Gantry crane loading and unloading system based on electronic anti-swing and intelligent algorithm and operation method of portal crane loading and unloading system

Abstract

The invention relates to a portal crane loading and unloading system based on an electronic anti-swing and intelligent algorithm and an operation method of the portal crane loading and unloading system, and aims to improve the safety and the working efficiency of a crane in the cargo loading and unloading process. According to the system, various sensors including a tilt angle sensor, a speed sensor, a trolley position sensor, a laser distance measuring sensor and an ultrasonic or visual sensor are integrated, and the swing state and the surrounding environment of the lifting appliance are monitored in real time. The PLC or the industrial computer calculates the swing error of the lifting appliance according to the feedback data of the sensor and generates a control signal, and the movement of the trolley is accurately adjusted through the intelligent servo driver so as to quickly offset the swing of the lifting appliance. The human-computer interface provides a real-time parameter adjusting function, so that an operator can optimize a control strategy in real time according to different working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of lifting machinery, and in particular to a portal crane loading and unloading system based on electronic anti-sway, which is specifically applied to crane loading and unloading systems in large-scale cargo loading and unloading operations such as ports, docks, and shipyards. Background Art

[0002] Gantry cranes are key equipment in ports, docks, shipyards and other bulk cargo loading and unloading scenarios. Their main task is to lift and move large cargo. However, in actual operation, due to the suspension connection between the spreader and the cargo, the spreader of the crane often swings during the process of lifting, moving and lowering the cargo. This swing not only reduces the loading and unloading efficiency, but also increases the risk of cargo damage and operator injury. Therefore, how to effectively suppress the swing of the spreader and maintain the stability of the spreader has become a key technical issue in improving the loading and unloading efficiency and safety of gantry cranes.

[0003] At present, many crane anti-sway systems use mechanical damping devices or simple PID (proportional integral differential) control algorithms to reduce the swing of the spreader. However, these traditional anti-sway technologies often show problems such as insufficient response and insufficient control accuracy when dealing with complex operating environments and changing working conditions (such as wind interference and uneven load). In addition, the existing anti-sway system mainly relies on data feedback from a single sensor, lacks multi-dimensional real-time feedback capabilities, and cannot cope with complex control requirements in dynamic environments.

[0004] With the development of sensor technology, intelligent control algorithms and automation systems, intelligent anti-sway systems based on electronic control and multi-sensor fusion have gradually become a research hotspot. By combining data feedback from multiple sensors (such as inclination sensors, speed sensors and trolley position sensors, etc.) and improved control algorithms (such as adaptive fuzzy adaptive fuzzy PID control algorithms), the dynamic response capability of the system and the accuracy of anti-sway control can be greatly improved. This type of technology can dynamically adjust control parameters according to real-time working conditions, while ensuring the stability of the spreader and optimizing the loading and unloading efficiency of the crane.

[0005] In order to overcome the above-mentioned defects, the present invention proposes a portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm. Summary of the invention

[0006] The present invention provides a portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm, which can effectively suppress the swing of the spreader through multi-sensor feedback and an improved adaptive fuzzy PID control algorithm, and significantly improve the safety and operating efficiency of the crane.

[0007] The technical solution of the present invention is as follows: A portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm includes a redundant sensor system composed of multiple sensors, including: Inclination sensor detects the swing angle of the spreader in real time; Speed ​​sensor, which measures the swing speed of the spreader and provides dynamic feedback to the system; Trolley position sensor, which detects the horizontal position and speed of the crane trolley; Laser distance sensor to measure the distance between the spreader and the ground or cargo; Ultrasonic or visual sensors to assist in detecting surroundings and obstacles; It also includes: PLC or industrial computer receives data from sensors and generates control signals based on adaptive fuzzy PID control algorithm; Intelligent servo drive, which receives control signals and realizes precise driving of the trolley; The human-machine interface HMI provides a real-time adaptive fuzzy PID control algorithm parameter adjustment function. The adaptive fuzzy PID control algorithm calculates and outputs control signals in real time based on the swing angle, speed and trolley position feedback of the spreader.

[0008] Furthermore, the adaptive fuzzy PID control algorithm includes proportional integral derivative control, wherein: Control error (t), where Target swing angle, (t) is the real-time swing angle of the spreader; Control output u(t)= (t)+ ,in , and are proportional, integral and differential coefficients respectively, u(t) is the control signal used to drive the car to move. (t) is a proportional term, proportional to the current error, is the integral term, accumulating past errors and correcting steady-state errors, is the differential term.

