Robot multi-frequency vibration composite suppression method and system based on inertia disturbance analysis

Abstract

The application relates to a robot multi-frequency vibration composite suppression method and system based on inertia disturbance analysis, which comprises the following steps: acquiring dynamic operation data in the operation process of a robot; inputting the operation data into an inertia disturbance analysis module, so that low-frequency vibration sources caused by a mechanical structure and high-frequency vibration sources caused by a driving system can be identified; and generating a disturbance frequency distribution diagram, according to which, an adaptive filtering algorithm is used to suppress the high-frequency vibration sources, a model predictive control-based compensation algorithm is used to suppress the low-frequency vibration sources, and the two strategies are collaboratively processed to generate comprehensive control instructions, so that the advantages of the two algorithms can be fully exerted, and effective composite suppression of multi-frequency vibration can be realized.

Description

Technical Field

[0001] This application relates to the fields of intelligent control and robotics, and in particular to a method and system for composite suppression of multi-frequency vibrations in robots based on inertia disturbance analysis. Background Technology

[0002] In the field of robotics, especially in industrial robot applications, operational stability is paramount. With the widespread use of robots in high-precision machining and complex assembly tasks, the need for vibration suppression during operation is becoming increasingly prominent. Robot vibration can affect its positioning accuracy and repeatability, reduce work efficiency, and may even lead to equipment damage and product quality degradation. In actual operation, the sources of robot vibration are complex, with different vibration sources exhibiting different frequency characteristics.

[0003] To address robot vibration issues, existing technologies typically employ single vibration suppression methods, such as traditional filtering algorithms or simple feedback control strategies. While these methods can mitigate robot vibration to some extent, their effectiveness is limited because they fail to comprehensively consider the characteristics of vibration sources at different frequencies. For instance, when low-frequency and high-frequency vibrations coexist, a single suppression method cannot effectively address vibration sources of different frequencies.

[0004] However, existing solutions suffer from a significant technical problem: a lack of ability to coordinate the processing of vibration sources at different frequencies. Because they fail to analyze and differentiate dynamic disturbance sources in detail, it is impossible to develop targeted suppression strategies based on the characteristics of vibration sources at different frequencies. This makes it difficult to achieve effective, comprehensive suppression of multi-frequency vibrations in robots under complex operating conditions. Summary of the Invention

[0005] The main objective of this application is to provide a method and system for composite suppression of multi-frequency vibrations of robots based on inertia perturbation analysis, which can achieve effective composite suppression of multi-frequency vibrations of robots.

[0006] To achieve the above objectives, embodiments of the present invention provide a method for suppressing the composite multi-frequency vibration of a robot based on inertia perturbation analysis, the method comprising:

[0007] The robot acquires dynamic operation data collected by sensors during operation, including joint angle change information, acceleration data, and torque feedback signals.

[0008] The dynamic operating data is input into the inertia disturbance analysis module, and the dynamic disturbance source in the robot operation is identified by the analysis algorithm. The dynamic disturbance source includes low-frequency vibration source caused by mechanical structure and high-frequency vibration source caused by drive system.

[0009] Based on the identification results of the dynamic disturbance sources, a corresponding disturbance frequency distribution map is generated, which includes the frequency range and amplitude characteristics of each disturbance source.

[0010] Based on the disturbance frequency distribution diagram, the high-frequency vibration source and the low-frequency vibration source are processed collaboratively to generate a comprehensive control command corresponding to the multi-frequency vibration composite suppression strategy. The suppression of the high-frequency vibration source adopts an adaptive filtering algorithm, and the suppression of the low-frequency vibration source adopts a compensation algorithm based on model predictive control.

[0011] The integrated control command is sent to the robot actuator to achieve multi-frequency vibration composite suppression of the robot.

[0012] Accordingly, this application also provides a robot multi-frequency vibration composite suppression system based on inertia perturbation analysis, the system comprising:

[0013] The acquisition module is used to acquire dynamic operation data collected by the robot through sensors during operation. The dynamic operation data includes joint angle change information, acceleration data, and torque feedback signals.

[0014] The motion source identification module is used to input the dynamic operation data into the inertia disturbance analysis module, and identify the dynamic disturbance sources in the robot operation through the analysis algorithm. The dynamic disturbance sources include low-frequency vibration sources caused by mechanical structures and high-frequency vibration sources caused by the drive system.

[0015] The distribution map generation module is used to generate a corresponding disturbance frequency distribution map based on the identification results of the dynamic disturbance source. The disturbance frequency distribution map includes the frequency range and amplitude characteristics of each disturbance source.

[0016] The suppression module is used to perform collaborative processing on the high-frequency vibration source and the low-frequency vibration source according to the disturbance frequency distribution map, so as to generate a comprehensive control command corresponding to the multi-frequency vibration composite suppression strategy. The suppression of the high-frequency vibration source adopts an adaptive filtering algorithm, and the suppression of the low-frequency vibration source adopts a compensation algorithm based on model predictive control.

[0017] The sending module is used to send the integrated control commands to the robot actuator to achieve multi-frequency vibration composite suppression of the robot.

[0018] In summary, the technical solution of this application firstly allows for a comprehensive understanding of the robot's operational status by acquiring dynamic operational data during its operation. Inputting this data into the inertia disturbance analysis module accurately identifies low-frequency vibration sources caused by the mechanical structure and high-frequency vibration sources caused by the drive system, providing a foundation for subsequent targeted processing. The generated disturbance frequency distribution map includes the frequency range and amplitude characteristics of each disturbance source, providing a clear understanding of the characteristics of vibration sources at different frequencies. Based on this distribution map, an adaptive filtering algorithm is used to suppress high-frequency vibration sources, and a compensation algorithm based on model predictive control is used to suppress low-frequency vibration sources. The two strategies are then processed collaboratively to generate a comprehensive control command, fully leveraging the advantages of both algorithms to achieve effective composite suppression of multi-frequency vibrations. Finally, the comprehensive control command is sent to the robot's actuator, significantly improving the stability and accuracy of robot operation, reducing the adverse effects of vibration on robot work, and enhancing the robot's performance and reliability under complex working conditions. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of a scenario for the robot multi-frequency vibration composite suppression method based on inertia disturbance analysis in the embodiments of this application;

[0020] Figure 2 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] This application provides a method and system for composite suppression of multi-frequency vibration of a robot based on inertia disturbance analysis, which will be described in detail below.

[0023] In this embodiment, the robot multi-frequency vibration composite suppression method based on inertia perturbation analysis is a comprehensive robot vibration suppression scheme. This method aims to solve the multi-frequency vibration problem during robot operation. By analyzing inertia perturbations, it identifies vibration sources of different frequencies and formulates corresponding suppression strategies for these sources. Inertia perturbation analysis involves analyzing and processing the robot's dynamic operating data to determine the characteristics of the vibration sources. Multi-frequency vibration composite suppression utilizes multiple algorithms to collaboratively process high-frequency and low-frequency vibration sources, thereby effectively suppressing vibration. This method can dynamically adjust the suppression strategy according to changes in vibration sources during robot operation, improving the stability and accuracy of robot operation, and is applicable to various industrial robot application scenarios.

[0024] As shown in Figure 1, a scenario for a robot multi-frequency vibration composite suppression method based on inertia disturbance analysis is provided. In the robot processing and manufacturing scenario, it mainly includes an industrial robot, sensors, and a control platform; the industrial robot, sensors, and control platform are connected through a wired network.

[0025] Taking an automotive parts processing workshop as an example, in this scenario, industrial robots are responsible for tasks such as handling, assembling, and processing parts. Industrial robots typically consist of multiple movable joints, which generate various vibrations during movement.

[0026] Sensors are installed in key parts of industrial robots, such as joints and end effectors, to collect dynamic operational data during robot operation. These sensors include angle sensors, accelerometers, and torque sensors. Angle sensors measure joint angle changes in real time. For example, in a six-joint industrial robot, the angle sensor for each joint can accurately record the joint's rotation angle; these angle changes reflect the robot's motion posture. Accelerometers collect the robot's acceleration data. When the robot performs rapid starts, stops, or speed changes, the accelerometer captures changes in acceleration, which is crucial for analyzing the robot's dynamic characteristics. Torque sensors obtain torque feedback signals from the joints. When the robot is under load, the torque feedback signals reflect the magnitude and changes in the load borne by the joints. By comprehensively analyzing the data collected by these sensors, a complete understanding of the robot's operational status can be obtained.

[0027] After acquiring dynamic operational data uploaded by sensors, the control platform processes and analyzes this data. First, the dynamic operational data is input into the inertia disturbance analysis module, where an analytical algorithm identifies the sources of dynamic disturbances in the robot's operation. During this process, the dynamic operational data is preprocessed to remove noise interference and extract feature data related to the dynamic disturbances. For example, a filtering algorithm is used to filter the acquired data, removing the influence of high-frequency noise. Then, the feature data is input into the inertia disturbance analysis module, where a Fourier transform algorithm is used to calculate the spectral distribution of the feature data. Based on the spectral distribution, the frequency ranges of low-frequency and high-frequency vibration sources are identified. For example, resonance in mechanical structures may cause low-frequency vibrations, typically in the range of tens of hertz; while electrical interference in the drive system may cause high-frequency vibrations, potentially in the range of hundreds of hertz. Based on the frequency range, combined with torque feedback signals and acceleration data from the dynamic operational data, the amplitude characteristics of the low-frequency and high-frequency vibration sources are determined.

[0028] Next, based on the identification results of the dynamic disturbance sources, a corresponding disturbance frequency distribution map is generated. The disturbance frequency distribution map intuitively displays the frequency range and amplitude characteristics of each disturbance source, providing an important basis for subsequent formulation of suppression strategies.

[0029] Based on the disturbance frequency distribution diagram, high-frequency and low-frequency vibration sources are processed collaboratively to generate a comprehensive control command corresponding to the multi-frequency vibration composite suppression strategy. For high-frequency vibration sources, an adaptive filtering algorithm is used for suppression. This algorithm automatically adjusts filter parameters, such as filter order and step size factor, according to the energy proportion of the high-frequency vibration source. For low-frequency vibration sources, a compensation algorithm based on model predictive control is used for suppression. This algorithm adjusts the prediction time domain length and control time domain length according to the energy proportion of the low-frequency vibration source. The suppression strategies for high-frequency and low-frequency vibration sources are then superimposed to generate a comprehensive control command.

