Industrial robot control method and system using PLC

Abstract

The application relates to the technical field of robot control, and particularly discloses an industrial robot control method and system applying PLC, the method comprising collecting servo driving data of an industrial robot body and communication data between a PLC and a robot controller; calculating a robot state index based on the servo driving data, and calculating a control system state index based on the communication data; fusing the robot state index and the control system state index to generate a motion state index; generating a control strategy based on the motion state index, the robot state index and the control system state index, and sending the control strategy to the robot controller; the application applies PLC to construct a closed-loop control framework, combines an industrial robot, a robot controller and a programmable logic controller (PLC) in a closed-loop control architecture, solves the problem of rigid control and lack of adaptive capacity in a traditional system, and improves overall adaptability and stability.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, specifically to an industrial robot control method and system using a PLC. Background Technology

[0002] Industrial robots, as core equipment in intelligent manufacturing, have been widely used in complex industrial scenarios such as welding, handling, spraying, and assembly. The performance of their control systems directly determines production efficiency and product quality.

[0003] Traditional industrial robot control systems generally adopt a two-level architecture of "industrial control computer / host computer + robot controller". The programmable logic controller is mainly responsible for logical sequence control, signal processing and communication with the upper-level information system, while the robot controller focuses on performing high-precision trajectory interpolation and servo drive.

[0004] As industrial applications continue to demand greater flexibility and intelligence, the limitations of this traditional architecture are becoming increasingly apparent. The control logic is mostly preset and fixed, unable to dynamically adjust based on changes in the robot's real-time operating status or fluctuations in the control system's own communication status. When encountering sudden load changes, wear and tear on mechanical parts, or network interference, the system lacks self-optimization capabilities, which can easily lead to product quality fluctuations or even equipment safety accidents. Therefore, how to construct a real-time dynamic adjustment scheme to control the robot's operation is the technical problem that this invention aims to solve. Summary of the Invention

[0005] The purpose of this invention is to provide an industrial robot control method and system using a PLC to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for controlling an industrial robot using a PLC, the method comprising:

[0008] Collect servo drive data of the industrial robot body and communication data between the PLC and the robot controller;

[0009] The robot state index is calculated based on the servo drive data, and the control system state index is calculated based on the communication data; wherein, the robot state index is a function of position tracking error, torque fluctuation rate, and volume positioning accuracy, and the control system state index is a function of network jitter, cycle time mismatch, and command smoothness.

[0010] The motion state index is generated by combining the robot state index and the control system state index.

[0011] The motion state index, robot state index, and control system state index are compared with multiple preset threshold ranges. A control strategy is generated based on the comparison results and sent to the robot controller.

[0012] As a further aspect of the present invention: the step of calculating the robot state index based on the servo drive data includes:

[0013] The absolute position error of the robot's end effector is processed by substituting it into the maximum-minimum normalization formula to obtain the position tracking error index.

[0014] The torque variability is obtained by dividing the standard deviation of torque by the maximum average torque.

[0015] The volume positioning accuracy is obtained by subtracting the positioning error volume from 1 and then dividing by the total volume of the workspace.

[0016] The robot state index is calculated based on the position tracking error index, torque fluctuation rate, and volume positioning accuracy.

[0017] As a further aspect of the present invention: the step of calculating the control system state index based on the communication data includes:

[0018] The network jitter index is obtained by dividing the network jitter time by the control period.

[0019] The difference between the actual period and the nominal period is taken as an absolute value, and then divided by the nominal period to obtain the period-time mismatch index.

[0020] The control system state index is calculated based on the network jitter index and the cycle time mismatch index.

[0021] As a further aspect of the present invention: the step of comparing the motion state index, robot state index, and control system state index with multiple preset threshold intervals, generating a control strategy based on the comparison results, and sending it to the robot controller includes:

[0022] The motion state index, robot state index, and control system state index are compared with multiple preset threshold ranges.

[0023] Based on the comparison results, select a target control strategy from the preset set of predefined control strategies;

[0024] The robot's motion control parameters are adjusted according to the target control strategy, and the adjusted control commands are sent to the robot controller.

[0025] As a further aspect of the present invention: the content of selecting a target control strategy from a preset set of predefined control strategies includes:

[0026] When the motion state index is within a preset first interval, a high-precision control strategy is selected, which uses preset maximum speed and acceleration parameters.

[0027] When the motion state index drops to a preset second range, a robust control strategy is selected. The robust control strategy maintains system stability by reducing motion speed and acceleration.

[0028] When the motion state index further drops to the preset third interval, a degradation control strategy is selected. The degradation control strategy switches the task to a backup program with lower accuracy requirements and sends an early warning signal to the upper-level system.