[0009] Furthermore, the control signal The output is sent to the motor driver, and the stable control of the swing of the spreader is achieved by adjusting the acceleration a(t) of the trolley.

[0010] Furthermore, the motor driver adjusts the acceleration of the car The speed of the trolley can be controlled to reduce the swing angle of the spreader and ultimately achieve an anti-sway effect.

[0011] Furthermore, in the adaptive fuzzy PID control algorithm, the parameters , and Adjust through the human-machine interface to meet the anti-sway requirements under different working conditions.

[0012] Furthermore, the system has an adaptive adjustment function, and dynamically adjusts the parameters of the adaptive fuzzy PID control algorithm through real-time feedback of sensor data to adapt to the anti-sway requirements under different working conditions. The adaptive fuzzy PID control algorithm includes: fuzzifying the input variable error e(t) and the error change rate Δe(t) into fuzzy sets and determining their membership; inferring the adjustment strategy of the adaptive fuzzy PID control algorithm parameters according to the fuzzy rule base, including the proportional coefficient , and Real-time dynamic adjustment; the fuzzy reasoning results are converted into specific adaptive fuzzy PID control algorithm parameters through the defuzzification process to optimize the control signal.

[0013] The control method of the present invention, the PLC or industrial computer calculates the swing angle error of the hanger in real time (t), and based on the adaptive fuzzy PID control algorithm, the control signal u(t) is output, which is transmitted to the motor driver to adjust the acceleration and movement direction of the trolley to reduce the swing of the spreader.

[0014] Furthermore, the operation steps of the operation method of the portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm include: (1) The inclination sensor detects the swing angle of the hanger in real time (t); (2) The speed sensor detects the swing speed of the spreader; (3) The trolley position sensor detects the horizontal position and speed of the trolley; (4) Laser ranging sensor measures the distance between the spreader and the ground or cargo to ensure accurate positioning; (5) Ultrasonic or visual sensors detect obstacles in the environment to enhance operational safety; (6) PLC or industrial computer based on the swing angle of the hanger (t), target angle Calculation error ; (7) Calculate the control signal u(t) based on the improved adaptive fuzzy PID control algorithm and mobilize the movement of the car through the intelligent servo drive; (8) The acceleration or deceleration of the trolley gradually reduces the swing of the sling until the swing becomes stable.

[0015] The sensor system adopts a multi-sensor redundancy solution to ensure the reliability and accuracy of data to improve the overall stability of the system.

[0016] The present invention significantly improves the anti-sway performance and operation safety of the gantry crane by adopting multiple sensors and an improved adaptive fuzzy PID control algorithm. First, the inclination sensor and the speed sensor are used to detect the swing angle and speed of the spreader, respectively, to provide real-time and accurate dynamic feedback data. These data are used to quickly identify the swing trend of the spreader. Combined with the differential term in the adaptive fuzzy PID control algorithm, the swing can be predicted in advance and adjusted in time, significantly reducing the swing amplitude and time. In addition, the trolley position sensor ensures that the horizontal position and speed data of the crane trolley are accurate, which enables the system to accurately control the movement of the trolley, avoid unnecessary secondary swings, and further improve the operation accuracy and stability.

[0017] The laser distance sensor ensures the accuracy of the spreader positioning by measuring the distance between the spreader and the ground or cargo, especially in complex loading and unloading environments, to avoid the spreader's positioning errors. At the same time, ultrasonic or visual sensors assist in detecting surrounding obstacles, providing safety protection to prevent the spreader from colliding with other objects, and enhancing the operational safety of the entire system.

[0018] The PLC or industrial computer used in the present invention calculates the swing error of the spreader in real time by fusing data from multiple sensors and using an improved adaptive fuzzy PID control algorithm, and outputs a control signal to drive the trolley to move. After receiving the control signal, the intelligent servo driver can accurately adjust the acceleration and position of the trolley, thereby quickly and effectively reducing the swing of the spreader. Through this control mechanism, the system achieves rapid response and precise control of the swing of the spreader, especially when dealing with complex operating environments, and can maintain the efficiency and stability of the system.

[0019] In addition, the human-machine interface (HMI) allows the operator to adjust the control parameters of the adaptive fuzzy PID control algorithm in real time according to the specific working conditions, so that the system can adapt to the anti-sway requirements under different working conditions. This flexible adjustment capability further improves the applicability of the system and ensures the best anti-sway effect in a changing environment. The multi-sensor redundant design ensures the reliability of the data. Even if some sensors are interfered with or fail, the system can still provide reliable data through redundant sensors to ensure the continuous stability of anti-sway control.