[0030] Finally, the control platform sends integrated control commands to the actuators of the industrial robot, such as servo motors. The actuators adjust the robot's motion parameters according to the integrated control commands, achieving comprehensive suppression of multi-frequency vibrations. During robot operation, dynamic operating data is continuously collected, and the parameters in the multi-frequency composite suppression strategy are dynamically adjusted to adapt to changes in the robot's operating state, improving the adaptability and robustness of the strategy. For example, when the robot's load changes, the characteristics of the vibration source also change accordingly. By dynamically adjusting the parameters of the suppression strategy, it is possible to effectively suppress vibrations under different working conditions, improving the robot's operational stability and machining accuracy.

[0031] This application provides a method for suppressing the composite multi-frequency vibration of a robot based on inertia perturbation analysis. The executing entity of this method can be a computer device (which can serve as a control platform). This computer device can be a single computer device or a cluster of multiple computer devices. The computer device can be a terminal device or a server, etc. The specific methods for suppressing the composite multi-frequency vibration of a robot based on inertia perturbation analysis provided in this application include:

[0032] S10: Acquire dynamic operation data collected by sensors during robot operation, including joint angle change information, acceleration data, and torque feedback signals.

[0033] In this application, dynamic operational data is a key dataset comprehensively describing the robot's operational state. Joint angle change information refers to the real-time changes in the angles of each joint during robot movement. In the robot's structure, joints are crucial for motion, and changes in joint angles directly determine the robot's posture. For example, in a typical six-joint industrial robotic arm, the angle change information of each joint precisely reflects the process of the arm moving from one position to another. Joint angle change information is one of the fundamental parameters describing robot kinematics, reflecting the rotational state of the robot's joints. Accurate acquisition of this parameter is essential for understanding the robot's posture and controlling its trajectory. The technical advantage lies in the fact that accurate joint angle change information provides precise joint state information for subsequent motion analysis and vibration suppression, ensuring the robot moves according to the expected posture and avoiding motion deviations and task failures caused by angle errors.

[0034] Acceleration data refers to the changes in the magnitude and direction of a robot's acceleration during movement. Acceleration reflects the rate of change of the robot's velocity and is closely related to its dynamic characteristics. For example, when a robot performs rapid starts, stops, or changes in speed, the acceleration data will change significantly. Acceleration data is an important basis for analyzing the robot's vibration characteristics, reflecting the inertial forces and impact forces experienced by the robot during movement. From a technical perspective, accurate acceleration data can help identify vibration sources during robot movement, providing crucial information for subsequent vibration suppression.

[0035] Torque feedback signals refer to the feedback information regarding the magnitude and direction of the torque experienced by a robot's joints during movement. During robot operation, joints need to overcome various resistances and loads to achieve motion, and torque feedback signals can reflect the load conditions borne by the joints in real time. For example, when a robot is handling heavy objects, the torque feedback signal of the joints will increase significantly. Torque feedback signals are an important indicator for evaluating the stability and load-bearing capacity of a robot, reflecting the stress state of the robot's joints. The technical advantage lies in the fact that accurate torque feedback signals can help determine whether the robot is in a normal working state, promptly detect potential faults and anomalies, and also provide important reference for the formulation of vibration suppression strategies.

[0036] This application embodiment can comprehensively understand the robot's operating status by acquiring dynamic operating data composed of these three types of data. From a technical perspective, obtaining this data completely and accurately provides the foundation for subsequent inertia disturbance analysis and vibration suppression, ensuring effective handling of vibration issues during robot operation.

[0037] In one embodiment, a high-precision angle sensor, such as an absolute photoelectric encoder, can be installed at each joint to provide information on joint angle changes. An absolute photoelectric encoder uses multiple concentric code tracks etched on a code disk, each track containing both transparent and opaque fan-shaped areas distributed according to a specific pattern. When the joint rotates, light emitted from a light source passes through the code disk and slits to illuminate a photoelectric element. The photoelectric element determines the absolute angular position of the joint based on different combinations of the received light signals. This type of sensor can directly output information on joint angle changes and features high precision and high reliability.

[0038] For acceleration data, a three-axis accelerometer can be used. A three-axis accelerometer can simultaneously measure acceleration components along three coordinate axes, providing a comprehensive reflection of the robot's acceleration. During installation, the three-axis accelerometer is fixed to key parts of the robot, such as the end effector of the robotic arm or the base. When the robot moves, the accelerometer can collect acceleration data in three directions in real time and transmit this data to the control platform for processing.

[0039] For torque feedback signals, torque sensors can be installed at the joints. Torque sensors typically employ the strain gauge principle, indirectly measuring the magnitude of the torque exerted on the joint by measuring the change in resistance of the strain gauge. During robot operation, the torque sensors collect the torque feedback signals from the joints in real time and transmit them to the control platform. The control platform then monitors and evaluates the robot's operational status in real time based on the collected torque feedback signals.

[0040] S20: Input the dynamic operation data into the inertia disturbance analysis module, and identify the dynamic disturbance source in the robot operation through the analysis algorithm. The dynamic disturbance source includes low-frequency vibration source caused by mechanical structure and high-frequency vibration source caused by drive system.

[0041] In this application, the inertia disturbance analysis module is the core module for processing and analyzing the robot's dynamic operation data to identify dynamic disturbance sources. Dynamic disturbance sources refer to various factors that cause vibrations in the robot; different disturbance sources have different frequency characteristics. During robot operation, the mechanical structure and drive system are the main vibration sources.

[0042] Low-frequency vibrations caused by mechanical structures are usually due to manufacturing errors, assembly deviations, or wear in the robot's mechanical components. For example, joint clearances and elastic deformation of links can cause low-frequency vibrations. The frequency range of low-frequency vibrations is generally low, typically below tens of hertz. Low-frequency vibrations can lead to decreased positioning accuracy in robots, affecting their work efficiency and product quality. From a technical perspective, accurately identifying the source of low-frequency vibrations can provide targeted solutions for subsequent vibration suppression, reducing the impact of low-frequency vibrations on robot operation.

[0043] High-frequency vibrations caused by the drive system are mainly due to the electrical characteristics of the drive motor, imperfections in the control algorithm, or electromagnetic interference. For example, current fluctuations in the drive motor and harmonics in the pulse width modulation (PWM) signal can all cause high-frequency vibrations. The frequency range of high-frequency vibrations is relatively high, typically above several hundred hertz. High-frequency vibrations affect the robot's control accuracy and increase system noise and energy consumption. The technical advantage lies in accurately identifying the high-frequency vibration source, enabling effective measures to suppress it, and improving the robot's operational stability and control accuracy.

[0044] This application embodiment analyzes dynamic operating data using an inertial disturbance analysis module, accurately identifying low-frequency and high-frequency vibration sources, thus providing a foundation for developing targeted vibration suppression strategies. From a technical perspective, identifying vibration sources of different frequencies enables effective combined suppression of multi-frequency vibrations in the robot, improving its operational performance.

[0045] In one embodiment, a Fourier transform algorithm can be used to perform spectral analysis on the dynamic operation data. First, the dynamic operation data is preprocessed to remove noise interference and extract feature data related to dynamic disturbances. For example, a low-pass filter is used to remove high-frequency noise, and a high-pass filter is used to remove low-frequency noise. Then, the processed feature data is input into the inertia disturbance analysis module, and the time-domain data is converted to frequency-domain data using a Fourier transform algorithm to obtain the spectral distribution of the feature data. Based on the spectral distribution, the frequency ranges of the low-frequency and high-frequency vibration sources are identified. Simultaneously, by combining the torque feedback signal and acceleration data in the dynamic operation data, the amplitude characteristics of the low-frequency and high-frequency vibration sources are determined. In this way, the dynamic disturbance sources in the robot's operation can be accurately identified.

[0046] S30: Based on the identification results of the dynamic disturbance sources, a corresponding disturbance frequency distribution map is generated, which includes the frequency range and amplitude characteristics of each disturbance source.

[0047] In this application, the disturbance frequency distribution map is an important tool for intuitively displaying the frequency characteristics of dynamic disturbance sources in a robot. It graphically presents vibration sources of different frequencies and their amplitude characteristics, providing a clear basis for subsequent vibration suppression strategies. By analyzing and organizing the identification results of dynamic disturbance sources, the frequency range and amplitude characteristics of each disturbance source are displayed in a visual form.

[0048] Frequency range refers to the frequency interval corresponding to each disturbance source. Different disturbance sources have different frequency characteristics. For example, the frequency range of low-frequency vibration sources is usually below tens of hertz, while the frequency range of high-frequency vibration sources is usually above hundreds of hertz. Determining the frequency range helps to clarify the distribution of different vibration sources, providing a basis for subsequent targeted processing. Its technical advantage lies in the fact that clear frequency range information can help select appropriate suppression algorithms and parameters, improving the effectiveness of vibration suppression.

[0049] Amplitude characteristics refer to the magnitude of the vibration amplitude of each disturbance source at different frequencies. Amplitude characteristics reflect the intensity and degree of influence of the vibration source, and different amplitude characteristics require different suppression strategies. For example, vibration sources with larger amplitudes require more powerful suppression measures. Accurate acquisition of amplitude characteristics provides crucial information for formulating vibration suppression strategies, ensuring their effectiveness.

[0050] The disturbance frequency distribution map generated in this application embodiment can comprehensively display the frequency characteristics of dynamic disturbance sources in the robot, providing an intuitive reference for subsequent vibration suppression. From a technical perspective, the disturbance frequency distribution map helps to quickly understand the distribution and characteristics of vibration sources, providing a basis for formulating targeted vibration suppression strategies and improving the efficiency and effectiveness of vibration suppression.

