Device and method for controlling height of lifting hook in amplitude variation process of crane
By using multi-sensor fusion detection and intelligent predictive control, combined with neural networks and fuzzy PID algorithms, high-precision, fast, and stable control of the hook height during crane luffing is achieved. This solves the problems of low control accuracy, slow response speed, and poor adaptability in existing technologies, thereby improving the crane's operating efficiency and safety.
Patent Information
- Application Number
- CN202511409655.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-29
- Publication Date
- 2025-11-21
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing cranes suffer from low control accuracy, slow response speed, and poor adaptability in hook height control during luffing, making it difficult to meet the high precision and high efficiency requirements of modern lifting operations.
Employing multi-sensor fusion detection, intelligent predictive control, and adaptive parameter adjustment, key parameters are detected by laser rangefinder, tilt sensor, encoder, and wind speed sensor. Combined with neural network prediction algorithm and fuzzy PID control, predictive-compensation dual closed-loop control is achieved. The actuator is driven by an AC permanent magnet synchronous servo motor and an electro-hydraulic proportional valve.
It achieves high-precision and stable control of hook height, improving control accuracy by two orders of magnitude, with a response time of less than 0.5 seconds. It is highly adaptable, flexible in operation, improves work efficiency and safety, and reduces cargo damage rate and equipment maintenance costs.
Smart Images
Figure CN120987200A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of crane control technology, and in particular to a hook height control device and method during crane luffing. Background Technology
[0002] Cranes are indispensable pieces of equipment in modern industrial production, port loading and unloading, and construction. The luffing mechanism is a key component for adjusting the crane's working range. During crane operation, the horizontal position of the hook is frequently adjusted by changing the boom angle to adapt to different operational requirements. However, during luffing, the change in boom angle alters the vertical distance from the hook to the boom hinge point. Without appropriate compensation measures, the hook height will fluctuate significantly. This height fluctuation not only affects operational efficiency but can also cause safety hazards such as cargo collisions and wire rope impacts. Especially in precision assembly and container handling, where strict height control is required, the stability of the hook height directly affects operational quality and safety. Therefore, developing a control device that can maintain a stable hook height during luffing has significant engineering application value.
[0003] Existing crane luffing control technologies mainly employ the following methods: First, they use mechanical linkage mechanisms, utilizing cams or linkages to synchronously adjust the length of the winch wire rope during luffing. However, this method is structurally complex, difficult to adjust, and can only achieve a fixed compensation pattern, failing to adapt to different load conditions. Second, they use simple electrical control methods, controlling the winch motor through a preset compensation curve to wind and unwind the wire rope at a fixed ratio during luffing. However, due to the lack of real-time feedback, the control accuracy is poor, and it cannot cope with interference factors such as wind load and inertia. Third, they use a PLC control system in conjunction with position sensors to achieve closed-loop control. However, traditional PID control algorithms have slow response speeds, are difficult to tune parameters, and have unsatisfactory control effects under complex working conditions. These existing technologies generally suffer from low control accuracy, slow response, and poor adaptability, making it difficult to meet the high-precision and high-efficiency requirements of modern lifting operations.
[0004] With the rapid development of sensor technology, intelligent control theory, and computer technology, new technical means have been provided for the upgrading and transformation of crane control systems. The continuously decreasing cost of high-precision detection equipment such as laser rangefinders and MEMS tilt sensors has made multi-sensor fusion detection possible; the mature application of intelligent algorithms such as neural networks and fuzzy control has provided effective methods for controlling complex nonlinear systems; and the widespread adoption of high-performance embedded processors and FPGAs has provided hardware guarantees for the implementation of real-time control algorithms. However, a complete solution integrating these advanced technologies for crane hook height control during luffing is currently lacking. Therefore, there is an urgent need to develop a new control device integrating advanced technologies such as multi-sensor fusion detection, intelligent predictive control, and adaptive parameter adjustment to achieve accurate, rapid, and stable control of the hook height during crane luffing. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a hook height control device and method during the luffing process of a crane. By using multi-sensor fusion detection, intelligent predictive control and adaptive parameter adjustment, high-precision and stable control of the hook height during the luffing process is achieved, solving the problems of low control accuracy, slow response speed and poor adaptability in the prior art.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0007] A hook height control device for crane luffing process, comprising:
[0008] The multi-sensor fusion detection module includes a laser rangefinder installed on the fixed bracket above the hook, an inclination sensor installed at the hinge point at the root of the boom, an encoder group installed on the drum shaft of the hoisting mechanism and the drive shaft of the luffing mechanism respectively, a load sensor installed on the support shaft of the fixed pulley group, and a wind speed sensor installed at the top of the boom.
