Grab bucket composite anti-swing control system based on angle feedback of steel wire rope at head of trunk beam and control method thereof

Abstract

The invention provides a grab bucket composite anti-swing control system based on angle feedback of a steel wire rope at the head of a trunk beam and a control method of the grab bucket composite anti-swing control system, and relates to the technical field of portal crane grab bucket anti-swing. The core problems of response lag, control strategy solidification and poor working condition adaptability of the open-loop anti-swing system are systematically solved. And on the basis of a closed-loop control strategy of swing angle prediction and multi-cycle compensation, the response delay of a traditional open-loop system is reduced, the swing trend is inhibited in advance through a feedforward prediction instruction, and the swing attenuation time of the grab bucket is reduced. Through cooperative compensation of the swing angle and the speed, the portal crane boom and the grab bucket are kept dynamically synchronous all the time, the repeated positioning precision is improved, the fineness of manual operation is matched, and meanwhile manual operation errors are avoided. Through a parameter self-adaption mechanism and a swing angle elimination frequency prediction function, the system can automatically adapt to various complex working conditions, the stable anti-swing effect can be kept without manually adjusting parameters, and the optimal balance of the portal crane between efficient operation and safety control is achieved.

Description

Technical Field

[0001] This invention relates to the field of anti-sway technology for gantry crane grab buckets, and in particular to a composite anti-sway control system and control method for grab buckets based on the angle feedback of the steel wire rope at the head of the elephant trunk. Background Technology

[0002] The existing anti-sway technology for gantry crane grab buckets has the following core defects: 1. Open-loop control response lag: Traditional systems adjust motor operation by preset speed curves using frequency converters, and cannot sense the grab bucket swing status in real time. When the luffing or rotating mechanism starts or stops, or when there is a sudden change in load, the grab bucket swing cycle can be as long as 812 seconds, resulting in low operating efficiency.

[0003] 2. Insufficient angle detection accuracy: Existing mechanical limit or laser sensors (such as the Chinese invention patent with publication number CN120135952A) are easily affected by dust and water mist. In harsh environments such as ports, the angle detection error exceeds ±2°, which cannot meet the closed-loop control requirements.

[0004] 3. Single control strategy: The mainstream PID control parameters are fixed and cannot adapt to complex working conditions such as load quality changes (such as 20% to 100% load fluctuations) and sudden changes in wind speed (0 to 10 m / s), resulting in unstable anti-sway effect. Summary of the Invention

[0005] Purpose of the invention: To propose a composite anti-sway control system and control method for grab bucket based on the angle feedback of the steel wire rope at the head of the elephant trunk beam. By integrating millimeter-wave radar into the head of the elephant trunk beam to detect the sway angle of the steel wire rope in real time, and using a dual closed-loop PID fuzzy adaptive control algorithm, the time for the grab bucket swing amplitude to decay from ±1.5m to ±0.1m is shortened to less than 3.5 seconds, solving the technical problems of slow response, low accuracy and poor robustness of traditional technology.

[0006] In a first aspect, the present invention provides a grab bucket composite anti-sway control system based on the angle feedback of the steel wire rope at the head of the elephant trunk, comprising: At least two millimeter-wave radars, symmetrically arranged on both sides of the gantry crane's trunk head, are used to collect raw point cloud data of the steel wire rope. The edge computing coprocessing unit is used to receive the raw point cloud data of the millimeter-wave radar, extract the feature points of the steel wire rope based on the point cloud clustering algorithm and calculate the ranging difference Δd between the two radars. Based on the horizontal baseline distance L between the two millimeter-wave radars, the swing angle α of the steel wire rope is obtained according to the formula α=arcsin(Δd / L), and the obtained swing angle α is Kalman filtered and then output as real-time swing angle data. The PLC is used to receive the real-time swing angle data output by the edge computing coprocessing unit, and to perform fuzzy PID control and feedforward compensation based on the real-time swing angle data and other operating conditions to generate frequency / torque commands for the inverter. Inverters and drive motors are used to respond to frequency / torque commands issued by the PLC to drive the rotation and amplitude-changing mechanisms and provide encoder feedback. The safety interlock module is used to display the system's operating status and to trigger an alarm or emergency braking when preset safety conditions are met. The edge computing coprocessor unit and the PLC transmit swing angle data through a real-time industrial Ethernet communication link, forming a closed-loop control data stream from the angle of the wire rope to the execution of the frequency converter.

