Load self-adaptive control method and system for scrap steel conveying

Abstract

The invention relates to the technical field of industrial automation control, in particular to a load self-adaptive control method and system for waste steel conveying, and the method comprises the steps: obtaining the power of a conveying motor at a current time node and a plurality of historical time nodes and the pressure of a waste steel material on a conveying belt, and constructing a pressure sequence; the load impact degree, the inertia lag degree and the dynamic adaptation deviation degree under the current time node are calculated, the target power of the conveying motor is output through an improved PID control algorithm, and self-adaptation control is conducted on the conveying motor based on the target power. The problems that an existing PID control algorithm is generally insufficient in adaptability and low in stability in a waste steel conveying scene are solved.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology. More specifically, this invention relates to a load adaptive control method and system for scrap steel conveying. Background Technology

[0002] With the rapid development of the iron and steel smelting and recycled resource processing industries, scrap steel, as an important metallurgical raw material, is now transported using continuous equipment such as belt conveyors, chain conveyors, or plate conveyors. In actual production environments, scrap steel materials typically exhibit significant characteristics such as irregular shape, discrete mass distribution, large size differences, and poor feeding continuity. During transport, they are prone to instantaneous accumulation, impact loading, and sudden load changes, subjecting the conveyor motor and transmission mechanism to complex conditions of strong disturbance, strong nonlinearity, and time-varying loads for extended periods. Simultaneously, scrap steel conveying systems often need to operate continuously in harsh environments with high dust, high noise, and high impact, placing higher demands on the stability, response speed, and anti-interference capabilities of the control system.

[0003] Existing scrap steel conveying equipment often employs traditional PID control algorithms, which use pre-set proportional, integral, and derivative parameters to achieve closed-loop regulation of the conveyor motor's speed or power. However, these control algorithms are typically based on assumptions of linear systems and relatively stable loads, meaning their control parameters remain constant during operation. This makes it difficult to reflect the actual characteristics of scrap steel loads changing drastically over time. When scrap steel is fed in concentrated bursts or accumulates locally, the pressure on the conveyor belt and the motor load rise sharply in a short period. Fixed proportional parameters can easily lead to excessive amplification of the control output, causing system overshoot or even oscillation. Furthermore, under conditions of rapid load changes or frequent impacts, the continuous accumulation of historical errors in the integral stage can easily lead to significant integral saturation, resulting in a slow system response and difficulty in returning to a stable operating state in a timely manner.

[0004] Furthermore, due to the inherent continuity and cumulative nature of load impacts during scrap steel transportation, mechanical structures and motor systems exhibit significant inertial lag. Traditional PID control algorithms lack the comprehensive ability to characterize the history of load impacts and inertial effects, relying solely on current errors for adjustment. This makes it difficult to promptly identify potential overload risks and hidden abnormal operating conditions. Moreover, during periods of low load fluctuation or no-load operation, fixed differential parameters may be overly sensitive to measurement noise and minor disturbances, leading to frequent fluctuations in control output and reduced system stability. Consequently, existing PID control algorithms generally suffer from insufficient adaptability and decreased stability in scrap steel transportation scenarios. Summary of the Invention

[0005] To address the problems of insufficient adaptability and decreased stability of existing PID control algorithms in scrap steel conveying scenarios, as mentioned in the background section, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a load adaptive control method for scrap steel conveying, comprising: acquiring the power of the conveyor motor at the current time node and at multiple historical time nodes, and the pressure of the scrap steel material on the conveyor belt, and constructing a pressure sequence; calculating the load impact degree at the current time node, wherein the load impact degree is positively correlated with the standard deviation and maximum value of the pressure sequence, and negatively correlated with the average value of the pressure sequence; calculating the inertial lag degree at the current time node, wherein the inertial lag degree is positively correlated with the load impact degree at the current time node; calculating the dynamic adaptation deviation degree at the current time node, wherein the dynamic adaptation deviation degree is positively correlated with the inertial lag degree and the power; outputting the target power of the conveyor motor using an improved PID control algorithm, and performing adaptive control of the conveyor motor based on the target power; wherein the improved PID control algorithm includes proportional gain. Integral gain With differential gain The proportional gain The integral gain is positively correlated with the dynamic adaptation deviation. The dynamic adaptation deviation is inversely correlated, and the differential gain... It is positively correlated with the degree of load impact.

