Tower leg automatic welding machine motion control method and system

Abstract

The application discloses a tower leg automatic welding machine motion control method and system, relates to the tower leg automatic welding machine motion control field, and is used for improving welding quality and comprises the following steps: acquiring geometric information of a to-be-welded path; acquiring real-time deviation information of a welding gun tail end of a mechanical arm and the to-be-welded path; acquiring motion capability limitation parameters of the mechanical arm; determining predicted motion deviation of the welding gun tail end within a preset time period based on the real-time deviation information, the geometric information and the motion capability limitation parameters; calculating a corrected motion track according to the predicted motion deviation and the motion capability limitation parameters of the mechanical arm; the corrected motion track is used for maintaining the stability of the motion of the mechanical arm under the premise of ensuring the accuracy of weld tracking; and the motion instruction of the mechanical arm is generated according to the corrected motion track.

Description

Technical Field

[0001] This invention relates to the field of motion control for automatic tower foot welding machines, and more particularly to a motion control method and system for automatic tower foot welding machines. Background Technology

[0002] In automated welding of large structural components, especially wind turbine tower legs, the weld paths exhibit varied and intricate curvature characteristics due to the increasing complexity of workpiece designs. To address these complex paths and improve production efficiency, existing motion control systems often enhance trajectory fitting accuracy by adjusting interpolation logic. However, this optimization strategy can lead to excessively frequent and abrupt accelerations and decelerations between adjacent path segments when handling continuous small-radius arcs or S-curves. This aggressive motion pattern generates significant heat in the robot arm's servo motors and reducers, resulting in uneven thermal expansion of the robot arm's linkage structure. This uneven thermal expansion causes a continuous deviation between the robot arm's actual geometry and the fixed kinematic model within its control system. Ultimately, this end-effector position uncertainty caused by the robot arm's thermal expansion results in a persistent mismatch between the data captured by the laser vision sensor in real-time and the geometric relationship established by the robot's hand-eye calibration matrix. This prevents the laser weld seam tracking data processing program from accurately determining the true centerline of the weld when attempting to fuse visual information with robot pose information. This persistent information gap prevents the welding torch from accurately and stably welding along the weld seam trajectory, leading to problems such as welding path deviation, uneven weld width, and insufficient or excessive penetration, which seriously affects the final welding quality and yield. Summary of the Invention

[0003] This application discloses a motion control method and system for an automatic welding machine for tower legs, which aims to solve the technical problems caused by the thermal expansion effect due to the aggressive motion mode of the robotic arm when the existing automated welding system handles complex weld paths, resulting in uncertainty in the end position and inaccurate weld tracking and reduced welding quality.

[0004] In a first aspect, this application discloses a motion control method for an automatic tower foot welding machine, comprising the following steps: obtaining geometric information of the path to be welded;

[0005] Obtain real-time deviation information between the welding torch tip of the robotic arm and the path to be welded;

[0006] Obtain the motion capability limitation parameters of the robotic arm;

[0007] Based on real-time deviation information, geometric information, and motion capability limitation parameters, the predicted motion deviation of the welding torch tip within a preset time period is determined.

[0008] Based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm, a corrected motion trajectory is calculated; the corrected motion trajectory is used to maintain the stability of the robotic arm's motion while ensuring the accuracy of weld seam tracking.

[0009] Based on the corrected motion trajectory, motion commands for the robotic arm are generated.

[0010] Optionally, based on the corrected motion trajectory, motion commands for the robotic arm are generated, including:

[0011] Identify the local geometric features of the path to be welded;

[0012] Measure the relative pose between the tip of the welding torch and local geometric features;

[0013] Based on the relative pose, the local relative pose deviation is obtained through processing;

[0014] Based on local relative pose deviations, fine-tuning instructions are generated for the end joints of the robotic arm.

[0015] By integrating and correcting motion trajectories with fine-tuning instructions, motion commands for the robotic arm are generated.

[0016] Optionally, based on local relative pose deviations, fine-tuning commands are generated for the end effector joints of the robotic arm, including:

[0017] Obtain the coupling effect parameters between each drive shaft of the micro actuator corresponding to the end joint, the operating temperature of the micro actuator, and the cumulative operating data of the micro actuator;

[0018] The gain and phase of the drive signal of the micro actuator are adjusted based on the local relative pose deviation, coupling effect parameters, operating temperature, and accumulated operating data.

[0019] Optionally, the gain of the drive signal of the micro actuator can be adjusted, including:

[0020] Acquire the instantaneous motion state information of each drive shaft of the micro actuator;

[0021] Acquire minute vibration signals transmitted by the robotic arm;

[0022] Based on instantaneous motion state information and minute vibration signals, the coupling effect parameters are corrected;

[0023] Obtain the local temperature distribution of key components of the micro actuator;

[0024] Adjust the operating temperature based on the local temperature distribution;

[0025] The gain of the drive signal of the micro actuator is adjusted based on the local relative pose deviation, the corrected coupling effect parameters, the corrected operating temperature, and the accumulated operating data.

[0026] Optionally, the phase of the drive signal of the micro actuator can be adjusted, including:

[0027] Acquire local electromagnetic field intensity information and local sound wave intensity information;

[0028] Based on local electromagnetic field intensity information and local acoustic wave intensity information, the instantaneous damping characteristic changes of key materials of the micro actuator are determined;

[0029] The phase of the drive signal of the micro actuator is adjusted according to the change in instantaneous damping characteristics.

[0030] Optionally, the phase of the drive signal of the micro actuator can be adjusted according to the change in instantaneous damping characteristics, including:

[0031] Obtain the instantaneous strain rate of the key viscoelastic material in the micro-actuator;

[0032] Based on the instantaneous strain rate, determine the influence of the instantaneous damping characteristic change on the phase of the driving signal;

[0033] The phase of the drive signal of the micro actuator is adjusted according to the instantaneous damping characteristic changes and their influence.

[0034] Optionally, based on the instantaneous strain rate, determine the influence of the instantaneous damping characteristic change on the phase of the driving signal, including:

[0035] Obtain historical data on the cumulative strain and current aging level of the key viscoelastic material in the micro-actuator;

[0036] Based on historical data of accumulated strain and the current degree of aging, the nonlinear response relationship between instantaneous strain rate and changes in damping characteristics is corrected;

[0037] Based on the corrected nonlinear response relationship, the influence of the instantaneous damping characteristic change on the phase of the driving signal is determined.

[0038] Optionally, based on historical cumulative strain data and the current degree of aging, the nonlinear response relationship between instantaneous strain rate and damping characteristic changes is corrected, including:

[0039] Obtain strain history data of the key viscoelastic material of the micro actuator in different motion directions;

[0040] To determine the degree of anisotropic degradation of the key viscoelastic material in a micro actuator;

[0041] Based on historical data of cumulative strain, current aging degree, historical strain data, and degree of anisotropic degradation, the nonlinear response relationship between instantaneous strain rate and damping characteristic changes is directionally corrected.

[0042] Optionally, based on the predicted motion deviation and the robotic arm's motion capability limitation parameters, a corrected motion trajectory is calculated, including:

[0043] Based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm, an objective function is generated;

[0044] Solve the objective function to obtain the corrected motion trajectory;

[0045] The objective function satisfies the following relationship:

[0046] J=Σ[w1*E (t+k) 2 +w2*V (t+k) 2 +w3*A (t+k) 2 ];

[0047] Among them, E (t+k) V is the predicted weld deviation at the future time (t+k). (t+k) A is the predicted welding torch speed at the future time (t+k). (t+k) It is the predicted welding torch acceleration at the future time (t+k), and w1, w2, and w3 are weighting coefficients.