[0020] The system loops through the above steps through real-time feedback of sensor data, and continuously and dynamically adjusts the parameters of the adaptive fuzzy PID control algorithm to adapt to the swing amplitude of the spreader, environmental changes or load fluctuations. Each feedback link will select different fuzzy rules according to the current spreader swing state and error change rate to achieve adaptive adjustment of the adaptive fuzzy PID control algorithm parameters.

[0021] This system can also add adaptive fuzzy PID algorithm control: Input: error e(t), error change rate Δe(t), and possible external disturbances.

[0022] Fuzzification: Convert input variables into fuzzy sets and give them a linguistic description.

[0023] Fuzzy reasoning: Infer the adaptive fuzzy PID control algorithm parameter adjustment strategy based on the preset fuzzy rule base.

[0024] Defuzzification: Convert the results of fuzzy reasoning into specific adaptive fuzzy PID control algorithm parameter adjustment values.

[0025] Adaptive fuzzy PID control algorithm control: According to the adjusted adaptive fuzzy PID control algorithm parameters, a control signal u(t) is generated to drive the crane trolley to counteract the swing.

[0026] Adaptive Adjustment: Dynamically adjust the adaptive fuzzy PID control algorithm parameters through real-time sensor feedback to ensure system stability and fast response.

[0027] This adaptive fuzzy PID control is suitable for handling complex and changing environments. For example, when cranes are performing loading and unloading operations in ports and docks where wind speeds change and loads are uneven, they can maintain the stability and efficient control of the spreader in real-time adjustments.

[0028] In summary, the present invention significantly improves the anti-sway control accuracy, response speed and safety of the gantry crane through the combination of sensor fusion, improved adaptive fuzzy PID control algorithm and intelligent servo drive, and is suitable for various complex operating environments. DETAILED DESCRIPTION

[0029] The present invention provides a portal crane based on electronic anti-sway, which realizes precise control of the swing of the spreader through multi-sensor real-time data feedback and an improved adaptive fuzzy PID control algorithm, effectively improving the operation safety and efficiency of the crane. The technical implementation process of the present invention is described below in conjunction with specific implementation methods.

[0030] The present invention provides a portal crane based on electronic anti-sway. The system adjusts the movement of the trolley in real time through multi-sensor feedback and improved adaptive fuzzy adaptive fuzzy PID control algorithm to achieve effective control of the swing of the spreader. The system is mainly composed of the following parts and works through the following steps: 1. System structure: The system includes multiple hardware modules and sensors to monitor the dynamic state of the spreader in real time and control the movement of the trolley. The specific components are as follows: Tilt sensor: Installed on the spreader, used to detect the swing angle of the spreader in real time and use it as the main feedback input of the anti-sway system.

[0031] Speed ​​sensor: used to detect the swing speed of the spreader, provide dynamic feedback data, assist the differential term in the adaptive fuzzy PID control algorithm, and predict the swing trend.

[0032] Car position sensor: used to detect the horizontal position and speed of the car in real time to ensure accurate control of the car's movement.

[0033] Laser distance sensor: used to measure the vertical distance between the spreader and the ground or cargo to ensure accurate positioning and avoid collision between the spreader and cargo or the ground.

[0034] Ultrasonic or visual sensors: used to monitor the surrounding environment, detect obstacles during operation, and enhance the safety of the system.

[0035] PLC or industrial computer: used to receive feedback data from the above sensors and calculate the control signal of the car based on the improved adaptive fuzzy adaptive fuzzy PID control algorithm.

[0036] Intelligent servo drive: used to execute the control signal output by PLC, adjust the acceleration and speed of the trolley, and ensure the accuracy of the trolley's movement.

[0037] Human Machine Interface (HMI): Used to display system status, monitor sensor data, and allow operators to adjust adaptive fuzzy PID control algorithm control parameters in real time.

[0038] 2. Workflow:

[0039] Data collection: After the system is started, each sensor starts to collect the status data of the spreader and the trolley in real time. The inclination sensor measures the swing angle of the spreader, the speed sensor measures the swing speed of the spreader, the trolley position sensor monitors the horizontal position and speed of the trolley, the laser distance sensor detects the distance between the spreader and the ground, and the ultrasonic or visual sensor monitors obstacles in the working area.