[0029] When the motion state index drops to the preset fourth range, a safety protection strategy is selected;

[0030] When either the robot state index or the control system state index falls below its corresponding preset safety threshold, a safety protection strategy is selected; the safety protection strategy includes safe shutdown, maintaining the current torque, and reversing along the path.

[0031] As a further aspect of the present invention, the method further includes:

[0032] Run the benchmark test program while the system is in good health, and record the calculated robot state index, control system state index and motion state index as the health benchmark values ​​for each index.

[0033] The multiple threshold ranges are set based on the health benchmark values.

[0034] The present invention also provides an industrial robot control system using a PLC, the system comprising:

[0035] The data acquisition module is used to collect servo drive data of the industrial robot body and communication data between the PLC and the robot controller;

[0036] The state evaluation module is used to calculate the robot state index based on the servo drive data and the control system state index based on the communication data; wherein, the robot state index is a function of position tracking error, torque fluctuation rate and volume positioning accuracy, and the control system state index is a function of network jitter, cycle time mismatch and command smoothness.

[0037] A motion state generation module is used to fuse the robot state index and the control system state index to generate a motion state index.

[0038] The strategy generation and sending module is used to compare the motion state index, robot state index and control system state index with multiple preset threshold intervals, generate a control strategy based on the comparison results, and send it to the robot controller.

[0039] As a further aspect of the present invention: the state assessment module includes:

[0040] The error determination unit is used to process the absolute position error of the robot end effector into the maximum-minimum normalization formula to obtain the position tracking error index.

[0041] The torque analysis unit calculates the torque variability by dividing the standard deviation of torque by the maximum average torque.

[0042] The positioning result analysis unit is used to obtain the volume positioning accuracy by subtracting the positioning error volume from 1 and then dividing by the total volume of the workspace.

[0043] The numerical application unit is used to calculate the robot state index based on the position tracking error index, torque fluctuation rate, and volume positioning accuracy.

[0044] As a further aspect of the present invention: the state assessment module further includes:

[0045] The network analysis unit is used to divide the network jitter time by the control period to obtain the network jitter index;

[0046] The mismatch parameter determination unit is used to calculate the difference between the actual period and the nominal period, take the absolute value, and then divide it by the nominal period to obtain the period-time mismatch index.

[0047] The calculation unit is used to calculate the control system state index based on the network jitter index and the cycle time mismatch index.

[0048] As a further aspect of the present invention: the strategy generation and sending module includes:

[0049] An index comparison unit is used to compare the motion state index, robot state index, and control system state index with multiple preset threshold intervals.

[0050] The strategy selection unit is used to select a target control strategy from a preset set of predefined control strategies based on the comparison results.

[0051] The adjustment instruction generation unit is used to adjust the robot's motion control parameters according to the target control strategy and send the adjusted control instructions to the robot controller.

[0052] Compared with the prior art, the beneficial effects of the present invention are: the present invention uses PLC to build a closed-loop control framework, combining industrial robot, robot controller and programmable logic controller PLC in a closed-loop control architecture, which solves the problems of rigid control and lack of adaptive ability, disconnect between state monitoring and decision control, and separation between the state of robot body and control system in traditional systems, thus achieving the effect of improving overall adaptability and stability. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0054] Figure 1 This is a flowchart illustrating the control method for industrial robots using PLC.

[0055] Figure 2 This is a block diagram showing the composition of an industrial robot control system that uses a PLC. Detailed Implementation

[0056] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0057] Figure 1 The flowchart illustrates an industrial robot control method using a PLC. In this embodiment of the invention, an industrial robot control method using a PLC includes:

[0058] Step S100: Collect servo drive data of the industrial robot body and communication data between the PLC and the robot controller;

[0059] Step S200: Calculate the robot state index based on the servo drive data, and calculate the control system state index based on the communication data; wherein, the robot state index is a function of position tracking error, torque fluctuation rate and volume positioning accuracy, and the control system state index is a function of network jitter, cycle time mismatch and command smoothness;

[0060] Step S300: Combine the robot state index and the control system state index to generate a motion state index;

[0061] The above content provides a process for monitoring and evaluating the execution status of a programmable logic controller (PLC). It quantifies the status of the robot body and the control system. First, servo drive data of the industrial robot body and communication data between the PLC and the robot controller are collected. This data, being known, provides the basic input for subsequent status evaluation. Then, a robot status index is calculated based on the servo drive data. This index is a function of position tracking error, torque fluctuation rate, and volume positioning accuracy, thus transforming the robot's mechanical performance status into a measurable indicator. Simultaneously, a control system status index is calculated based on the communication data. This index is a function of network jitter, cycle time mismatch, and command smoothness, thereby achieving a quantitative evaluation of the control system's stability. Finally, a motion status index is generated by fusing the robot status index and the control system status index. This comprehensive index eliminates the disconnect between the robot body and control system status, providing a unified basis for overall decision-making.

