An adaptive control system for cable winding speed under dynamic operating conditions

Abstract

This invention relates to the field of automatic control technology and discloses an adaptive control system for cable reel speed under dynamic operating conditions. The system includes an intrinsic vibration sensing module configured on the cable reel support bearing and a system state decoding module. The decoding module can decode in parallel from the single raw electrical signal converted by the intrinsic vibration sensing module, extracting multi-dimensional state information such as real-time tension after temperature self-calibration, overall system rotational inertia, and frictional abrupt changes. This information then drives the adaptive control module to match and execute the corresponding speed control strategy. This invention transforms the core bearing structure of the reel into an information source of its own state, avoiding the signal delay and system vulnerability caused by traditional multi-sensor layouts. Control decisions are made based on information reflecting the current system state. Furthermore, the system also has the function of optimizing control strategies based on historical data and compensating for temperature effects.

Description

Technical Field

[0001] This invention relates to an adaptive control system for cable winding speed under dynamic operating conditions, belonging to the field of automatic control technology. Background Technology

[0002] In the field of adaptive control of cable winding speed under dynamic operating conditions, how to achieve real-time synchronization between control commands and the physical state of the system is a long-standing and continuously concerned technical challenge. To address this challenge, the current industry practice is to build a monitoring network consisting of multiple external independent sensors around a central controller. By collecting data from components such as encoders and tension sensors, closed-loop regulation of the winding process is achieved. This approach provides a feasible technical path to ensure the smoothness of cable winding under relatively steady-state operating conditions.

[0003] However, as engineering operations increasingly demand higher dynamic response performance, the limitations of the aforementioned control methods have become more apparent. In scenarios involving reel acceleration and deceleration, sudden load changes, or frequent external environmental disturbances, the accumulated time required for signals to travel from various distributed sensors to the central controller means that the state information relied upon by the control system is actually a snapshot of the physical world from a moment ago. This time lag between control decisions and physical reality directly leads to a delayed response to cable tension fluctuations. While industry efforts to improve control accuracy, such as increasing the number of sensors or raising their sampling frequency, have enriched the data dimensions to some extent, they have also increased the complexity of the system hardware architecture and maintenance costs, and have not changed the requirement for long-distance signal transmission.

[0004] Specifically, existing technologies face several challenges in dynamic applications: 1. Lag in control response, a problem stemming from the multi-point, remote information acquisition and transmission architecture, making it difficult for the system to respond promptly and effectively to instantaneous physical disturbances; 2. The contradiction between system architecture complexity and reliability, where added sensors, while enhancing information acquisition capabilities, also introduce more potential failure points, reducing the overall system's long-term operational stability; 3. The system lacks direct understanding of its own dynamic characteristics, such as load inertia, and its adaptive adjustments rely on modeling and extrapolating lag data, limiting the matching accuracy of control strategies. Therefore, the technical problem this invention aims to solve is how to construct a control system that no longer relies on a combination of external multi-point sensors, but can directly and synchronously analyze multiple dimensions of operational characteristics, such as real-time tension, overall rotational inertia, and contact surface friction state, from the system's core load-bearing structure, and possess self-calibration capabilities for measurement errors caused by changes in operating temperature without adding new hardware sensors. This would allow the control system to achieve real-time perception and adaptation to its own state in a simpler and more reliable architecture. Summary of the Invention

[0005] This invention provides an adaptive control system for cable winding speed under dynamic operating conditions. Its main purpose is to solve the problems of signal delay, system fragility, and inability to directly perceive its own dynamic characteristics caused by the reliance on external multi-point sensors in the existing technology.

[0006] To achieve the above objectives, the present invention provides an adaptive control system for cable winding speed under dynamic operating conditions, comprising:

[0007] An endogenous vibration sensing module is configured to convert the mechanical micro-vibrations generated by the cable reel's support bearing structure due to cable tension and system dynamic disturbances during cable winding into raw electrical signals.

[0008] A system state decoding module, electrically connected to an endogenous vibration sensing module, is configured to: extract a first characteristic quantity from the low-frequency component of the original electrical signal and extract a third characteristic quantity from the high-frequency component of the original electrical signal during continuous system operation; and extract a second characteristic quantity from the original electrical signal during the transient process of system start-up and shutdown.

[0009] A temperature drift self-calibration unit is configured to: infer the operating temperature change of the intrinsic vibration sensing module based on the phase current parameters of the drive motor of the system, and generate calibration coefficients for dynamic calibration of the first characteristic quantity.