[0007] The aforementioned technical solution differentiates and adaptively adjusts the proportional, integral, and derivative control strengths in the control algorithm, maintaining flexible and stable control characteristics under stable load conditions, and rapidly enhancing regulation capabilities while suppressing oscillations and overshoot when load shocks intensify or inertial effects become significant. This significantly improves the stability, response speed, and robustness of the scrap steel conveying system under strong disturbances and highly nonlinear conditions, reduces overload risk and energy consumption fluctuations, and minimizes mechanical shock and equipment wear, thereby improving overall conveying efficiency and operational safety.

[0008] Furthermore, Load impact at specific time points for: , The standard deviation of the pressure series. The maximum value in the pressure sequence. This is the mean of all pressure data in the pressure series. These are the preset hyperparameters.

[0009] The aforementioned technical solution achieves precise quantitative characterization of load impact intensity by comprehensively coupling the fluctuation amplitude and instantaneous peak characteristics of the pressure sequence during scrap steel transportation with the overall load level. It simultaneously considers both volatility and suddenness, thus avoiding the risk of misjudgment caused by relying solely on a single extreme value or average level. By introducing dispersion to reflect the severity of pressure changes and combining it with the sensitive characterization of instantaneous accumulation, impact, or abnormal compression by pressure peaks, and then using the overall average level for normalization constraints, this indicator is not abnormally amplified under high-load but stable transportation conditions, while maintaining sufficient identification sensitivity even under low-load conditions with sudden impacts.

[0010] Furthermore, Inertial lag at a given time point for: , for The degree of load impact at a given time point, For The time node is the latest historical setting within the time window. The degree of load impact at each time point Set the total number of time nodes within the specified time window for the history. For the natural constant An exponential function with base 0. This is a preset threshold.

[0011] The aforementioned technical solution achieves dynamic quantification of the inertial hysteresis characteristics of the conveyor system by exponentially coupling the current load impact level with the cumulative effect of load impacts over a historical period. This reflects the system's response hysteresis and cumulative load impact under continuous or repeated impacts. When load impacts are occasional and brief, a low level is maintained to effectively suppress misjudgments and prevent the controller from over-responding to instantaneous fluctuations. Conversely, when impacts continuously accumulate or show a cumulative trend over time, the level is rapidly amplified to accurately reveal the inertial hysteresis and potential overload risks of the conveyor motor and conveyor belt system caused by repeated stress.

[0012] Furthermore, Dynamic adaptation deviation at time points for: , for The degree of inertial lag at a given time point, for The power of the transmission motor at a given time point. This is the preset desired power.

[0013] The above technical solution achieves a comprehensive quantification of the system's dynamic adaptability by coupling the inertial hysteresis characteristics of the conveying system with the degree to which the motor power deviates from the ideal operating state. When the system load is stable and the power is close to the expected value, it remains at a low level to avoid over-responding to minor fluctuations; while when the system experiences continuous shocks or the power deviates significantly from the expected value, it rises rapidly, thereby accurately revealing the risks of slow response, potential overload, and decreased adaptability of the conveying motor under dynamic operating conditions.

[0014] Furthermore, the proportional gain for: , This is the initial proportional gain. As a proportional adjustment factor, It is the hyperbolic tangent function. for Dynamic adaptation deviation at a given time point.

[0015] The above technical solution nonlinearly couples the proportional gain with the dynamic adaptation deviation of the system, enabling the proportional regulation to adaptively enhance with the combined risk of load shocks and power deviations, thereby achieving dynamic amplification and constraint of the error response. When the system operates smoothly and the deviation is low, the proportional gain is close to the initial set value, ensuring the flexibility and stability of the control output and avoiding oversensitivity to small fluctuations. However, when the load shock is significant or the power deviation intensifies, the proportional gain gradually increases with the degree of deviation, enabling the controller to quickly respond to abnormal operating conditions and effectively suppress overshoot and oscillation.

[0016] Furthermore, the integral gain for: , This is the initial integral gain. For the natural constant An exponential function with base 0. for Dynamic adaptation deviation at a given time point.

[0017] Furthermore, the differential gain for: , For the initial differential gain, As the differential adjustment factor, for The degree of load impact at a given time point.

[0018] Furthermore, it also includes noise reduction and standardization processing of the power of the conveyor motor at the current time point and at multiple historical time points, as well as the pressure of the scrap steel material on the conveyor belt.

[0019] Furthermore, the value range of the proportional adjustment factor is [0.5, 3].