[0062] Step S400: Compare the motion state index, robot state index, and control system state index with multiple preset threshold intervals, generate a control strategy based on the comparison results, and send it to the robot controller.

[0063] After obtaining the motion state index, robot state index, and control system state index, these indices are compared with multiple preset threshold ranges to achieve hierarchical identification of the system state. This hierarchical comparison provides an evaluation benchmark for subsequent strategy selection. Based on the comparison results, a target control strategy is selected from multiple predefined control strategies, thereby realizing dynamic decision-making based on state evaluation. This avoids the control rigidity problem caused by traditional fixed logic. The robot's motion control parameters are adjusted according to the target control strategy, and the adjusted control commands are sent to the robot controller to complete the real-time optimization of control behavior.

[0064] Thus, the above technical solution forms a closed-loop control mechanism, in which the state monitoring and evaluation steps provide input for adaptive decision-making, while the adaptive decision-making and control steps dynamically adjust parameters based on the input, enabling the system to autonomously optimize control behavior according to real-time state changes. This effectively solves the problems of industrial robot control systems lacking adaptive capabilities, the disconnect between state monitoring and decision-making control, and the separation of the robot body's state from the control system's state.

[0065] As a preferred embodiment of the technical solution of the present invention, regarding step S200, the step of calculating the robot state index based on the servo drive data includes:

[0066] The absolute position error of the robot's end effector is processed by substituting it into the maximum-minimum normalization formula to obtain the position tracking error index.

[0067] The torque variability is obtained by dividing the standard deviation of torque by the maximum average torque.

[0068] The volume positioning accuracy is obtained by subtracting the positioning error volume from 1 and then dividing by the total volume of the workspace.

[0069] The robot state index is calculated based on the position tracking error index, torque fluctuation rate, and volume positioning accuracy.

[0070] The above content explains the application process of servo drive data. The absolute position error of the robot end effector or joint is substituted into the maximum-minimum normalization formula for processing to obtain the position tracking error index; the torque standard deviation is divided by the maximum average torque to obtain the torque fluctuation rate; and the difference between 1 and the positioning error volume divided by the total volume of the workspace is used to obtain the volume positioning accuracy.

[0071] The robot state index is calculated as follows: ;

[0072] in For the position tracking error weights, As the weight for torque volatility, Weights for volume positioning accuracy. ,and , as well as All greater than ; The position tracking error index. For torque ripple rate, Dimensionless, and , For volume positioning accuracy, Dimensionless, and , This is the robot's state index.

[0073] In this embodiment, the position tracking error index is a dimensionless index obtained by processing the absolute position error of the robot's end effector or joint using a maximum-minimum normalization formula. It can be implemented using dynamic scaling to eliminate the influence of differences in error dimensions and ranges under different working conditions, ensuring the comparability of evaluation results. Torque fluctuation rate is a relative proportional index obtained by converting the ratio of the torque standard deviation to the maximum average torque. It can be implemented using a load-based normalization method to isolate the interference of load changes on the absolute torque value, thus reflecting only the degree of abnormal fluctuation. Volumetric positioning accuracy is a three-dimensional spatial positioning performance index obtained by subtracting the positioning error volume from 1 and then dividing it by the total volume of the workspace. It can be implemented using an overall error distribution analysis method to comprehensively capture the degradation of the robot's positioning capability in the workspace.

[0074] Furthermore, the aforementioned technical solution addresses the inaccuracy in robot state assessment caused by differences in data dimensions, ranges, and ambiguous weight settings by introducing standardized quantitative methods. Specifically, the position tracking error index achieves unified comparison across different working conditions by normalizing the maximum and minimum values ​​of the absolute position error; the torque fluctuation rate effectively avoids interference from load conditions on the assessment results by introducing the maximum average torque as a benchmark; and the volume positioning accuracy provides a more comprehensive basis for positioning performance evaluation from a holistic three-dimensional spatial perspective. Based on this, the robot state index is calculated using a weighted sum formula... Perform calculations, where and Reverse-engineering problematic indicators into healthy indicators, and weighting them. , , The dynamic adjustment mechanism allows for flexible allocation of the contribution of each component according to actual needs, while ensuring that the RSI value monotonically increases in the [0,1] interval, thus providing a reliable basis for subsequent adaptive decision-making.

[0075] As a preferred embodiment of the technical solution of the present invention, regarding step S200, the step of calculating the control system state index based on the communication data includes:

[0076] The network jitter index is obtained by dividing the network jitter time by the control period.

[0077] The difference between the actual period and the nominal period is taken as an absolute value, and then divided by the nominal period to obtain the period-time mismatch index.