[0051] In one embodiment, specialized plotting software, such as MATLAB, can be used to generate a disturbance frequency distribution map based on the identification results of dynamic disturbance sources. First, the frequency range and amplitude characteristics of each disturbance source are compiled into a data table. Then, using the plotting software's plotting functions, the frequency range is used as the horizontal axis and the amplitude characteristics as the vertical axis to plot the disturbance frequency distribution map. During the plotting process, different colors or line styles can be used to distinguish different disturbance sources, making the graph clearer and easier to understand.

[0052] S40: Based on the disturbance frequency distribution diagram, the high-frequency vibration source and the low-frequency vibration source are processed collaboratively to generate a comprehensive control command corresponding to the multi-frequency vibration composite suppression strategy. The suppression of the high-frequency vibration source adopts an adaptive filtering algorithm, and the suppression of the low-frequency vibration source adopts a compensation algorithm based on model predictive control.

[0053] In this application, collaborative processing refers to the process of comprehensively considering the characteristics of high-frequency and low-frequency vibration sources, using different algorithms to process them separately, and integrating the processing methods or strategies into a multi-frequency composite suppression strategy. The multi-frequency vibration composite suppression strategy is a comprehensive solution developed for the multi-frequency vibration problem of robots. It combines the advantages of adaptive filtering algorithms and compensation algorithms based on model predictive control, enabling effective suppression of vibration sources of different frequencies.

[0054] Adaptive filtering algorithms are algorithms that automatically adjust filter parameters based on the statistical characteristics of the input signal. For high-frequency vibration sources, adaptive filtering algorithms can automatically adjust the filter order and step size factor according to the energy proportion of the high-frequency vibration source to achieve the best filtering effect. For example, when the energy proportion of the high-frequency vibration source is large, increasing the filter order can improve the filtering accuracy, while decreasing the step size factor can enhance the filter's stability. The advantage of adaptive filtering algorithms lies in their ability to adapt to changes in high-frequency vibration sources in real time, improving vibration suppression. Their technical effect is that adaptive filtering algorithms can effectively suppress high-frequency vibration sources and reduce the impact of high-frequency vibration on robot operation.

[0055] Model predictive control (MMDC)-based compensation algorithms are a type of control strategy that calculates control inputs by predicting the future state of the system to compensate for system errors. For low-frequency vibration sources, MMDC-based compensation algorithms can adjust the prediction and control time domains based on the energy proportion of the low-frequency vibration source to effectively suppress it. For example, when the energy proportion of the low-frequency vibration source is large, increasing the prediction time domain length can improve prediction accuracy, while decreasing the control time domain length can enhance control timeliness. The advantage of MMDC-based compensation algorithms lies in their ability to predict the changing trends of low-frequency vibrations in advance and take corresponding control measures for compensation. The technical effect is that using MMDC-based compensation algorithms can effectively suppress low-frequency vibration sources and improve the operational stability of robots.

[0056] This application embodiment, through collaborative processing of high-frequency and low-frequency vibration sources, generates integrated control commands that fully leverage the advantages of adaptive filtering algorithms and model predictive control-based compensation algorithms to achieve effective composite suppression of multi-frequency vibrations in the robot. Technically, the integrated control commands can be tailored to the characteristics of vibration sources of different frequencies, improving the effectiveness and efficiency of vibration suppression.

[0057] In one embodiment, the frequency range and amplitude characteristics of the high-frequency and low-frequency vibration sources are first obtained from the disturbance frequency distribution map, and their respective energy proportions are calculated. Then, based on the energy proportion of the high-frequency vibration source, the parameter configuration of the adaptive filtering algorithm, such as the filter order and step size factor, is determined. Based on the energy proportion of the low-frequency vibration source, the parameter configuration of the compensation algorithm based on model predictive control, such as the prediction time domain length and control time domain length, is determined. Finally, the suppression strategies of the high-frequency and low-frequency vibration sources are superimposed to generate a comprehensive control command.

[0058] S50: Send the integrated control command to the robot actuator to achieve multi-frequency vibration composite suppression of the robot.

[0059] In this application, the robot actuator refers to the component in the robot responsible for performing motion tasks, such as servo motors and cylinders. The integrated control command is a control signal generated based on a multi-frequency vibration composite suppression strategy, which includes suppression information for both high-frequency and low-frequency vibration sources. The integrated control command is sent to the robot actuator, which adjusts the robot's motion parameters according to the command, thereby achieving composite suppression of the robot's multi-frequency vibrations.

[0060] The actuator adjusts the robot's motion parameters, such as motor speed and torque, according to integrated control commands. By adjusting these parameters, the robot's motion state can be changed, reducing vibration.

[0061] For example, in one embodiment, the actuator adjusts the motor speed according to integrated control commands. Changes in motor speed directly affect the movement speed of the robot joints. When a high-frequency vibration source causes the robot to produce rapid and minute vibrations, reducing the motor speed can reduce the amplitude of these high-frequency vibrations. This is because a lower speed makes the robot's movement smoother, reducing high-frequency vibrations caused by rapid movement. For instance, when an industrial robotic arm performs precision assembly tasks, high-frequency vibrations can affect assembly accuracy. By appropriately reducing the motor speed, the end effector of the robotic arm becomes more stable, effectively reducing errors caused by high-frequency vibrations and improving assembly accuracy. Technically, adjusting the motor speed can specifically suppress high-frequency vibrations, improving the robot's working stability in high-frequency vibration environments.

[0062] Adjusting the motor torque also plays a significant role in suppressing vibration. Low-frequency vibration sources are usually caused by changes in the robot's load or imbalances in its mechanical structure. When low-frequency vibration occurs, appropriately increasing the motor torque can enhance the robot's ability to overcome load changes and mechanical resistance. For example, during the process of a robot carrying heavy objects, uneven weight distribution or the robot's inertia during movement may cause low-frequency swaying. By increasing the motor torque, the robot's joints can maintain stability more effectively, reducing the amplitude of low-frequency swaying. This adjustment can improve the robot's stability under varying load conditions, reduce the impact of low-frequency vibration on the robot's motion accuracy, and thus improve the robot's performance under complex working conditions.

[0063] In practical applications, the actuator precisely adjusts motion parameters based on integrated control commands. These commands may contain specific control information for vibration sources of different frequencies, which the actuator can identify and adjust accordingly. For example, the control command might specify how much the motor speed should decrease or how much the torque should increase within a certain time period. The actuator, through its internal control system, converts these commands into specific electrical signals, driving the motor to achieve the corresponding speed and torque adjustments. This precise parameter adjustment ensures effective suppression of multi-frequency vibrations, enabling the robot to operate stably even in complex vibration environments.

[0064] From a system-wide perspective, sending integrated control commands to the robot's actuators and adjusting their parameters forms a closed-loop vibration suppression system. During robot operation, sensors continuously collect dynamic operational data. This data is analyzed and identified by the inertial disturbance analysis module to identify vibration sources, generate a disturbance frequency distribution map, and then formulate multi-frequency vibration suppression strategies and integrated control commands. After the actuators adjust their motion parameters according to the commands, the sensors collect new operational data again for further analysis and adjustment. This closed-loop system can monitor and respond to the robot's vibration in real time, continuously optimize vibration suppression strategies, ensure the robot is always in optimal operating condition, and improve robot efficiency and product quality.

[0065] In one embodiment, a communication interface can be used to transmit integrated control commands from the control platform to the robot actuator. For example, an industrial Ethernet interface can be used, which features high-speed and stable communication, ensuring that control commands are transmitted to the actuator quickly and accurately. After receiving the commands, the actuator parses and processes them through its internal controller. The controller adjusts the control signals of the motor driver according to the parameter requirements in the commands, thereby achieving precise control of the motor speed and torque. Simultaneously, the actuator can also use a feedback mechanism to feed back the actual motion parameters to the control platform, allowing the control platform to monitor the actuator's operating status in real time, further optimize the integrated control commands, and improve vibration suppression.

[0066] In one embodiment, step S40 can be implemented as follows:

[0067] S401: Obtain the frequency range and amplitude characteristics of the high-frequency vibration source and the low-frequency vibration source from the disturbance frequency distribution diagram, and calculate the energy ratio of the high-frequency vibration source and the low-frequency vibration source respectively.

[0068] In this application, frequency range represents the frequency distribution interval of different vibration sources, and amplitude characteristics reflect the intensity of the vibration source. Energy proportion refers to the proportion of energy from high-frequency and low-frequency vibration sources in the total vibration energy. By calculating the energy proportion, the influence of different frequency vibration sources on robot vibration can be quantified, providing an important reference for determining the parameter configuration of the suppression algorithm. Let the energy of the high-frequency vibration source be E_hf, the energy of the low-frequency vibration source be E_lf, and the total vibration energy E_total = E_hf + E_lf, then the energy proportion of the high-frequency vibration source P_hf = E_hf / E_total. For example, if the energy proportion of the high-frequency vibration source is large, it indicates that the high-frequency vibration has a more significant impact on the robot and needs to be given more attention in the suppression strategy. The technical effect is that accurate energy proportion calculation can make the subsequent parameter configuration more reasonable, improving the pertinence and effectiveness of vibration suppression.

[0069] In one embodiment, the energy of the high-frequency and low-frequency vibration sources can be calculated using an integration method. First, the frequency ranges corresponding to the high-frequency and low-frequency vibration sources are determined based on the disturbance frequency distribution diagram. Then, the power spectrum of the vibration signal is integrated within its respective frequency range to obtain the energy values ​​of the high-frequency and low-frequency vibration sources. Finally, each energy value is divided by the total vibration energy to obtain the energy percentage.

[0070] S402: Based on the energy proportion of the high-frequency vibration source, determine the parameter configuration of the adaptive filtering algorithm to obtain the suppression strategy of the high-frequency vibration source. The parameter configuration includes the filter order and step size factor.