[0010] An adaptive control module, connected to the system state decoding module, is configured to: automatically select and execute a speed control strategy stored in the adaptive control module based on a second feature value, and perform safety monitoring and speed correction based on a calibrated first and third feature values.

[0011] Preferably, the intrinsic vibration sensing module is a piezoelectric element pre-embedded or pasted on the bearing housing supporting the bearing.

[0012] Preferably, the first characteristic quantity is the fundamental frequency amplitude of the low-frequency component of the original electrical signal, which is used to characterize the real-time cable tension. The second characteristic quantity is the oscillation attenuation rate of the original electrical signal during the system start-up and shutdown transient process, which is used to characterize the overall rotational inertia of the system. The third characteristic quantity is the pulse of the energy value of the high-frequency component of the original electrical signal within the time window, which is used to characterize the abrupt change in the friction state between the cable and the guiding device.

[0013] Preferably, the adaptive control module includes a mapping table storing multiple sets of operating conditions and their corresponding speed control strategies. The adaptive control module is configured to query the mapping table using a second feature to match the speed control strategy. The mapping table includes mapping the second feature with an oscillation decay rate within a first preset range to a first type of high-response speed control strategy, and mapping the second feature with an oscillation decay rate within a second preset range to a second type of flexible start-up control strategy.

[0014] Preferably, the temperature drift self-calibration unit is configured to: record the relationship between motor phase current and speed during the initial stage of equipment cold start to calibrate the reference motor winding resistance; monitor the real-time phase current during equipment operation and compare it with the theoretical current determined by the reference motor winding resistance at the same speed to calculate the micro-increase in phase current and the calibration coefficient. Determined by the following rules: ,in, For a small increment of phase current, A compensation coefficient or calibration coefficient stored in the system. It is used to perform multiplication correction on the first characteristic.

[0015] Preferably, when the system state decoding module parses the third feature quantity, it is configured to: input the original electrical signal into a bandpass filter, calculate the root mean square value of the energy of the signal after filtering by the bandpass filter, and when the root mean square value of the energy exceeds an energy mutation threshold stored in the system, generate the third feature quantity to trigger the adaptive control module to execute a speed correction command to increase the power of the electric motor.

[0016] Preferably, the system further includes a control strategy self-optimization module, which is configured to: monitor the human intervention operation signal applied to the system while the adaptive control module executes the speed control strategy; when a human intervention operation signal is detected, record the intervention event including the intervention command and the first and second characteristic quantities at the time of intervention; based on the analysis of the stored historical intervention events, identify the human intervention pattern that recurs under similar first and second characteristic quantity conditions; and when the frequency of a certain human intervention pattern exceeds a frequency threshold, automatically generate an optimized correction scheme for the speed control strategy based on the human intervention pattern.

[0017] Preferably, the system status decoding module is also configured to perform cable slack risk warning, specifically: monitoring the occurrence of a composite event, which is defined as: detecting an energy pulse in the high-frequency component of the original electrical signal, and within a time window after the detection of the energy pulse, a first characteristic quantity drops below a cable slack threshold. When the composite event occurs, a cable slack risk warning signal is generated, and the adaptive control module is triggered to perform safety braking.

[0018] Preferably, the adaptive control module is configured to: determine a malignant load change and perform emergency braking when performing safety monitoring, if the calibrated first characteristic exceeds a safety threshold stored in the system.

[0019] Preferably, the mapping table also includes a third type of sinusoidal compensation control strategy for periodic load changes, and the system state decoding module is also configured to parse attenuated harmonics from the oscillation waveform of the second characteristic quantity. When attenuated harmonics that conform to the characteristics of periodic external force are identified, the adaptive control module matches and applies the third type of sinusoidal compensation control strategy from the mapping table.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. By configuring an endogenous vibration sensing module on the support bearing structure of the cable reel and processing the raw electrical signal acquired by this module with the system state decoding module, the existing technology avoids the reliance on external multi-point, multi-type sensor arrangements. The system no longer treats the winding and unwinding device as a black box requiring external monitoring, but rather makes its core supporting structure a self-expressor of its own operating state. The information acquisition path changes from external, multi-stage transmission to direct structural response, thereby reducing the time delay between control information and physical disturbances. This allows control decisions to be made based on a more immediate and real system state, which improves the suppression of tension mutations and stacking disorder caused by signal lag under dynamic operating conditions.