[0009] The intelligent control processing module includes an industrial control computer and an FPGA coprocessor. The industrial control computer has a neural network prediction algorithm module and a data fusion algorithm module. The FPGA coprocessor is electrically connected to each sensor and executes real-time control algorithm calculations.
[0010] The execution drive module includes a hoisting mechanism driven by an AC permanent magnet synchronous servo motor and a variable amplitude hydraulic cylinder controlled by an electro-hydraulic proportional valve. The servo motor and the electro-hydraulic proportional valve are electrically connected to the intelligent control processing module, respectively.
[0011] The intelligent control processing module adopts a predictive-compensation dual closed-loop control structure, with the outer loop being the hook height and position control loop and the inner loop being the hoist speed control loop.
[0012] The hook height control device during the luffing process of the aforementioned crane includes a multi-sensor fusion detection module, an intelligent control processing module, an execution drive module, and a human-machine interaction module. The multi-sensor fusion detection module includes a laser rangefinder, tilt sensor, encoder group, load sensor, and wind speed sensor, used to detect key parameters such as hook height, boom angle, winch and luffing mechanism position, actual load, and environmental wind load. The intelligent control processing module adopts a dual-core architecture of an industrial control computer and an FPGA coprocessor. The industrial control computer runs neural network prediction algorithms and data fusion algorithms, while the FPGA coprocessor performs high-speed data acquisition and real-time control calculations, ensuring a control cycle of less than 1ms. The control strategy adopts a predictive-compensation dual closed-loop structure, with the outer loop being a hook height position control loop based on fuzzy PID, and the inner loop being a winch speed control loop with feedforward compensation. The execution drive module includes a winch mechanism driven by an AC permanent magnet synchronous servo motor and a luffing hydraulic system controlled by an electro-hydraulic proportional valve, communicating with the control module in real time via a high-speed bus.
[0013] Furthermore, the laser rangefinder uses a 905nm wavelength pulsed laser rangefinder with a measurement range of 0.5-500 meters, a measurement accuracy of ±3mm, and a sampling frequency of 100Hz; the tilt sensor uses a dual-axis MEMS tilt sensor with a measurement range of ±90° and a resolution of 0.01°; the encoder on the drum shaft of the encoder group is a 17-bit absolute encoder, and the encoder on the drive shaft of the amplitude transformer is a 5000 pulses / revolution incremental encoder.
[0014] Furthermore, the neural network prediction algorithm module adopts a three-layer BP neural network structure. The input layer contains 8 neurons, corresponding to boom angle, angular velocity, angular acceleration, load weight, wind speed, wind direction, wire rope length, and target height, respectively. The hidden layer contains 20 neurons, and the output layer contains 1 neuron, which outputs the predicted value of hook height change within the next 2 seconds.
[0015] Furthermore, the position control loop uses a fuzzy PID controller, which dynamically adjusts the PID parameters based on the height error e and the error change rate ec using a fuzzy rule table; the speed control loop uses a PI controller with feedforward compensation, and the feedforward amount is calculated based on the boom angular velocity and the current load.
[0016] Furthermore, the servo motor controller communicates with the intelligent control processing module via an EtherCAT bus with a communication cycle of 1ms; the electro-hydraulic proportional valve is a Rexroth 4WREE series proportional directional valve, and the control signal is ±10V analog voltage or 4-20mA current signal.
[0017] Furthermore, it also includes a human-machine interaction module and a safety protection system. The human-machine interaction module includes a touch screen display, and the safety protection system includes a height limit switch, an overload protection device, an emergency stop button, and a fail-safe brake.