[0071] In this application, the parameter configuration of the adaptive filtering algorithm is dynamically adjusted based on the energy proportion of the high-frequency vibration source. The filter order determines the filter's complexity and filtering performance; a higher order may result in higher filtering accuracy, but the computational load will also increase accordingly. The step size factor controls the update speed of the filter parameters; a larger step size factor results in a faster response to changes in the input signal, but may lead to instability in the filtering results. By reasonably adjusting these parameters according to the energy proportion of the high-frequency vibration source, the adaptive filtering algorithm can better adapt to the characteristics of the high-frequency vibration source and improve the suppression effect. For example, when the energy proportion of the high-frequency vibration source is large, appropriately increasing the filter order and decreasing the step size factor can enhance the filtering accuracy and stability. The technical effect is that, through reasonable parameter configuration, the adaptive filtering algorithm can more effectively suppress high-frequency vibration sources and reduce the impact of high-frequency vibration on the robot.

[0072] In one embodiment, a mapping table between energy percentage and filter parameter configuration can be pre-established. Based on the calculated energy percentage of the high-frequency vibration source, the corresponding filter order and step size factor are looked up from the mapping table. If the energy percentage exceeds the range of the mapping table, a linear interpolation method can be used to determine suitable parameter values.

[0073] In one embodiment, step S402 can be implemented by the following steps:

[0074] S4021: Obtain the ratio of the energy percentage of the high-frequency vibration source to the preset energy threshold.

[0075] In this application, the preset energy threshold is a pre-defined reference value used to measure the proportion of energy from the high-frequency vibration source. For example, if the proportion of energy from the high-frequency vibration source is P_hf and the preset energy threshold is T_hf, then the ratio R_hf = P_hf / T_hf. By calculating the ratio of the proportion of energy from the high-frequency vibration source to the preset energy threshold, the relative proportion of energy from the high-frequency vibration source compared to the preset standard can be intuitively determined. This ratio serves as the basis for subsequent adjustments to the parameters of the adaptive filtering algorithm. For example, if the ratio is greater than 1, it indicates that the proportion of energy from the high-frequency vibration source is relatively large, requiring more stringent filtering measures; if the ratio is less than 1, it indicates that the proportion of energy from the high-frequency vibration source is relatively small, and the filtering intensity can be appropriately reduced. The technical advantage is that the calculation of this ratio provides a clear judgment standard for subsequent parameter adjustments, making the parameter adjustments more scientific and reasonable.

[0076] In one embodiment, a preset energy threshold can be stored in the system's parameter table. After obtaining the energy percentage of the high-frequency vibration source, it is directly divided by the preset energy threshold to obtain the ratio.

[0077] S4022: If the ratio is greater than the first energy threshold, then increase the filter order of the adaptive filtering algorithm and decrease the step size factor.

[0078] In this application, the first energy threshold is a critical value used to determine whether the filtering effect needs to be strengthened. When the ratio is greater than the first energy threshold, it indicates that the energy proportion of the high-frequency vibration source is relatively large, and its impact on the robot is more significant. Increasing the filter order can increase the complexity of the filter, improve the filtering accuracy, and more effectively remove high-frequency noise. Decreasing the step size factor can slow down the update speed of the filter parameters, enhance the stability of the filter, and avoid unstable filtering results due to excessively rapid parameter updates. The technical effect is that, through this parameter adjustment method, the filtering effect of the adaptive filtering algorithm can be improved when the energy proportion of the high-frequency vibration source is large, and high-frequency vibration can be suppressed more effectively.

[0079] In one embodiment, a pre-set increment for the filter order and a pre-set decrement for the step size factor can be used. When the ratio is greater than a first energy threshold, the current filter order is increased by the increment, and the current step size factor is decreased by the decrement.

[0080] S4023: If the ratio is less than or equal to the first energy threshold, then reduce the filter order of the adaptive filtering algorithm and increase the step size factor.

[0081] In this application, when the ratio is less than or equal to the first energy threshold, it indicates that the energy proportion of the high-frequency vibration source is relatively small, and its impact on the robot is relatively small. Let the original filter order be N_0, and the adjusted filter order be N_1 = N_0 - ΔN (ΔN is the amount by which the order is reduced); let the original step size factor be μ_0, and the adjusted step size factor be μ_1 = μ_0 + Δμ (Δμ is the amount by which the step size factor is increased). Reducing the filter order can reduce the computational load and improve the algorithm's operating efficiency. Increasing the step size factor allows the filter to adapt to changes in the input signal more quickly, improving the real-time performance of the filtering. The technical effect is that, when the energy proportion of the high-frequency vibration source is small, this parameter adjustment method can improve the algorithm's operating efficiency and real-time performance while ensuring a certain filtering effect.

[0082] In one embodiment, a reduction in order and an increase in step size factor can be preset. When the ratio is less than or equal to a first energy threshold, the current filter order is subtracted from the reduction in order, and the current step size factor is added to the increase in step size factor.

[0083] S4024: Reconfigure the parameters of the adaptive filtering algorithm based on the adjusted filter order and step size factor.

[0084] In this application, reconfiguring the parameters of the adaptive filtering algorithm is a crucial step to ensure that the algorithm runs according to the adjusted parameters. Based on the adjusted filter order and step size factor, the relevant parameters in the adaptive filtering algorithm are updated, enabling the algorithm to filter high-frequency vibration sources with the new parameter settings. The technical advantage lies in the fact that accurate parameter reconfiguration ensures that the adaptive filtering algorithm operates according to the expected parameter settings, achieving effective suppression of high-frequency vibration sources.

[0085] In one embodiment, the adjusted filter order and step size factor can be written into the corresponding parameter positions by modifying the code or configuration file of the adaptive filtering algorithm.

[0086] S4025: The reconfigured adaptive filtering algorithm is applied to the suppression of high-frequency vibration sources to obtain the suppression strategy for high-frequency vibration sources.

[0087] In this application, a reconfigured adaptive filtering algorithm is applied to suppress high-frequency vibration sources, enabling targeted filtering based on the actual conditions of the high-frequency vibration source. Through filtering, noise and interference from the high-frequency vibration source are removed, resulting in a suppression strategy. This suppression strategy effectively reduces the impact of high-frequency vibrations on the robot, improving its operational accuracy. The key technical advantage lies in the fact that the reconfigured adaptive filtering algorithm more effectively suppresses high-frequency vibration sources, enhancing the overall suppression effect.

[0088] In one embodiment, the signal from the high-frequency vibration source can be input into a reconfigured adaptive filtering algorithm for processing. The filtering result output by the algorithm is the suppression strategy for the high-frequency vibration source.

[0089] S403: Based on the energy proportion of the low-frequency vibration source, determine the parameter configuration of the compensation algorithm based on model predictive control to obtain the suppression strategy of the low-frequency vibration source. The parameter configuration includes the prediction time domain length and the control time domain length.

[0090] In this application, the compensation algorithm based on model predictive control calculates the control quantity by predicting the future state of the system to compensate for system errors. The prediction time domain length determines the time range of the prediction; the longer the length, the richer the predicted information, but the computational load also increases. The control time domain length defines the time interval of the control action, affecting the timeliness and stability of the control. Adjusting these parameters according to the energy proportion of the low-frequency vibration source allows the compensation algorithm based on model predictive control to better adapt to the characteristics of the low-frequency vibration source. For example, when the energy proportion of the low-frequency vibration source is large, increasing the prediction time domain length can improve the accuracy of the prediction, while decreasing the control time domain length can enhance the timeliness of the control. The technical effect is that reasonable parameter configuration enables the compensation algorithm based on model predictive control to more effectively suppress low-frequency vibration sources and improve the operational stability of the robot.

[0091] In one embodiment, an experimental method can be used to determine the optimal parameter configuration under different energy ratios. Multiple experiments are conducted on the robot under different low-frequency vibration source energy ratios, recording the vibration suppression effect under different combinations of prediction and control time domain lengths. By comparing the experimental results, the optimal parameter configuration is determined, and a correspondence between energy ratios and parameter configurations is established.

[0092] In one embodiment, step S403 can be implemented as follows:

[0093] S4031: Obtain the ratio of the energy percentage of the low-frequency vibration source to the preset energy threshold.

[0094] In this application, the preset energy threshold is a pre-defined reference value used to measure the proportion of energy from low-frequency vibration sources. Let the proportion of low-frequency vibration source energy be P_lf, and the preset energy threshold be T_lf, then the ratio R_lf = P_lf / T_lf. Calculating the ratio of the proportion of low-frequency vibration source energy to the preset energy threshold allows for a direct assessment of the low-frequency vibration source energy proportion relative to the preset standard. This ratio is a crucial basis for subsequent adjustments to the compensation algorithm parameters based on model predictive control. For example, a larger ratio indicates a larger proportion of low-frequency vibration source energy, requiring corresponding adjustments to the algorithm parameters to enhance the suppression effect. The technical advantage lies in that the calculation of this ratio provides a clear criterion for subsequent parameter adjustments, facilitating a more rational configuration of the algorithm parameters.

[0095] In one embodiment, a preset energy threshold can be stored in the system's database. After obtaining the energy proportion of the low-frequency vibration source, the ratio is obtained by dividing it by the preset energy threshold using a software program.

[0096] S4032: If the ratio is greater than the second energy threshold, then increase the prediction time domain length of the model-based predictive control and decrease the control time domain length.

[0097] In this application, the second energy threshold is a critical value used to determine whether the effect of the compensation algorithm based on model predictive control needs to be strengthened. When the ratio is greater than the second energy threshold, it indicates that the energy proportion of the low-frequency vibration source is relatively large, and its impact on the robot is relatively significant. Let the original prediction time domain length be Tp_0, and the adjusted prediction time domain length be Tp_1 = Tp_0 + ΔTp (ΔTp is the increase in prediction time domain length); let the original control time domain length be Tc_0, and the adjusted control time domain length be Tc_1 = Tc_0 - ΔTc (ΔTc is the decrease in control time domain length). Increasing the prediction time domain length allows the algorithm to obtain more future information, improves the accuracy of prediction, and thus better copes with changes in low-frequency vibration. Decreasing the control time domain length can enhance the timeliness of control, enabling the algorithm to compensate for low-frequency vibration more quickly. The technical effect is that, through this parameter adjustment method, the suppression effect of the compensation algorithm based on model predictive control can be improved when the energy proportion of the low-frequency vibration source is large, thereby enhancing the operational stability of the robot.

[0098] In one embodiment, a predicted time domain length increase and a control time domain length decrease can be preset. When the ratio is greater than a second energy threshold, the current predicted time domain length is added to the predicted time domain length increase, while the current control time domain length is subtracted from the control time domain length decrease.