[0022] 2. The system state decoding module of this invention can perform multi-dimensional parallel decoding of the raw electrical signals from a single endogenous vibration sensing module. That is, during continuous system operation, it can analyze the low-frequency characteristics representing tension and the high-frequency characteristics representing frictional abrupt changes. At the same time, it can analyze the transient characteristics representing system inertia by utilizing the inevitable process of equipment start-up and shutdown. This information mining method of one source and multiple uses not only simplifies the hardware architecture and reduces the risk of information pollution or single point of failure that may occur due to multiple independent sensors, but more importantly, it establishes a logical connection, so that the system's understanding of the working condition is no longer a patchwork of scattered information, but a holistic insight into different aspects of the same physical process. This completeness and synergy of information acquisition is the foundation for realizing subsequent adaptive control.

[0023] 3. This invention solves two long-standing problems of control systems in real and complex environments by setting up a temperature drift self-calibration unit and a control strategy self-optimization module. The temperature drift self-calibration unit uses the electrical parameters of the drive motor itself to infer and compensate for the temperature drift of the intrinsic vibration sensing module. This ensures the long-term accuracy of the core tension sensing without increasing the hardware cost or failure point of any temperature sensor, making the system reliable even in outdoor scenarios with huge day-night temperature differences. The control strategy self-optimization module regards the operator's manual intervention in automatic mode as valuable experience data. By learning the patterns of these manual interventions, it optimizes the speed control strategy in reverse. This makes the system's intelligence no longer fixed at the factory, but can continuously evolve and grow in actual application, thereby better adapting to the gradual wear of undefined tooling or the equipment itself. The combination of these two features gives the system not only initial adaptability, but also the ability to continuously improve itself and maintain performance throughout its entire life cycle. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall functional architecture of an adaptive control system for cable winding speed under dynamic working conditions according to the present invention.

[0025] Figure 2 This is a schematic diagram of the information processing flow of an adaptive control system for cable winding speed under dynamic working conditions according to the present invention.

[0026] Figure 3 This is a schematic diagram of the hardware and software architecture of an adaptive control system for cable winding speed under dynamic working conditions according to the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] In a cable reel operation scenario such as that of a container spreader on a port quay crane, the cable reel system continuously responds to load changes and external environmental disturbances. Existing technologies typically rely on a combination of multiple external sensors. The time delay in the signal transmission and processing chain can cause control decisions to lag behind changes in the physical state. To address this challenge, the inherent vibration sensing module of this invention is configured as a piezoelectric element embedded or attached to the bearing seat of the cable reel support bearing. Since this bearing seat is the structural path through which cable tension and system dynamic disturbances are transmitted to the main body of the equipment, changes in cable tension will cause mechanical micro-vibrations in the bearing seat. These vibrations can be captured by the piezoelectric element and converted into a single raw electrical signal. This signal acquisition method aims to shorten the physical link of information collection, enabling the information relied upon by the control system to reflect the occurrence of physical disturbances more promptly. The task of the system state decoding module is to parse the vibration from this single raw electrical signal. To characterize the multi-dimensional state of the system, given that real-time monitoring of cable tension is a prerequisite for ensuring operational safety during continuous operation, the system state decoding module is configured to input the original electrical signal into a low-pass filter during continuous system operation and extract the fundamental frequency amplitude from the low-frequency components of its output as the first characteristic quantity characterizing the real-time cable tension. Simultaneously, to address the risk of sudden changes in the friction coefficient due to foreign matter contaminating the cable surface, the decoding module is configured to input another path of the original electrical signal in parallel into a band-pass filter. By calculating the root mean square (RMS) value of the filtered signal, the energy of the high-frequency components is monitored. When the RMS value exceeds an energy change threshold within a time window, an energy pulse is generated as the third characteristic quantity characterizing the sudden change in the friction state between the cable and the guiding device. Thus, by analyzing the same signal source in different frequency domains, the system can simultaneously obtain information about cable tension and the friction state of the contact surface.