[0007] As a preferred embodiment of the first aspect, the edge computing coprocessing unit uses the DBSCAN point cloud clustering algorithm to extract the feature points of the wire rope, and after angle calculation, sends the Kalman-filtered smoothed swing angle α to the PLC through the Profinet interface at a predetermined period. The frequency converter supports sensorless vector control and receives frequency / torque commands from the PLC via the Profinet interface. The drive motor is equipped with an encoder at its shaft end to provide real-time speed and / or position information to the PLC to form a speed closed loop.

[0008] As a preferred embodiment of the first aspect, the PLC has a built-in or cooperative control module, which includes a fuzzy PID controller, a feedforward compensator, and a parameter adaptive unit. The fuzzy PID controller uses the swing angle deviation e(t) and the swing angle change rate de(t) / dt as fuzzy control inputs, and outputs corrections to the PID parameters; the feedforward compensator calculates and predicts the swing angle α based on the acceleration a(t) of the rotary luffing mechanism and the load mass m. pred The prediction results are then combined with the fuzzy PID output to form the inverter frequency command f(t); The parameter adaptive unit estimates the load mass m in real time based on the current and speed curve of the drive motor, and periodically adjusts the parameters of the fuzzy PID controller.

[0009] As a preferred embodiment of the first aspect, the edge computing coprocessing unit performs the following when preprocessing the radar raw data: time-domain filtering to remove Gaussian white noise, frequency-domain FFT to identify the main vibration frequencies, and combining the phase difference method and Kalman filtering to output a smooth angle in the angle calculation stage; when the smooth angle jumps more than a preset threshold, the sensor self-test program is triggered.

[0010] As a preferred embodiment of the first aspect, the PLC is equipped with a working state machine for executing a dual-condition triggering mechanism: When the operating speed reaches the preset speed and the distance to the target is less than or equal to the preset distance, it switches to closed-loop anti-sway control based on angle feedback; When the swing angle is less than the preset angle value and the speed and / or position meet the fine stopping threshold, it enters the locked state.

[0011] As a preferred embodiment of the first aspect, the safety interlocking module performs the following mechanism: When the real-time absolute value of the swing angle |α| exceeds the first preset threshold, the PLC command executes an emergency brake and issues an alarm. When the deviation between radar speed measurement and encoder speed measurement exceeds the second preset threshold, an alarm is triggered and the system switches to safe mode according to preset logic.

[0012] Based on the grab bucket composite anti-sway control system disclosed in the first aspect and its preferred embodiments, the second aspect of the present invention further proposes a control method comprising the following steps: S1. Perform self-testing and calibration on the millimeter-wave radars on both sides of the gantry crane's trunk head, confirm the horizontal baseline distance L between the two millimeter-wave radars, and initialize system parameters and communication links. S2. Two millimeter-wave radars simultaneously acquire ranging data of the steel wire rope. The edge computing coprocessing module performs point cloud clustering and temporal filtering on the data to extract feature points of the steel wire rope. S3. Calculate the distance difference Δd between the two radars, and obtain the swing angle α of the wire rope according to the formula α=arcsin(Δd / L). Perform Kalman filtering on the swing angle α to smooth it, and send the real-time swing angle data to the PLC main controller according to the set period. S4, the PLC main controller estimates the load mass m based on the current and speed feedback of the drive motor, and identifies the current working condition type by combining speed, acceleration and swing angle change rate; S5. Call the fuzzy PID control unit, taking the swing angle deviation e(t) and the swing angle change rate de(t) / dt as inputs, and output the PID correction; call the feedforward compensation unit, taking the acceleration a(t) of the rotating or luffing mechanism and the load mass m as inputs, and predict the swing angle α. pred And generate a feedforward compensation signal; the two are then combined to generate the inverter frequency / torque command f(t); S6. The frequency conversion drive module performs rotation or amplitude control according to the f(t), and returns the speed and position signals to the PLC main controller through the encoder; the PLC continuously corrects the control output based on the feedback information and the swing angle α, forming a closed loop. S7. When the fine stop condition is met, the PLC issues a fine stop command and enters the locked state, completing one anti-shake operation cycle.

[0013] As a preferred option, this method also includes parameter adjustment mechanisms for load adaptation and distance adaptation; Load Adaptive: Dynamically adjusts key gain parameters in real time based on load quality m. and To adapt to the anti-sway requirements under different load conditions, , ,in This represents the basic gain proportional to the swing angle under no-load conditions. The base gain represents the change in swing angle under no-load conditions. The parameter is the load-dependent effect of the swing angle. The load influence coefficient is the swing angle variation. Indicates the rated load mass; Distance adaptive: based on the current speed of the gantry crane Dynamically change the deceleration start distance parameter , ,in This represents the speed influence coefficient (typical value 0.5-1.0). Indicates the maximum operating speed of the mechanism. This indicates the basic deceleration distance (typical value 2m).