[0099] S4033: If the ratio is less than or equal to the second energy threshold, then reduce the prediction time domain length of the model predictive control and increase the control time domain length.

[0100] In this application, when the ratio is less than or equal to the second energy threshold, it indicates that the energy proportion of the low-frequency vibration source is relatively small, and its impact on the robot is relatively small. Let the original prediction time domain length be Tp_0, and the adjusted prediction time domain length be Tp_1 = Tp_0 - ΔTp (ΔTp is the reduction in prediction time domain length); let the original control time domain length be Tc_0, and the adjusted control time domain length be Tc_1 = Tc_0 + ΔTc (ΔTc is the increase in control time domain length). Reducing the prediction time domain length can reduce the computational load and improve the algorithm's operating efficiency. Increasing the control time domain length can make the control action more stable and avoid excessively frequent control. The technical effect is that, when the energy proportion of the low-frequency vibration source is small, this parameter adjustment method can improve the algorithm's operating efficiency and control stability while ensuring a certain suppression effect.

[0101] In one embodiment, a predicted time domain length reduction amount and a control time domain length increase amount can be preset. When the ratio is less than or equal to a second energy threshold, the predicted time domain length reduction amount is subtracted from the current predicted time domain length, while the control time domain length increase amount is added to the current control time domain length.

[0102] S4034: Reconfigure the parameters of the compensation algorithm based on model predictive control according to the adjusted prediction time domain length and control time domain length.

[0103] In this application, reconfiguring the parameters of the model predictive control-based compensation algorithm is a crucial step to ensure that the algorithm operates according to the adjusted parameters. Based on the adjusted prediction and control time domain lengths, the relevant parameters in the algorithm are updated, enabling the algorithm to compensate for low-frequency vibration sources with the new parameter settings. The technical advantage lies in the fact that accurate parameter reconfiguration ensures that the model predictive control-based compensation algorithm operates according to the expected parameter settings, achieving effective suppression of low-frequency vibration sources.

[0104] In one embodiment, the adjusted prediction time domain length and control time domain length can be written into the corresponding parameter positions by modifying the code or configuration file of the compensation algorithm based on model predictive control.

[0105] S4035: The reconfigured model predictive control-based compensation algorithm is applied to the suppression of low-frequency vibration sources to obtain a suppression strategy for low-frequency vibration sources.

[0106] In this application, a reconfigured model predictive control-based compensation algorithm is applied to suppress low-frequency vibration sources, enabling targeted compensation based on the actual conditions of the low-frequency vibration sources. Through compensation, the impact of low-frequency vibration sources on the robot is reduced, resulting in a low-frequency vibration source suppression strategy. This suppression strategy effectively improves the robot's operational stability in low-frequency vibration environments. The technical advantage lies in the fact that the reconfigured model predictive control-based compensation algorithm can more effectively suppress low-frequency vibration sources, thus improving the effectiveness of low-frequency vibration suppression.

[0107] In one embodiment, relevant data from the low-frequency vibration source can be input into a reconfigured model predictive control-based compensation algorithm for processing. The compensation result output by the algorithm is the suppression strategy for the low-frequency vibration source.

[0108] S404: Superimpose the suppression strategies for high-frequency vibration sources and low-frequency vibration sources to generate a comprehensive control command.

[0109] In this application, the superposition processing is a process of integrating suppression strategies for high-frequency and low-frequency vibration sources. The suppression strategies for high-frequency and low-frequency vibration sources are determined based on their respective characteristics and energy proportions. The resulting integrated control command, generated after superposition processing, can simultaneously suppress both high-frequency and low-frequency vibrations. This superposition processing method fully considers the characteristics of vibration sources of different frequencies, enabling composite suppression of multi-frequency vibrations. Its technical effect is that the generated integrated control command can comprehensively and effectively suppress multi-frequency vibrations of the robot, improving the robot's operational accuracy and stability.

[0110] In one embodiment, a frequency domain superposition method can be used for processing. First, the control commands corresponding to the suppression strategies of the high-frequency vibration source and the low-frequency vibration source are respectively transformed in the frequency domain to extract their respective frequency components. Then, these frequency components are superimposed in the frequency domain to obtain the frequency domain integrated control command. Finally, the frequency domain integrated control command is inversely transformed to convert it into a time domain integrated control command.

[0111] In one embodiment, step S404 can be implemented as follows:

[0112] S4041: Obtain the control commands corresponding to the suppression strategies for high-frequency vibration sources and low-frequency vibration sources respectively.

[0113] In this application, the control commands corresponding to the suppression strategies for high-frequency and low-frequency vibration sources are specific control signals generated based on their respective suppression strategies. These control commands contain action requirements for the robot actuators, used to suppress vibrations of the corresponding frequencies. Acquiring these control commands separately provides the basic data for subsequent superposition processing. The technical advantage lies in the fact that accurately acquiring the control commands ensures the accuracy of subsequent superposition processing, providing a foundation for generating effective integrated control commands.

[0114] In one embodiment, control commands corresponding to the suppression strategies for high-frequency and low-frequency vibration sources can be extracted from the outputs of the adaptive filtering algorithm and the compensation algorithm based on model predictive control.

[0115] S4042: Perform frequency domain transformation on the control command of the high-frequency vibration source and extract its high-frequency components.

[0116] In this application, frequency domain transformation is performed on the control commands for the high-frequency vibration source to convert the time-domain signal to the frequency domain, enabling better analysis and processing of high-frequency components. High-frequency components refer to the signal components in the control commands that fall within the frequency range of the high-frequency vibration source. By extracting high-frequency components, high-frequency vibration can be suppressed more effectively. The technical advantage lies in the fact that accurate extraction of high-frequency components improves the precision of high-frequency vibration suppression and reduces interference from low-frequency components.

[0117] In one embodiment, the control command signal of the high-frequency vibration source is denoted as a time-domain signal x(t), which is converted to the frequency domain using a Fast Fourier Transform (FFT) to obtain X(f), where f represents the frequency. In the frequency domain, high-frequency components are typically located in a higher frequency range.

[0118] In one embodiment, step S4042 can be implemented as follows:

[0119] A: Obtain the control command signal of the high-frequency vibration source, and convert the control command signal from the time domain to the frequency domain using the fast Fourier transform algorithm.

[0120] In this application, the control command signal for the high-frequency vibration source is generated by an adaptive filtering algorithm based on the characteristics and energy proportion of the high-frequency vibration source. This signal is used to control the robot's actuator movements to suppress high-frequency vibrations. The Fast Fourier Transform (FFT) algorithm is an efficient algorithm for converting time-domain signals to frequency-domain signals. It can transform large amounts of data in a short time, greatly improving processing efficiency. After converting the control command signal from the time domain to the frequency domain using the FFT algorithm, the frequency components of the signal are clearly presented. Each frequency component corresponds to a different vibration frequency, and its amplitude represents the intensity of that frequency component. This conversion process provides the basis for subsequent screening of high-frequency components. Its technical advantage lies in its ability to efficiently and accurately convert time-domain signals to frequency-domain signals, facilitating the analysis and processing of high-frequency vibration components.

[0121] In one embodiment, a specialized signal processing library, such as the NumPy library in Python, can be used to call its FFT function to process the control command signal of the high-frequency vibration source. First, the control command signal is input into the FFT function as an array. The function automatically performs a Fast Fourier Transform and outputs the frequency domain signal X(f).

[0122] B: In the frequency domain, frequency components corresponding to high-frequency vibration sources are selected according to the preset frequency threshold range to form high-frequency component data.

[0123] In this application, the preset frequency threshold range is pre-set based on the typical frequency range of high-frequency vibration sources during robot operation. Let the preset frequency threshold range be [f1, f2]. In the frequency domain signal X(f), each frequency component is traversed. When the frequency f satisfies f1 ≤ f ≤ f2, the frequency component and its amplitude information are retained, forming high-frequency component data X_hf(f). The formed high-frequency component data only contains frequency components related to the high-frequency vibration source, removing other unnecessary interference components. This allows for more targeted suppression of high-frequency vibrations, improving the suppression effect. Its technical advantage lies in accurately filtering out the frequency components corresponding to the high-frequency vibration source, providing a cleaner signal for subsequent processing.

[0124] In one embodiment, a program can be written to traverse each frequency component in the frequency domain signal and determine whether its frequency is within a preset frequency threshold range. If it is within the range, the frequency component and its amplitude information are retained to form high-frequency component data.

[0125] C: The high-frequency component data is normalized to ensure that its amplitude characteristics match the input requirements of the robot actuator.

[0126] In this application, amplitude normalization is used to adjust the amplitude of high-frequency component data to a suitable range, ensuring that its amplitude characteristics meet the input requirements of the robot actuator. Let the high-frequency component data be X_hf(f), with its maximum amplitude being max(|X_hf(f)|). Amplitude normalization is performed using the formula X_norm_hf(f) = X_hf(f) / max(|X_hf(f)|), resulting in the normalized high-frequency component data X_norm_hf(f). Since different robot actuators have different requirements for the amplitude of the input signal, if the amplitude of the high-frequency component data is too large or too small, it may cause the actuator to fail to respond normally or over-respond. Amplitude normalization unifies the amplitude of the high-frequency component data to a suitable range, ensuring that the robot actuator can accurately execute control commands. Its technical effect is to match the amplitude characteristics of the high-frequency component data with the input requirements of the robot actuator, improving the effectiveness and reliability of the control commands.

[0127] In one embodiment, a linear normalization method can be used to find the maximum and minimum values ​​for each data sequence, and then normalize each data point according to the formula so that its amplitude range is between [0, 1].

[0128] S4043: Perform frequency domain transformation on the control command of the low-frequency vibration source and extract its low-frequency components.