[0029] Furthermore, to enable the control strategy to adapt to changes in the overall system inertia caused by changes in the cable winding state, the system state decoding module is further configured to calibrate the system's dynamic characteristics using the equipment start-up and shutdown process. Specifically, during the transient process when the system receives a start-up and shutdown command, the module synchronously acquires the original electrical signal and analyzes the oscillation waveform caused by the sudden change in motor torque. The oscillation decay rate of this waveform from its peak to its steady state is interpreted as a second characteristic quantity characterizing the overall system inertia. This second characteristic quantity is used by the adaptive control module to query a mapping table storing multiple sets of operating conditions and their corresponding speed control strategies to match a speed control strategy adapted to the current overall system inertia. The matching and application process described here is as follows: After receiving the second characteristic quantity characterizing the overall system inertia, the adaptive control module uses it as an index to query the mapping table and determine the optimal speed control strategy. Subsequently, the module executes this strategy and generates the final speed control command based on the preset control algorithm within the strategy, issuing it to the drive motor. For example, a speed control command calibrated as having an oscillation decay rate at the first... The second characteristic quantity within the preset range can be mapped to the first type of high-response speed control strategy, while a second characteristic quantity calibrated as having an oscillation decay rate within the second preset range can be mapped to the second type of flexible start control strategy. Furthermore, when a decaying harmonic conforming to the periodic external force characteristics is extracted from the oscillation waveform of the second characteristic quantity, the adaptive control module can match and apply the third type of sinusoidal compensation control strategy for periodic load changes from the mapping table, thereby enabling the control behavior to match the changes in the physical characteristics of the system. It should be noted that, considering that the charge sensitivity of the piezoelectric element may be affected by changes in the ambient temperature, a temperature drift self-calibration unit is set in the system. This unit infers the temperature change by reading the phase current parameters of the system drive motor without adding an external temperature sensor. Its working procedure is set as follows: during the cold start of the equipment, the relationship between the motor phase current and speed in the initial stage is recorded to calibrate the reference motor winding resistance. In subsequent operation, the unit monitors the real-time phase current and compares it with the theoretical current determined by the reference motor winding resistance at the same speed to calculate the micro-increase of the phase current. A calibration coefficient By rules It was determined that, among them, This is a compensation coefficient stored within the system. The first characteristic quantity is calibrated using this coefficient before being used for security monitoring decisions. The multiplication correction is designed to compensate for measurement biases that may be caused by temperature changes.

[0030] In terms of safety monitoring logic, the adaptive control module is configured to determine a severe load change and execute emergency braking when the received calibrated first characteristic quantity exceeds a safety threshold stored in the system. Simultaneously, to address situations where cable tension disappears instantaneously, the system state decoding module is also configured to monitor a specific composite event, defined as: detecting an energy pulse in the high-frequency component of the original electrical signal, and within a time window after detecting the energy pulse, the first characteristic quantity drops below a cable slack threshold. When this composite event occurs, the system generates a cable slack risk warning signal and triggers the adaptive control module to execute safety braking. This aims to distinguish tension drops caused by different reasons. Finally, to enable the system to... To adapt to changes that may occur during long-term operation, the system may also include a control strategy self-optimization module. While the adaptive control module executes the speed control strategy, this module monitors the human intervention operation signals applied to the system. When human intervention is detected, the system records an intervention event, which includes the intervention command and the first and second characteristic quantities at the time of intervention. By analyzing the stored historical intervention events, the system can identify the recurring patterns of human intervention under similar first and second characteristic quantity conditions. When the frequency of a certain human intervention pattern exceeds a frequency threshold, the system automatically generates an optimized correction scheme for the speed control strategy based on the human intervention pattern, thus providing a way for iterative updates of the control logic.

[0031] Example 1: During nighttime operations at a coastal port, a quay crane equipped with the control system of this invention is performing a heavy-duty container lifting task. Strong winds and cold rain occur, causing a rapid drop in ambient temperature. Simultaneously, the rainwater alters the friction state of the cable surface. Under these conditions, the system starts operating. The temperature drift self-calibration unit compares the real-time phase current of the drive motor with the initially calibrated reference motor winding resistance to calculate the micro-increase in phase current reflecting temperature changes. And generate calibration coefficients instantly. The raw electrical signal output by the intrinsic vibration sensing module is processed. This process compensates for the measurement deviation introduced by changes in ambient temperature when the system analyzes the first characteristic quantity representing the real-time cable tension, thus providing calibrated data input for subsequent judgment and control. During the lifting process, the cable surface state changes. The system state decoding module detects an energy pulse in the high-frequency component of the raw electrical signal through a bandpass filter and analyzes this event as the third characteristic quantity. After receiving the third characteristic quantity, the adaptive control module executes a speed correction command, slightly increasing the motor's driving force. The decision of this command is based on a first characteristic quantity that has been compensated by the temperature drift self-calibration unit as a reference, so that the judgment of the friction state is not affected by changes in ambient temperature.

[0032] Meanwhile, due to container loading and strong winds, the reel system is subjected to a periodic oscillating load. During the transient process of equipment start-up and shutdown, the system state decoding module has analyzed the damped harmonics unique to this load from the oscillation waveform of the second characteristic quantity representing the overall rotational inertia of the system. Therefore, the adaptive control module matches and applies a third type of sinusoidal wave compensation control strategy for this specific damped harmonic from its internally stored mapping table. The execution of this control strategy makes the torque output of the drive motor more synchronized with the changes in the external periodic load, thereby suppressing the speed oscillations that may be caused by load oscillations. When the operation ends, the container winding process is completed, and the cables are stacked neatly. During this period, the system has dealt with conditions such as the drop in ambient temperature, changes in the friction state of the cable surface, and periodic load oscillations. The entire process requires no manual intervention.

