Robotic arm joint friction identification method and system for ship hull plate bending

Abstract

Provided in the present invention are a robotic arm joint friction identification method and system for ship hull plate bending. The method comprises the following steps: acquiring the speed and a friction state parameter of a robotic arm joint; acquiring the actual curvature of a bent plate; establishing a LuGre friction model, and the LuGre friction model obtaining a joint friction force F on the basis of the speed and the friction state parameter of the robotic arm; establishing a disturbance observer, and using the disturbance observer and the joint friction force F to obtain an estimated friction force disturbance; and determining a friction force compensation control strategy on the basis of the estimated friction force disturbance. The present invention improves the automation level of ship manufacturing, makes robotic arm joint friction identification during ship hull plate bending more accurate, and improves the overall level of automation, thereby not only reducing the reliance on manual operations, but also improving the production efficiency and quality control.

Description

A method and system for identifying joint friction of a robotic arm in ship bending forming Technical Field

[0001] This invention relates to the fields of robot control and shipbuilding technology, specifically to a method and system for identifying joint friction of a robotic arm during the bending and forming process of a ship. Background Technology

[0002] In shipbuilding, plate bending is a crucial step. Traditional plate bending methods mainly include machine cold bending and linear hot-and-water forming. Among these, hot-and-water bending is widely used because it allows for better control of the curved surface shape. The degree of automation in this process directly affects the efficiency and quality of shipbuilding. However, due to the complex curved shape of the steel plate and the thermal deformation characteristics during the heating process, automating this process presents significant challenges.

[0003] To improve the automation level of water-fire bending processes, industrial robotic arms have been introduced into this field. High-precision motion control is required when robotic arms perform bending of complex curved surfaces. However, the frictional characteristics of robotic arm joints are complex and difficult to predict, significantly impacting motion control accuracy. Therefore, identifying and compensating for the friction of robotic arm joints has become a key technology for improving their operational precision.

[0004] In existing technologies, both domestic and international research on ship bending plate processing and robotic arms has yielded certain results. Internationally, the development of water-fire bending plate processes is relatively mature, with various heating algorithms and technologies proposed. For example, Shin et al. proposed a comprehensive line heating algorithm for automatically forming curved panels. Domestically, research on the automation of water-fire bending plate processes is also actively underway. Dalian University of Technology has conducted research on cantilevered water-fire processing robots, aiming to improve processing accuracy and efficiency. The kinematics and dynamics of robotic arms are fundamental to achieving precise control. Research shows that reasonable trajectory planning and impedance control can improve the operational accuracy of robotic arms. Existing research mainly focuses on friction modeling and identification methods for mechanical systems. For example, Bauml et al., in studying robot dynamic tasks, proposed several friction compensation methods to improve the operational stability of robotic arms.

[0005] In existing technologies, the identification and compensation of joint friction in robotic arms are not precise enough, leading to significant errors during operation. The curved steel plates involved in surface forming are not on a single plane; the robotic arm's posture changes considerably with variations in the motion space during the process of handling the curved plate. This results in control errors due to friction and other nonlinear factors, indicating insufficient precision and stability when handling complex curved surfaces. Furthermore, existing impedance control cannot be effectively adjusted before the robotic arm contacts the target, leading to excessive impact and unstable operation. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for identifying joint friction of a robotic arm in the process of forming a ship bending plate, which can improve the automation level and processing accuracy of the water-fire bending plate process in ship manufacturing.

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

[0008] A method for identifying joint friction in a robotic arm for ship bending plate forming includes the following steps:

[0009] Obtain the speed and friction parameters of the robotic arm joints; obtain the actual curvature of the bending plate;

[0010] A LuGre friction model is established, which derives the joint friction force F based on the robot arm speed and friction state parameters.

[0011] A perturbation observer is established, and using the perturbation observer and the joint friction force F, the estimated frictional perturbation is obtained.

[0012] Based on the estimated frictional disturbance Determine the friction compensation control strategy.