[0020] In a second aspect, the present invention provides a load adaptive control system for scrap steel conveying, comprising a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the load adaptive control method for scrap steel conveying described in any one of the above embodiments is implemented.

[0021] The beneficial effects of this invention are as follows: This invention adaptively adjusts the proportional, integral, and derivative gains of the traditional PID control algorithm, enabling the controller to maintain flexible and stable regulation characteristics under stable loads, while rapidly enhancing response capabilities and suppressing overshoot and oscillations when load shocks are significant or power deviations intensify. It also effectively suppresses noise and abnormal fluctuations. This invention can proactively identify transient shocks, accumulated loads, and potential overload risks, enabling forward-looking control of the conveyor motor. This significantly improves stability, robustness, and response speed in highly nonlinear and disturbed scrap steel conveying environments, reduces mechanical shock and equipment wear, and enhances conveying efficiency and operational safety. Furthermore, it provides a reliable engineering foundation for industrial applications. Attached Figure Description

[0022] Figure 1 This is a flowchart schematically illustrating a load adaptive control method for scrap steel conveying according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the comparison of dynamic adjustment of gain parameters in the PID control algorithm before and after an improvement of a load adaptive control method for scrap steel conveying according to an embodiment of the present invention; Figure 3 This is a schematic block diagram illustrating the structure of a load adaptive control system for scrap steel conveying according to an embodiment of the present invention. Detailed Implementation

[0023] An embodiment of a load adaptive control method for scrap steel conveying.

[0024] like Figure 1 As shown, a flowchart of a load adaptive control method for scrap steel conveying according to an embodiment of the present invention is included, comprising the following steps: S1: Obtain the power of the conveyor motor at the current time node and at multiple historical time nodes, as well as the pressure of the scrap steel material on the conveyor belt, and construct a pressure sequence.

[0025] In a preferred embodiment, the power data of the conveyor motor at the current time point and at multiple historical time points are acquired in real time, and the pressure data generated by the scrap steel material carried on the conveyor belt is acquired simultaneously. The power data can be acquired by power sensors arranged on the power supply side or drive control unit of the conveyor motor, reflecting the energy consumption variation characteristics of the conveyor motor under different load conditions; the pressure data can be acquired by a pressure sensor array located below the conveyor belt or at the idler support structure, used to characterize the actual stacking state of the scrap steel material on the conveyor belt and its temporal variation characteristics.

[0026] Furthermore, based on the pressure data collected at the current time node and multiple historical time nodes, a corresponding pressure sequence is constructed in chronological order, thereby forming time-series pressure characteristic data that reflects the load change pattern during the scrap steel material transportation process.

[0027] It also includes preprocessing the power and pressure data of the conveyor motor at the current time point and at multiple historical time points. The preprocessing operation includes at least noise reduction and standardization. Specifically, noise reduction can be performed using methods such as moving average filtering, median filtering, or low-pass filtering to suppress high-frequency noise components introduced by sensor jitter, electromagnetic interference, or random impacts from scrap steel materials, thereby improving the stability and reliability of the power and pressure data.

[0028] Furthermore, the denoised power and pressure data are standardized to eliminate scale differences between data of different dimensions and magnitudes. This standardization can employ extreme value normalization, Z-score normalization, or adaptive normalization based on historical statistical characteristics, ensuring that the power and pressure data are expressed within a unified data space.

[0029] S2: Calculate the load impact level, inertial lag level, and dynamic adaptation deviation at the current time point.

[0030] In a preferred embodiment, Load impact at specific time points for: , The standard deviation of the pressure series. The maximum value in the pressure sequence. This is the mean of all pressure data in the pressure series. These are the preset hyperparameters.

[0031] By utilizing pressure fluctuations to reflect the severity of load changes, peak pressure characterizes extreme load conditions caused by instantaneous impacts or localized accumulation, and average pressure is used to normalize the overall load level, thus avoiding misjudgment of normal fluctuations under high baseline load conditions. It not only exhibits higher sensitivity to sudden impacts, transient material accumulation, and abnormal compression, but also demonstrates good suppression of long-term stable conveying conditions, thereby enabling accurate characterization of real impact risks in complex scrap steel conveying environments.

[0032] Inertial lag at a given time point for: , for The degree of load impact at a given time point, For The time node is the latest historical setting within the time window. The degree of load impact at each time point Set the total number of time nodes within the specified time window for the history. For the natural constant An exponential function with base 0. This is a preset threshold.