[0040] Error calculation: PLC or industrial computer calculates the swing error of the hanger based on the data fed back by the sensor.

[0041] Control error (t), where Target swing angle, (t) is the real-time swing angle of the spreader.

[0042] Control output u(t)= (t)+ ,in , and are proportional, integral and differential coefficients respectively, u(t) is the control signal used to drive the car to move. (t) is a proportional term, proportional to the current error, is the integral term, accumulating past errors and correcting steady-state errors, is the differential term.

[0043] Trolley motion control: After receiving the control signal u(t), the intelligent servo drive adjusts the trolley acceleration (a(t)) in real time. The trolley quickly suppresses the swing by moving in the opposite direction of the spreader swing. By controlling the acceleration and speed of the trolley, the swing of the spreader is gradually reduced until it stabilizes.

[0044] Real-time feedback and closed-loop control: The system continuously receives the latest data from the sensor, calculates new errors in real time and generates new control signals to form a closed-loop control system to ensure that the swing of the spreader remains stable within the specified range.

[0045] Furthermore, the sensor system adopts a multi-sensor redundancy solution to ensure the reliability and accuracy of data to improve the overall stability of the system.

[0046] Furthermore, the system has an adaptive adjustment function, and dynamically adjusts the parameters of the adaptive fuzzy PID control algorithm through real-time feedback sensor data to adapt to the anti-sway requirements under different working conditions. The adaptive fuzzy PID control algorithm includes: fuzzifying the input variable error e(t) and the error change rate Δe(t) into fuzzy sets and determining their membership; inferring the adjustment strategy of parameters according to the fuzzy rule base, including the proportional coefficient , and Real-time dynamic adjustment; the fuzzy reasoning results are converted into specific adaptive fuzzy PID control algorithm parameters through the defuzzification process to optimize the control signal.

[0047] In the adaptive fuzzy PID control algorithm of the present invention, the error change rate Δe(t) is one of the key input parameters, which represents the rate of change of the error over time. It is used in the control system to help determine the change trend of the current error and ensure the rapid response of the system under different working conditions. The specific calculation method of the error change rate Δe(t) is as follows: ; in: It represents the error value at the current time t, that is, the difference between the currently detected swing angle of the spreader and the target angle.

[0048] Indicates the previous moment (i.e. ) error value.

[0049] is the sampling time interval of the system, which is used to obtain error data at discrete time points.

[0050] This calculation method can effectively capture the error change trend and avoid complex differential calculations by taking the difference between adjacent sampling points. It can be set to a fixed value, such as 0.1 second, according to the response requirements of the system to ensure the real-time and accuracy of the calculation.

[0051] Determination of fuzzy reasoning results: The adaptive fuzzy PID control algorithm of the present invention performs fuzzy reasoning on the swing error e(t) and the error change rate Δe(t) of the hanger through the fuzzy rule base, and outputs the adaptive fuzzy PID control algorithm parameters. , and The fuzzy adjustment instructions. The fuzzy reasoning results are generated based on the "if-then" rules in the rule base.

[0052] Definition of fuzzy sets: The error e(t) and the error change rate Δe(t) are divided into fuzzy sets, such as "positive large", "positive small", "zero", etc.

[0053] Fuzzy rule base: The fuzzy rule base defines the adjustment strategies of the adaptive fuzzy PID control algorithm parameters under different error states, for example: If e(t) is positive and large and Δe(t) is rapidly increasing, the inference may be increasing. a little bit", "keep unchanged”, “decreased a little".

[0054] These fuzzy inference results represent the weights of parameter adjustments through membership functions.

[0055] Defuzzification process

[0056] The fuzzy inference results need to be defuzzified and converted into specific adaptive fuzzy PID control algorithm parameter values ​​in order to be actually applied to system control. The present invention uses the centroid method to perform defuzzification and calculate the specific adaptive fuzzy PID control algorithm parameters. , and .

[0057] Defuzzification calculation formula: = ; = ; = ; in is the membership value of each fuzzy set, , , The incremental value of the adaptive fuzzy PID control algorithm parameters corresponding to each fuzzy set.

[0058] Example: Assume that the current fuzzy inference output is "increase The degree of membership of "one point" is 0.7, the corresponding increment is 0.5, and "maintain The membership degree of "unchanged" is 1, and the corresponding increment is 0, and the membership degree of "reduced" is 1. The "one point" membership is 0.4, and the corresponding reduction is 0.3. The adaptive fuzzy PID control algorithm parameters after defuzzification calculation are: = ; = ;

[0059] The final specific adaptive fuzzy PID control algorithm parameters are applied to the controller for dynamically adjusting the system response.