[0078] The control system state index is calculated based on the network jitter index and the cycle time mismatch index.

[0079] The above content describes the application process of communication data in detail. The network jitter index is obtained by dividing the network jitter time by the control period; the period-time mismatch index is obtained by taking the absolute value of the difference between the actual period and the nominal period and then dividing it by the nominal period; the control system state index is calculated as follows: ;

[0080] in, For network jitter weight, As the periodic time mismatch weight, For instruction smoothness weights, ,and , as well as All greater than ; This refers to the network jitter index. This is the periodic time mismatch index. For instruction smoothness, ,and Dimensionless This is the state index of the control system.

[0081] In this embodiment, the network jitter index is a quantitative indicator obtained by normalizing the network jitter time. It can be achieved by dividing the network jitter time by the control cycle, aiming to eliminate measurement bias caused by differences in hardware configuration or control cycle, thereby ensuring the comparability of network jitter assessment under different operating conditions. The cycle time mismatch index is a quantitative indicator obtained by normalizing the deviation between the actual cycle and the nominal cycle. It can be achieved by calculating the absolute value of the difference between the actual cycle and the nominal cycle and dividing it by the nominal cycle, aiming to unify the scale of cycle stability assessment and avoid calculation distortion caused by differences in nominal cycle units. The command smoothness is a quantitative indicator used to characterize the continuity and stability of control system commands. It can be directly generated by a preset algorithm or model and limited to the range of [0,1], aiming to provide a healthy positive indicator that intuitively reflects the quality of communication.

[0082] Furthermore, the above technical solution addresses the lack of a unified standard for communication status assessment by defining specific quantification methods for network jitter index, cycle time mismatch index, and command smoothness. The network jitter index converts the original time quantity into a dimensionless index through normalization, with its value range limited to [0,1], making network jitter assessment unaffected by specific hardware configurations and facilitating standardized comparisons under different operating conditions. The cycle time mismatch index eliminates directional interference through absolute value calculations and normalizes the mismatch quantity to a unified scale, ensuring its value range is within [0,1], eliminating calculation distortion caused by differences in nominal cycle units, and ensuring consistency in cycle stability assessment. Command smoothness directly characterizes communication quality and provides a positive health indicator through quantification limited to the [0,1] range. Based on this, the calculation method for the control system status index adopts... ,in and The negative impacts of network jitter and period mismatch are transformed into a positive health indicator, directly representing instruction smoothness and limited to the range [0,1]; weights , , The sum of all components is 1 and all are greater than 0, allowing for dynamic adjustment of the contribution ratio of each component according to the actual scenario, while ensuring... With the overall range of [0,1], this design effectively integrates the multi-dimensional characteristics of the communication state, generating a comprehensive and quantitative index, which provides a reliable input basis for the subsequent fusion of motion state indices.

[0083] In a preferred embodiment of the present invention, the motion state index is calculated as follows: ;

[0084] in, For robot state weights, For the control system state weights, For coupling weights, ,and , as well as All greater than , This is the robot's state index. For the control system state index, This is a motion state index.

[0085] In this embodiment, the coupling weight γ is a parameter used to characterize the mutual influence between the robot state index and the control system state index. It can be implemented using empirical values ​​obtained by fitting experimental data, with the aim of capturing the synergistic effect when the two indices change simultaneously. In practical applications, the robot state weight α and the control system state weight β can be understood as coefficients used to measure the contribution of the robot's state and the control system's state to the overall motion performance, respectively. They can be dynamically adjusted through expert system presets or online learning algorithms to ensure that the contribution of each state dimension to the comprehensive index is positive.

[0086] The above scheme introduces a coupling weight γ multiplied by the sum of two exponents, i.e. While retaining independent weighted terms ( and Based on their respective state representation capabilities, this design effectively solves the problem of neglecting the mutual influence between the robot's state and the control system state during fusion. When robot joint wear leads to increased position tracking error (reduced RSI), and network jitter intensifies (reduced CSI), the coupling term further amplifies the decrease in the motion state index, thus truly reflecting the accelerated deterioration trend of the overall system performance. This design avoids the evaluation bias caused by the assumption of state independence in traditional methods, enabling the generated motion state index to trigger subsequent adaptive decision-making steps more promptly and accurately.

[0087] Furthermore, weight constraints Furthermore, the design that all weights are strictly greater than zero ensures the rationality and stability of the motion state index's value range, preventing distortion of evaluation results due to unbalanced weight allocation. This fusion mechanism based on coupling, combined with the precise calculation of the robot's state index and the control system's state index in the aforementioned state monitoring and evaluation steps, forms a complete system health status assessment system, thereby enhancing the robustness and continuous operation capability of industrial robot systems under complex working conditions.