[0033] Example 2: To verify the technical solution of the present invention, a test platform consisting of a cable reel driven by a servo motor, a programmable magnetic powder brake, a high-precision tensile sensor, and a temperature-controlled environment chamber was built. This platform applies a variable cable load through the programmable magnetic powder brake, sets different operating ambient temperatures through the temperature-controlled environment chamber, and uses the high-precision tensile sensor as a reference to compare the values ​​of the state parameters resolved by the system of the present invention. The experiment aims to verify the compensation effect of the temperature drift self-calibration unit on the first characteristic quantity under different temperature environments. During the experiment, the system was first run in a standard environment of 25℃, applying a constant load of 5000N measured by the reference sensor. At this time, the uncalibrated first characteristic quantity was recorded as 5012N. Subsequently, the temperature of the temperature-controlled environment chamber was lowered to -20℃. Under the same load, the uncalibrated value decreased to 4535N, while when the temperature rose to 50℃, the value increased to 5506N. If the calibration coefficient output by the temperature drift self-calibration unit is... The corrected first characteristic value was observed throughout the test range of -20℃ to 50℃. Its value always fluctuated around the reference tension of 5000N. For example, it was 4989N at -20℃ and 5024N at 50℃. It did not show a unidirectional drift trend related to temperature changes. This result shows that the temperature drift self-calibration unit can compensate for the measurement deviation of the piezoelectric element caused by the temperature effect according to the changes in the electrical parameters of the motor.

[0034] Subsequent experiments aimed to verify the system's ability to distinguish different system moments of inertia using the second characteristic quantity. Two operating conditions were set up: Condition A, where the reel was nearly empty and the overall system moment of inertia was low; and Condition B, where the reel was nearly full and the overall system moment of inertia was high. The same motor start command was executed under both conditions. Under Condition A, the system recorded that the time required for the oscillation to decay to 10% of its peak value was 148ms, and based on this, the first type of high-response speed control strategy was selected from the mapping table. Under Condition B, this decay time was extended to 652ms, and the system correspondingly matched the second type of flexible start control strategy. These experimental results demonstrate that the system of this invention can utilize the transient process of equipment start-up and shutdown to distinguish the dynamic characteristics of its own load and use this as a basis for adjusting control behavior. In summary, the results show that the system of this invention can simultaneously analyze temperature-compensated tension information and dynamic characteristics representing the overall system moment of inertia from a single vibration signal source. Its data processing and decision-making logic achieved the preset functions under the set dynamic operating conditions and temperature environments, confirming the feasibility of the technical solution.

[0035] Example 3: This example combines Figures 1 to 3 This section describes an adaptive control system for cable winding speed under dynamic operating conditions, such as... Figure 1 As shown in the figure, this diagram illustrates the complete flow path of information within the system and the closed-loop control logic. The cable reel system, acting as a physical disturbance source, generates micro-mechanical vibrations, which are captured by the endogenous vibration sensing module and converted into a single raw electrical signal. This signal is input to the system state decoding module. Simultaneously, the drive motor provides its phase current parameters to the temperature drift self-calibration unit, which infers the temperature and generates calibration coefficients based on these parameters. The coefficient is also input into the system state decoding module. The system state decoding module integrates the original electrical signal and the calibration coefficient, and decodes in parallel the multi-dimensional state information including real-time tension, moment of inertia, and friction. This information is then sent to the adaptive control module, which matches and executes the optimal speed control strategy and generates speed control commands to be applied to the drive motor. In addition, the system also includes a control strategy self-optimization module. This module learns from human intervention signals and combines them with the state characteristics obtained from the system state decoding module to generate an optimized correction scheme to iteratively update the speed control strategy within the adaptive control module, thereby forming a closed-loop control system with control strategy self-optimization function.