[0129] In this application, the purpose of frequency domain transformation and low-frequency component extraction of the control command for a low-frequency vibration source is similar to that of high-frequency component extraction. Let the control command signal for the low-frequency vibration source be a time-domain signal y(t), which is transformed to the frequency domain using a Fast Fourier Transform (FFT) to obtain Y(f). In the frequency domain, low-frequency components are typically located in a lower frequency range. Assuming a preset low-frequency threshold range of [f3, f4], in the frequency domain signal Y(f), when the frequency f satisfies f3 ≤ f ≤ f4, this frequency component and its amplitude information are retained, forming the low-frequency component data Y_lf(f). Transforming the time-domain control command to the frequency domain allows for clearer separation of the signal components of the low-frequency vibration source. Low-frequency components refer to the signal components in the control command that fall within the frequency range of the low-frequency vibration source. Extracting low-frequency components helps to specifically suppress low-frequency vibrations. The technical effect is that accurate extraction of low-frequency components improves the suppression effect of low-frequency vibrations and reduces interference from high-frequency components.

[0130] In one embodiment, a method similar to that used for processing high-frequency vibration source control commands can be employed, using an FFT algorithm to convert the control command signal of the low-frequency vibration source to the frequency domain, and then filtering out the low-frequency component data according to a preset low-frequency threshold range.

[0131] S4044: The high-frequency component and the low-frequency component are superimposed in the frequency domain to generate a frequency domain integrated control command.

[0132] In this application, the normalized high-frequency component data X_norm_hf(f) and low-frequency component data Y_lf(f) are superimposed in the frequency domain to generate a frequency-domain integrated control command Z(f) = X_norm_hf(f) + Y_lf(f). Superimposing the high-frequency and low-frequency components in the frequency domain integrates vibration suppression signals of two different frequencies. The frequency-domain integrated control command contains comprehensive suppression information for both high-frequency and low-frequency vibrations. By superimposing in the frequency domain, interference and distortion that may occur with time-domain superposition can be avoided. The technical advantage is that the generated frequency-domain integrated control command can effectively suppress both high-frequency and low-frequency vibrations simultaneously, improving the overall suppression effect of multi-frequency vibrations.

[0133] In one embodiment, the corresponding frequency components of the high-frequency component data and the low-frequency component data can be directly added together to obtain the frequency domain integrated control command.

[0134] S4045: Perform an inverse transformation on the frequency domain integrated control command to generate a time domain integrated control command.

[0135] In this application, the frequency-domain integrated control command Z(f) is subjected to an inverse fast Fourier transform (IFFT) to obtain the time-domain integrated control command z(t). This inverse transform of the frequency-domain integrated control command is to convert the frequency-domain signal back to a time-domain signal so that the robot actuator can understand and execute it. The time-domain integrated control command is the signal ultimately used to control the robot actuator's actions. Its technical advantage lies in the ability to accurately convert the suppression strategy designed and optimized in the frequency domain into specific control actions of the robot during actual operation, achieving composite suppression of multi-frequency vibrations.

[0136] In one embodiment, step S20 can be implemented as follows:

[0137] S201: Preprocess the dynamic operation data to remove noise interference and extract feature data related to dynamic disturbances.

[0138] In this application, dynamic operational data may be subject to various noise interferences during the acquisition process, such as sensor noise and environmental noise. These noises can affect the accurate identification of dynamic disturbance sources. The purpose of preprocessing is to remove these noise interferences, making the data cleaner. Simultaneously, extracting feature data related to dynamic disturbances can highlight information related to the vibration source in the data and reduce interference from irrelevant information. For example, noise can be removed through filtering and smoothing, and feature extraction algorithms can be used to extract feature data such as vibration frequency and amplitude changes. Let the dynamic operational data be D(t), and after preprocessing, feature data D_pre(t) is obtained. The technical effect is that it improves the quality and usability of the data, provides more accurate input for subsequent analytical algorithms, and thus improves the accuracy of dynamic disturbance source identification.

[0139] In one embodiment, a low-pass filter can be used to remove high-frequency noise, and a median filter algorithm can be used to remove impulse noise. For feature extraction, a wavelet transform algorithm can be used, which can decompose the signal at different scales and extract different features of the signal.

[0140] S202: Input the feature data into the inertia disturbance analysis module and calculate the spectral distribution of the feature data using the Fourier transform algorithm.

[0141] In this application, the time-domain signal is converted into a frequency-domain signal using the Fourier transform algorithm. Although the feature data has been preprocessed to extract information related to dynamic disturbances, its frequency characteristics are difficult to visualize intuitively in the time domain. The Fourier transform algorithm transforms the feature data D_pre(t) from the time domain to the frequency domain, yielding the spectral distribution D_freq(f). The spectral distribution reflects the vibration components and their intensities at different frequencies, providing parameters for identifying the frequency ranges of low-frequency and high-frequency vibration sources. The technical advantage lies in clearly presenting the frequency characteristics of the feature data, facilitating subsequent analysis and identification of vibration sources at different frequencies.

[0142] In one embodiment, the feature data can be processed using the Discrete Fourier Transform (DFT) algorithm or its fast implementation, the Fast Fourier Transform (FFT) algorithm. The feature data is input into the algorithm as an array, and the algorithm outputs the spectral distribution of the feature data.

[0143] S203: Based on the spectrum distribution, identify the frequency ranges of the low-frequency vibration source and the high-frequency vibration source.

[0144] In this application, the spectral distribution visually illustrates the distribution of different frequency components in the feature data. Based on the peak positions and frequency distribution in the spectral distribution, the frequency ranges of low-frequency and high-frequency vibration sources can be determined. Let the spectral distribution be D_freq(f). By analyzing the spectrum, the frequency range [f_lf_min, f_lf_max] corresponding to the peaks in the low-frequency band is identified as the frequency range of the low-frequency vibration source, and the frequency range [f_hf_min, f_hf_max] corresponding to the peaks in the high-frequency band is identified as the frequency range of the high-frequency vibration source. Accurately identifying vibration sources in different frequency ranges provides a basis for subsequently employing different suppression algorithms. Its technical effect lies in clarifying the frequency ranges of low-frequency and high-frequency vibration sources, thereby improving the targeting of vibration suppression.

[0145] S204: Based on the frequency range, and in conjunction with the torque feedback signal and acceleration data in the dynamic operation data, determine the amplitude characteristics of the low-frequency vibration source and the high-frequency vibration source.

[0146] In this application, the frequency range defines the approximate frequency intervals of the low-frequency and high-frequency vibration sources. However, knowing only the frequency range is insufficient to fully understand the characteristics of the vibration sources. Torque feedback signals and acceleration data contain information about the force conditions and motion state changes of the robot during its movement. Combining this data allows for a more accurate determination of the amplitude characteristics of the low-frequency and high-frequency vibration sources. Let the torque feedback signal be T(t) and the acceleration data be a(t). Based on the frequency range, torque feedback signals T_lf(t) and T_hf(t) and acceleration data a_lf(t) and a_hf(t) for the corresponding frequencies are selected, and further processing is performed to determine the amplitude characteristics. The technical advantage lies in comprehensively considering multiple data sources to more accurately determine the amplitude characteristics of the low-frequency and high-frequency vibration sources, providing a reference for developing more effective vibration suppression strategies.

[0147] In one embodiment, step S204 of this application embodiment can be implemented in the following manner:

[0148] S2041: Based on the frequency range, extract the torque feedback signal and acceleration data corresponding to the low-frequency vibration source and the high-frequency vibration source from the dynamic operation data.

[0149] In this application, the frequency ranges for low-frequency and high-frequency vibration sources have been clearly defined. Extracting torque feedback signals and acceleration data corresponding to these frequency ranges from dynamic operating data allows for focusing on data relevant to the vibration source and reducing interference from irrelevant data. For example, by setting filters based on the frequency range, torque feedback signals and acceleration data within specific frequency intervals can be selected. The extracted data thus more accurately reflects the characteristics of low-frequency and high-frequency vibration sources. The technical advantage lies in improving the relevance and effectiveness of the data, providing more accurate foundational data for subsequent determination of amplitude characteristics.

[0150] In one embodiment, a bandpass filter can be used. The passband of the filter is set according to the frequency range of the low-frequency vibration source and the high-frequency vibration source. The torque feedback signal and acceleration data in the dynamic operation data are filtered to extract the data within the corresponding frequency range.

[0151] S2042: Normalize the torque feedback signal and acceleration data respectively to eliminate the influence of amplitude differences.

[0152] In this application, the torque feedback signal and acceleration data may exhibit amplitude differences due to factors such as different sensor characteristics and measurement ranges. These amplitude differences can affect the accurate subsequent judgment of the vibration source's amplitude characteristics. Normalization unifies the data amplitudes to a specific range, eliminating the impact of these amplitude differences. Through normalization, different data can be compared and processed on the same scale, improving the accuracy of the analysis. Its technical effect is to make the torque feedback signal and acceleration data comparable in amplitude, facilitating subsequent superposition calculations and amplitude analysis.

[0153] In one embodiment, a linear normalization method can be used to find the maximum and minimum values ​​for each data sequence, and then normalize each data point according to the formula so that its amplitude range is between [0, 1].

[0154] S2043: The normalized torque feedback signal and acceleration data are superimposed to obtain the superimposed signal.

[0155] In this application, the normalized torque feedback signal and acceleration data are superimposed to comprehensively consider the information contained in these two types of data. The torque feedback signal reflects the force on the robot joints, while the acceleration data reflects the changes in the robot's motion state. Superimposing them provides a more comprehensive reflection of the vibration source's characteristics. The superimposed signal contains integrated information from both types of data, enabling a more accurate representation of the characteristics of both low-frequency and high-frequency vibration sources. The technical advantage lies in the fact that the superimposed signal obtained through this operation better represents the actual situation of the vibration source, providing more reliable data for subsequent amplitude analysis.

[0156] In one embodiment, the normalized torque feedback signal and the corresponding data points of the acceleration data can be added together to obtain a superimposed signal.

[0157] S2044: Perform amplitude analysis on the superimposed signal to determine the amplitude characteristics of the low-frequency vibration source and the high-frequency vibration source.