[0048] Secondly, this application also discloses a motion control system for an automatic tower leg welding machine, the system comprising:

[0049] The acquisition module is used to acquire the geometric information of the path to be welded;

[0050] The acquisition module is also used to acquire real-time deviation information between the welding gun tip of the robotic arm and the path to be welded;

[0051] The acquisition module is also used to acquire motion capability limitation parameters of the robotic arm;

[0052] The trajectory calculation module is used to determine the predicted motion deviation of the welding torch tip within a preset time period based on real-time deviation information, geometric information, and motion capability limitation parameters.

[0053] The trajectory calculation module is also used to calculate and correct the motion trajectory based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm; the corrected motion trajectory is used to maintain the stability of the robotic arm's motion while ensuring the accuracy of weld seam tracking.

[0054] The motion command generation module is used to generate motion commands for the robotic arm based on the corrected motion trajectory.

[0055] Beneficial effects

[0056] This application discloses a motion control method for an automatic welding machine for tower legs. By acquiring the geometric information of the path to be welded, the real-time deviation information of the welding torch end of the robotic arm, and the motion capability limitation parameters of the robotic arm, the current state and future motion trend of the robotic arm can be comprehensively evaluated. Based on this, this application determines the predicted motion deviation of the welding torch end within a preset time period based on this information, and calculates a corrected motion trajectory according to the predicted motion deviation and the motion capability limitation parameters of the robotic arm. This corrected motion trajectory can effectively maintain the stability of the robotic arm's motion while ensuring the accuracy of weld seam tracking, avoiding the problem of frequent and violent acceleration and deceleration of the robotic arm caused by pursuing trajectory fitting accuracy in the prior art. In this way, this application effectively solves the problems of overheating of the robotic arm servo motor and reducer, uneven thermal expansion of the linkage structure, and the resulting deviation between the actual geometry of the robotic arm and the kinematic model. Finally, this application can eliminate the phenomenon of mismatch between laser vision sensor data and robot hand-eye calibration matrix, ensuring that the welding torch accurately and stably welds along the weld seam trajectory, thereby significantly improving welding quality and yield, and overcoming the shortcomings of the prior art such as welding path deviation, uneven weld seam width, and insufficient or excessive penetration depth. Attached Figure Description

[0057] Figure 1 This is a schematic flowchart of a motion control method for an automatic tower foot welding machine provided in an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of another motion control method for an automatic tower foot welding machine provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the motion control system of an automatic tower foot welding machine provided in an embodiment of the present invention. Detailed Implementation

[0060] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0061] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0062] First, let's introduce the terminology used in this application.

[0063] "Automatic tower base welding machine" usually refers to an automated welding equipment equipped with a robotic arm and welding gun, which is mainly used for welding large structural components such as wind turbine tower bases.

[0064] "Robotic arm" refers to the robotic arm in the welding machine that can perform multi-degree-of-freedom movements, and its end is usually equipped with a "welding torch".

[0065] The "tip of the welding torch" is the part of the welding torch that contacts the workpiece, and its movement accuracy directly affects the welding quality.

[0066] "Welding path" refers to the pre-planned weld trajectory on the workpiece, and its "geometric information" includes the shape, size, curvature, etc. of the path.

[0067] "Real-time deviation information" refers to the actual positional deviation between the tip of the welding torch and the path to be welded during the welding process.

[0068] "Motion capability limiting parameters" refer to the physical limitations that the robotic arm experiences during movement, such as maximum speed, maximum acceleration, and joint angle limitations. These parameters are crucial for ensuring the safe and stable operation of the robotic arm.

[0069] "Preset time period" refers to the future time window considered when predicting motion deviation. "Predicted motion deviation" refers to the possible motion deviation of the welding torch tip relative to the path to be welded within the preset time period.

[0070] The "corrected trajectory" is an optimized trajectory calculated based on predicted motion deviations and motion capability limitation parameters, designed to correct deviations and maintain motion stability.

[0071] "Motion commands" are specific instructions that control the movement of each joint of the robotic arm, such as joint angle, speed, or torque commands.

[0072] The following specific embodiments will provide a detailed description and explanation of the motion control method for an automatic tower foot welding machine provided in this application.

[0073] Reference Figure 1 This invention provides a motion control method for an automatic tower leg welding machine, comprising the following steps:

[0074] S1, obtain the geometric information of the path to be welded.

[0075] Specifically, pre-designed weld path data can be directly imported through CAD / CAM software. This data typically includes detailed geometric features such as the path's three-dimensional coordinates, normal vector, and curvature.

[0076] Another approach is to use a vision sensor or laser scanner to scan the actual workpiece, generating point cloud data, and then extract the geometric information of the welding path using point cloud processing algorithms. For example, an image processing-based edge detection algorithm can be used to identify weld edges and then fit the weld path.

[0077] S2. Obtain real-time deviation information between the welding torch tip of the robotic arm and the path to be welded.

[0078] Specifically, a laser vision sensor can be used to scan the weld area in real time to obtain the actual position and shape of the weld, and compare it with the preset welding path to calculate the real-time deviation between the welding torch tip and the welding path. Alternatively, a force / torque sensor installed at the tip of the welding torch can sense the contact force between the torch and the workpiece, indirectly inferring the deviation information. For example, when the welding torch deviates from the weld, the contact force may change; by analyzing these mechanical signals, the deviation can be estimated.

[0079] S3. Obtain the motion capability limitation parameters of the robotic arm.

[0080] Among these, motion capability limitation parameters are inherent physical characteristics of the robotic arm, typically provided by the manufacturer at the time of manufacture or obtained through experimental calibration. For example, the maximum rotational speed, maximum acceleration, maximum torque, and range of motion (angle limitations) of each joint can be obtained. These parameters are crucial for ensuring the robotic arm operates within safe limits and avoiding overload or collisions. For instance, during high-speed welding, it is necessary to ensure that the robotic arm's speed and acceleration do not exceed their limits to prevent vibration or instability.

[0081] S4. Based on real-time deviation information, geometric information, and motion capability limitation parameters, determine the predicted motion deviation of the welding torch tip within a preset time period.

[0082] For example, state estimation algorithms such as Kalman filters or extended Kalman filters can be used, combined with real-time deviation information and the kinematic model of the robotic arm, to predict the future motion state of the welding torch tip. Simultaneously, geometric information of the path to be welded, such as the curvature variation trend of the path, can be used to help predict potential future deviations of the welding torch tip. For instance, if the curvature of the path to be welded changes drastically within a certain time period in the future, it can be predicted that the welding torch tip is more likely to deviate significantly during that time period.

[0083] S5. Calculate the corrected motion trajectory based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm.

[0084] Among them, the motion trajectory correction is used to maintain the stability of the robotic arm's movement while ensuring the accuracy of weld seam tracking.

[0085] Specifically, an objective function can be generated based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm; the corrected motion trajectory can be obtained by solving the objective function.

[0086] The objective function satisfies the following relationship:

[0087] J=Σ[w1*E (t+k) 2 +w2*V (t+k) 2 +w3*A (t+k) 2 ];

[0088] Among them, E (t+k) V is the predicted weld deviation at the future time (t+k). (t+k) A is the predicted welding torch speed at the future time (t+k). (t+k) It is the predicted welding torch acceleration at the future time (t+k), and w1, w2, and w3 are weighting coefficients.