[0018] A method for controlling the hook height during crane luffing includes the following steps:
[0019] Step 1: After the system starts up, it performs self-test and calibration, including sensor zero-point calibration, actuator response test and communication link check;
[0020] Step 2: Set the target hook height, luffing speed limit, and control mode parameters through the human-machine interface module. The system calculates the initial state based on the current sensor data.
[0021] Step 3: After receiving the luffing command, based on the target boom angle and the current load, use a neural network model to predict the trajectory of the hook height change during the luffing process, and calculate the compensation amount and compensation speed curve;
[0022] Step 4: During the variable amplitude execution, the control process is executed cyclically at a frequency of 1kHz: read the current values of all sensors, fuse the laser rangefinder and encoder data through the Kalman filter algorithm to obtain the actual height of the hook, calculate the height error and input it to the position controller, the position controller outputs the speed command to the speed controller, the speed controller calculates the motor torque command or proportional valve control signal according to the speed deviation, and at the same time corrects the control signal according to the feedforward compensation amount output by the prediction model to drive the actuator to move;
[0023] Step 5: Monitor the control effect in real time. When the height error exceeds the set threshold, automatically adjust the control parameters and optimize the control performance through online learning.
[0024] Furthermore, the control modes include four types: precision mode, fast mode, energy-saving mode, and emergency mode. In precision mode, the high control accuracy is ±10mm and the response time is 0.8 seconds; in fast mode, the high control accuracy is ±50mm and the response time is 0.3 seconds; in energy-saving mode, the compensation frequency is reduced to 5Hz and the allowable error is ±100mm; in emergency mode, the maximum compensation speed is limited to 50% of the rated value.
[0025] Furthermore, in step 4, the sampling period of the position loop is 10ms, the sampling period of the velocity loop is 1ms, the fuzzy control error domain of the position controller is [-1,1]m, the error change rate domain is [-0.5,0.5]m / s, and the output domain is [-1,1]; the neural network prediction time domain is 2 seconds, and the prediction step size is 0.1 seconds.
[0026] This invention also provides a method for controlling the hook height during crane luffing. First, the system performs self-checks and sensor calibrations to ensure all components are functioning correctly. Then, control parameters and operating modes are set via a human-machine interface. Upon receiving a luffing command, a trained three-layer BP neural network model is used to predict the hook height change trajectory within the next two seconds based on eight input parameters, including boom angle, angular velocity, load, and wind load. During luffing, the system cyclically executes the control algorithm at a frequency of 1 kHz, using Kalman filtering to fuse multi-sensor data to obtain an accurate hook height. The position controller dynamically adjusts PID parameters based on the height error and outputs speed commands. The speed controller, combined with feedforward compensation, generates the final actuator control signal. The system monitors the control effect in real time and continuously optimizes control parameters through online learning. Depending on different operational needs, the system provides four operating modes: precision mode, fast mode, energy-saving mode, and emergency mode, optimizing control accuracy, response speed, energy efficiency, and safety performance respectively.
[0027] The beneficial effects of this invention are:
[0028] Compared with the prior art, the present invention has the following beneficial effects:
[0029] First, control accuracy is significantly improved. By fusing Kalman filtering data from the laser rangefinder and encoder, precise measurement of the hook height is achieved. A neural network prediction model is used to predict height changes 2 seconds in advance, combined with fuzzy PID adaptive control, enabling hook height control accuracy to reach ±10mm, which is two orders of magnitude higher than traditional control methods.
[0030] Secondly, the system has a fast response time. It adopts FPGA hardware acceleration to achieve a 1ms control cycle, and with the predictive-compensation dual closed-loop control strategy, the system response time is less than 0.5 seconds. It can quickly suppress height fluctuations during amplitude changes, making it particularly suitable for fast-paced port loading and unloading operations.
[0031] Third, it is highly adaptable. The neural network model can automatically adjust the prediction parameters according to different working conditions such as load (0-100 tons) and wind load (0-15m / s); the fuzzy controller can dynamically optimize the PID parameters according to the error magnitude; the system has online learning capabilities, and the control performance is continuously improved during use, with the prediction accuracy increasing from 85% to over 94%.