[0013] Furthermore, a LuGre friction model is established, specifically as follows:

[0014] The friction state parameters z of the i-th sample at different speeds of the robotic arm are obtained using the Euler method. i , i∈(1……n), where n is the total number of experiments;

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

[0016]

[0017] Wherein, g(v i ) is the static friction function related to velocity; v i Let be the speed of the robotic arm during the i-th sampling.

[0018] The sum of squared residuals between the observed data and the model predictions is minimized using the nonlinear least squares method, specifically as follows:

[0019]

[0020] Among them, F i σi represents the frictional force from the i-th sampling, where σ0 is the stiffness coefficient, σ1 is the damping coefficient, and σ2 is the viscous friction coefficient.

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

[0022] Establish the LuGre model:

[0023]

[0024] In the formula: F is the joint friction force, v is the robot arm speed, and z represents the variable friction state parameter. σ 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 as follows:

[0026] State estimation, specifically:

[0027] The observer formula is: in, The frictional state variables are estimated by the system matrix, where A, B, and C are the system matrices, L is the observer gain with a value range of (0 to 1), and y is the system torque.

[0028] Friction state variables estimated from the system matrix using the observer formula.

[0029] Perturbation estimation, based on the observer output The estimated frictional disturbance is obtained Specifically:

[0030]

[0031] in: These are the estimated frictional state variables; It is the estimated rate of change of the frictional state variable.

[0032] Furthermore, based on the estimated frictional disturbance The friction compensation control strategy is determined as follows:

[0033] Using estimated frictional perturbation Calculate the compensation control input Δu: Where K is the compensation gain;

[0034] The compensated 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 robotic arm can be made to operate precisely during the bending process.

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

[0037] The actual curvature y of the curved plate is collected and fused with the displacement, velocity, and torque data of the robotic arm to form a feedback control u. feedback :

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

[0039] Based on feedback control u feedback Adjust the movement path and force of the robotic arm.

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

[0041] The beneficial effects of this invention are as follows:

[0042] 1. The method for identifying joint friction of a robotic arm in the process of ship bending forming, as described in this invention, improves the level of automation in ship manufacturing. It enables more accurate identification of joint friction during the bending forming process, enhancing the overall automation level. This not only reduces reliance on manual operation but also improves production efficiency and quality control. The application of automated robotic arms reduces physical labor in manual operations, lowers worker workload and operational risks, and improves the safety of the working environment.

[0043] 2. The robotic arm joint friction identification method for the ship bending forming process described in this invention, employing a LuGre friction identification model combined with a disturbance observer, can significantly improve the operational accuracy and efficiency of the robotic arm during the bending forming process. Experiments show that the single-pass forming rate using the robotic arm for spiral heating reaches approximately 80%, a significant improvement compared to the 55% forming rate of ordinary equipment. Due to the increased forming efficiency and reduced rework frequency (significantly reducing the need for minor reheating only in a few locations), the overall production cost is reduced. Furthermore, automated operation reduces reliance on highly skilled workers, further lowering labor costs.

[0044] 3. The method for identifying joint friction of a robotic arm in the process of forming a ship bending plate, as described in this invention, adopts the LuGre friction identification model combined with a disturbance observer to achieve high-precision identification of joint friction of the robotic arm, thus ensuring the accuracy and stability of the robotic arm in actual operation.

[0045] 4. The method for identifying joint friction of a robotic arm in the process of forming a ship bending plate, as described in this invention, adjusts and optimizes the control strategy of the robotic arm by accurately identifying the friction force, thereby ensuring stable operation under complex trajectories (such as spiral heating trajectories).

[0046] 5. The friction identification method for robotic arm joints in the process of forming a ship bending plate, as described in this invention, enables the robotic arm to adapt to these thermal deformations during the bending process, thus ensuring processing accuracy. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings described below are some embodiments of the present invention. For those skilled in the art, it is obvious that other drawings can be obtained from these drawings without creative effort.