[0033] By using the current load impact level as the baseline and introducing the cumulative effect of load impacts over a historical time period, the persistence and superposition of historical impacts are nonlinearly amplified exponentially. This characterizes the inertial hysteresis characteristics of the conveyor system under multiple impacts, thus avoiding judgments based solely on the impact amplitude at a single moment. It comprehensively considers whether the impact has continuity, accumulation, and evolutionary trends. When load impacts are sporadic and short-lived, the inertial hysteresis remains at a low level, effectively suppressing false triggering. However, when impacts continuously accumulate over time, the inertial hysteresis increases rapidly, accurately reflecting the response hysteresis and fatigue accumulation effects of repeated stress on the mechanical structure, conveyor belt, and motor system.

[0034] Dynamic adaptation deviation at time points for: , for The degree of inertial lag at a given time point, for The power of the transmission motor at a given time point. This is the preset desired power.

[0035] By squared-processing the power deviation to enhance sensitivity to abnormal offsets, and using the inertial lag level as a weighting factor to dynamically modulate the power deviation results, the system can reflect both the deviation between the current energy consumption state and the expected operating conditions, and the adaptability to power changes under continuous load and historical impacts. When the system is in a state of weak inertial effect and stable load changes, even if short-term power fluctuations occur, they remain at a low level, effectively suppressing misjudgments. However, under conditions of significant inertial lag and continuous accumulation of load impacts, once the power deviates from the reasonable range, it will rapidly amplify, thereby accurately revealing the slow response, abnormal energy consumption, or potential overload risks of the conveyor motor during the dynamic adaptation process.

[0036] S3: Utilize an improved PID control algorithm to output the target power of the conveyor motor, and perform adaptive control of the conveyor motor based on the target power.

[0037] like Figure 2 The figure shows a comparison of the dynamic adjustment of the gain parameter in the PID control algorithm before and after the improvement of the load adaptive control method for scrap steel conveying according to an embodiment of the present invention.

[0038] In a preferred embodiment, the improved PID control algorithm includes proportional gain. Integral gain With differential gain The proportional gain for: , This is the initial proportional gain. As a proportional adjustment factor, It is the hyperbolic tangent function. for The dynamic adaptation deviation at a given time point. In this embodiment, the value range of the proportional adjustment factor is [0.5, 3], but it can also be set according to the actual situation.

[0039] By introducing a nonlinear mapping function with smooth saturation characteristics, the proportional gain adjustment intensity can adaptively adjust to changes in dynamic deviation, thereby avoiding control oscillations caused by sudden increases or decreases in proportional gain under abnormal operating conditions. When the system operating state is close to the desired condition and the dynamic deviation is low, the proportional gain remains close to the initial setpoint, which helps maintain the stability of the control process and its anti-interference capability. However, when the system exhibits abnormal signs such as significant load accumulation and decreased power adaptability, the proportional gain will gradually increase with the degree of deviation, making the controller's response to errors more rapid and powerful. At the same time, the saturation characteristics of the nonlinear function constrain the gain growth, preventing over-adjustment.

[0040] The integral gain for: , This is the initial integral gain. For the natural constant An exponential function with base 0. for Dynamic adaptation deviation at a given time point.

[0041] By inversely correlating the cumulative effect of the integral term on historical errors with the current dynamic adaptive deviation state of the system, and introducing an exponential decay mechanism, the integral adjustment intensity adaptively weakens as the degree of operational deviation increases. This avoids integral saturation or control lag caused by continuous error accumulation under conditions of significant load shocks and system inertial lag. When the system operates smoothly and the degree of deviation is low, the integral gain remains close to the initial setpoint, which is beneficial for gradually eliminating steady-state errors and improving long-term control accuracy. However, when the system experiences large deviations, decreased responsiveness, or drastic load changes, the integral effect is rapidly suppressed, allowing the controller to focus more on instantaneous adjustment rather than the accumulation of historical errors.

[0042] The differential gain for: , For the initial differential gain, As the differential adjustment factor, for The degree of load impact at a given time point.

[0043] By directly linking the derivative element's ability to proactively suppress system changes to the intensity of the current load impact, the derivative adjustment strength can adaptively increase with changes in the load impact level. This allows the controller to proactively dampen and suppress load changes when the conveyor system experiences sudden impacts or rapid load variations. When the load changes gradually and the operating state is stable, the derivative gain remains close to the initial setting, which helps reduce the excessive amplification of minor fluctuations and noise. However, when the material carried by the conveyor belt experiences significant impacts or rapid transient pressure changes, the derivative effect will increase accordingly, enabling the controller to detect system changes earlier and effectively suppress potential overshoot and oscillations.