[0032] Fourth, it is flexible in operation. It offers four preset working modes that can be switched with one click according to the work requirements; the human-machine interface is user-friendly and the parameter settings are simple and intuitive; it has comprehensive safety protection functions, including height limit, overload protection, and emergency braking.
[0033] Fifth, the economic benefits are significant. After adopting this invention, the crane's loading and unloading efficiency increases by 15%, the damage rate decreases by 90%, equipment maintenance costs decrease by 20%, and the investment payback period is approximately 18 months, demonstrating its excellent value for widespread application.
[0034] This invention integrates multiple advanced technologies to achieve intelligent and precise control of the hook height during crane luffing, which not only improves operational efficiency and safety, but also provides a complete solution for the intelligent upgrading of crane control systems. It can be widely used in various lifting equipment such as port cranes, construction tower cranes, and marine cranes. Attached Figure Description
[0035] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0036] Figure 1 This is a schematic diagram of the overall system structure of the present invention;
[0037] Figure 2 This is a schematic diagram of the multi-sensor fusion detection module of the present invention;
[0038] Figure 3 This is a schematic diagram of the intelligent control processing module architecture of the present invention;
[0039] Figure 4 This is a schematic diagram of the execution driver module structure of the present invention;
[0040] Figure 5 This is a schematic diagram of the control method of the present invention. Detailed Implementation
[0041] See Figures 1 to 5 As shown
[0042] This invention provides a hook height control device during crane luffing. The device mainly comprises four parts: a multi-sensor fusion detection module, an intelligent control processing module, an execution drive module, and a human-machine interaction module. The multi-sensor fusion detection module includes a laser rangefinder, a tilt sensor, an encoder assembly, a load sensor, and a wind speed sensor. The laser rangefinder, mounted on a fixed bracket above the crane hook, uses a 905nm wavelength pulsed laser rangefinder with a measurement range of 0.5-500 meters, a measurement accuracy of ±3mm, and a sampling frequency of 100Hz. It is used to measure the vertical distance from the bottom of the hook to the ground or a target plane in real time. The tilt sensor, installed near the hinge point at the base of the crane boom, uses a dual-axis MEMS tilt sensor with a measurement range of ±90° and a resolution of 0.01°. It is used to monitor the boom's pitch angle changes in real time. The encoder assembly... The system includes an absolute encoder mounted on the drum shaft of the hoisting mechanism and an incremental encoder mounted on the drive shaft of the luffing mechanism. The absolute encoder has a resolution of 17 bits and can accurately measure the winding and unwinding length of the wire rope, while the incremental encoder has a resolution of 5000 pulses / revolution and is used to detect the luffing speed. The load sensor is a strain gauge tension sensor mounted on the support shaft of the fixed pulley block. The range is selected according to the rated load of the crane, and the measurement accuracy is 0.1%FS. It is used to detect the actual lifting weight in real time. The wind speed sensor is a three-dimensional ultrasonic anemometer and wind vane mounted on the top of the boom. The measurement range is 0-60m / s and it is used to monitor the wind load conditions of the working environment.
[0043] The intelligent control processing module is the core of the entire control system. It adopts an architecture of an industrial-grade control computer and an FPGA coprocessor. The industrial control computer uses an Intel Core i7 processor with a main frequency of 3.5GHz and 16GB of memory, running a real-time Linux operating system. It is responsible for running neural network prediction algorithms, data fusion algorithms, and human-machine interface programs. The FPGA coprocessor uses a Xilinx Kintex-7 series chip with a main frequency of 200MHz. It is responsible for high-speed data acquisition, real-time control algorithm calculation, and PWM signal generation, ensuring a control cycle of less than 1ms. The control algorithm adopts a predictive-compensation dual closed-loop control strategy. The outer loop is the hook height and position control loop, and the inner loop is the hoisting speed control loop. The position control loop uses an improved fuzzy PID algorithm. Based on the height error e and the error change rate ec, the PID parameters are dynamically adjusted through a fuzzy rule table. The speed control loop uses a PI regulator with feedforward compensation. The feedforward amount is calculated based on the boom angular velocity and the current load. The neural network prediction model adopts a three-layer BP neural network structure. The input layer contains 8 neurons, corresponding to boom angle, angular velocity, angular acceleration, load weight, wind speed, wind direction, wire rope length, and target height, respectively. The hidden layer contains 20 neurons, using the ReLU activation function. The output layer contains 1 neuron, which outputs the predicted value of the hook height change within the next 2 seconds. The network is trained offline using 10,000 sets of historical operating data. After training, it is deployed to the control system for online prediction.
