, the manipulator lacks precision and stability when processing complex surfaces. Existing impedance control cannot be effectively adjusted before the manipulator and the target are in contact, resulting in excessive impact and unstable operation. Summary of the Invention

[0006] In response to the deficiencies in the prior art, the present invention provides a method and system for identifying friction in the joints of a robotic arm during the ship bending process, which can improve the degree of automation and processing accuracy of the water and fire bending process during ship manufacturing.

[0007] The present invention achieves the above technical objectives through the following technical means.

[0008] A method for identifying friction of manipulator joints for ship bending forming includes the following steps:

[0009] Obtain the speed and friction state parameters of the robot arm joint; obtain the actual curvature of the bent plate;

[0010] Establish a LuGre friction model, which derives the joint friction force F based on the robot arm speed and friction state parameters;

[0011] Establish a disturbance observer and use the disturbance observer and joint friction force F to obtain the estimated friction disturbance

[0012] According to the estimated friction disturbance Determine the friction compensation control strategy.

[0013] Furthermore, the LuGre friction model is established, specifically:

[0014] The friction state parameter z of the i-th sampling at different speeds of the manipulator is obtained by the Euler method. i , i∈(1……n), n is the total number of experiments;

[0015] The friction state parameter z of the i-th sampling i The derivative of Satisfy the following relationship:

[0016]

[0017] Among them, g(v i ) is the static friction function associated with velocity; v i is the speed of the robotic arm at the i-th sampling;

[0018] The nonlinear least squares method is used to minimize the sum of squared residuals between the observed data and the model predictions, specifically:

[0019]

[0020] Among them, F i is the friction force of the i-th sampling, σ0 is the stiffness coefficient; σ1 is the damping coefficient; σ2 is the viscous friction coefficient;

[0021] The stiffness coefficient σ0, damping coefficient σ1 and viscous friction coefficient σ2 are obtained by fitting;

[0022] Build the LuGre model:

[0023]

[0024] Where: F is the joint friction force, v is the speed of the robot arm, z represents the variable friction state parameter, is the derivative of z; σ0 is the stiffness coefficient; σ1 is the damping coefficient; σ2 is the viscous friction coefficient.

[0025] Furthermore, a disturbance observer is established, specifically:

[0026] State estimation, specifically:

[0027] The observer formula is: in, is the friction state variable estimated by the system matrix, A, B and C are the system matrices respectively, L is the observer gain, and its value range is (0~1]; y is the system torque;

[0028] The friction state variables of the system matrix are estimated using the observer formula

[0029] Disturbance estimation, based on the output of the observer Get the estimated friction disturbance Specifically:

[0030]

[0031] in: is the estimated friction state variable; is the rate of change of the estimated friction state variable.

[0032] Furthermore, according to the estimated friction disturbance Determine the friction compensation control strategy, specifically:

[0033] Using the estimated friction perturbation Calculate the compensation control input Δu: Wherein, K is the compensation gain;

[0034] The compensation control input Δu is superimposed on the original control input u to obtain the new control input u new =u+Δu;

[0035] By compensating for friction in real time, the precise operation of the robot arm during the bending process is ensured.

[0036] Furthermore, by compensating for friction in real time, the precise operation of the robot arm during the plate bending process is ensured, specifically:

[0037] The collected actual curvature y of the bending plate is integrated with the displacement, speed and torque data of the robot arm to form the feedback control u feedback :

[0038] Among them, u feedback is the feedback control input, Kp, Ki, Kd are the control gains, r is the desired curvature, and y is the actual curvature;

[0039] According to the feedback control u feedback Adjust the motion path and strength of the robotic arm.

[0040] A mechanical arm joint friction identification system for ship bending forming comprises a storage medium; the storage medium stores a program written using the mechanical arm joint friction identification method for ship bending forming.

[0041] The beneficial effects of the present invention are:

[0042] 1. The present invention's method for identifying friction in robotic arm joints during ship plate bending improves the automation level of shipbuilding. This allows for more precise identification of friction in robotic arm joints during ship plate bending, enhancing the overall level of automation. This not only reduces reliance on manual operation but also improves production efficiency and quality control. The use of automated robotic arms reduces the physical labor associated with manual operations, lowering worker workload and operational risks, and improving the safety of the work environment.

[0043] 2. The method for identifying friction of the joints of the manipulator used in the ship bending forming process described in the present invention adopts the LuGre friction identification model combined with the disturbance observer method, which can significantly improve the operating accuracy and efficiency of the manipulator in the bending forming process. Experiments show that the single forming rate of spiral heating using a manipulator reaches about 80%, which is a significant improvement compared to the 55% forming rate of ordinary equipment. Due to the improvement in forming efficiency and the reduction in the number of reworks (the situation where only a small amount of re-firing is required in individual positions is greatly reduced), the overall production cost is reduced. In addition, automated operation reduces dependence on highly skilled workers, further reducing labor costs.

[0044] 3. The method for identifying friction in the joints of the manipulator used in the ship bending forming process described in the present invention adopts the LuGre friction identification model combined with the disturbance observer method to achieve high-precision identification of the friction force in the joints of the manipulator, thereby ensuring the accuracy and stability of the manipulator in actual operation.