[0014] As a preferred option, this method also includes: constructing a prediction model for the number of swing angle eliminations, and using the formula Estimated number of stable hooking attempts required Based on this prediction result, the deceleration start distance will be dynamically adjusted further. ,in Indicates the initial swing angle. This indicates the permissible residual swing angle (typically 0.05 rad). This indicates the rate of decrease in the swing angle for each steady hook (typical value 0.3-0.5).

[0015] Beneficial Effects: Through the aforementioned key technological innovations, this invention systematically solves the core problems of open-loop anti-sway systems, such as response lag, rigid control strategies, and poor adaptability to operating conditions. First, the closed-loop control strategy based on swing angle prediction and multi-cycle compensation significantly reduces the response delay of traditional open-loop systems. By using feedforward prediction commands to suppress swaying trends in advance, it reduces the grab's swing amplitude decay time, significantly improving anti-sway efficiency. Second, the algorithm simulating manual hook stabilization operation, through coordinated compensation of swing angle and speed, ensures that the gantry crane boom remains dynamically synchronized with the grab, improving repeatability and matching the precision of manual operation while avoiding human error. Third, the parameter adaptive mechanism and swing angle elimination frequency prediction function enable the system to automatically adapt to various complex operating conditions, maintaining a stable anti-sway effect without manual parameter adjustment, ultimately achieving the optimal balance between efficient operation and safe control for gantry cranes. Attached Figure Description

[0016] Figure 1This is a side view of an embodiment of the present invention showing a millimeter-wave radar installed on the head of an elephant's trunk.

[0017] Figure 2 This is a top view of an embodiment of the present invention showing a millimeter-wave radar installed on the head of an elephant's trunk.

[0018] Figure 3 This is a flowchart of the operation of the grab bucket composite anti-sway control system in an embodiment of the present invention. Detailed Implementation

[0019] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0020] The anti-sway system used in the automated operation of conventional gantry cranes does not rely on sensor feedback (such as tilt sensors or encoders calculating rope length differences) to measure the grab bucket's swing angle in real time. It operates the crane based on pre-set operating modes and control strategies, minimizing the inertial force impact that causes swaying by controlling the acceleration, deceleration, and speed curves of the drive mechanism. This open-loop anti-sway system has the following technical disadvantages: 1. Lag in dynamic response leads to low anti-shake efficiency. The open-loop system relies on a preset speed curve model and lacks a real-time feedback mechanism. When the grab bucket sways due to external disturbances (such as gusts of wind or sudden load changes), the system cannot dynamically adjust the control parameters. If the anti-sway optimization is restarted due to lag, the time is too long, which does not meet the requirements of on-site operations.

[0021] 2. Fixed control strategies cannot adapt to complex operating conditions. The control parameters (such as acceleration and deceleration curves) of an open-loop system are fixed at the factory, and the control strategy cannot be automatically optimized according to real-time operating conditions (such as emergency stop or collision, load fluctuations).

[0022] The core objective of this invention is to construct a real-time closed-loop control system for gantry crane grab bucket anti-swaying through systematic technological innovation, specifically including the following objectives: 1. Construction of a high-precision real-time detection system Dual millimeter-wave radars are symmetrically deployed at the head of the gantry crane's trunk. Through multi-source signal fusion and hardware-level acceleration processing, high-precision real-time detection of the wire rope swing angle is achieved, and the sensor signal response time is compressed to less than 5ms. At the same time, the communication link between the sensor and the controller and actuator is optimized, and the control delay of the entire process from angle detection to motor adjustment is controlled to less than 50ms, providing data support for millisecond-level dynamic anti-sway.

[0023] 2. Implementation of Adaptive Intelligent Control Strategy This innovative approach integrates the rapid response characteristics of PID control with the robust advantages of fuzzy logic to construct a dynamic parameter adjustment mechanism. By collecting operating parameters such as load mass, wind speed, and mechanism operating speed in real time, the fuzzy rule base is driven to autonomously optimize the proportional coefficient (Kp), integral time (Ti), and derivative time (Td) of the PID controller. This enables adaptive anti-sway control under different operating scenarios (such as load fluctuations of 20% to 100% and sudden changes in wind speed of 0 to 10 m / s), solving the technical bottleneck of traditional fixed parameter control being unable to match complex operating conditions.