[0088] As a preferred embodiment of the technical solution of the present invention, regarding step S400, the step of comparing the motion state index, robot state index, and control system state index with multiple preset threshold intervals, generating a control strategy based on the comparison results, and sending it to the robot controller includes:

[0089] The motion state index, robot state index, and control system state index are compared with multiple preset threshold ranges.

[0090] Based on the comparison results, select a target control strategy from the preset set of predefined control strategies;

[0091] The robot's motion control parameters are adjusted according to the target control strategy, and the adjusted control commands are sent to the robot controller.

[0092] Specifically, selecting a target control strategy from a predefined set of control strategies includes:

[0093] When the motion state index is within a preset first interval, a high-precision control strategy is selected, which uses preset maximum speed and acceleration parameters.

[0094] When the motion state index drops to a preset second range, a robust control strategy is selected. The robust control strategy maintains system stability by reducing motion speed and acceleration.

[0095] When the motion state index further drops to the preset third interval, a degradation control strategy is selected. The degradation control strategy switches the task to a backup program with lower accuracy requirements and sends an early warning signal to the upper-level system.

[0096] When the motion state index drops to the preset fourth range, a safety protection strategy is selected;

[0097] When either the robot state index or the control system state index falls below its corresponding preset safety threshold, a safety protection strategy is selected; the safety protection strategy includes safe shutdown, maintaining the current torque, and reversing along the path.

[0098] The first, second, third, and fourth intervals are respectively referred to as the normal interval, the attention interval, the warning interval, and the alarm interval. When the motion state index is in the normal interval (first interval), a high-precision control strategy is selected, which uses preset maximum speed and acceleration parameters. When the motion state index drops to the attention interval (second interval), a robust control strategy is selected, which maintains system stability by reducing motion speed and acceleration. When the motion state index further drops to the warning interval (third interval), a degraded control strategy is selected, which switches the task to a backup program with lower precision requirements and sends a warning signal to the upper-level system. When the motion state index drops to the alarm interval (fourth interval) or either the robot state index or the control system state index falls below its safety threshold, a safety protection strategy is selected, which includes safe shutdown, maintaining the current torque, or reversing along the path.

[0099] In the above, the first normal range refers to the range where the motion state index is near the healthy baseline value, which can be achieved using a confidence interval obtained from statistical analysis of historical operating data. The second attention range refers to the range where the motion state index begins to fluctuate slightly but has not yet significantly affected system performance, which can be achieved by setting a threshold slightly lower than the normal range. The third warning range refers to the range where the motion state index deviates significantly from the normal range and may affect the quality of task completion, which can be achieved using a dynamically adjusted adaptive threshold. The fourth alarm range refers to the range where the motion state index deviates severely from the normal range and has potential safety risks, which can be achieved through real-time monitoring and prediction models. The purpose of dividing these ranges is to establish a graded response mechanism to ensure that the system can take appropriate control strategies according to different states.

[0100] Furthermore, in actual operation, the specific values ​​of each state index must first be determined, and then compared with multiple preset threshold ranges. When the motion state index is in the first normal range, the system will select a high-precision control strategy and use the highest speed and acceleration parameters. This fully utilizes the system's full performance potential in a healthy state. As the motion state index gradually decreases, the system will sequentially select a robust control strategy, a degraded control strategy, and a safety protection strategy, reflecting a progressive intervention process from preventive optimization to safety protection. In particular, when either the robot's state index or the control system's state index falls below its safety threshold, the system will immediately trigger the safety protection strategy even if the motion state index has not yet reached the fourth alarm range. This effectively solves the protection delay problem caused by the blind spot of single-dimensional monitoring in traditional control. At the same time, this hierarchical response mechanism is closely integrated with the aforementioned state monitoring and evaluation steps to form a complete adaptive control system, which can make precise interventions in the early stages of performance degradation, avoiding overreaction or underreaction.

[0101] In a preferred embodiment of the present invention, the robust control strategy is further adjusted based on the relative relationship between the robot state index and the control system state index:

[0102] If the control system state index is significantly lower than the robot state index, prioritize optimizing the communication network or adjusting the PLC program to improve instruction smoothness.

[0103] If the robot's state index is significantly lower than the control system's state index, then the robot's kinematic performance parameters should be reduced first.

[0104] In this embodiment, the robot state index is a quantitative indicator reflecting the operating state of the industrial robot body. It can be obtained by analyzing parameters such as position tracking error, torque fluctuation rate, and volume positioning accuracy in the servo drive data. In practical applications, this index can be used to evaluate the health status and motion accuracy of the robot's mechanical components. The control system state index is a quantitative indicator reflecting the communication quality and control performance between the PLC and the robot controller. It can be obtained by analyzing parameters such as network jitter, cycle time mismatch, and command smoothness. This index is mainly used to evaluate the stability of the communication network and the execution quality of control commands.