[0045] 4. The method for identifying friction of the manipulator joints used in the ship bending forming process described in the present invention adjusts and optimizes the control strategy of the manipulator through accurate identification of friction force, ensuring stable operation under complex trajectories (such as spiral heating trajectories).

[0046] 5. The friction identification method of the robot arm joint used in the ship bending process described in the present invention, since the steel plate will be heated and cooled during the bending process, resulting in thermal deformation, the friction identification method of the present invention enables the robot arm to adapt to these thermal deformations to ensure processing accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. The drawings described below are some embodiments of the present invention. For ordinary technicians in this field, it is obvious that other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 This is a control principle diagram of the method for identifying friction of manipulator joints in the ship bending forming process described in the present invention. DETAILED DESCRIPTION

[0049] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0050] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "axial", "radial", "vertical", "horizontal", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0051] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0052] The method for identifying friction of a manipulator joint for ship bending forming according to the present invention comprises the following steps:

[0053] S01: data collection and preliminary processing;

[0054] Data Collection: As the robotic arm performs the standard ship bending process, it collects data on the displacement, friction parameters, speed, torque, and curvature of each joint. Displacement, speed, and torque of each joint are typically acquired using the robotic arm's built-in or external sensors. Curvature data can be collected using devices such as laser scanners or contact measuring instruments.

[0055] Data processing: De-noising and filtering are performed on the collected data to remove noise and outliers to ensure the accuracy and reliability of the data.

[0056] S02: Establish LuGre friction model, specifically:

[0057] S2.1: Obtain the friction state parameter z of the i-th sampling at different speeds of the manipulator using the Euler method i , can be measured directly or indirectly using existing sensors, which is an existing measurement method. i∈(1……n), n is the total number of experiments;

[0058] The friction state parameter z of the i-th sampling i The derivative of Satisfy the following relationship:

[0059]

[0060] Among them, g(v i ) is the static friction function associated with velocity, which is the static friction function in common joints; v i is the speed of the robotic arm at the i-th sampling;

[0061] S2.2: Use nonlinear least squares to minimize the sum of squared residuals between the observed data and the model predictions, specifically:

[0062]

[0063] Among them, Fi is the friction force of the i-th sampling, σ0 is the stiffness coefficient; σ1 is the damping coefficient; σ2 is the viscous friction coefficient;

[0064] The stiffness coefficient σ0, damping coefficient σ1 and viscous friction coefficient σ2 are obtained by fitting.

[0065] The least squares method (LSM) is a standard method for parameter estimation. Its goal is to minimize the sum of squared residuals between observed data and model predictions. Applying the Least Squares method to the LuGre model fitting can determine model parameters, making the model more accurately describe the friction behavior of the actual system.

[0066] S2.3: Build LuGre model:

[0067]

[0068] Where: F is the joint friction force, v is the speed of the robot arm, z represents the variable friction state parameter, is the derivative of z; σ0 is the stiffness coefficient; σ1 is the damping coefficient; σ2 is the viscous friction coefficient.

[0069] S03: Establish a disturbance observer, specifically:

[0070] The LuGre model can be used to predict the friction in the system in advance and directly compensate for it during the control process, thereby reducing the impact of friction on control accuracy. However, even if the LuGre model is used to compensate for friction, uncompensated friction may still exist due to model incompleteness or parameter estimation errors. In this case, a disturbance observer can be used to further estimate and compensate for these unmodeled friction forces and other disturbances, further improving system performance. The disturbance observer can monitor the deviation between the friction model and the actual friction force and use it as feedback information to dynamically adjust the parameters of the LuGre model so that the model can more accurately reflect the actual situation.

[0071] S3.1: State estimation, specifically:

[0072] Observer design formula: in, is the friction state variable estimated by the system matrix, A, B, C are the system matrices, L is the observer gain, and its value range is (0~1]; y is the system torque.

[0073] The friction state variables of the system matrix are estimated using the observer design formula.

[0074] S3.2: Disturbance estimation, based on the output of the observer Get the estimated friction disturbance Specifically:

[0075] The disturbance estimation formula is:

[0076] in: is the estimated friction state variable; is the rate of change of the estimated friction state variable;

[0077] S04: Friction compensation control strategy

[0078] S4.1: Using Estimated Friction Perturbations Calculate the compensation control input Δu: Where K is the compensation gain.

[0079] S4.2: Superimpose the compensation control input Δu on the original control input u to obtain the new control input u new :u new =u+Δu;

[0080] S4.3: Ensure accurate operation of the robot arm during the bending process by compensating for friction in real time. Specifically:

[0081] The collected actual curvature y of the bending plate is integrated with the displacement, speed and torque data of the robot arm to form the feedback control u feedback :

[0082] Among them, u feedback is the feedback control input, Kp, Ki, Kd are the control gains, r is the desired curvature, and y is the actual curvature.

[0083] According to the fused data, the motion path and force of the robotic arm are adjusted to ensure the accuracy of the bent plate forming.