[0024] 3. Systematic breakthrough in addressing the core defects of open-loop systems Addressing the technical pain points of existing open-loop anti-sway systems, such as slow response, low detection accuracy, and poor adaptability to working conditions (only able to handle single load conditions), this system achieves a reduction of more than 50% in the time it takes for the grab bucket swing amplitude to decay from ±1.5m to ±0.1m through triple innovation of "dual millimeter-wave radar detection + adaptive algorithm control + full-link delay optimization". This significantly improves the operating efficiency and safety of gantry cranes and provides technical support for high-precision lifting operations in harsh environments such as ports and mines.

[0025] The following sections will elaborate on the implementation plan in detail from five aspects: hardware design, system composition, operation process, compensation algorithm logic implementation, and system performance indicators.

[0026] I. Hardware Design 1. Millimeter-wave radar configuration scheme It uses a 77GHz industrial-grade millimeter-wave radar, with core features adapted to gantry crane operating conditions: horizontal angular resolution ±0.5° (improved to ±0.3° through dual radar interferometry technology), vertical angle coverage 15°~+15° (matching the wire rope swing range); effective detection distance 0.550m (covering the entire lifting stroke of the grab bucket 530m), distance accuracy ±5cm, and supports 100 frames of data output per second (meeting the real-time tracking of swing frequencies below 2Hz). Installation layout: Radar brackets are symmetrically fixed on both sides of the elephant trunk head of the gantry crane. The baseline length of the two radars is L=1m (to eliminate blind spots of a single radar through triangulation). The brackets are made of 304 stainless steel (corrosion resistant) and are fastened to the elephant trunk head with 4 M12 high-strength bolts (to prevent vibration from loosening). 2. Controller Architecture The main controller, Siemens S71500 PLC, serves as the core control unit of the system. Its communication interfaces include two ProfinetIO interfaces (supporting IRT real-time communication with a cycle of 1ms, connecting the frequency converter and HMI). Expansion modules: Configure analog input module (16-bit AD, 10kHz sampling rate, for receiving radar angle signals) and digital input / output module (for monitoring radar and motor status signals). Coprocessing Unit: Integrated edge computing module (aids in processing complex algorithms): Hardware configuration: Intel Core i5 10400E processor (6 cores, 2.2GHz), 8GB DDR4 memory, supports GPU acceleration (for point cloud clustering and angle calculation). Functional division of labor: Receive raw point cloud data from dual radars, extract feature points of the steel wire rope using a point cloud segmentation algorithm based on DBSCAN, calculate the swing angle using trigonometric geometric formula (α=arcsin(Δd / L), where Δd is the distance difference between the two radars), and after Kalman filtering (with dynamic adjustment of process noise covariance Q), send the data to the PLC main controller via Profinet at 40ms intervals.

[0027] 3. Variable frequency drive system Inverter: Yaskawa H1000 series high-voltage inverter is selected, compatible with gantry crane trolley drive motor: Control performance: Supports sensorless vector control (speed control accuracy ±0.2% of rated speed), torque response time <10ms (fast tracking PLC compensation instructions), speed range 1:100 (0.550Hz, covering trolley running speed 0.110m / min). Communication and protection: Integrated Profinet interface (receives PLC speed commands in real time, with a cycle of 1ms), supports overcurrent, overvoltage, overheat, and motor stall protection (protection action response time <10ms), and has a built-in braking unit (compatible with a 20kW braking resistor to absorb emergency stop energy).

[0028] Drive motor: Siemens 1LE0001 series three-phase asynchronous motor (with encoder) is selected. Motor parameters: Rated power 22kW, rated speed 1480r / min, rated torque 143N·m, protection class IP55 (dustproof and splashproof), insulation class F (allowable temperature rise 105K). Feedback configuration: A 1024-line incremental encoder (accuracy ±0.1°) is installed at the shaft end, and the real-time speed is fed back to the frequency converter via Profinet (cycle 1ms) to form a speed closed loop.

[0029] II. System Composition 1. Radar signal processing system The radar signal processing system first performs data preprocessing, including removing Gaussian white noise through time-domain filtering (ensuring a signal-to-noise ratio (SNR) > 20 dB), identifying the wire rope vibration frequency (mainly concentrated in the 0.32 Hz range) through FFT in frequency-domain analysis, and extracting wire rope feature points based on the DBSCAN algorithm (cluster radius ε = 0.1 m). In the angle calculation stage, a dual-radar interferometry method is used to calculate the wire rope deflection angle through phase difference (the formula is...). It combines historical angle data with Kalman filtering to output a smooth angle (filter bandwidth 0.53Hz), and also has an anomaly detection function. When the angle jump is >5° / frame, the sensor self-test program is automatically triggered.