[0089] Solving the objective function to obtain the corrected motion trajectory refers to using optimization algorithms to find the motion trajectory that minimizes the objective function J, while satisfying the limitations of the robotic arm's motion capabilities (such as joint angle, velocity, and acceleration limits). This process can employ various optimization methods, such as quadratic programming and model predictive control (MPC). By solving this optimization problem, a series of optimal welding torch tip positions, velocities, and acceleration sequences can be obtained over a predetermined future time period; these sequences collectively constitute the corrected motion trajectory.

[0090] S6. Generate motion commands for the robotic arm based on the corrected motion trajectory.

[0091] Specifically, inverse kinematics algorithms can be used to convert the welding torch end-effector pose (position and orientation) in the corrected motion trajectory into angle or angular velocity commands for each joint of the robotic arm. These commands are then sent to the robotic arm's servo drive system, driving the robotic arm to move according to the corrected motion trajectory. For example, if the corrected motion trajectory requires the welding torch end-effector to reach a specific position and orientation at a certain moment, the inverse kinematics algorithm will calculate the angles that each joint of the robotic arm should rotate to at that time and send these angle commands to the servo motors.

[0092] The motion control method for the automatic welding machine at the tower foot of this application effectively predicts the future movement deviation of the welding torch end by comprehensively considering the geometric information of the path to be welded, the real-time deviation information of the welding torch tip, and the motion capability limitation parameters of the robotic arm. Based on this, by calculating and correcting the motion trajectory, it not only ensures the accuracy of weld seam tracking but also maintains the stability of the robotic arm's movement, avoiding the problems of frequent acceleration / deceleration and thermal expansion of the robotic arm caused by the pursuit of high precision in traditional methods.

[0093] Compared with existing technologies, the core innovation of this application lies in the introduction of the determination of the "predicted motion deviation" of the welding torch tip, and the calculation of the "corrected motion trajectory" based on this predicted deviation and the "motion capability limitation parameters" of the robotic arm. Traditional methods often focus on real-time feedback control, that is, making immediate adjustments based on the current deviation information. This can easily lead to unstable robotic arm movement in highly dynamic or complex path welding. For example, when the curvature of the weld path changes drastically, traditional real-time feedback control may cause the robotic arm to frequently accelerate and decelerate rapidly, thereby causing thermal expansion of the robotic arm and uncertainty in the end-effector position.

[0094] This application enables the control system to plan ahead rather than react passively by predicting future motion deviations. This proactive control strategy, combined with consideration of the robot arm's motion capability limiting parameters, allows the calculated corrective motion trajectory to actively optimize the robot arm's kinematic performance while correcting deviations, such as limiting abrupt changes in velocity and acceleration, thereby effectively reducing vibration and heat generation. For example, when a significant deviation of the welding torch tip is predicted at a future moment, the method in this application adjusts the robot arm's motion trajectory in advance for a smooth transition, rather than drastically correcting it after the deviation occurs. This proactive stability maintenance mechanism significantly improves the stability of the welding process and the quality of the weld, effectively solving the problem of end-effector position uncertainty caused by the thermal expansion of the robot arm in existing technologies, thereby improving welding quality and yield.

[0095] In some of the embodiments described above in this application, motion commands for the robotic arm are generated based on the modified motion trajectory. However, in the actual welding process, due to the possible slight changes or measurement errors in the local geometric features of the path to be welded, relying solely on the pre-calculated modified motion trajectory may not be able to fully guarantee the ultimate tracking accuracy of the welding torch tip on the weld seam. Especially in scenarios requiring high-precision local adjustments, there may still be slight tracking deviations.

[0096] like Figure 2 As shown, in order to generate motion commands for the robotic arm based on the corrected motion trajectory, this application may further include the following steps:

[0097] S101. Identify the local geometric features of the path to be welded.

[0098] Specifically, identifying the local geometric features of the welding path refers to acquiring images or point cloud data of the area to be welded in real time using visual sensors, laser scanners, or other non-contact sensors, and then automatically detecting and extracting representative local geometric features such as weld edges, corners, and curve segments using image processing or pattern recognition algorithms. The purpose is to provide high-precision local reference points or lines for subsequent fine-tuning.

[0099] S102. Measure the relative pose between the tip of the welding torch and local geometric features.

[0100] Measuring the relative pose between the welding torch tip and local geometric features can be understood as using real-time data acquired by the aforementioned sensors to calculate the position and orientation of the welding torch tip (e.g., the wire outlet or nozzle center) relative to the identified local geometric features in three-dimensional space. This typically involves coordinate transformation and geometric matching algorithms, the purpose of which is to quantify the real-time deviation between the welding torch tip and the ideal weld position.

[0101] S103. Based on the relative pose, process to obtain the local relative pose deviation.

[0102] Specifically, the measured relative pose data can be preprocessed by filtering, smoothing, and coordinate system transformation to eliminate noise and convert it into deviation quantities that are easily understood by the control system, such as position and attitude deviations in the weld seam normal and tangential directions. The purpose is to provide accurate and reliable input for generating fine-tuning commands.

[0103] S104. Based on the local relative pose deviation, generate fine-tuning instructions for the end joints of the robotic arm.

[0104] Among these, generating fine-tuning commands for the end effector joints of the robotic arm based on local relative pose deviations refers to calculating the minute angle or displacement adjustments required for the end effector joints (such as the wrist joint) based on the calculated local relative pose deviations using inverse kinematics algorithms or preset control strategies. These fine-tuning commands aim to locally and quickly correct deviations at the welding torch tip, with the goal of achieving precise tracking of the weld seam.

[0105] S105: Integrate and correct the motion trajectory with fine-tuning instructions to generate motion instructions for the robotic arm.

[0106] Specifically, the corrected motion trajectory obtained from global planning (providing smooth and accurate movement of the robotic arm as a whole) can be superimposed or weighted and fused with the real-time generated end-joint fine-tuning commands (providing local and precise deviation compensation). For example, the fine-tuning commands can serve as real-time disturbance compensation for the corrected motion trajectory, or the two can be combined through a hierarchical control structure. The goal is to generate the final robotic arm motion commands that ensure both overall stability and high-precision local tracking.

[0107] This application's solution, by introducing real-time identification of local geometric features and measurement of the relative pose between the welding torch tip and these features, can dynamically sense and quantify minute changes in the weld and real-time deviations of the welding torch. Based on these local relative pose deviations, the system can generate fine-tuning commands for the robotic arm's end-effector joints, thereby providing immediate and precise local compensation to the welding torch tip while correcting the motion trajectory. Ultimately, by integrating the global corrected motion trajectory with the local fine-tuning commands, the robotic arm's motion commands not only possess overall stability and accuracy but also achieve an ultimate ability to track weld details.

[0108] The above technical solution significantly improves the weld seam tracking accuracy of the automatic tower foot welding machine on complex welding paths or paths with minor errors. This solution, through a real-time local feedback mechanism, effectively compensates for potential local tracking deficiencies when relying solely on preset trajectories. This allows the welding torch tip to more closely conform to the actual weld seam, thereby improving weld quality, reducing welding defects caused by local deviations, and further enhancing the system's adaptability and robustness to changes in actual working conditions.

[0109] In some preferred embodiments, it is assumed that the welding path has slight undulations or local bends during the tower leg welding process. First, local edge features of the weld are identified in real time using a vision sensor mounted near the welding torch. Next, the system measures the real-time position and orientation deviation of the welding torch tip relative to these local edge features. For example, if the welding torch tip deviates by 0.5 mm in the direction perpendicular to the weld and has a 2-degree tilt, the control system immediately calculates the small adjustments required by the robotic arm wrist joints based on these deviations; for example, one joint needs to rotate 0.1 degrees clockwise, and another joint needs to rotate 0.05 degrees counterclockwise. These fine-tuning instructions are then integrated into a corrected motion trajectory generated by a global planning module, jointly driving the robotic arm movement. Thus, even with slight changes in the weld path, the welding torch tip can always be precisely aligned with the weld, ensuring welding quality.