[0036] like Figure 2As shown, the mechanical micro-vibration of the cable reel system is transmitted to the vibration sensing system (labeled 1.0), generating a raw electrical signal that is then passed to the decoding multidimensional state stage (labeled 2.0). This decoding stage extracts the first characteristic quantity (tension), the second characteristic quantity (inertia), and the third characteristic quantity (friction) from the signal. The second and third characteristic quantities are directly used in the execution adaptive control stage (labeled 3.0), which makes decisions based on data obtained from the mapping table and threshold library and outputs speed control commands to the drive motor. The first characteristic quantity first enters the calibration tension signal stage (labeled 4.0), which uses the motor phase current parameters obtained from the drive motor to perform temperature drift calibration and transmits the calibrated first characteristic quantity to the execution adaptive control stage (labeled 3.0) for safety monitoring and speed correction. In addition, the manual intervention signal generated by the operator and the state characteristic quantity output by the decoding stage are sent to the self-optimizing control strategy stage (labeled 5.0). This stage generates an optimization correction scheme based on the analysis of historical intervention events. The arrows of this scheme point to the mapping table and historical intervention events, respectively, to update the control model and achieve iterative optimization.

[0037] like Figure 3 As shown, the entire system is centered around an embedded field control unit. This unit integrates a system status decoding module (as a software component), an adaptive control module, a temperature drift self-calibration unit, a control strategy self-optimization module, and a configuration and log database for storing data such as mapping tables, thresholds, and historical events. In terms of physical connection, the intrinsic vibration sensing module inputs the collected data to the field control unit through analog signals. The field control unit then communicates bidirectionally with the drive motor system through a control / feedback bus to achieve command issuance and status feedback. At the same time, the field control unit is connected to the upper-level human-machine interface terminal via an industrial Ethernet, which provides an interactive interface for monitoring and manual control applications.

[0038] Example 4: Before a newly installed cable reel used in a specific industrial device is put into operation for the first time, an offline system calibration procedure needs to be performed to match its internal control parameters with the physical characteristics of the device. This procedure provides definite values ​​for the mapping table, thresholds, and key coefficients in the temperature drift self-calibration unit of the adaptive control module. The procedure first addresses the compensation coefficients in the temperature drift self-calibration unit. Calibration was performed by placing the drive motor used in the system on a test bench. The motor body temperature was precisely controlled by an external device, and the real-time resistance of the motor windings and the phase current fed back by the driver were measured using instruments. Within a temperature range of 0℃ to 80℃, multiple temperature points were selected at 10℃ intervals, and the incremental changes in phase current of the motor under no-load uniform rotation were recorded. The relationship between the measured temperature rise and the actual temperature rise is obtained by linearly fitting these data points to obtain a calibration coefficient specific to this motor model. compensation coefficient Subsequently, an in-machine test is performed on the equipment to initialize the mapping table and thresholds. The operator first drives the reel to start with a preset acceleration while the reel is unloaded. The system status decoding module records the second characteristic quantity during this process, namely the oscillation decay rate, and establishes a correlation between this value and the first type of high response speed control strategy in the mapping table. Then, the cable is completely wound onto the reel, and the start-up process is repeated. The recorded oscillation decay rate, which slows down due to the increase in the overall rotational inertia of the system, is then correlated with the second type of flexible start-up control strategy in the mapping table. By repeating this process under different winding amounts, the mapping table can be interpolated and filled.

[0039] To determine the safety threshold of the first characteristic quantity, the operator uses an auxiliary winch with controllable tension to slowly pull the cable in the opposite direction, simulating a load exceeding the rated value. The system state decoding module continuously records the changes in the first characteristic quantity. When the external tension reaches 75% of the limit load of the equipment's mechanical structure, the amplitude of the first characteristic quantity at this moment is set as the safety threshold in the adaptive control module. To determine the energy mutation threshold of the third characteristic quantity, lubricant is applied to a section of the cable surface. During the winding process, when this lubricated section passes through the guide device, the system state decoding module records a high-frequency energy pulse caused by a change in friction. The peak value of the root mean square value of the pulse's energy is multiplied by a coefficient of 1.5, and the product is set as the energy mutation threshold. After the above calibration is completed, all key parameters and control models inside the control system have been configured according to the measured physical characteristics of the specific equipment, and the system thus has the initial conditions to perform adaptive control in actual operation.

[0040] After the system's initial deployment or major maintenance, its key internal parameters are calibrated through a set of deterministic procedures. The temperature drift self-calibration unit's calibration function involves recording the incremental phase current of the drive motor under no-load, uniform rotation at multiple preset temperature points within a temperature-controlled environment. This generates a calibration coefficient that uses the incremental phase current as input. The output discrete data mapping table is used to calculate the calibration coefficient in real time during system operation by performing Lagrange interpolation on the two data points in the table that are closest to the current measured phase current micro-increment. Meanwhile, the speed control strategy mapping table in the adaptive control module records the oscillation decay rate during system startup one by one under multiple discrete winding states of the cable reel, such as no load, one-quarter load, one-half load, three-quarter load, and full load. For each rate point, a set of proportional, integral, and derivative terms is configured. The optimal control parameters, including those included, are finally fitted using the B-spline interpolation function, a mathematical tool for generating smooth, continuous curves between discrete data points. This fits these discrete inertia feature-control parameter data pairs to construct a mathematical model that can continuously and deterministically output a speed control strategy based on any input second feature quantity.