[0044] The solution of this invention can accurately identify complex operating conditions such as instantaneous accumulation of scrap steel, impact loads, and power deviations, dynamically adjust the response strength and smoothness of the controller, rapidly enhance the adjustment capability to suppress overshoot and oscillation during sudden load changes, and maintain flexible control to avoid overreaction when the load is stable or fluctuates slightly. Simultaneously, noise reduction and standardization processes improve the reliability and robustness of the data. Therefore, the stability, response speed, and shock resistance of the entire conveying system under high disturbance and nonlinear conditions are significantly improved, effectively reducing the risk of mechanical shock and motor overload, extending equipment life, optimizing energy consumption, and providing a solid technical guarantee for the safety, reliability, and intelligent control of the scrap steel conveying process.

[0045] An example of a load adaptive control system for scrap steel conveying: like Figure 3 As shown, a structural block diagram of a load adaptive control system for scrap steel conveying according to an embodiment of the present invention includes a processor and a memory.

[0046] This invention also provides a load adaptive control system for scrap steel conveying. For example... Figure 3 As shown, the system includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the load adaptive control method for scrap steel conveying according to the present invention.

[0047] The load adaptive control system for scrap steel conveying also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0048] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0049] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0050] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A load adaptive control method for scrap steel conveying, characterized in that, include: Obtain the power of the conveyor motor at the current time point and at multiple historical time points, as well as the pressure of the scrap steel material on the conveyor belt, and construct a pressure sequence; Calculate the load impact level at the current time point. The load impact level is positively correlated with the standard deviation and maximum value of the pressure sequence, and negatively correlated with the average value of the pressure sequence. Calculate the inertial lag level at the current time point. The inertial lag level is positively correlated with the load impact level at the current time point. Calculate the dynamic adaptation deviation level at the current time point. The dynamic adaptation deviation level is positively correlated with the inertial lag level and the power. An improved PID control algorithm is used to output the target power of the conveyor motor, and adaptive control of the conveyor motor is performed based on the target power. Among them, the improved PID control algorithm includes proportional gain. Integral gain With differential gain The proportional gain The integral gain is positively correlated with the dynamic adaptation deviation. The dynamic adaptation deviation is inversely correlated, and the differential gain... It is positively correlated with the degree of load impact.

2. The load adaptive control method for scrap steel conveying according to claim 1, characterized in that, Load impact at specific time points for: , The standard deviation of the pressure series. The maximum value in the pressure sequence. This is the mean of all pressure data in the pressure series. These are the preset hyperparameters.

3. The load adaptive control method for scrap steel conveying according to claim 1, characterized in that, Inertial lag at a given time point for: , for The degree of load impact at a given time point, For The time node is the latest historical setting within the time window. The degree of load impact at each time point Set the total number of time nodes within the specified time window for the history. For the natural constant An exponential function with base 0. This is a preset threshold.

4. The load adaptive control method for scrap steel conveying according to claim 1, characterized in that, Dynamic adaptation deviation at time points for: , for The degree of inertial lag at a given time point, for The power of the transmission motor at a given time point. This is the preset desired power.

5. The load adaptive control method for scrap steel conveying according to claim 1, characterized in that, The proportional gain for: , This is the initial proportional gain. As a proportional adjustment factor, It is the hyperbolic tangent function. for Dynamic adaptation deviation at a given time point.

6. The load adaptive control method for scrap steel conveying according to claim 1, characterized in that, The integral gain for: , This is the initial integral gain. For the natural constant An exponential function with base 0. for Dynamic adaptation deviation at a given time point.

7. The load adaptive control method for scrap steel conveying according to claim 1, characterized in that, The differential gain for: , For the initial differential gain, As the differential adjustment factor, for The degree of load impact at a given time point.

8. The load adaptive control method for scrap steel conveying according to claim 1, characterized in that, It also includes noise reduction and standardization processing of the power of the conveyor motor at the current time point and at multiple historical time points, as well as the pressure of the scrap steel material on the conveyor belt.

9. The load adaptive control method for scrap steel conveying according to claim 5, characterized in that, The value range of the proportional adjustment factor is [0.5, 3].

10. A load adaptive control system for scrap steel conveying, characterized in that, It includes a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the load adaptive control method for scrap steel conveying according to any one of claims 1 to 9 is implemented.

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