[0110] In some embodiments described above, this application proposes a scheme for generating fine-tuning commands for the end effector joints of a robotic arm based on local relative pose deviations. However, in practical applications, the performance of the micro-actuator at the end effector joint of the robotic arm may be affected by various complex factors when responding to these fine-tuning commands, such as the coupling effect between the drive shafts inside the micro-actuator, operating temperature fluctuations caused by changes in ambient temperature, and cumulative performance degradation caused by long-term operation. If these factors are not effectively compensated, they may lead to a deviation between the actual movement of the micro-actuator and the expected fine-tuning command, thereby affecting the final accuracy and smoothness of weld seam tracking. To address this, this application further proposes a more refined fine-tuning command generation method, aiming to significantly improve the accuracy and robustness of fine-tuning control by dynamically adjusting the drive signal of the micro-actuator to adapt to changes in its internal state and external environment.

[0111] In response, based on local relative pose deviations, fine-tuning commands are generated for the end effector joints of the robotic arm, including:

[0112] S201. Obtain the coupling effect parameters between each drive shaft of the micro actuator corresponding to the end joint, the operating temperature of the micro actuator, and the cumulative operating data of the micro actuator.

[0113] Specifically, coupling effect parameters refer to the mutual influence between different drive shafts within a micro-actuator. For example, when one drive shaft moves, it may generate a small force or displacement on other drive shafts, leading to unexpected motion. Obtaining these parameters helps to build a more accurate actuator motion model, thereby enabling pre-compensation when generating drive signals. These parameters can be obtained through factory calibration, online identification, or simulation based on physical models. Operating temperature refers to the actual temperature at which the micro-actuator operates, affecting the physical properties of the internal materials, such as the elastic modulus, damping coefficient, and the performance of electronic components. Temperature variations can lead to changes in actuator response speed, accuracy, and force output. By monitoring the operating temperature in real time, temperature compensation can be performed on the drive signal, ensuring consistent actuator performance under different thermal environments. Operating temperature is typically measured in real time using a temperature sensor integrated into or near the actuator body. Furthermore, cumulative operating data includes the micro-actuator's operating history since it was put into use, such as total operating time, total stroke, maximum load cycles, and fault records. This data can reflect the actuator's wear, aging degree, and potential performance degradation trends. By analyzing accumulated operating data, future performance changes of the actuator can be predicted, and the gain and phase of the drive signal can be adjusted in advance to extend its service life and maintain stable performance.

[0114] S202. Adjust the gain and phase of the drive signal of the micro actuator based on the local relative pose deviation, coupling effect parameters, operating temperature and accumulated operating data.

[0115] In practical applications, adjusting the gain of the drive signal mainly affects its amplitude, thereby controlling the output force or displacement of the actuator; phase adjustment affects the timing of the drive signal and can be used to compensate for the dynamic response delay or lead of the actuator. By comprehensively utilizing local relative pose deviation, coupling effect parameters, operating temperature, and accumulated operating data, an adaptive control strategy can be constructed to dynamically adjust the gain and phase of the drive signal, enabling the micro-actuator to respond more accurately and stably to fine-tuning commands, thus achieving precise control of the welding torch tip position.

[0116] This application's solution constructs a more comprehensive actuator state awareness model by introducing coupling effect parameters between the drive axes of the micro-actuator, operating temperature, and accumulated operating data. Specifically, when the system needs to generate fine-tuning commands for the end effector joints of the robotic arm, it first acquires the current local relative pose deviation, which represents the instantaneous error between the welding torch tip and the path to be welded. Simultaneously, the system acquires the coupling effect parameters of the micro-actuator in real time to quantify the mutual interference between internal drive axes; acquires the operating temperature to assess the impact of environmental thermal effects on its performance; and references accumulated operating data to consider performance degradation caused by long-term wear and aging. Based on this comprehensive information, the system can more accurately predict the response characteristics of the micro-actuator to the drive signal in the current state. Subsequently, through intelligent algorithms, the gain and phase of the drive signal are finely adjusted. For example, if a high coupling effect is detected, the system may introduce reverse compensation into the drive signal to counteract undesirable linkage; if the operating temperature is too high, the gain may be appropriately adjusted to prevent the actuator from overheating or overshooting; if accumulated operating data shows a certain degree of performance degradation in the actuator, the phase may be pre-adjusted to compensate for response delay. Therefore, this solution can ensure that the micro-actuator can perform fine-tuning actions with high precision and high stability under various working conditions, thereby achieving accurate tracking of the weld path by the welding torch tip.

[0117] In some preferred embodiments, it is assumed that the automated welding machine for tower legs is welding a complex curved weld. At this time, a vision sensor detects in real time a minute local relative pose deviation between the welding torch tip and the path to be welded. To correct this deviation, the system needs to send fine-tuning commands to the micro-actuators at the end joints of the robotic arm. Specifically, the system first obtains the coupling effect parameters between the drive axes of the current micro-actuator using a built-in force / torque sensor or a pre-established dynamic model; for example, the minute displacement that may occur on the Y-axis when the X-axis is driven. Simultaneously, the operating temperature is monitored in real time using thermocouple sensors mounted on the actuator body; for example, the current temperature is 50°C. Furthermore, the control system queries the historical operating database of the micro-actuator to obtain data such as its cumulative operating time and total stroke to assess its current aging level. Based on this information, the control algorithm performs a comprehensive analysis: if the coupling effect parameters show that the X-axis motion has a significant impact on the Y-axis, then when generating the X-axis drive signal, it will simultaneously perform reverse phase compensation on the Y-axis drive signal; if the operating temperature is high, to prevent the actuator from overheating or overshooting, the gain of the drive signal may be appropriately reduced; if the cumulative operating data shows that the actuator is nearing its design life, the phase of the drive signal may be fine-tuned to compensate for the response delay caused by material fatigue. In this way, the gain and phase of the drive signal are dynamically adjusted, enabling the micro-actuator to perform fine-tuning actions with extremely high precision and stability, ensuring that the tip of the welding torch moves precisely along the path to be welded, thereby obtaining a high-quality weld.

[0118] The above-mentioned adjustment of the gain of the drive signal of the micro actuator includes:

[0119] S301. Obtain the instantaneous motion state information of each drive shaft of the micro actuator.

[0120] Specifically, acquiring the instantaneous motion state information of each drive axis of the micro-actuator refers to real-time monitoring and collection of kinematic or dynamic data such as position, velocity, and acceleration of each drive axis of the micro-actuator at the current moment. This information reflects the instantaneous response characteristics of the micro-actuator when executing fine-tuning commands.

[0121] S302. Acquire the minute vibration signals transmitted by the robotic arm.

[0122] Specifically, minute vibrations generated by the robot arm's movement, external disturbances, or the welding process itself can be detected and collected using sensors (such as accelerometers or strain gauges) mounted on the robotic arm or micro-actuator. These vibration signals can adversely affect the motion accuracy and stability of the micro-actuator.

[0123] S303. Based on instantaneous motion state information and minute vibration signals, correct the coupling effect parameters.

[0124] This step refers to using this real-time data to dynamically adjust the parameters describing the degree of interaction between the drive axes of the micro-actuator. For example, when the instantaneous motion state of a drive axis changes drastically or significant micro-vibrations are detected, its coupling effect on other drive axes may change accordingly. By correcting these parameters in real time, the actual working state of the micro-actuator can be more accurately reflected.