[0044] The execution drive module includes a hoisting mechanism drive system and a luffing mechanism control system. The hoisting mechanism is driven by an AC permanent magnet synchronous servo motor. The motor power is selected according to the crane tonnage, generally 1.2-1.5 times the rated load. It is equipped with a planetary gear reducer, and the reduction ratio is determined according to the drum diameter and rated speed. The motor controller adopts a vector control method and supports three control modes: position, speed, and torque. The controller communicates with the intelligent control module via EtherCAT bus with a communication cycle of 1ms. The luffing mechanism adopts an electro-hydraulic proportional control system, including a variable displacement piston pump, an electro-hydraulic proportional directional valve, a balance valve, and a hydraulic cylinder. The proportional directional valve adopts the Rexroth 4WREE series, and the rated flow rate is selected according to the luffing speed requirements. The valve core position is controlled by a proportional electromagnet. The control signal is ±10V analog voltage or 4-20mA current signal. By adjusting the valve core opening, the extension and retraction speed of the hydraulic cylinder is controlled, thereby achieving precise control of the boom angle. The safety protection system includes a height limit switch, overload protection device, emergency stop button, and fail-safe brake. When the system detects that the hook height exceeds the set range, the load exceeds 110% of the rated value, or other abnormal conditions occur, it automatically cuts off the power and applies the brake to ensure operational safety.
[0045] The implementation process of the control method is as follows: After the system starts, it first performs self-test and calibration, including sensor zero-point calibration, actuator response test and communication link check. After the self-test passes, it enters standby mode. The operator sets parameters such as target hook height, luffing speed limit and control mode through the touch screen. The system calculates the initial state based on the current sensor data. When the luffing command is received, the system first uses a neural network model to predict the hook height change trajectory during the luffing process based on the target boom angle and the current load, and calculates the required compensation amount and compensation speed curve. During the luffing execution, the control system cyclically executes the following steps at a frequency of 1kHz: reads the current values of all sensors, fuses laser ranging and encoder data through Kalman filtering algorithm to obtain the actual hook height, calculates the height error and inputs it to the position controller, the position controller outputs the speed command to the speed controller, the speed controller calculates the motor torque command or proportional valve control signal based on the speed deviation, and corrects the control signal based on the feedforward compensation amount output by the prediction model, and finally drives the actuator to move. The system monitors the control effect in real time. When the height error exceeds the set threshold, it automatically adjusts the control parameters and continuously optimizes the control performance through online learning.
[0046] Example 1
[0047] To verify the technical effect of the present invention, an 80-ton gantry crane at a port was used as the application object for implementation. The crane has a boom length of 45 meters, a maximum luffing angle of 75°, a minimum luffing angle of 25°, and a rated lifting height of 40 meters. The original system experienced hook height fluctuations of ±2.5 meters during luffing.
[0048] When installing the control device of this invention, a SICK DT500 laser rangefinder sensor is installed above the hook, a BWL328 tilt sensor is installed at the base of the boom, a Heidenhain ECN1313 absolute encoder is installed on the 50-ton winch drum, an Omron E6B2 incremental encoder is installed on the output shaft of the luffing gearbox, an HBM U9C / 50-ton tension sensor is installed on the fixed pulley bearing housing, and a Gill WindMasterPro three-dimensional anemometer is installed at the top of the boom. The control system uses an Advantech IPC-610 industrial computer equipped with a Xilinx Kintex-7XC7K325T FPGA board. The winch motor is a Siemens 1FT7 series 105kW permanent magnet synchronous motor with a SINAMICSS120 driver. The luffing hydraulic system uses a Rexroth A10VSO140 variable pump with a 4WREE10 proportional valve.