[0048] Figure 1 is a schematic diagram of the control principle of the robotic arm joint friction identification method for the ship bending forming process described in this invention. Detailed Implementation

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

[0050] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "axial," "radial," "vertical," "horizontal," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0051] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0052] The method for identifying joint friction of a robotic arm for ship bending forming as described in this invention includes the following steps:

[0053] S01: Data Acquisition and Preliminary Processing;

[0054] Data Acquisition: During the standard process of ship bending forming by the robotic arm, displacement, friction parameters, speed, torque, and bending plate curvature data of each joint are collected. The displacement, speed, and torque of each joint are typically acquired through built-in or external sensors of the robotic arm. Bending plate curvature data can be acquired using equipment such as laser scanners or contact measuring instruments.

[0055] Data processing: The collected data is processed by denoising, filtering, etc., to remove noise and outliers and ensure the accuracy and reliability of the data.

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

[0057] S2.1: Obtain the friction state parameters z of the i-th sample at different speeds of the robotic arm using the Euler method. i It can be measured directly or indirectly using existing sensors; this is the current measurement method. i∈(1…n), where n is the total number of experiments;

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

[0059]

[0060] Wherein, g(v i (v) is the static friction function related to velocity; static friction functions are common in joints. i Let be the speed of the robotic arm during the i-th sampling.

[0061] S2.2: Minimize the sum of squared residuals between the observed data and the model predictions using the nonlinear least squares method, specifically:

[0062]

[0063] Among them, F i σi represents the frictional force from the i-th sampling, where σ0 is the stiffness coefficient, σ1 is the damping coefficient, and σ2 is the viscous friction coefficient.

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

[0065] The least squares method is a standard approach 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 fitting the LuGre model can determine the model parameters, enabling the model to more accurately describe the frictional behavior of the actual system.

[0066] S2.3: Establish the LuGre model:

[0067]

[0068] In the formula: F is the joint friction force, v is the robot arm speed, and z represents the variable friction state parameter. σ 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] LuGre models can be used to predict frictional forces in a system beforehand and directly compensate for these forces during control, thereby reducing the impact of friction on control accuracy. However, even with LuGre models for frictional compensation, uncompensated frictional forces may still exist due to model incompleteness or parameter estimation errors. In this case, disturbance observers can be used to further estimate and compensate for these unmodeled frictional forces and other disturbances, further improving system performance. Disturbance observers can monitor the deviation between the frictional force model and the actual frictional force and use this as feedback information to dynamically adjust the parameters of the LuGre model, enabling the model to more accurately reflect the actual situation.

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

[0072] Observer design formula: in, The frictional state variables are estimated by the system matrix, where A, B, and C are the system matrices, L is the observer gain with a value range of (0 to 1), and y is the system torque.

[0073] Friction state variables for system matrix estimation are derived using the observer design formula.

[0074] S3.2: Perturbation estimation, based on the observer output. The estimated frictional disturbance is obtained Specifically:

[0075] The disturbance estimation formula is:

[0076] in: These are the estimated frictional state variables; It is the estimated rate of change of the frictional state variable;

[0077] S04: Friction Compensation Control Strategy

[0078] S4.1: Utilizing estimated frictional perturbation Calculate the compensation control input Δu: Where K is the compensation gain.

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

[0080] S4.3: By compensating for friction in real time, the precise operation of the robotic arm during the bending process is ensured, specifically:

[0081] The actual curvature y of the curved plate is collected and fused with the displacement, velocity, and torque data of the robotic arm to form a feedback control u. feedback :

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

[0083] Based on the fused data, the movement path and force of the robotic arm are adjusted to ensure the accuracy of the bending plate forming.

[0084] The present invention describes a method for identifying joint friction of a robotic arm in the process of forming ship plates, which improves the level of automation in ship manufacturing. The method enables more accurate identification of joint friction during the forming process, enhancing the overall automation level. This not only reduces reliance on manual operation but also improves production efficiency and quality control. The application of automated robotic arms reduces physical labor in manual operations, lowers worker workload and operational risks, and improves the safety of the working environment. The method using a LuGre friction identification model combined with a disturbance observer significantly improves the operational accuracy and efficiency of the robotic arm in the forming process. Experiments show that the single-pass forming rate using a robotic arm for spiral heating reaches approximately 80%, a significant improvement compared to the 55% forming rate of ordinary equipment. Due to the increased forming efficiency and reduced rework (significantly reducing the need for only minor reheating in a few locations), overall production costs are reduced. Furthermore, automated operation reduces reliance on highly skilled workers, further lowering labor costs.