[0030] 2. Anti-sway control system The core algorithm module of the anti-sway control system includes a fuzzy PID controller and a feedforward compensator. The fuzzy PID controller takes the angle deviation e(t) and the angle change rate de(t) / dt as input variables and outputs the correction values ​​of the PID parameters Kp, Ki, and Kd. The feedforward compensator receives the acceleration a(t) of the rotation and amplitude-changing mechanism and the load mass m as inputs and outputs the predicted sway angle α_pred and generates compensation commands in advance. At the same time, the system has an adaptive adjustment function for control parameters. Through load identification (real-time estimation of load mass based on motor current and speed curves with an error of <±5%), working condition classification (identification of 8 typical working conditions based on the combination of speed, acceleration, and angle change rate features), and parameter self-tuning (periodic optimization of the fuzzy rule base through a genetic algorithm for each work cycle), the anti-sway effect is ensured to be stable and reliable under different working scenarios.

[0031] 3. PLC control system The PLC control system's drive states include six modes: standby, start-up, running, deceleration, precise stopping, and fault detection, which can automatically switch according to the work progress. It also integrates mechanism safety interlock functions, triggering emergency braking when the absolute value of the wire rope swing angle |α|>15°, and issuing an alarm when the deviation between radar speed measurement and encoder speed measurement exceeds 10%, comprehensively ensuring system operational safety. In terms of communication management, the system interfaces with the automated operation system via a dedicated interface to receive target position commands; establishes real-time communication with the frequency converter, issuing torque commands at a 100Hz update frequency; and provides a human-machine interface via a touchscreen, offering real-time status monitoring and enabling visualized management of equipment operating status.

[0032] III. Work Procedures See Figure 3 The work process is as follows: 1. Start-up Phase After the automated operation system assigns the target location, it activates the anti-sway system and initializes the parameters, and the equipment enters the acceleration phase.

[0033] 2. Operation and Control Phase When the speed reaches 80% of the maximum speed, radar detection is activated, and dynamic adjustments are made using a fuzzy PID algorithm based on real-time angle feedback data.

[0034] 3. Deceleration and Fine Stopping Phase When the distance to the target is 2m, the deceleration program is activated, the feedforward compensation module is started to eliminate system lag, and the fine stop control is continuously adjusted until the swing angle is <0.5°.

[0035] 4. Lock-in phase Once the required accuracy is achieved, the position is locked, the anti-shake system is turned off, and the system awaits new instructions.

[0036] Key control points: Dual-condition detection: speed threshold (80%) It provides dual protection through both distance threshold (2m).

[0037] Closed-loop control: Angle feedback → fuzzy PID to form a real-time control closed loop.

[0038] Three-stage braking: speed management in three stages: acceleration, deceleration, and precise stopping.

[0039] Accuracy Guarantee: Feedforward compensation + precise stop control ensures positioning accuracy of ±0.5°.

[0040] IV. Implementation of the Compensation Algorithm Design a complete algorithm formula system to achieve swing angle prediction, multi-cycle hook stabilization control, and lead calculation. This algorithm will output inverter frequency commands to simulate and optimize the operator's manual hook stabilization operation. 1. Basic physical model and symbol definition f(t): Inverter output frequency command (Hz) Current wire rope swing angle (rad) Rate of change of pendulum angle (rad / s) v(t): Rotational speed (m / s) x(t): Rotation of the current position (m) Target location (m) L: Length of wire rope (m) m: Load mass (kg) =50Hz: Maximum output frequency of the frequency converter :correspond Maximum rotational speed (m / s) 2. Swing Angle Prediction Model Based on the dynamics of a pendulum, the swing of the grab bucket can be approximated as a damped simple harmonic motion:

[0041] in: Initial swing angle amplitude Undamped natural frequency Damping ratio (typical values ​​0.05-0.1) Damped natural frequency Initial phase angle 3. Frequency instruction formula based on operator experience 3.1 Basic Speed ​​Commands (Distance-Related)

[0042] in: Deceleration starting distance parameter (adjustable according to load, typical value 23m) Hyperbolic tangent function, used for smooth transitions. 3.2 Swing Angle Compensation Command (Key Hook Stabilization Section)