[0125] S304. Obtain the local temperature distribution of key components of the micro actuator.

[0126] Specifically, thermocouples, infrared sensors, and other equipment can be used to measure the real-time temperature of key components inside the micro actuator (such as motor windings, bearings, and drive circuits) at different locations. These local temperature distributions can more precisely reflect the thermal state of the micro actuator, rather than just an average operating temperature.

[0127] S305. Adjust the operating temperature according to the local temperature distribution.

[0128] Specifically, based on this detailed temperature data, the overall operating temperature parameters used for gain adjustment can be more accurately calibrated. For example, the operating temperature can be updated based on the highest temperature point or the temperature of critical components to more accurately reflect the impact of temperature on actuator performance.

[0129] S306. Adjust the gain of the drive signal of the micro actuator based on the local relative pose deviation, the corrected coupling effect parameters, the corrected operating temperature, and the accumulated operating data.

[0130] This application's solution addresses the problem of insufficient gain adjustment accuracy caused by the static nature of parameters in traditional methods by introducing a real-time dynamic correction mechanism for the internal parameters of the micro-actuator. Specifically, when the robotic arm is performing welding operations, its instantaneous motion state and transmitted minute vibration signals directly reflect the dynamic load and external disturbances experienced by the micro-actuator. By acquiring this information and correcting the coupling effect parameters accordingly, it is ensured that the gain adjustment model can accurately capture the real-time changes in the interaction between the drive shafts, avoiding control errors caused by inaccurate coupling effect parameters. Simultaneously, the local temperature distribution of key components of the micro-actuator provides more detailed thermal state information than a single operating temperature. Since temperature significantly affects material properties, electronic component performance, and mechanical clearances, correcting the operating temperature can more accurately compensate for the impact of temperature changes on the micro-actuator's performance, thereby ensuring the consistency and accuracy of the drive signal gain under different thermal environments. It is precisely because of the real-time correction of these dynamically changing parameters that subsequent gain adjustments based on these corrected parameters can better match the actual working conditions of the micro-actuator, thus improving control precision and response speed.

[0131] In some preferred embodiments, a specific example is given below. Assume that during automated welding of the tower legs, a robotic arm is tracking a welding path with complex curvature at high speed. At this time, the micro-actuators need to be frequently fine-tuned to maintain precise alignment between the welding torch tip and the path. The system continuously acquires instantaneous motion state information of each drive shaft of the micro-actuators, such as position, velocity, and acceleration data obtained through encoders and accelerometers. Simultaneously, high-sensitivity sensors mounted on the robotic arm monitor and acquire minute vibration signals generated by the robotic arm's own movement or the welding arc in real time. This instantaneous motion state information and minute vibration signals are input into the control system to dynamically correct the coupling effect parameters between the drive shafts of the micro-actuators. For example, when resonance or abnormal vibration is detected in a drive shaft at a specific frequency, the corresponding coupling effect parameters are adjusted in real time to reduce its impact on other drive shafts. Meanwhile, key components inside the micro-actuators, such as the drive motor and precision gear set, are equipped with multiple miniature temperature sensors to obtain their local temperature distribution. For example, the temperature of the motor windings may rise due to prolonged high-load operation, while the temperature of the bearings may change due to friction. The system adjusts the overall operating temperature parameters of the micro-actuator based on these local temperature distributions, such as key hotspot temperatures or weighted average temperatures. Finally, combining these corrected coupling effect parameters and operating temperature with current local relative pose deviations and historical operating data, the control system precisely calculates and adjusts the gain of the micro-actuator drive signal. For example, in high-speed motion and high vibration conditions, the gain may be dynamically adjusted to improve response speed and stability; while as the temperature rises, the gain may be fine-tuned to compensate for the effects of material expansion or performance degradation. In this way, even in dynamic and complex environments, the welding torch tip maintains high-precision tracking, thereby achieving high-quality automated welding.

[0132] In some embodiments described above, this application proposes adjusting the gain and phase of the drive signal of the micro-actuator based on local relative pose deviation, coupling effect parameters, operating temperature, and accumulated operating data. However, in actual welding processes, the physical properties of key materials inside the micro-actuator, especially its damping characteristics, may dynamically change due to instantaneous operating conditions (such as vibration, temperature fluctuations, electromagnetic interference, etc.). If phase adjustment relies solely on preset or slowly updated parameters, it may be impossible to compensate for these instantaneous changes in a timely and accurate manner, thereby affecting the real-time performance and stability of the micro-actuator's response and ultimately reducing the accuracy of weld seam tracking.

[0133] In response, this application further proposes adjusting the phase of the drive signal of the aforementioned micro actuator, including:

[0134] S401. Obtain local electromagnetic field intensity information and local sound wave intensity information.

[0135] Specifically, local electromagnetic field strength information refers to the electromagnetic field strength data collected in real time within the working area of ​​the micro-actuator by electromagnetic sensors (such as Hall sensors, coil sensors, etc.). This data can reflect the electromagnetic effects generated by internal components such as coils and magnetic materials under the action of driving current, as well as the influence of the external electromagnetic environment on the actuator. Local acoustic wave intensity information refers to the acoustic wave signal intensity data collected near the micro-actuator by acoustic sensors (such as miniature microphones, piezoelectric sensors, etc.). These acoustic wave signals may originate from the vibration, friction, or microscopic deformation of internal mechanical components of the actuator, and can indirectly reflect the mechanical state of the material and energy dissipation.

[0136] S402. Based on the local electromagnetic field strength information and the local acoustic wave intensity information, determine the instantaneous damping characteristic changes of the key materials of the micro-actuator.

[0137] The instantaneous damping characteristic change of key materials in micro-actuators can be understood as the change in the energy dissipation capacity or vibration suppression capacity of the materials constituting the core components of the micro-actuator (such as piezoelectric ceramics, magnetostrictive materials, viscoelastic damping layers, etc.) relative to their nominal state or the previous moment at a specific instant. This change directly affects the response speed and stability of the actuator. By analyzing local electromagnetic field intensity information and local acoustic wave intensity information, the instantaneous physical state of these key materials, such as temperature, stress, and microstructural changes, can be inferred, thereby indirectly or directly determining the instantaneous change in their damping characteristics. For example, abnormal fluctuations in electromagnetic field intensity may indicate changes in the distribution of internal current or magnetic field, affecting the response of magnetostrictive materials; an increase in acoustic wave intensity may indicate increased internal friction or vibration, reflecting changes in the damping performance of viscoelastic materials.

[0138] S403. Adjust the phase of the drive signal of the micro actuator according to the change of instantaneous damping characteristics.

[0139] In practical applications, adjusting the phase of the drive signal of a micro-actuator based on changes in instantaneous damping characteristics refers to real-time correction of the phase angle within the waveform of the drive signal. The purpose is to compensate for lag or lead in the actuator response caused by changes in material damping characteristics, ensuring optimal synchronization between the drive signal and the actual mechanical response of the actuator, thereby maintaining or restoring the high precision and rapid response capability of the micro-actuator. For example, when increased damping characteristics are detected, it may be necessary to appropriately lead the phase of the drive signal to compensate for the response lag; when damping characteristics weaken, it may be necessary to lag the phase to avoid overshoot.

[0140] In some embodiments, the instantaneous strain rate of the key viscoelastic material of the micro actuator can be obtained; based on the instantaneous strain rate, the influence of the instantaneous damping characteristic change on the phase of the drive signal can be determined; and based on the instantaneous damping characteristic change and its influence, the phase of the drive signal of the micro actuator can be adjusted.