[0085] The present invention discloses a robotic arm joint friction identification system for the ship bending forming process, comprising a storage medium storing a program written using the described robotic arm joint friction identification method for the ship bending forming process. The storage medium includes a hard disk, CD-ROM, optical storage device, magnetic storage device, or a combination thereof. Those skilled in the art will understand that the various features described herein can be implemented by methods, data processing systems, or computer program products. Therefore, these features can be implemented entirely in hardware, entirely in software, or in a combination of hardware and software. Furthermore, the above features can also be implemented as a computer program product stored on one or more computer-readable storage media, which contains computer-readable program code segments or instructions stored in the storage medium. Any usable computer-readable storage medium can be used, including hard disks, CD-ROMs, optical storage devices, magnetic storage devices, and / or combinations thereof.

[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 way of describing the specification is only for 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 detailed descriptions listed above are merely specific illustrations of feasible embodiments of the present invention and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying the friction of a joint of a robot arm for ship bending forming, characterized by, Includes the following steps: Obtain the speed and friction parameters of the robotic arm joints; obtain the actual curvature of the bending plate; A LuGre friction model is established, which derives the joint friction force F based on the robot arm speed and friction state parameters. A disturbance observer is established, and an estimated friction disturbance is obtained by using the disturbance observer and the joint friction force F According to the estimated friction disturbance Determine the friction compensation control strategy.

2. The method of claim 1, wherein The LuGre friction model is established as follows: The friction state parameter z of the ith sampling of the mechanical arm at different speeds is obtained by Euler method i , i ∈ (1 … n), and n is the total number of experiments friction state parameter z of the ith sample i derivative of satisfies the following relationship: where g(v i ) is a static friction function associated with the velocity; v i is the velocity of the robotic arm at the i-th sample. The nonlinear least squares method is used to minimize the sum of the squares of the residuals between the observed data and the model predictions, specifically: where F i is the friction force of the ith sample, σ0is the stiffness coefficient; σ1is the damping coefficient; σ2is the viscous friction coefficient; The stiffness coefficient σ0, damping coefficient σ1, and viscous friction coefficient σ2 were obtained by fitting. Establish LuGre model: where F is the joint friction, v is the robot velocity, and z represents the variable friction state parameter, σ is the derivative of z; σ0 is the stiffness coefficient; σ1 is the damping coefficient; σ2 is the viscous friction coefficient.

3. The method of claim 1, wherein, Establish a disturbance observer, specifically as follows: State estimation, specifically: The observer formula is: wherein, The frictional state variables are estimated by the system matrix, where A, B, and C are the system matrices, L is the observer gain with a value range of (0 to 1), and y is the system torque. The friction state variable of the system matrix estimation is derived using an observer formula Disturbance estimation, according to the output of the observer obtaining an estimated friction disturbance In particular: wherein: is an estimated friction state variable; It is the estimated rate of change of the frictional state variable.

4. The method of claim 1, wherein, According to the estimated friction disturbance The friction compensation control strategy is determined, in particular: Utilizing estimated friction disturbances Computing compensation control input Where K is the compensation gain; The compensation control input Δu is superimposed on the original control input u to obtain a new control input u new = u + Δu; By compensating for friction in real time, the robotic arm can be made to operate precisely during the bending process.

5. The method of claim 4, wherein, By compensating for friction in real time, the precise operation of the robotic arm during the bending process is ensured, specifically: The collected actual curvature y of the bending plate is fused with the displacement, speed and torque data of the mechanical arm to form a feedback control u feedback : wherein u feedback is the feedback control input, Kp, Ki, Kd are control gains, r is the desired curvature, and y is the actual curvature. According to the feedback control u feedback Adjust the movement path and force of the robot arm.

6. A mechanical arm joint friction identification system for ship bending forming, characterized in that, Includes a storage medium; the storage medium stores a program written for the joint friction identification method of a robotic arm for ship bending forming as described in any one of claims 1-5.

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