[0060] Relationship between defuzzification and adaptive fuzzy PID control algorithm parameters: Adaptive fuzzy PID control algorithm parameters after defuzzification , and The specific value of is calculated based on the membership value in the fuzzy reasoning result and the incremental value corresponding to the fuzzy set. Different combinations of errors and error change rates will produce different fuzzy reasoning results, which are converted into specific adaptive fuzzy PID control algorithm parameters after defuzzification, so that the system can adaptively adjust the control strategy under different working conditions to ensure the stability and anti-sway effect of the spreader.

Claims

1. A portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm, including a redundant sensor system composed of multiple sensors, include: Inclination sensor detects the swing angle of the spreader in real time; Speed ​​sensor, which measures the swing speed of the spreader and provides dynamic feedback to the system; Trolley position sensor, which detects the horizontal position and speed of the crane trolley; Laser distance sensor to measure the distance between the spreader and the ground or cargo; Ultrasonic or visual sensors to assist in detecting surroundings and obstacles; It also includes: PLC or industrial computer receives data from sensors and generates control signals based on adaptive fuzzy PID control algorithm; Intelligent servo drive, which receives control signals and realizes precise driving of the trolley; The human-machine interface HMI provides a real-time adaptive fuzzy PID control algorithm parameter adjustment function. The adaptive fuzzy PID control algorithm calculates and outputs control signals in real time based on the swing angle, speed and trolley position feedback of the spreader.

2. The portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm according to claim 1, It is characterized in that The adaptive fuzzy PID control algorithm includes proportional integral differential control, and the PLC or industrial computer calculates the swing angle error of the hanger in real time. (t), and outputs a control signal u(t) based on the adaptive fuzzy PID control algorithm. The control signal is transmitted to the motor driver to adjust the acceleration and movement direction of the trolley to reduce the swing of the spreader, where: Control error (t), where Target swing angle of the spreader, (t) is the real-time swing angle of the spreader; Control output u(t)= (t)+ ,in , and are proportional, integral and differential coefficients respectively, u(t) is the control signal used to drive the car to move. (t) is a proportional term, proportional to the current error, is the integral term, accumulating past errors and correcting steady-state errors, is the differential term.

3. The portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm according to claim 2, It is characterized in that The control signal The output is sent to the motor driver, and the stable control of the swing of the spreader is achieved by adjusting the acceleration a(t) of the trolley.

4. The portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm according to claim 3, It is characterized in that The motor driver adjusts the acceleration of the car Realize the speed control of the car.

5. The portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm according to claim 2, It is characterized in that In the adaptive fuzzy PID control algorithm, the parameters , and Adjustment via the human-machine interface.

6. The portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm according to claim 1, It is characterized in that The system has an adaptive adjustment function. Through real-time feedback of sensor data, the adaptive fuzzy PID algorithm parameters are dynamically adjusted to meet the anti-sway requirements under different working conditions. The adaptive fuzzy PID control algorithm includes: The input variable error e(t) and the error change rate Δe(t) are fuzzified into fuzzy sets, and their membership is determined; The adjustment strategy of the adaptive fuzzy PID control algorithm parameters, including the proportional coefficient, is inferred based on the fuzzy rule base Integration coefficient , differential coefficient Real-time dynamic adjustment; The fuzzy reasoning results are converted into specific adaptive fuzzy PID control algorithm parameters through the defuzzification process to optimize the control signal.

7. An operating method of a portal crane loading and unloading system based on electronic anti-sway and intelligent algorithm as claimed in any one of the above claims, It is characterized in that The operating steps of the system include: (1) The tilt sensor detects the swing angle of the hanger in real time (t); (2) The speed sensor detects the swing speed of the spreader; (3) The trolley position sensor detects the horizontal position and speed of the trolley; (4) Laser ranging sensor detects the distance between the spreader and the ground or cargo; (5) Ultrasonic or visual sensors detect obstacles in the environment; (6) PLC or industrial computer based on the swing angle of the hanger (t), target angle Calculation error ; (7) Calculate the control signal u(t) based on the improved adaptive fuzzy PID control algorithm and mobilize the movement of the car through the intelligent servo drive; (8) The acceleration or deceleration of the trolley gradually reduces the swing of the sling until the swing becomes stable.

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