[0141] Specifically, obtaining the instantaneous strain rate of the key viscoelastic material in a micro-actuator refers to obtaining the deformation rate of the key viscoelastic material in the micro-actuator at the current moment through real-time monitoring by sensors or calculation based on the kinematic model and material constitutive relations of the actuator. The damping characteristics of viscoelastic materials are strain rate dependent; therefore, accurately obtaining the instantaneous strain rate is crucial for a deeper understanding and quantification of its damping behavior.

[0142] Specifically, determining the influence of instantaneous damping characteristic changes on the phase of the driving signal based on the instantaneous strain rate can be understood as establishing a quantitative mapping relationship between the degree of influence of instantaneous strain rate and damping characteristic changes on the phase of the driving signal. This relationship can be obtained through experimental calibration, theoretical modeling, or by learning from historical data using machine learning algorithms. The aim is to accurately correlate the microscopic dynamic response of the material with the macroscopic phase adjustment requirements of the driving signal, thereby achieving more refined control.

[0143] In practical applications, the phase of the micro-actuator's drive signal is adjusted based on the instantaneous damping characteristic changes and their influence. Specifically, this involves fusing the instantaneous damping characteristic changes determined by local electromagnetic field strength and local acoustic wave intensity information with the influence calculated based on the instantaneous strain rate, using both as the basis for adjusting the drive signal phase. For example, when the instantaneous damping characteristic changes significantly, and the instantaneous strain rate indicates a significant impact of this change on the phase, the system can perform larger-amplitude or more precise phase compensation to ensure control accuracy.

[0144] Thus, by introducing the instantaneous strain rate of the key viscoelastic material in the micro-actuator and determining the impact of instantaneous damping characteristic changes on the phase of the drive signal, a more comprehensive and accurate understanding and quantification of the dynamic response of the internal materials of the micro-actuator can be achieved. Traditional methods that determine instantaneous damping characteristic changes based solely on local electromagnetic field intensity and local acoustic wave intensity information may not adequately consider the nonlinear damping behavior of viscoelastic materials at different deformation rates. By obtaining the instantaneous strain rate, the viscous dissipation mechanism of the material can be revealed more deeply, thereby accurately assessing its actual impact on the phase of the drive signal. It is precisely this refined consideration of the micromechanical behavior of the material that allows the phase adjustment of the drive signal to better adapt to the dynamic changes of the micro-actuator under complex operating conditions, avoiding control deviations caused by insufficient understanding of damping characteristics.

[0145] This application's solution captures the dynamic physical state changes of key materials within a micro-actuator under instantaneous operating conditions by acquiring real-time information on local electromagnetic field strength and local acoustic wave intensity in the micro-actuator's working environment. This information serves as an indirect or direct characterization of the material's instantaneous damping characteristics, enabling the system to overcome the limitations of relying solely on preset or slowly updated parameters. Specifically, electromagnetic field strength information reflects the real-time interaction between the drive current and the magnetic material, while acoustic wave intensity information reveals the microscopic vibrations and energy dissipation within the material. Through comprehensive analysis of this real-time data, the instantaneous damping characteristics changes of the micro-actuator's key materials can be accurately determined. Consequently, the phase of the drive signal can be dynamically and adaptively adjusted to precisely compensate for actuator response lag or lead caused by damping changes, ensuring optimal synchronization between the drive signal and the actuator's mechanical response. This real-time, dynamic phase adjustment mechanism effectively addresses the shortcomings of traditional methods in dealing with instantaneous material characteristic changes, significantly improving the response accuracy and stability of the micro-actuator.

[0146] Through the above technical solution, this application enables more precise and real-time control of the phase of the micro-actuator drive signal. By introducing local electromagnetic field strength information and local acoustic wave intensity information to dynamically evaluate the instantaneous damping characteristic changes of key materials, the system can promptly detect and compensate for response deviations caused by changes in material properties. This allows the micro-actuator to maintain excellent tracking accuracy and motion stability even when facing complex and variable working environments and high dynamic response requirements, effectively avoiding vibration, overshoot, or response hysteresis caused by phase asynchrony, thereby significantly improving the overall performance and welding quality of the automatic tower foot welding machine in complex weld seam tracking tasks.

[0147] In some preferred embodiments, a specific example is given below. Assume that during automated welding of the tower legs, a micro-actuator is responsible for high-frequency fine-tuning of the welding torch tip to precisely track the weld seam. During prolonged continuous operation or a sudden increase in the welding environment temperature, the piezoelectric ceramic material or viscoelastic damping layer inside the micro-actuator may experience changes in its instantaneous damping characteristics due to thermal effects or fatigue.

[0148] Specifically, the system monitors local electromagnetic field strength in real time using a miniature Hall sensor integrated near the micro-actuator, while simultaneously acquiring local acoustic wave intensity information via a miniature piezoelectric accelerometer or microphone. When a specific pattern of fluctuation in electromagnetic field strength (e.g., changes in harmonic components related to the drive frequency) or acoustic wave intensity (e.g., increased noise energy in a specific frequency band) exceeds a preset threshold, the control system interprets this as a signal of instantaneous damping characteristics of the micro-actuator's critical materials. For example, an increase in acoustic wave intensity might indicate increased internal friction or energy dissipation, implying enhanced damping characteristics.

[0149] Based on this, the control system maps the real-time acquired electromagnetic field and acoustic wave information to specific instantaneous changes in damping characteristics according to a pre-established physical model or machine learning model. For example, if the model predicts an X% increase in damping characteristics, the system calculates that the phase of the drive signal needs to be adjusted Y degrees in advance. Subsequently, the drive signal generator of the micro-actuator adjusts the phase of its output signal according to this Y degree. In this way, even when the material damping characteristics change instantaneously, the drive signal can maintain optimal synchronization with the actual mechanical response of the micro-actuator, ensuring that the welding torch tip can continuously and accurately track the path to be welded, thereby guaranteeing weld quality.

[0150] This application further proposes the following steps for determining the influence of the instantaneous damping characteristic change on the phase of the driving signal based on the instantaneous strain rate:

[0151] S501. Obtain historical data on the cumulative strain of the key viscoelastic material of the micro-actuator and its current aging status.

[0152] Specifically, the cumulative strain history data of the key viscoelastic material in a micro-actuator refers to the record of all strain loading and unloading processes the material has undergone since it was put into use, including information such as strain amplitude, frequency, and duration. This data can reflect the cumulative fatigue damage and plastic deformation of the material. The current aging degree refers to the degree of performance degradation of the material at the current point in time due to environmental factors (such as temperature, humidity, and chemical corrosion) and mechanical loads (such as creep and stress relaxation). It can be quantitatively assessed through the material's physical or chemical testing indicators (such as hardness, modulus, fracture toughness, and molecular chain breakage rate).

[0153] S502. Based on historical data of accumulated strain and the current degree of aging, correct the nonlinear response relationship between instantaneous strain rate and changes in damping characteristics.

[0154] The correction of the nonlinear response relationship between instantaneous strain rate and damping characteristic changes refers to introducing historical cumulative strain data and current aging level as correction factors into the traditional instantaneous strain rate-damping characteristic relationship model to more accurately describe the true damping behavior of materials under different service conditions. For example, this correction can be achieved by establishing a multivariate regression model, a neural network model, or a constitutive model based on physical mechanisms, so that the nonlinear response relationship can dynamically adapt to changes in material properties.

[0155] S503. Based on the corrected nonlinear response relationship, determine the influence of the instantaneous damping characteristic change on the phase of the driving signal.