[0105] During the execution of the robust control strategy, by comparing the relative relationship between the robot's state index and the control system's state index, the system can dynamically identify the main causes of current performance degradation. When the control system's state index is detected to be significantly lower than the robot's state index, it indicates a significant defect in the communication network or instruction generation process. In this case, priority is given to optimizing the communication network configuration or adjusting the PLC program logic, directly intervening in communication problems such as network jitter and cycle mismatch. This approach avoids ineffective adjustments to the robot's body parameters, thereby maximizing motion efficiency while maintaining system stability. When the robot's state index is detected to be significantly lower than the control system's state index, it indicates that the robot's mechanical performance is the main bottleneck. In this case, reducing the robot's kinematic performance parameters can effectively alleviate mechanical load pressure, prevent further deterioration of the state, and avoid wasting resources in the communication process. This decision-making logic based on the relative relationship of indices allows the control strategy to be adjusted specifically according to the working conditions, significantly improving the system's adaptability and operational robustness under complex working conditions.

[0106] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0107] Run the benchmark test program while the system is in good health, and record the calculated robot state index, control system state index and motion state index as the health benchmark values ​​for each index.

[0108] The multiple threshold ranges are set based on the health benchmark values.

[0109] In one embodiment of the technical solution of the present invention, an initialization step is also introduced, the initialization step including:

[0110] Run the benchmark test program when the system is in a healthy state, and record the robot state index, control system state index and motion state index calculated at this time as the health benchmark value of each index; based on the health benchmark value, set the multiple threshold intervals.

[0111] In this embodiment, the initialization step refers to executing a specific benchmark test program under ideal, fault-free operating conditions of the industrial robot system to obtain quantitative indicators reflecting the system's optimal performance. In practical applications, the system's health status can be confirmed in various ways, such as through manual inspection or automatic diagnosis to ensure that mechanical components are free from abnormal wear and that the communication network is stable and interference-free. The benchmark test program can employ standardized motion trajectories and load conditions to ensure the repeatability and representativeness of the collected data. The recording process of health benchmark values ​​needs to ensure the consistency of the data acquisition environment and avoid the influence of external factors on the test results. Its purpose is to provide a reliable reference standard for subsequent status monitoring, thereby improving the accuracy of adaptive decision-making.

[0112] The above solution, by running benchmark testing programs under healthy system conditions, first ensures that the collected robot state index, control system state index, and motion state index accurately reflect the inherent characteristics of the system. These health benchmark values ​​not only include the servo drive characteristics of the robot itself but also integrate key information on the communication status of the control system, forming a foundation for comprehensively evaluating the overall system performance. Multiple threshold ranges set on this basis can be dynamically adjusted according to the initial performance level of the specific robot, rather than using a uniform fixed threshold. This threshold setting method based on health benchmark values ​​makes state monitoring more closely aligned with actual system characteristics, and can more accurately determine the degree of performance degradation when state indices deviate. Furthermore, because the threshold ranges are directly correlated with the health benchmark values, the entire control method maintains high adaptability and reliability when facing industrial robots of different models or configurations, effectively avoiding misjudgments caused by individual differences. Through this technical solution, personalized threshold settings for specific robots are achieved, significantly improving the accuracy of state monitoring and the timeliness of adaptive decision-making.

[0113] In a preferred embodiment of the present invention, the programmable logic controller (PLC) is configured as follows:

[0114] It communicates with industrial robot controllers and network devices to obtain the servo drive data and communication data;

[0115] An internally integrated or connected state assessment unit is used to perform the state monitoring and assessment steps;

[0116] It integrates an adaptive decision-making unit to execute the adaptive decision-making and control steps.

[0117] In this embodiment, in practical applications, communication connection refers to data interaction methods implemented through industrial Ethernet, fieldbus, or other industrial communication protocols, which can be implemented using standard communication protocols such as TCP / IP, Modbus, and EtherCAT. The state evaluation unit refers to a functional module with data processing and algorithmic computation capabilities, which can be implemented using embedded processors, FPGAs, or dedicated computing chips, with the aim of transforming raw data into quantitative indicators. Specifically, the adaptive decision-making unit refers to an intelligent module capable of dynamically adjusting the control strategy based on the input state index, which can be implemented using rule engines, fuzzy logic controllers, or machine learning models, with the aim of improving the system's real-time response capability and adaptability.