[0158] In this application, amplitude analysis processes superimposed signals to determine the magnitude and variation patterns of the amplitudes of low-frequency and high-frequency vibration sources. By analyzing the amplitude of the superimposed signal across different frequency ranges, the amplitude characteristics of each vibration source can be obtained. For example, spectral analysis can be used to perform a Fourier transform on the superimposed signal to obtain its spectral distribution, and then the amplitudes of the low-frequency and high-frequency vibration sources can be determined based on the frequency range. The technical advantage lies in accurately determining the amplitude characteristics of low-frequency and high-frequency vibration sources, providing key parameters for developing effective vibration suppression strategies.

[0159] S205: Output the frequency range and amplitude characteristics as the identification result of the dynamic disturbance source.

[0160] In this application, frequency range and amplitude characteristics are output as the identification results of dynamic disturbance sources, providing complete information for the subsequent formulation of vibration suppression strategies. The frequency range clarifies the frequency characteristics of different vibration sources, while the amplitude characteristics reflect the intensity of the vibration source. This information forms the basis for developing targeted vibration suppression strategies. Subsequently, adaptive filtering algorithms can be used to suppress high-frequency vibration sources, and model predictive control-based compensation algorithms can be used to suppress low-frequency vibration sources. The technical advantage lies in providing accurate and complete information on dynamic disturbance sources for subsequent vibration suppression, thereby improving the effectiveness of vibration suppression strategies.

[0161] In one embodiment, the dynamic adjustment of parameters in the multi-band composite suppression strategy is to enable the vibration suppression strategy to adapt to changes in the vibration source during robot operation. Since factors such as the robot's working environment and load may change, the characteristics of the vibration source also change. By dynamically adjusting the parameters, the suppression strategy can be guaranteed to maintain optimal suppression effect, improving the strategy's adaptability and robustness. The dynamically adjusted parameters can be as follows:

[0162] S61: Determine the energy proportion distribution characteristics of high-frequency vibration sources and low-frequency vibration sources within the current monitoring period, and collect the energy proportion change trend within at least three consecutive monitoring periods, wherein the at least three consecutive monitoring periods include the current monitoring period and the two previous historical monitoring periods.

[0163] This application determines the energy proportion distribution characteristics of high-frequency and low-frequency vibration sources within the current monitoring period, enabling an understanding of the relative intensity of vibration sources at different frequencies at the current moment. Let the current monitoring period be n, the energy proportion of the high-frequency vibration source be P_hf(n), and the energy proportion of the low-frequency vibration source be P_lf(n). The energy proportion change trend is collected over at least three consecutive monitoring periods, namely, P_hf(n-2), P_hf(n-1), P_hf(n) and P_lf(n-2), P_lf(n-1), P_lf(n). By observing the change trend over multiple periods, future changes in vibration source characteristics can be predicted, providing a basis for parameter adjustment. For example, if the energy proportion of the high-frequency vibration source shows an increasing trend over several consecutive periods, it indicates that the influence of high-frequency vibration is gradually increasing, requiring corresponding adjustments to the suppression strategy parameters. The technical advantage lies in providing historical data and trend information for dynamic parameter adjustment, making the adjustment more scientific and reasonable.

[0164] S62: Based on the energy proportion change trend, calculate the mean and variance of the energy proportion of high-frequency vibration sources and low-frequency vibration sources in each monitoring cycle.

[0165] In this application, the mean energy percentage reflects the average level of the energy percentage of high-frequency vibration sources and low-frequency vibration sources over a certain period of time, while the variance reflects the degree of fluctuation in the energy percentage. For high-frequency vibration sources, the mean energy percentage is μ_hf = (P_hf (n - 2) + P_hf (n - 1) + P_hf (n)) / 3, and the variance is σ_hf^2 = [(P_hf (n - 2) - μ_hf)^2 + (P_hf (n - 1) - μ_hf)^2 + (P_hf (n) - μ_hf)^2] / 3; similarly, for low-frequency vibration sources, the mean energy percentage is μ_lf = (P_lf (n - 2) + P_lf (n - 1) + P_lf (n)) / 3, and the variance is σ_lf^2 = [(P_lf (n - 2) - μ_lf)^2 + (P_lf (n - 1) - μ_lf)^2 + (P_lf (n) - μ_lf)^2] / 3. By calculating the mean and variance, a more comprehensive understanding of the statistical characteristics of the energy proportion can be obtained. For example, a large variance indicates significant fluctuations in the energy proportion, suggesting instability in the characteristics of the vibration source and necessitating more frequent parameter adjustments. The technical advantage lies in providing quantified statistical information for generating dynamic adjustment factors, enabling these factors to more accurately reflect changes in the energy proportion.

[0166] In one embodiment, statistical analysis software, such as the NumPy library in Python, can be used to call the mean and variance calculation functions to process the energy proportion data within at least three consecutive monitoring periods, and calculate the mean and variance of the energy proportion of high-frequency vibration sources and low-frequency vibration sources within each monitoring period.

[0167] S63: Based on the mean and variance, generate a dynamic adjustment factor, which is used to reflect the degree of influence of changes in energy proportion on parameter configuration.

[0168] In this application, the dynamic adjustment factor is a coefficient generated based on the mean and variance of the energy proportion, reflecting the degree of influence of changes in the energy proportion on parameter configuration. Let the dynamic adjustment factor for the high-frequency vibration source be k_hf, and the dynamic adjustment factor for the low-frequency vibration source be k_lf. These can be generated using the formulas k_hf = f (μ_hf, σ_hf^2) and k_lf = f (μ_lf, σ_lf^2), where f is a predefined function that determines the value of the dynamic adjustment factor based on the magnitude of the mean and variance. If the mean or variance of the energy proportion changes significantly, it indicates a significant change in the characteristics of the vibration source, requiring substantial adjustments to the parameter configuration. The dynamic adjustment factor can be dynamically calculated based on the magnitude of the mean and variance, making parameter adjustment more flexible and accurate. Its technical advantage lies in providing a reasonable basis for parameter configuration adjustments based on the actual changes in the energy proportion, thereby improving the adaptability of the suppression strategy.

[0169] In one embodiment, a piecewise function can be designed as the f function. For example, when the variance exceeds a certain threshold, the value of the dynamic adjustment factor is increased; when the mean deviates significantly from the normal range, the dynamic adjustment factor is also adjusted accordingly.

[0170] S64: Apply the dynamic adjustment factor to the parameter configuration process of the current monitoring cycle, and adjust the filter order, step size factor, prediction time domain length and control time domain length of the adaptive filtering algorithm and the model predictive control.

[0171] In this application, the dynamic adjustment factor is applied to the parameter configuration process of the current monitoring period to adjust the parameters of the suppression strategy in real time according to the changes in energy proportion. Let the filter order of the original adaptive filtering algorithm be N_0, and the step size factor be μ_0; let the prediction time domain length of the original model predictive control be Tp_0, and the control time domain length be Tc_0. The adjusted filter order N_1 = N_0 × k_hf, the adjusted step size factor μ_1 = μ_0 / k_hf; the adjusted prediction time domain length Tp_1 = Tp_0 × k_lf, and the adjusted control time domain length Tc_1 = Tc_0 / k_lf. The filter order and step size factor of the adaptive filtering algorithm, and the prediction and control time domain lengths of the model predictive control, directly affect the suppression effect on high-frequency and low-frequency vibration sources. By dynamically adjusting these parameters, the suppression strategy can better adapt to changes in the characteristics of the vibration sources. For example, if the dynamic adjustment factor indicates that the influence of high-frequency vibration sources is increasing, the filter order of the adaptive filtering algorithm is appropriately increased, and the step size factor is decreased. Its technical advantage lies in enabling the parameters of the suppression strategy to be dynamically adjusted according to the actual situation, thereby improving the vibration suppression effect.

[0172] In one embodiment, the filter order, step size factor, prediction time domain length, and control time domain length of the adaptive filtering algorithm can be adjusted according to the above formula based on the magnitude of the dynamic adjustment factor. The adjusted parameters are then updated in the corresponding algorithm.

[0173] S65: Apply the adjusted parameter configuration to the suppression strategies for high-frequency and low-frequency vibration sources to improve the adaptability and robustness of the multi-band composite suppression strategy.

[0174] In this application, the adjusted parameter configuration is applied to the suppression strategies for high-frequency and low-frequency vibration sources, thus implementing the results of dynamic adjustment into the actual vibration suppression process. By adjusting the parameters, the suppression strategy can better adapt to changes in the characteristics of the vibration source, improving the ability to suppress multi-frequency vibrations under different working conditions. Adaptability is reflected in the ability to adjust the strategy in a timely manner according to changes in the vibration source, while robustness is reflected in the strategy maintaining good suppression effects under different working conditions. The adjusted parameter configuration is input into an adaptive filtering algorithm and a compensation algorithm based on model predictive control to regenerate the suppression strategies for high-frequency and low-frequency vibration sources, thereby generating comprehensive control commands and sending them to the robot actuator. The technical effect is that it improves the performance of the multi-band composite suppression strategy, enabling the robot to operate stably in complex and changing working environments and reducing the impact of vibration on its operation.

[0175] In one embodiment, the adjusted parameter configuration is updated in the adaptive filtering algorithm and the model predictive control-based compensation algorithm to regenerate suppression strategies for high-frequency and low-frequency vibration sources. The integrated control commands are then sent to the robot actuator to effectively suppress multi-frequency vibrations. Simultaneously, the robot's operating status and vibration conditions are continuously monitored, and the dynamic adjustment process is continuously optimized.

[0176] Accordingly, to better implement the above methods, this application also provides a robot multi-frequency vibration composite suppression system based on inertia perturbation analysis. This robot multi-frequency vibration composite suppression system based on inertia perturbation analysis includes:

[0177] The acquisition module is used to acquire dynamic operation data collected by the robot through sensors during operation. The dynamic operation data includes joint angle change information, acceleration data, and torque feedback signals.

[0178] The motion source identification module is used to input the dynamic operation data into the inertia disturbance analysis module, and identify the dynamic disturbance sources in the robot operation through the analysis algorithm. The dynamic disturbance sources include low-frequency vibration sources caused by mechanical structures and high-frequency vibration sources caused by the drive system.

[0179] The distribution map generation module is used to generate a corresponding disturbance frequency distribution map based on the identification results of the dynamic disturbance source. The disturbance frequency distribution map includes the frequency range and amplitude characteristics of each disturbance source.