[0041] Example 5: When the number of intervention events stored in the self-optimization module of the control strategy reaches a preset number, the system initiates a set of established verification and review procedures to generate and confirm a new speed control strategy. In this procedure, the system first filters the stored intervention events, removing operation signals whose amplitude exceeds a preset limit or whose direction reverses more than a preset frequency per unit time. The system then clusters the filtered intervention events based on their first and second characteristic values ​​at the time of occurrence. When the cumulative frequency of intervention events in a certain category exceeds the occurrence frequency threshold, the system calculates the average adjustment amount of all intervention operations in that category and applies it to... The corresponding original speed control strategy generates a candidate optimization correction scheme. This candidate scheme is placed in the internal simulation environment, and the system calls historical working condition data to backtest the candidate scheme to generate a set of performance indicators including expected tension fluctuations and energy consumption. When the performance indicators of the candidate scheme and the corresponding indicators of the original speed control strategy meet the preset comparison logic, and the simulation process does not trigger the virtual safety boundary, the candidate scheme and its performance indicators are submitted to a human-computer interaction interface for authorized personnel to review. After review and confirmation, the optimization correction scheme is added to the mapping table of the adaptive control module as a new selectable speed control strategy.

[0042] Example 6: After the system completes the initial parameter calibration, in order to establish a benchmark reference model that can be used for subsequent operational status monitoring, a baseline data acquisition procedure needs to be executed. This procedure first establishes a reference benchmark characterizing the mechanical coupling state between the endogenous vibration sensing module and the equipment body. With the equipment stopped and unloaded, the system drives a built-in micro-impact exciter to apply a transient mechanical impact to the support bearing structure of the cable reel with a preset energy. The system status decoding module simultaneously acquires and calculates the total energy of the original electrical signal output by the endogenous vibration sensing module within a time window after the impact, and stores this total energy value as the benchmark feature of the mechanical coupling state in the system. Subsequently, the system establishes a reference benchmark characterizing the vibration characteristics of the equipment itself. The system operates smoothly at a preset low speed. At the same time, the system state decoding module performs spectrum analysis on the acquired and unfiltered raw electrical signal, generating a power spectral density map covering a preset frequency range. The system stores this power spectral density map as the vibration characteristic benchmark of the equipment and identifies the frequency distribution boundaries corresponding to the tension signal and friction noise in the spectrum. It sets the initial cutoff frequency and passband range for the low-pass filter used to analyze the first characteristic quantity and the band-pass filter used to analyze the third characteristic quantity, so that the channel parameters of the signal decoding match the vibration characteristics of the equipment in the initial state.

[0043] To monitor the physical coupling between the intrinsic vibration sensing module and the device body online, the system executes a reference feature acquisition procedure immediately after initial calibration. This procedure applies a preset instantaneous standard torque pulse as excitation to the drive motor and simultaneously acquires the response signal output by the intrinsic vibration sensing module. Subsequently, the transfer function of the system in the initial state is generated through Fourier transform. (i.e., the reference transfer function), which characterizes the vibration transmission characteristics from the mechanical excitation point to the intrinsic vibration sensing module and is stored in the system; in subsequent equipment operation cycles, the system automatically repeats the above excitation and acquisition process in convenient static states such as each start-up or shutdown, generating the current transfer function. And calculate a formula Defined system health deviation index ,in, The Euclidean norm (L2 norm) of a vector is used to quantify changes in the physical properties of a system by calculating the normalized Euclidean distance between the current transfer function and the reference transfer function in the frequency domain. More than three standard deviations from the statistical distribution of the index itself based on the initial stable operation. The determined drift threshold When this happens, the system will automatically trigger a complete parameter recalibration process or send an early warning signal to the upper-level system that a physical inspection and maintenance is required.