[0118] The above solution restructures the hardware and functional architecture of the programmable logic controller (PLC), upgrading it from a traditional logic control unit into a core control node with state awareness and intelligent decision-making capabilities. First, the PLC establishes direct communication connections with the industrial robot controller and network devices, ensuring real-time acquisition of servo drive and communication data. This design avoids data delays and information distortion caused by relying on a host computer for relaying data. Second, the internally integrated state assessment unit transforms the collected raw data into robot state indices and control system state indices, further fusing them to generate motion state indices. This deep processing mechanism provides a unified and accurate basis for assessing the system's health status. Finally, the adaptive decision-making unit dynamically selects control strategies and adjusts motion parameters based on these quantitative indicators, enabling the system to optimize and adjust according to real-time conditions.

[0119] Figure 2 The present invention provides a block diagram of the structure of an industrial robot control system using a PLC. In this embodiment, the system 10 includes:

[0120] The data acquisition module 11 is used to acquire servo drive data of the industrial robot body and communication data between the PLC and the robot controller.

[0121] The state evaluation module 12 is used to calculate the robot state index based on the servo drive data and the control system state index based on the communication data; wherein, the robot state index is a function of position tracking error, torque fluctuation rate and volume positioning accuracy, and the control system state index is a function of network jitter, cycle time mismatch and command smoothness.

[0122] Motion state generation module 13 is used to fuse the robot state index and the control system state index to generate a motion state index;

[0123] The strategy generation and sending module 14 is used to compare the motion state index, robot state index and control system state index with multiple preset threshold intervals, generate a control strategy based on the comparison results, and send it to the robot controller.

[0124] Furthermore, the state assessment module 12 includes:

[0125] The error determination unit is used to process the absolute position error of the robot end effector into the maximum-minimum normalization formula to obtain the position tracking error index.

[0126] The torque analysis unit calculates the torque variability by dividing the standard deviation of torque by the maximum average torque.

[0127] The positioning result analysis unit is used to obtain the volume positioning accuracy by subtracting the positioning error volume from 1 and then dividing by the total volume of the workspace.

[0128] The numerical application unit is used to calculate the robot state index based on the position tracking error index, torque fluctuation rate, and volume positioning accuracy.

[0129] Specifically, the state assessment module 12 further includes:

[0130] The network analysis unit is used to divide the network jitter time by the control period to obtain the network jitter index;

[0131] The mismatch parameter determination unit is used to calculate the difference between the actual period and the nominal period, take the absolute value, and then divide it by the nominal period to obtain the period-time mismatch index.

[0132] The calculation unit is used to calculate the control system state index based on the network jitter index and the cycle time mismatch index.

[0133] Furthermore, the strategy generation and sending module 14 includes:

[0134] An index comparison unit is used to compare the motion state index, robot state index, and control system state index with multiple preset threshold intervals.

[0135] The strategy selection unit is used to select a target control strategy from a preset set of predefined control strategies based on the comparison results.

[0136] The adjustment instruction generation unit is used to adjust the robot's motion control parameters according to the target control strategy and send the adjusted control instructions to the robot controller.

[0137] This invention combines an industrial robot, a robot controller, and a programmable logic controller (PLC) in a closed-loop control architecture, thereby solving the problems of rigid control and lack of adaptability, disconnect between state monitoring and decision control, and separation of the robot body and the control system in traditional systems, thus improving the overall adaptability and stability.

[0138] In this system, the industrial robot, acting as the execution terminal, provides basic input for state assessment through its operational status data, enabling the system to perceive changes in the robot's performance. The robot controller, as the instruction execution unit, receives and executes the control parameters adjusted by the PLC, ensuring that adaptive decisions are translated into actual motion control. The programmable logic controller (PLC), as the decision-making core, collects servo drive data and communication data, calculates the robot state index, control system state index, and motion state index, and selects the target control strategy based on the index comparison results, achieving dynamic parameter adjustment of the robot controller. These three components work collaboratively: the PLC continuously integrates the robot's state and control system state information to generate a unified motion state index, guiding control strategy switching; the robot controller precisely controls the industrial robot's actions according to PLC instructions; and the industrial robot provides real-time status data feedback, forming a closed loop of monitoring-decision-execution. This ensures that the system automatically optimizes its control behavior under scenarios such as load changes, mechanical wear, or communication interference, avoiding performance degradation or malfunctions, while eliminating the disconnect between the robot's state and the control system state, achieving overall operational adaptability and stability.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for controlling an industrial robot using a PLC, characterized in that, The method includes: Collect servo drive data of the industrial robot body and communication data between the PLC and the robot controller; The robot state index is calculated based on the servo drive data, and the control system state index is calculated based on the communication data; wherein, the robot state index is a function of position tracking error, torque fluctuation rate, and volume positioning accuracy, and the control system state index is a function of network jitter, cycle time mismatch, and command smoothness. The motion state index is generated by combining the robot state index and the control system state index. The motion state index, robot state index, and control system state index are compared with multiple preset threshold ranges. A control strategy is generated based on the comparison results and sent to the robot controller. The step of calculating the robot state index based on the servo drive data includes: The absolute position error of the robot's end effector is processed by substituting it into the maximum-minimum normalization formula to obtain the position tracking error index. The torque variability is obtained by dividing the standard deviation of torque by the maximum average torque. The volume positioning accuracy is obtained by subtracting the difference between 1 and the total volume of the workspace. The robot state index is calculated based on the position tracking error index, torque fluctuation rate, and volume positioning accuracy. The step of calculating the control system state index based on the communication data includes: The network jitter index is obtained by dividing the network jitter time by the control period. The difference between the actual period and the nominal period is taken as an absolute value and then divided by the nominal period to obtain the period-time mismatch index. Calculate the control system state index based on network jitter index and cycle time mismatch index; The motion state index is calculated as follows: ; in, For robot state weights, For the control system state weights, For coupling weights, ,and , as well as All greater than , This is the robot's state index. For the control system state index, This is a motion state index.