[0049] System parameter settings: Position loop sampling period 10ms, speed loop sampling period 1ms, initial parameters for the position controller PID: Kp = 2.5, Ki = 0.15, Kd = 0.8, speed controller PI parameters: Kp = 15, Ki = 5, neural network prediction time domain 2 seconds, prediction step size 0.1 seconds, height holding accuracy threshold ±20mm, fuzzy control error domain [-1, 1] meters, error change rate domain [-0.5, 0.5] meters / second, output domain [-1, 1]. The system is configured with four operating modes: Precision mode: height control accuracy ±10mm, response time 0.8 seconds; Fast mode: height control accuracy ±50mm, response time 0.3 seconds; Energy-saving mode: compensation frequency reduced to 5Hz, allowable error ±100mm; Emergency mode: maximum compensation speed limited to 50% of the rated value.
[0050] Actual tests were conducted under different working conditions: Working condition 1 was no-load luffing, from 25° to 75°, with a luffing speed of 0.5° / second. Under traditional control, the hook height decreased from 40 meters to 37.8 meters and then increased to 42.3 meters, fluctuating by 4.5 meters. After adopting the present invention, the hook height was maintained within the range of 40 ± 0.015 meters. Working condition 2 was a full-load 80-ton luffing, from 75° to 25°, with a luffing speed of 0.3° / second. Under traditional control, the height fluctuated by 3.2 meters. After adopting the present invention, the height fluctuation was controlled within ± 0.025 meters. Working condition 3 was a 25°-60° reciprocating luffing test under a 40-ton load in a level 6 wind (wind speed 13.8 m / s). The system could adjust the compensation amount in real time according to the wind load change, and the height control accuracy was maintained within ± 0.05 meters. Continuous operation tests showed that the control accuracy of the system did not decrease significantly after 1000 amplitude variation cycles. The neural network model improved its prediction accuracy from the initial 85% to 94% through online learning, which proved the reliability and adaptability of the system.
[0051] Energy consumption analysis shows that in the precision mode, energy consumption increases by about 8% compared to the traditional method due to frequent compensation. However, in the fast mode and energy-saving mode, energy consumption is reduced by 5% and 12% respectively by optimizing the compensation strategy. Economic benefit analysis shows that after adopting this invention, loading and unloading efficiency is improved by 15%, cargo damage caused by high fluctuations is reduced by 90%, equipment maintenance costs are reduced by 20%, and the investment payback period is about 18 months.
[0052] This invention achieves precise control of the hook height during the luffing process of a crane through multi-sensor information fusion, intelligent predictive control, and adaptive parameter adjustment. Compared with existing technologies, it has advantages such as high control accuracy, fast response speed, strong adaptability, and high degree of intelligence. It can be widely used in the upgrading and transformation of various types of lifting machinery and has good application prospects and promotion value.
[0053] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.
[0054] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A hook height control device during the luffing process of a crane, characterized in that, include: The multi-sensor fusion detection module includes a laser rangefinder installed on the fixed bracket above the hook, an inclination sensor installed at the hinge point at the root of the boom, an encoder group installed on the drum shaft of the hoisting mechanism and the drive shaft of the luffing mechanism respectively, a load sensor installed on the support shaft of the fixed pulley group, and a wind speed sensor installed at the top of the boom. The intelligent control processing module includes an industrial control computer and an FPGA coprocessor. The industrial control computer is equipped with a neural network prediction algorithm module and a data fusion algorithm module. The FPGA coprocessor is electrically connected to each sensor and performs real-time control algorithm calculations. The execution drive module includes a hoisting mechanism driven by an AC permanent magnet synchronous servo motor and a variable-amplitude hydraulic cylinder controlled by an electro-hydraulic proportional valve. The servo motor and the electro-hydraulic proportional valve are electrically connected to the intelligent control processing module, respectively. The intelligent control processing module adopts a predictive-compensation dual closed-loop control structure, with the outer loop being the hook height position control loop and the inner loop being the hoist speed control loop.