[0084] The method for identifying friction in the joints of a robotic arm used in the ship plate bending process described in the present invention improves the level of automation in shipbuilding. The identification of friction in the joints of the robotic arm used in the ship plate bending process can be more accurate, thereby increasing the overall degree of automation. This not only reduces reliance on manual operation but also improves production efficiency and quality control. The use of automated robotic arms reduces the physical labor involved in manual operation, reduces the labor intensity and operational risks of workers, and improves the safety of the working environment. The LuGre friction identification model combined with a disturbance observer method can significantly improve the operational accuracy and efficiency of the robotic arm in the plate bending process. Experiments have shown that the single-shot forming rate using a robotic arm for spiral heating reaches approximately 80%, a significant improvement over the 55% forming rate of conventional equipment. Due to the increased forming efficiency and reduced rework (significantly reducing the number of instances where only a small amount of re-firing is required in individual locations), overall production costs are reduced. Furthermore, automated operations reduce reliance on highly skilled workers, further reducing labor costs.

[0085] The robot arm joint friction identification system for ship bending and forming processes described in the present invention includes a storage medium; the storage medium stores a program written using the robot arm joint friction identification method for ship bending and forming processes described above. The storage medium includes a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, or a combination thereof. Those skilled in the art will appreciate that the various features described herein can be implemented via methods, data processing systems, or computer program products. Therefore, these features can be partially implemented in hardware, entirely in software, or in a combination of hardware and software. In addition, the above features can also be implemented in the form of a computer program product stored on one or more computer-readable storage media, wherein the computer-readable storage medium contains computer-readable program code segments or instructions stored in the storage medium. Any commonly used computer-readable storage medium can be used, including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, and / or combinations of the above devices.

[0086] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0087] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent embodiments or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying friction of manipulator joints for ship bending forming, characterized in that: The steps include: Obtain the speed and friction state parameters of the robot arm joint; obtain the actual curvature of the bent plate; Establish a LuGre friction model, which derives the joint friction force F based on the robot arm speed and friction state parameters; Establish a disturbance observer and use the disturbance observer and joint friction force F to obtain the estimated friction disturbance According to the estimated friction disturbance Determine the friction compensation control strategy.

2. The method for identifying joint friction of a manipulator for ship bending according to claim 1, characterized in that: Establish the LuGre friction model, specifically: The friction state parameter z of the i-th sampling at different speeds of the manipulator is obtained by the Euler method. i , i∈(1……n), n is the total number of experiments; The friction state parameter z of the i-th sampling i The derivative of Satisfy the following relationship: Among them, g(v i ) is the static friction function associated with velocity; v i is the speed of the robotic arm at the i-th sampling; The nonlinear least squares method is used to minimize the sum of squared residuals between the observed data and the model predictions, specifically: Among them, F i is the friction force of the i-th sampling, σ0 is the stiffness coefficient; σ1 is the damping coefficient; σ2 is the viscous friction coefficient; The stiffness coefficient σ0, damping coefficient σ1 and viscous friction coefficient σ2 are obtained by fitting; Build the LuGre model: Where: F is the joint friction force, v is the speed of the robot arm, z represents the variable friction state parameter, is the derivative of z; σ0 is the stiffness coefficient; σ1 is the damping coefficient; σ2 is the viscous friction coefficient.

3. The method for identifying joint friction of a manipulator for ship bending according to claim 1, characterized in that: Establish a disturbance observer, specifically: State estimation, specifically: The observer formula is: in, is the friction state variable estimated by the system matrix, A, B and C are the system matrices respectively, L is the observer gain, and its value range is (0~1]; y is the system torque; The friction state variables of the system matrix are estimated using the observer formula Disturbance estimation, based on the output of the observer Get the estimated friction disturbance Specifically: in: is the estimated friction state variable; is the rate of change of the estimated friction state variable.

4. The method for identifying joint friction of a manipulator for ship bending according to claim 1, characterized in that: According to the estimated friction disturbance Determine the friction compensation control strategy, specifically: Using the estimated friction perturbation Calculate the compensation control input Δu: Wherein, K is the compensation gain; The compensation control input Δu is superimposed on the original control input u to obtain the new control input u new =u+Δu; By compensating for friction in real time, the precise operation of the robot arm during the bending process is ensured.

5. The method for identifying joint friction of a manipulator for ship bending according to claim 4, characterized in that: By compensating for friction in real time, the precise operation of the robot arm during the bending process is ensured, specifically: The collected actual curvature y of the bending plate is integrated with the displacement, speed and torque data of the robot arm to form the feedback control u feedback : Among them, u feedback is the feedback control input, Kp, Ki, Kd are the control gains, r is the desired curvature, and y is the actual curvature; According to the feedback control u feedback Adjust the motion path and strength of the robotic arm.

6. A mechanical arm joint friction identification system for ship bending forming, characterized by: It includes a storage medium; the storage medium stores a program written using the method for identifying friction of a manipulator joint for ship bending forming as described in any one of claims 1 to 5.

Images