2. The industrial robot control method using PLC according to claim 1, characterized in that, The step of comparing the motion state index, robot state index, and control system state index with multiple preset threshold intervals, generating a control strategy based on the comparison results, and sending it to the robot controller includes: The motion state index, robot state index, and control system state index are compared with multiple preset threshold ranges. Based on the comparison results, select a target control strategy from the preset set of predefined control strategies; The robot's motion control parameters are adjusted according to the target control strategy, and the adjusted control commands are sent to the robot controller.

3. The industrial robot control method using PLC according to claim 2, characterized in that, Selecting a target control strategy from a predefined set of control strategies includes: When the motion state index is within a preset first interval, a high-precision control strategy is selected, which uses preset maximum speed and acceleration parameters. When the motion state index drops to a preset second range, a robust control strategy is selected. The robust control strategy maintains system stability by reducing motion speed and acceleration. When the motion state index further drops to the preset third interval, a degradation control strategy is selected. The degradation control strategy switches the task to a backup program with lower accuracy requirements and sends an early warning signal to the upper-level system. When the motion state index drops to the preset fourth range, a safety protection strategy is selected; When either the robot state index or the control system state index falls below its corresponding preset safety threshold, a safety protection strategy is selected; the safety protection strategy includes safe shutdown, maintaining the current torque, and reversing along the path.

4. The industrial robot control method using PLC according to claim 1, characterized in that, The method further includes: Run the benchmark test program while the system is in good health, and record the calculated robot state index, control system state index and motion state index as the health benchmark values ​​for each index. The multiple threshold ranges are set based on the health benchmark values.

5. An industrial robot control system using a PLC, the system being used to implement the industrial robot control method using a PLC as described in any one of claims 1 to 4, characterized in that, The system includes: The data acquisition module is used to collect servo drive data of the industrial robot body and communication data between the PLC and the robot controller; The state evaluation module is used to calculate the robot state index based on the servo drive data and the control system state index based on the communication data; wherein, the robot state index is a function of position tracking error, torque fluctuation rate and volume positioning accuracy, and the control system state index is a function of network jitter, cycle time mismatch and command smoothness. A motion state generation module is used to fuse the robot state index and the control system state index to generate a motion state index. The strategy generation and sending module is used to compare the motion state index, robot state index and control system state index with multiple preset threshold intervals, generate a control strategy based on the comparison results, and send it to the robot controller.

6. The industrial robot control system using PLC according to claim 5, characterized in that, The status assessment module includes: The error determination unit is used to process the absolute position error of the robot end effector into the maximum-minimum normalization formula to obtain the position tracking error index. The torque analysis unit is used to divide the torque standard deviation by the maximum average torque to obtain the torque fluctuation rate. The positioning result analysis unit is used to obtain the volume positioning accuracy by subtracting the difference between 1 and the positioning error volume divided by the total volume of the workspace. The numerical application unit is used to calculate the robot state index based on the position tracking error index, torque fluctuation rate, and volume positioning accuracy.

7. The industrial robot control system using PLC according to claim 6, characterized in that, The status assessment module also includes: The network analysis unit is used to divide the network jitter time by the control period to obtain the network jitter index; The mismatch parameter determination unit is used to calculate the difference between the actual period and the nominal period, take the absolute value, and then divide it by the nominal period to obtain the period-time mismatch index. The calculation unit is used to calculate the control system state index based on the network jitter index and the cycle time mismatch index.

8. The industrial robot control system using PLC according to claim 7, characterized in that, The strategy generation and sending module includes: An index comparison unit is used to compare the motion state index, robot state index, and control system state index with multiple preset threshold intervals. The strategy selection unit is used to select a target control strategy from a preset set of predefined control strategies based on the comparison results. The adjustment instruction generation unit is used to adjust the robot's motion control parameters according to the target control strategy and send the adjusted control instructions to the robot controller.

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