[0043] in: Swing angle proportional gain (unit: Hz / rad) Gain in the rate of change of swing angle (unit: Hz·s / rad) Velocity compensation gain (unit: Hz·s / m) 3.3 Feedforward Prediction Instructions (Lead Calculation)

[0044] in: : The amplitude of the predicted swing angle for the i-th time T: Prediction period N: Number of prediction steps Prediction gain coefficient 3.4 Integrated Frequency Command

[0045] in: : Swing angle sign function, ensuring that the compensation direction is consistent with the swing angle direction. The function ensures that negative frequencies are not output. 4. Parameter adaptive adjustment mechanism To adapt to different operating conditions, the parameters should be dynamically adjusted according to the load and operating status: 4.1 Load Adaptive

[0046]

[0047] in: , Base gain under no-load conditions Load Influence Coefficient Rated load capacity 4.2 Distance Adaptation

[0048] in: Basic deceleration distance (typical value 2m) Speed ​​influence coefficient (typical value 0.5-1.0) 5. Prediction of the number of times the swing angle is eliminated Predict the number of stable hooking attempts required, N:

[0049] in: Allowable residual swing angle (typical value 0.05 rad) : The attenuation rate of the pendulum angle for each steady hook (typical value 0.3-0.5) : Floor function Based on the predicted N, dynamically adjust the deceleration starting distance:

[0050] in: The distance increment required for each hook stabilization (typically 0.5-1m). 6. Algorithm Execution Flow 6.1 Real-time acquisition of current position x(t), velocity v(t), and swing angle and rate of change of swing angle

[0051] 6.2 Calculate the basic speed command

[0052] 6.3 Calculate the current swing angle compensation command

[0053] 6.4 Predict the swing angles for the next N times and calculate the feedforward instructions.

[0054] 6.5. Generate frequency commands

[0055] 6.6 Check if the conditions for sperm cessation are met: and

[0056] 6.7 If the condition is met, output the fine stop frequency instruction; otherwise, continue the loop.

[0057] This algorithm transforms operator experience into a mathematical model, achieving efficient anti-sway control of the grab bucket through real-time feedback and predictive control. The key lies in... Simulates the operation of "following the grab", through It enables advance prediction and adapts to different working conditions through parameter adaptive mechanism.

[0058] V. System Performance Indicators

[0059] In summary, the core key points of this invention are as follows: 1. Closed-loop control strategy based on swing angle prediction and multi-cycle compensation: The core of this invention lies in the innovative combination of a swing angle prediction model and a multi-cycle compensation mechanism. Through a swing angle prediction model constructed based on the dynamics of a physical pendulum, the swing trend of the grab bucket can be accurately predicted in advance, thereby generating feedforward prediction commands. This predictive mechanism, which adjusts the inverter's output frequency in advance, is significantly different from existing technologies that rely solely on real-time feedback for control. Simultaneously, the multi-cycle compensation mechanism utilizes... According to the real-time swing angle and rate of change of swing angle By dynamically adjusting the compensation intensity and cycling multiple times until the sway angle is eliminated, the accuracy and stability of the anti-sway control are greatly improved. This multi-cycle closed-loop control strategy is a key innovation that distinguishes it from traditional anti-sway algorithms.

[0060] 2. Algorithm Implementation for Simulating Manual Hook Stabilization Operation: The algorithm accurately simulates the hook stabilization operation commonly used by gantry crane operators. It precisely mimics the operator's technique of quickly adjusting the gantry crane boom speed to follow the grab's swing based on the grab's swaying motion. Mid-swing angle proportional gain Gain of swing angle change rate and speed compensation gain The synergistic effect ensures that the gantry crane boom can keep up with the position of the grab bucket in a timely and accurate manner, and restores the operation logic of manual hook stabilization to the greatest extent, realizing intelligent and efficient automatic hook stabilization control. This is an important breakthrough in traditional anti-sway control methods and provides a brand-new idea and method for automated anti-sway control.

[0061] 3. Parameter Adaptive Adjustment Mechanism: This invention designs parameter adjustment mechanisms that are adaptive to load and adaptive to distance. Regarding load adaptation, the adjustment mechanism is based on load quality... Real-time dynamic adjustment of key gain parameters and ,like , , This is to adapt to the anti-sway requirements under different load conditions and ensure the stability of the control effect. For distance adaptation, it is based on the current speed of the vehicle. Dynamically change the deceleration start distance parameter ,Right now This ensures that the anti-sway control process can be started accurately and efficiently at different operating speeds, greatly enhancing the algorithm's adaptability to complex working conditions. This parameter adaptive mechanism is a key technical point for improving the practicality and universality of this invention.