[0044] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An adaptive control system for cable winding speed under dynamic operating conditions, characterized in that, include: An endogenous vibration sensing module is configured to convert the mechanical micro-vibrations generated by the cable reel's support bearing structure due to cable tension and system dynamic disturbances during cable winding into raw electrical signals. A system state decoding module, electrically connected to an endogenous vibration sensing module, is configured to: during continuous operation of the system, parse a first characteristic quantity from the low-frequency component of the original electrical signal and parse a third characteristic quantity from the high-frequency component of the original electrical signal; During the transient process of system start-up and shutdown, the second characteristic quantity is extracted from the original electrical signal; A temperature drift self-calibration unit is configured to: infer the operating temperature change of the intrinsic vibration sensing module based on the phase current parameters of the drive motor of the system, and generate calibration coefficients for dynamic calibration of the first characteristic quantity. An adaptive control module is connected to the system state decoding module and is configured to: automatically select and execute a speed control strategy stored in the adaptive control module based on a second feature value, and perform safety monitoring and speed correction based on a calibrated first and third feature values; Among them, the first characteristic quantity is the fundamental frequency amplitude of the low-frequency component of the original electrical signal, which is used to characterize the real-time cable tension; the second characteristic quantity is the oscillation decay rate of the original electrical signal during the system start-up and shutdown transient process, which is used to characterize the overall rotational inertia of the system; the third characteristic quantity is the pulse of the energy value of the high-frequency component of the original electrical signal within the time window, which is used to characterize the abrupt change in the friction state between the cable and the guiding device. When the system state decoding module parses the third feature quantity, it is configured to: input the original electrical signal into a bandpass filter, calculate the root mean square value of the energy of the signal after filtering by the bandpass filter, and when the root mean square value of the energy exceeds an energy mutation threshold stored in the system, generate the third feature quantity to trigger the adaptive control module to execute a speed correction command to increase the power of the electric motor. The adaptive control module is configured to determine a malicious load change and perform emergency braking when performing safety monitoring, if the calibrated first characteristic exceeds a safety threshold stored in the system.

2. The adaptive control system for cable winding speed under dynamic operating conditions as described in claim 1, characterized in that, The intrinsic vibration sensing module is a piezoelectric element that is pre-embedded or pasted onto the bearing housing supporting the bearing.

3. The adaptive control system for cable winding speed under dynamic operating conditions as described in claim 1, characterized in that, The adaptive control module includes a mapping table that stores multiple sets of operating conditions and their corresponding speed control strategies. The adaptive control module is configured to query the mapping table using a second feature to match the speed control strategy. The mapping table includes mapping the second feature with an oscillation decay rate within a first preset range to a first type of high-response speed control strategy, and mapping the second feature with an oscillation decay rate within a second preset range to a second type of flexible start-up control strategy.

4. The adaptive control system for cable winding speed under dynamic operating conditions as described in claim 1, characterized in that, The temperature drift self-calibration unit is configured to: record the relationship between motor phase current and speed during the initial stage of equipment cold start to calibrate the reference motor winding resistance; monitor the real-time phase current during equipment operation and compare it with the theoretical current determined by the reference motor winding resistance at the same speed to calculate the micro-increase in phase current and the calibration coefficient. Determined by the following rules: ,in, For a small increment of phase current, A compensation coefficient or calibration coefficient stored in the system. It is used to perform multiplication correction on the first characteristic.

5. The adaptive control system for cable winding speed under dynamic operating conditions as described in claim 1, characterized in that, The system also includes a control strategy self-optimization module, which is configured to: monitor the human intervention operation signals applied to the system while the adaptive control module executes the speed control strategy; when a human intervention operation signal is detected, record the intervention event containing the intervention command and the first and second characteristic quantities at the time of intervention; based on the analysis of the stored historical intervention events, identify the human intervention patterns that recur under similar first and second characteristic quantity conditions; and when the frequency of a certain human intervention pattern exceeds a frequency threshold, automatically generate an optimized correction scheme for the speed control strategy based on the human intervention pattern.

6. The adaptive control system for cable winding speed under dynamic operating conditions as described in claim 1, characterized in that, The system status decoding module is also configured to perform cable slack risk warning, specifically: monitoring the occurrence of a composite event, which is defined as: detecting an energy pulse in the high-frequency component of the original electrical signal, and within a time window after the detection of the energy pulse, the first characteristic quantity drops below a cable slack threshold. When the composite event occurs, a cable slack risk warning signal is generated, and the adaptive control module is triggered to perform safety braking.

7. The adaptive control system for cable winding speed under dynamic operating conditions as described in claim 3, characterized in that, The mapping table also includes a third type of sinusoidal compensation control strategy for periodic load changes. The system state decoding module is also configured to parse attenuated harmonics from the oscillation waveform of the second characteristic quantity. When attenuated harmonics that conform to the characteristics of periodic external force are identified, the adaptive control module matches and applies the third type of sinusoidal compensation control strategy from the mapping table.

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