2. The hook height control device during crane luffing process according to claim 1, characterized in that, The laser rangefinder uses a 905nm wavelength pulsed laser rangefinder with a measurement range of 0.5-500 meters, a measurement accuracy of ±3mm, and a sampling frequency of 100Hz. The tilt sensor uses a dual-axis MEMS tilt sensor with a measurement range of ±90° and a resolution of 0.01°. The encoder on the drum shaft of the encoder group is a 17-bit absolute encoder, and the encoder on the drive shaft of the amplitude transformer is a 5000 pulses / revolution incremental encoder.
3. The hook height control device during crane luffing process according to claim 1, characterized in that, The neural network prediction algorithm module adopts a three-layer BP neural network structure. The input layer contains 8 neurons, corresponding to boom angle, angular velocity, angular acceleration, load weight, wind speed, wind direction, wire rope length and target height, respectively. The hidden layer contains 20 neurons, and the output layer contains 1 neuron, which outputs the predicted value of hook height change in the next 2 seconds.
4. The hook height control device during crane luffing process according to claim 1, characterized in that, The position control loop uses a fuzzy PID controller, which dynamically adjusts the PID parameters based on the height error e and the error change rate ec using a fuzzy rule table; the speed control loop uses a PI regulator with feedforward compensation, and the feedforward amount is calculated based on the boom angular velocity and the current load.
5. The hook height control device during crane luffing process according to claim 1, characterized in that, The servo motor controller communicates with the intelligent control processing module via an EtherCAT bus with a communication cycle of 1ms; the electro-hydraulic proportional valve is a Rexroth 4WREE series proportional directional valve, and the control signal is ±10V analog voltage or 4-20mA current signal.
6. The hook height control device during crane luffing process according to claim 1, characterized in that, It also includes a human-machine interaction module and a safety protection system. The human-machine interaction module includes a touch screen display, and the safety protection system includes a height limit switch, an overload protection device, an emergency stop button, and a fail-safe brake.
7. A method for controlling the hook height during the luffing process of a crane, employing the control device described in any one of claims 1-6, characterized in that, Includes the following steps: Step 1: After the system starts up, it performs self-test and calibration, including sensor zero-point calibration, actuator response test and communication link check; Step 2: Set the target hook height, luffing speed limit, and control mode parameters through the human-machine interface module. The system calculates the initial state based on the current sensor data. Step 3: After receiving the luffing command, based on the target boom angle and the current load, use a neural network model to predict the trajectory of the hook height change during the luffing process, and calculate the compensation amount and compensation speed curve; Step 4: During the variable amplitude execution, the control process is executed cyclically at a frequency of 1kHz: read the current values of all sensors, fuse the laser rangefinder and encoder data through the Kalman filter algorithm to obtain the actual height of the hook, calculate the height error and input it to the position controller, the position controller outputs the speed command to the speed controller, the speed controller calculates the motor torque command or proportional valve control signal according to the speed deviation, and at the same time corrects the control signal according to the feedforward compensation amount output by the prediction model to drive the actuator to move; Step 5: Monitor the control effect in real time. When the height error exceeds the set threshold, automatically adjust the control parameters and optimize the control performance through online learning.
8. The method for controlling the hook height during crane luffing as described in claim 7, characterized in that, The control modes include four types: precision mode, fast mode, energy-saving mode, and emergency mode. In precision mode, the high control accuracy is ±10mm and the response time is 0.8 seconds; in fast mode, the high control accuracy is ±50mm and the response time is 0.3 seconds; in energy-saving mode, the compensation frequency is reduced to 5Hz and the allowable error is ±100mm; in emergency mode, the maximum compensation speed is limited to 50% of the rated value.
9. The method for controlling the hook height during crane luffing as described in claim 7, characterized in that, In step 4, the sampling period of the position loop is 10ms, the sampling period of the velocity loop is 1ms, the fuzzy control error domain of the position controller is [-1,1]m, the error change rate domain is [-0.5,0.5]m / s, and the output domain is [-1,1]; the neural network prediction time domain is 2 seconds, and the prediction step size is 0.1 seconds.