[0156] A saddle point is a point where the objective function value increases in some directions and decreases in others. Its characteristic is that the gradient is close to zero, but the Hessian matrix has positive and negative eigenvalues. A flat region, on the other hand, refers to a region where the objective function value changes very little over a large range, and the gradient is also close to zero. By analyzing curvature and gradient, these two local states can be accurately distinguished.

[0157] This application's solution, by incorporating historical cumulative strain data and current aging status of the key viscoelastic material in the micro-actuator, enables a more comprehensive assessment of the material's actual condition. Because the damping characteristics of viscoelastic materials change nonlinearly with cumulative strain and aging, traditional models based solely on instantaneous strain rate struggle to accurately capture these long-term effects. By acquiring this historical and aging data and using it to correct the nonlinear response relationship between instantaneous strain rate and changes in damping characteristics, the determined influence quantities can more closely approximate the material's true response. This correction mechanism ensures that even with material performance degradation, adjustments to the drive signal phase can still accurately compensate for changes in damping characteristics, thereby maintaining the high-precision control performance of the micro-actuator.

[0158] In some preferred embodiments, it is assumed that there exists an initial nonlinear response function f(ε_inst) between the instantaneous strain rate and the change in damping characteristics of the critical viscoelastic material of the micro-actuator, where ε_inst is the instantaneous strain rate. To correct this relationship, the cumulative strain history data of the material can first be obtained, such as the total number of strain cycles N and the maximum cumulative strain ε_cum_max, as well as the current aging degree A_age, for example, the relative hardness value obtained through material hardness testing. Then, a correction factor M(N, ε_cum_max, A_age) can be constructed, which can be an empirical formula, a lookup table function, or an output obtained by training a machine learning model. Finally, the corrected nonlinear response relationship can be expressed as f_corrected(ε_inst) = f(ε_inst) * M(N, ε_cum_max, A_age). For example, when the cumulative strain history data shows that the material is close to its fatigue life or has a high degree of aging, the correction factor M will cause f_corrected(ε_inst) to exhibit a larger damping change, thereby giving a larger phase adjustment when determining the influence amount to compensate for the decline in material performance.

[0159] This application further proposes the above-mentioned method for correcting the nonlinear response relationship between instantaneous strain rate and damping characteristic changes based on historical cumulative strain data and current aging level, including:

[0160] S601. Obtain the strain history data of the key viscoelastic material of the micro actuator in different motion directions.

[0161] Specifically, acquiring strain history data of the key viscoelastic material in a micro-actuator under different motion directions involves recording the dynamic information of the material, such as strain amplitude, frequency, and duration, in real time or periodically under different stress directions by arranging multi-axis strain sensors at key parts of the material or by using simulation methods such as finite element analysis. The aim is to comprehensively capture the actual deformation history of the material under multi-dimensional stress fields, providing multi-angle and detailed data support for subsequent precise corrections.

[0162] S602. Obtain the degree of anisotropic degradation of the key viscoelastic material of the micro actuator.

[0163] The anisotropic degradation degree of the key viscoelastic material in micro-actuators can be understood as assessing the differences in performance degradation caused by long-term operation, fatigue accumulation, and environmental factors (such as temperature and humidity) in different directions. For example, this degradation degree can be quantified by measuring the rate of change of dynamic mechanical property parameters such as the material's elastic modulus and loss factor in different directions, or by characterizing the damage accumulation and microstructural changes in the material's internal structure in different directions using non-destructive testing techniques (such as ultrasonic testing and X-ray diffraction). The aim is to quantify the anisotropic changes in material properties, thereby more accurately reflecting its current state.

[0164] S603. Based on the cumulative strain history data, current aging degree, strain history data, and anisotropic degradation degree, the nonlinear response relationship between instantaneous strain rate and damping characteristic change is directionally corrected.

[0165] In practical applications, the nonlinear response relationship between instantaneous strain rate and damping characteristic changes is directionally corrected based on accumulated strain history data, current aging level, strain history data, and anisotropic degradation level. Specifically, this involves constructing a multi-dimensional adaptive correction model. This model not only comprehensively considers the overall aging effect and accumulated strain history of the material but also incorporates the strain history and anisotropic degradation information of the material in specific directions as correction factors. For example, constitutive models based on tensor analysis, anisotropic viscoelastic models, or combinations of machine learning algorithms can be used as inputs to dynamically adjust the coefficients or functional forms in the nonlinear response relationship, enabling it to output more accurate predictions of damping characteristic changes for instantaneous strain rates in different motion directions. The aim is to improve the accuracy and applicability of the correction model, especially when the material exhibits significant anisotropic behavior, ensuring that the adjustment of the driving signal phase accurately matches the actual response of the material.

[0166] This application's solution, by introducing strain history data and anisotropic degradation levels of the key viscoelastic material in the micro-actuator across different motion directions, can more comprehensively and precisely characterize the material's actual mechanical behavior. Because the damping characteristics of viscoelastic materials are often closely related to their stress direction and historical strain state, and their degradation process can also exhibit anisotropy, relying solely on accumulated strain history and overall aging degree for correction may not accurately reflect the material's instantaneous response in a specific motion direction. By acquiring strain history data in different motion directions, the true response mode of the material under multidimensional stress states can be captured; simultaneously, by obtaining the anisotropic degradation level, the differential decay of material performance in different directions can be quantified. This additional information is used to directionally correct the nonlinear response relationship between instantaneous strain rate and damping characteristic changes, enabling the corrected model to more accurately predict changes in the material's damping characteristics in any motion direction, thus providing a more reliable basis for precise adjustment of the drive signal phase.

[0167] Through the above technical solution, this application can significantly improve the prediction accuracy of changes in the damping characteristics of key viscoelastic materials in micro-actuators, especially when the material exhibits anisotropic behavior or undergoes a complex multi-directional strain history. This directional correction mechanism allows the phase adjustment of the drive signal to more accurately adapt to the instantaneous state and directional characteristics of the material, thereby effectively compensating for changes in damping characteristics caused by material anisotropic degradation and complex strain history, further improving the response accuracy and stability of the micro-actuator. Consequently, during the automatic welding of tower legs, the fine-tuning commands of the robotic arm's end joints can be executed more precisely, ensuring the tracking accuracy of the welding torch tip on the welding path and maintaining the stability of the robotic arm's movement, ultimately improving welding quality and efficiency.

[0168] In some preferred embodiments, it is assumed that the key viscoelastic material of the micro-actuator is a fiber-reinforced composite material, which exhibits significant differences in viscoelastic properties and aging characteristics along and perpendicular to the fiber direction. In conventional correction methods, the nonlinear response relationship may be corrected solely based on the material's overall cumulative strain and average aging degree. This can lead to significant deviations in the prediction of damping characteristic changes in certain motion directions, thereby affecting the control accuracy of the micro-actuator.

[0169] Specifically, this application integrates a multi-axis strain sensor array onto a micro-actuator to acquire real-time instantaneous strain rate and strain history data of the composite material in the three orthogonal directions of X, Y, and Z. Simultaneously, the degree of anisotropic degradation is assessed by periodically performing anisotropic property tests on the material (e.g., measuring dynamic mechanical analysis parameters in different directions) or by utilizing a material microstructure model combined with fatigue damage accumulation theory. For example, it can be found that the damping decay rate of the material along the fiber direction is slower than that perpendicular to the fiber direction.