[0062] 4. Prediction of the number of swing angle eliminations and calculation of lead time: By constructing a prediction model for the number of swing angle eliminations, and using the formula... Accurately estimate the number of stable hooks required Based on this prediction result, the deceleration start distance will be dynamically adjusted further. This ability to calculate lead time and predict the number of swing angle eliminations allows the system to plan anti-sway control strategies in advance before operation, rationally arrange deceleration timing and the number of hook stabilization operations, effectively avoiding poor anti-sway performance due to excessive speed or improper operation timing. It provides a strong guarantee for achieving efficient and precise anti-sway control and has significant innovation and advancement in existing anti-sway technologies.

[0063] This invention, through the aforementioned key technological innovations, systematically solves the core problems of open-loop anti-sway systems, such as delayed response, rigid control strategies, and poor adaptability to operating conditions. Firstly, based on a closed-loop control strategy using swing angle prediction and multi-cycle compensation, the response delay of over 200ms in traditional open-loop systems is compressed to within 50ms. By using feedforward prediction commands to suppress swaying trends in advance, the grab's swing amplitude decay time is shortened from 8 seconds to less than 3.5 seconds, significantly improving anti-sway efficiency. Secondly, the algorithm simulating manual hook stabilization operation, through coordinated compensation of swing angle and speed, ensures that the gantry crane boom remains dynamically synchronized with the grab, improving repeatability from ±0.5m to ±0.05m, matching the precision of manual operation while avoiding human error. Thirdly, the parameter adaptive mechanism and swing angle elimination frequency prediction function enable the system to automatically adapt to complex operating conditions such as 20%-100% load fluctuations and 0-10m / s wind speed changes, maintaining a stable anti-sway effect without manual parameter adjustment, ultimately achieving the optimal balance between efficient operation and safe control for gantry cranes.

[0064] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. Although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A composite anti-sway control system for grab buckets based on the angle feedback of the steel wire rope at the head of an elephant's trunk, characterized in that, include: At least two millimeter-wave radars, symmetrically arranged on both sides of the gantry crane's trunk head, are used to collect raw point cloud data of the steel wire rope. The edge computing coprocessing unit is used to receive the raw point cloud data of the millimeter-wave radar, extract the feature points of the steel wire rope based on the point cloud clustering algorithm and calculate the ranging difference Δd between the two radars. Based on the horizontal baseline distance L between the two millimeter-wave radars, the swing angle α of the steel wire rope is obtained according to the formula α=arcsin(Δd / L), and the obtained swing angle α is Kalman filtered and then output as real-time swing angle data. The PLC is used to receive the real-time swing angle data output by the edge computing coprocessing unit, and to perform fuzzy PID control and feedforward compensation based on the real-time swing angle data and other operating conditions to generate frequency / torque commands for the inverter. Inverters and drive motors are used to respond to frequency / torque commands issued by the PLC to drive the rotation and amplitude-changing mechanisms and provide encoder feedback. The safety interlock module is used to display the system's operating status and to trigger an alarm or emergency braking when preset safety conditions are met. The edge computing coprocessor unit and the PLC transmit swing angle data through a real-time industrial Ethernet communication link, forming a closed-loop control data stream from the angle of the wire rope to the execution of the frequency converter.

2. The grab bucket composite anti-sway control system based on the angle feedback of the steel wire rope at the head of the elephant trunk as described in claim 1, characterized in that, The edge computing coprocessing unit uses the DBSCAN point cloud clustering algorithm to extract feature points of the wire rope, and after angle calculation, sends the Kalman-filtered smoothed swing angle α to the PLC through the Profinet interface at a predetermined period. The frequency converter supports sensorless vector control and receives frequency / torque commands from the PLC via the Profinet interface. The drive motor is equipped with an encoder at its shaft end to provide real-time speed and / or position information to the PLC to form a speed closed loop.

3. The grab bucket composite anti-sway control system based on the angle feedback of the steel wire rope at the head of the elephant trunk as described in claim 1, characterized in that, The PLC has a built-in or collaborative control module, which includes a fuzzy PID controller, a feedforward compensator, and a parameter adaptive unit. The fuzzy PID controller uses the swing angle deviation e(t) and the swing angle change rate de(t) / dt as fuzzy control inputs, and outputs corrections to the PID parameters; the feedforward compensator calculates and predicts the swing angle α based on the acceleration a(t) of the rotary luffing mechanism and the load mass m. pred The prediction results are then combined with the fuzzy PID output to form the inverter frequency command f(t); The parameter adaptive unit estimates the load mass m in real time based on the current and speed curve of the drive motor, and periodically adjusts the parameters of the fuzzy PID controller.