[0180] The suppression module is used to perform collaborative processing on the high-frequency vibration source and the low-frequency vibration source according to the disturbance frequency distribution map, so as to generate a comprehensive control command corresponding to the multi-frequency vibration composite suppression strategy. The suppression of the high-frequency vibration source adopts an adaptive filtering algorithm, and the suppression of the low-frequency vibration source adopts a compensation algorithm based on model predictive control.

[0181] The sending module is used to send the integrated control commands to the robot actuator to achieve multi-frequency vibration composite suppression of the robot.

[0182] The implementation details of each module are provided in the preceding method embodiments and will not be repeated here. The technical effects achieved by each module and device are described in the foregoing method embodiments.

[0183] like Figure 2 As shown, this application embodiment also provides a computer device 90, which includes a processor 901 and a memory 902, wherein the memory 902 stores a computer program, and when the computer program is executed by the processor 901, the processor 901 performs the steps of any of the methods described above.

[0184] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A robot multi-frequency vibration compound suppression method based on inertia disturbance analysis, characterized in that, The method comprises the following steps: acquiring dynamic operation data collected by sensors during operation of the robot, the dynamic operation data comprising joint angle change information, acceleration data and torque feedback signals; inputting the dynamic operation data into an inertia disturbance analysis module to identify dynamic disturbance sources in the operation of the robot by an analysis algorithm, the dynamic disturbance sources comprising low-frequency vibration sources caused by mechanical structures and high-frequency vibration sources caused by drive systems; generating a corresponding disturbance frequency distribution map according to the identification results of the dynamic disturbance sources, the disturbance frequency distribution map containing the frequency range and amplitude characteristics of each disturbance source; acquiring the frequency range and amplitude characteristics of the high-frequency vibration sources and the low-frequency vibration sources from the disturbance frequency distribution map and calculating the energy proportion of the high-frequency vibration sources and the low-frequency vibration sources respectively; determining the parameter configuration of an adaptive filtering algorithm according to the energy proportion of the high-frequency vibration sources to obtain a suppression strategy for the high-frequency vibration sources, the parameter configuration comprising filter order and step factor; determining the parameter configuration of a compensation algorithm based on model predictive control according to the energy proportion of the low-frequency vibration sources to obtain a suppression strategy for the low-frequency vibration sources, the parameter configuration comprising prediction time domain length and control time domain length; superimposing the suppression strategies for the high-frequency vibration sources and the low-frequency vibration sources to generate a comprehensive control instruction, wherein the suppression of the high-frequency vibration sources adopts the adaptive filtering algorithm and the suppression of the low-frequency vibration sources adopts the compensation algorithm based on model predictive control; sending the comprehensive control instruction to the robot actuator to implement multi-frequency vibration composite suppression of the robot.

2. The method of claim 1, wherein, The method of determining the parameter configuration of the adaptive filtering algorithm according to the energy proportion of the high-frequency vibration sources to obtain the suppression strategy for the high-frequency vibration sources comprises the following steps: acquiring the ratio of the energy proportion of the high-frequency vibration sources to a preset energy threshold value; if the ratio is greater than a first energy threshold value, increasing the filter order of the adaptive filtering algorithm and decreasing the step factor; if the ratio is less than or equal to the first energy threshold value, decreasing the filter order of the adaptive filtering algorithm and increasing the step factor; reconfiguring the parameters of the adaptive filtering algorithm according to the adjusted filter order and step factor; applying the reconfigured adaptive filtering algorithm to the suppression processing of the high-frequency vibration sources to obtain the suppression strategy for the high-frequency vibration sources.

3. The method of claim 2, wherein, The method of determining the parameter configuration of the compensation algorithm based on model predictive control according to the energy proportion of the low-frequency vibration sources to obtain the suppression strategy for the low-frequency vibration sources comprises the following steps: acquiring the ratio of the energy proportion of the low-frequency vibration sources to a preset energy threshold value; if the ratio is greater than a second energy threshold value, increasing the prediction time domain length of the model predictive control and decreasing the control time domain length; if the ratio is less than or equal to the second energy threshold value, decreasing the prediction time domain length of the model predictive control and increasing the control time domain length; reconfiguring the parameters of the compensation algorithm based on model predictive control according to the adjusted prediction time domain length and control time domain length; applying the reconfigured compensation algorithm based on model predictive control to the suppression processing of the low-frequency vibration sources to obtain the suppression strategy for the low-frequency vibration sources.

4. The method of claim 1, wherein, The superposition processing of the suppression strategies of the high-frequency vibration source and the low-frequency vibration source comprises the following steps: Respectively acquire control instructions corresponding to the suppression strategies of the high-frequency vibration source and the low-frequency vibration source; Perform frequency domain transformation on the control instruction of the high-frequency vibration source to extract high-frequency components thereof; Perform frequency domain transformation on the control instruction of the low-frequency vibration source to extract low-frequency components thereof; Perform superposition processing on the high-frequency components and the low-frequency components in the frequency domain to generate a frequency domain comprehensive control instruction; Perform inverse transformation on the frequency domain comprehensive control instruction to generate a time domain comprehensive control instruction.

5. The method of claim 4, wherein, The specific steps of performing frequency domain transformation on the control instruction of the high-frequency vibration source to extract high-frequency components thereof comprise: Acquire the control instruction signal of the high-frequency vibration source, and convert the control instruction signal from the time domain to the frequency domain through the fast Fourier transform algorithm; In the frequency domain, according to a preset frequency threshold range, filter out frequency components belonging to the high-frequency vibration source to form high-frequency component data; Perform amplitude normalization processing on the high-frequency component data to ensure that the amplitude characteristics thereof match the input requirements of the robot execution mechanism.

6. The method according to any one of claims 1 to 5, characterized in that, Input the dynamic running data into the inertia disturbance analysis module to identify dynamic disturbance sources in the robot running through an analysis algorithm, comprising the following steps: Preprocess the dynamic running data to remove noise interference and extract feature data related to dynamic disturbance; Input the feature data into the inertia disturbance analysis module to calculate the frequency spectrum distribution of the feature data through the Fourier transform algorithm; According to the frequency spectrum distribution, identify the frequency ranges of the low-frequency vibration source and the high-frequency vibration source respectively; Based on the frequency ranges, determine the amplitude characteristics of the low-frequency vibration source and the high-frequency vibration source in combination with the torque feedback signal and acceleration data in the dynamic running data; Output the frequency ranges and amplitude characteristics as the identification results of the dynamic disturbance sources.

7. The method of claim 6, wherein, Based on the frequency ranges, determine the amplitude characteristics of the low-frequency vibration source and the high-frequency vibration source in combination with the torque feedback signal and acceleration data in the dynamic running data, comprising the following steps: According to the frequency ranges, extract the torque feedback signal and acceleration data corresponding to the low-frequency vibration source and the high-frequency vibration source from the dynamic running data; Respectively perform normalization processing on the torque feedback signal and the acceleration data to eliminate the influence of amplitude differences; Perform superposition operation on the normalized torque feedback signal and acceleration data to obtain a superposition signal; Perform amplitude analysis on the superposition signal to determine the amplitude characteristics of the low-frequency vibration source and the high-frequency vibration source.

8. The method of claim 6, wherein, The method further comprises dynamically adjusting parameters in the multi-frequency band composite suppression strategy, comprising the following steps: Determine the energy proportion distribution characteristics of the high-frequency vibration source and the low-frequency vibration source in the current monitoring period, and collect the energy proportion change trend in at least three consecutive monitoring periods, the at least three consecutive monitoring periods comprising the current monitoring period and the two previous historical monitoring periods; Based on the energy proportion change trend, calculate the energy proportion mean and variance of the high-frequency vibration source and the low-frequency vibration source in each monitoring period; According to the mean and the variance, a dynamic adjustment factor is generated, which is used to reflect the influence degree of energy proportion change on parameter configuration; The dynamic adjustment factor is applied to the parameter configuration process of the current monitoring period to adjust the filter order, step factor of the adaptive filtering algorithm, and the prediction time domain length and control time domain length of the model predictive control; The adjusted parameter configuration is applied to the suppression strategy of the high-frequency vibration source and the low-frequency vibration source.

9. A robot multi-frequency vibration composite suppression system based on inertia disturbance analysis, characterized in that, The system comprises: An acquisition module is configured to acquire dynamic operation data collected by a sensor during operation of a robot, the dynamic operation data comprising joint angle change information, acceleration data, and torque feedback signals; A dynamic source identification module is configured to input the dynamic operation data into an inertia disturbance analysis module to identify dynamic disturbance sources in the operation of the robot through an analysis algorithm, the dynamic disturbance sources comprising a low-frequency vibration source caused by a mechanical structure and a high-frequency vibration source caused by a drive system; A distribution map generation module is configured to generate a corresponding disturbance frequency distribution map according to the identification results of the dynamic disturbance sources, the disturbance frequency distribution map containing the frequency range and amplitude characteristics of each disturbance source; A suppression module is configured to obtain the frequency range and amplitude characteristics of the high-frequency vibration source and the low-frequency vibration source from the disturbance frequency distribution map, and to calculate the energy proportion of the high-frequency vibration source and the low-frequency vibration source, respectively; according to the energy proportion of the high-frequency vibration source, to determine the parameter configuration of the adaptive filtering algorithm to obtain the suppression strategy of the high-frequency vibration source, the parameter configuration comprising a filter order and a step factor; according to the energy proportion of the low-frequency vibration source, to determine the parameter configuration of the compensation algorithm based on the model predictive control to obtain the suppression strategy of the low-frequency vibration source, the parameter configuration comprising a prediction time domain length and a control time domain length; to superimpose the suppression strategies of the high-frequency vibration source and the low-frequency vibration source to generate a comprehensive control instruction, wherein the suppression of the high-frequency vibration source adopts the adaptive filtering algorithm, and the suppression of the low-frequency vibration source adopts the compensation algorithm based on the model predictive control; A sending module is configured to send the comprehensive control instruction to a robot actuator to implement multi-frequency vibration composite suppression of the robot.

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