[0170] Subsequently, this directional strain history data and anisotropic degradation level, along with cumulative strain history data and current aging level, are input into a correction model based on tensor analysis and adaptive learning algorithms. This model dynamically adjusts the nonlinear response relationship between the instantaneous strain rate and damping characteristic changes based on the material's current direction of motion and instantaneous strain rate. For example, when the micro-actuator is fine-tuned in the X direction (along the fiber direction), the model uses the nonlinear relationship corrected for the X direction to determine the impact of damping characteristic changes on the phase of the drive signal; when fine-tuning is performed in the Y direction (perpendicular to the fiber direction), the relationship corrected for the Y direction is used. Through this directional correction, the phase adjustment of the drive signal can more accurately match the actual damping response of the material in a specific direction, thereby effectively eliminating control errors caused by material anisotropy and ensuring high precision and stability of the robotic arm's end effector joint in complex movements.

[0171] like Figure 3 As shown in the figure, this embodiment of the invention also provides a motion control system for an automatic tower leg welding machine. The system includes:

[0172] The acquisition module is used to acquire the geometric information of the path to be welded;

[0173] The acquisition module is also used to acquire real-time deviation information between the welding gun tip of the robotic arm and the path to be welded;

[0174] The acquisition module is also used to acquire motion capability limitation parameters of the robotic arm;

[0175] The trajectory calculation module is used to determine the predicted motion deviation of the welding torch tip within a preset time period based on real-time deviation information, geometric information, and motion capability limitation parameters.

[0176] The trajectory calculation module is also used to calculate and correct the motion trajectory based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm; the corrected motion trajectory is used to maintain the stability of the robotic arm's motion while ensuring the accuracy of weld seam tracking.

[0177] The motion command generation module is used to generate motion commands for the robotic arm based on the corrected motion trajectory.

[0178] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0179] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0180] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0181] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A motion control method for an automatic tower leg welding machine, characterized in that, include: Obtain the geometric information of the path to be welded; Obtain real-time deviation information between the welding torch tip of the robotic arm and the path to be welded; Obtain the motion capability limiting parameters of the robotic arm; Based on the real-time deviation information, the geometric information, and the motion capability limitation parameters, the predicted motion deviation of the welding torch tip within a preset time period is determined. Calculate the corrected motion trajectory based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm; The corrected motion trajectory is used to maintain the stability of the robotic arm's movement while ensuring the accuracy of weld seam tracking; Based on the corrected motion trajectory, motion commands for the robotic arm are generated.

2. The motion control method for an automatic tower leg welding machine according to claim 1, characterized in that, The step of generating motion commands for the robotic arm based on the corrected motion trajectory includes: Identify the local geometric features of the path to be welded; Measure the relative pose between the welding torch tip and the local geometric feature; Based on the relative pose, the local relative pose deviation is obtained through processing; Based on the local relative pose deviation, fine-tuning instructions are generated for the end joints of the robotic arm. The motion commands for the robotic arm are generated by combining the corrected motion trajectory with the fine-tuning instructions.

3. The motion control method for an automatic tower leg welding machine according to claim 2, characterized in that, The step of generating fine-tuning commands for the end effector joints of the robotic arm based on the local relative pose deviation includes: The coupling effect parameters between each drive shaft of the micro actuator corresponding to the end joint, the operating temperature of the micro actuator, and the cumulative operating data of the micro actuator are obtained. The gain and phase of the drive signal of the micro actuator are adjusted based on the local relative pose deviation, the coupling effect parameter, the operating temperature, and the accumulated operating data.

4. The motion control method for an automatic tower leg welding machine according to claim 3, characterized in that, Adjusting the gain of the drive signal of the micro actuator includes: Acquire the instantaneous motion state information of each drive shaft of the micro actuator; Acquire the minute vibration signals transmitted by the robotic arm; The coupling effect parameters are corrected based on the instantaneous motion state information and the minute vibration signal; Obtain the local temperature distribution of the key components of the micro actuator; The operating temperature is corrected based on the local temperature distribution; The gain of the drive signal of the micro actuator is adjusted based on the local relative pose deviation, the corrected coupling effect parameters, the corrected operating temperature, and the accumulated operating data.

5. The motion control method for an automatic tower leg welding machine according to claim 3, characterized in that, Adjusting the phase of the drive signal of the micro actuator includes: Acquire local electromagnetic field intensity information and local sound wave intensity information; Based on the local electromagnetic field intensity information and the local acoustic wave intensity information, the instantaneous damping characteristic changes of the key materials of the micro actuator are determined; The phase of the drive signal of the micro actuator is adjusted according to the change in the instantaneous damping characteristics.

6. The motion control method for an automatic tower leg welding machine according to claim 5, characterized in that, The step of adjusting the phase of the drive signal of the micro actuator according to the change in the instantaneous damping characteristics includes: Obtain the instantaneous strain rate of the key viscoelastic material in the micro-actuator; Based on the instantaneous strain rate, determine the influence of the instantaneous damping characteristic change on the phase of the driving signal; The phase of the drive signal of the micro actuator is adjusted according to the instantaneous damping characteristic change and the influence amount.

7. The motion control method for an automatic tower leg welding machine according to claim 6, characterized in that, The step of determining the influence of the instantaneous damping characteristic change on the phase of the driving signal based on the instantaneous strain rate includes: Obtain historical data on the cumulative strain and current aging level of the key viscoelastic material of the micro-actuator; Based on the accumulated strain history data and the current aging degree, the nonlinear response relationship between the instantaneous strain rate and the change in damping characteristics is corrected; Based on the corrected nonlinear response relationship, the influence of the instantaneous damping characteristic change on the phase of the driving signal is determined.

8. The motion control method for an automatic tower leg welding machine according to claim 7, characterized in that, The step of correcting the nonlinear response relationship between the instantaneous strain rate and the change in damping characteristics based on the accumulated strain history data and the current aging degree includes: Obtain the strain history data of the key viscoelastic material of the micro-actuator in different motion directions; The degree of anisotropic degradation of the key viscoelastic material of the micro-actuator was obtained; Based on the accumulated strain history data, the current aging degree, the strain history data, and the anisotropic degradation degree, the nonlinear response relationship between the instantaneous strain rate and the change in damping characteristics is directionally corrected.

9. The motion control method for an automatic tower leg welding machine according to claim 1, characterized in that, The step of calculating the corrected motion trajectory based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm includes: A target function is generated based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm; Solving the objective function yields the corrected motion trajectory; The objective function satisfies the following relationship: J=Σ[w1*E (t+k) 2 +w2*V (t+k) 2 +w3*A (t+k) 2 ]: Among them, E (t+k) V is the predicted weld deviation at the future time (t+k). (t+k) A is the predicted welding torch speed at the future time (t+k). (t+k) It is the predicted welding torch acceleration at the future time (t+k), and w1, w2, and w3 are weighting coefficients.

10. A motion control system for an automatic tower leg welding machine, characterized in that, The system includes: The acquisition module is used to acquire the geometric information of the path to be welded; The acquisition module is also used to acquire real-time deviation information between the welding gun end of the robotic arm and the path to be welded; The acquisition module is also used to acquire the motion capability limitation parameters of the robotic arm; The trajectory calculation module is used to determine the predicted motion deviation of the welding torch tip within a preset time period based on the real-time deviation information, the geometric information, and the motion capability limitation parameters. The trajectory calculation module is further configured to calculate a corrected motion trajectory based on the predicted motion deviation and the motion capability limitation parameters of the robotic arm; the corrected motion trajectory is used to maintain the stability of the robotic arm's motion while ensuring the accuracy of weld seam tracking. The motion command generation module is used to generate motion commands for the robotic arm based on the corrected motion trajectory.

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