4. The grab bucket composite anti-sway control system based on the angle feedback of the steel wire rope at the head of the elephant trunk as described in claim 1, characterized in that, The edge computing coprocessing unit performs the following when preprocessing the radar raw data: time-domain filtering to remove Gaussian white noise, frequency-domain FFT to identify the main vibration frequencies, and combining phase difference method and Kalman filtering to output a smooth angle in the angle calculation stage; when the smooth angle jumps more than a preset threshold, the sensor self-test program is triggered.

5. The grab bucket composite anti-sway control system based on the angle feedback of the steel wire rope at the head of the elephant trunk as described in claim 1, characterized in that, The PLC is equipped with a state machine for executing a dual-condition triggering mechanism: When the operating speed reaches the preset speed and the distance to the target is less than or equal to the preset distance, it switches to closed-loop anti-sway control based on angle feedback; When the swing angle is less than the preset angle value and the speed and / or position meet the fine stopping threshold, it enters the locked state.

6. The grab bucket composite anti-sway control system based on the angle feedback of the steel wire rope at the head of the elephant trunk as described in claim 1, characterized in that, The safety interlock module executes the following mechanism: When the real-time absolute value of the swing angle |α| exceeds the first preset threshold, the PLC command executes an emergency brake and issues an alarm. When the deviation between radar speed measurement and encoder speed measurement exceeds the second preset threshold, an alarm is triggered and the system switches to safe mode according to preset logic.

7. A control method for a grab bucket composite anti-sway control system based on the angle feedback of the steel wire rope at the head of the elephant trunk bridge, as described in any one of claims 1 to 6, characterized in that, Includes the following steps: S1. Perform self-testing and calibration on the millimeter-wave radars on both sides of the gantry crane's trunk head, confirm the horizontal baseline distance L between the two millimeter-wave radars, and initialize system parameters and communication links. S2. Two millimeter-wave radars simultaneously acquire ranging data of the steel wire rope. The edge computing coprocessing module performs point cloud clustering and temporal filtering on the data to extract feature points of the steel wire rope. S3. Calculate the distance difference Δd between the two radars, and obtain the swing angle α of the wire rope according to the formula α=arcsin(Δd / L). Perform Kalman filtering on the swing angle α to smooth it, and send the real-time swing angle data to the PLC main controller according to the set period. S4, the PLC main controller estimates the load mass m based on the current and speed feedback of the drive motor, and identifies the current working condition type by combining speed, acceleration and swing angle change rate; S5. Call the fuzzy PID control unit, taking the swing angle deviation e(t) and the swing angle change rate de(t) / dt as inputs, and output the PID correction amount; The feedforward compensation unit is invoked, using the acceleration a(t) of the rotating or luffing mechanism and the load mass m as inputs, to predict the swing angle α. pred And generate a feedforward compensation signal; the two are then combined to generate the inverter frequency / torque command f(t); S6. The frequency conversion drive module performs rotation or amplitude control according to the f(t), and returns the speed and position signals to the PLC main controller through the encoder. The PLC continuously adjusts the control output based on feedback information and the swing angle α, forming a closed loop. S7. When the fine stop condition is met, the PLC issues a fine stop command and enters the locked state, completing one anti-shake operation cycle.

8. The control method according to claim 7, characterized in that, It features parameter adjustment mechanisms for both load adaptation and distance adaptation; Load Adaptive: Dynamically adjusts key gain parameters in real time based on load quality m. and To adapt to the anti-sway requirements under different load conditions, , ,in This represents the basic gain proportional to the swing angle under no-load conditions. The base gain represents the change in swing angle under no-load conditions. The parameter is the load-dependent effect of the swing angle. The load influence coefficient is the rate of change of the swing angle. Indicates the rated load mass; Distance adaptive: based on the current speed of the gantry crane Dynamically change the deceleration start distance parameter , ,in Indicates the speed influence coefficient. Indicates the maximum operating speed of the mechanism. Indicates the basic deceleration distance.

9. The control method according to claim 7, characterized in that, Also includes: Construct a prediction model for the number of swing angle eliminations, and use the formula Estimated number of stable hooking attempts required Based on this prediction result, the deceleration start distance will be dynamically adjusted further. ,in Indicates the initial swing angle. Indicates the allowable residual swing angle. This indicates the rate of decrease in the swing angle for each steady hook.

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