Bridge portal crane sling control method based on multi-stage model predictive control

Abstract

The application provides a bridge gantry crane sling control method based on multi-stage model predictive control, and relates to the field of data processing. In the method, first, control parameters and initial state variables are obtained to define the operating boundary and the starting state, and a dynamic model containing the coupling relationship of trolley acceleration, sling rope length acceleration and sling swing angle is established; the distribution of obstacles is obtained by using a space perception device, and the dynamic safety height of the sling is calculated in combination with the obstacle parameters and the control parameters; whether the sling is in the obstacle avoidance stage or the target tracking stage is judged according to the initial state variables and the dynamic safety height; a model predictive controller is constructed, and the state weight matrix and the control input weight matrix are dynamically adjusted; finally, the control input sequence is generated through an optimization algorithm, and the trajectory safety and attitude stability control of the sling are realized. The implementation of the technical scheme facilitates the realization of effective avoidance of obstacles while the swing angle is suppressed, and ensures the safety of the sling in the whole operation process.

Description

Technical Field

[0001] This application relates to the technical field of data processing, specifically to a control method for bridge crane spreaders based on multi-stage model predictive control. Background Technology

[0002] In recent years, as port operations have continued to evolve towards unmanned and intelligent operations, gantry cranes, as key equipment, have seen their automatic control capabilities become a core support for intelligent scheduling and safe operation. However, in actual operations, due to the combined effects of trolley horizontal acceleration, changes in spreader rope length, and environmental disturbances, the spreader is prone to significant swaying during operation, which is difficult to suppress effectively in a short period of time, seriously affecting terminal positioning accuracy and system operational safety.

[0003] Existing anti-sway control methods have significant limitations: On the one hand, traditional control strategies based on proportional-integral-derivative (PID) typically rely on additional sway angle detection sensors, resulting in rigid control structures that struggle to adapt to the dynamic coupling characteristics of the system caused by changes in rope length. This is especially problematic when performing path avoidance maneuvers in obstacle-prone areas, often leading to sway angle overshoot and trajectory deviation. On the other hand, current mainstream model predictive control methods are largely based on the assumption of a fixed-length lifting rope, only predicting and controlling the horizontal displacement of the trolley. They neglect the synergistic relationship between dynamic adjustment of the lifting rope length and obstacle avoidance, resulting in a lack of flexibility and adaptability in complex operating environments.

[0004] Therefore, how to achieve effective obstacle avoidance while suppressing the swing angle of the spreader, and ensure the safety of the spreader throughout the entire operation process, is a key issue that urgently needs to be addressed in the current control system of bridge and gantry cranes. Summary of the Invention

[0005] This application provides a control method for the spreader of a bridge crane based on multi-stage model predictive control, which facilitates the spreader to effectively avoid obstacles while suppressing the swing angle, thus ensuring the safety of the spreader throughout the entire operation process.

[0006] A first aspect of this application provides a control method for a gantry crane spreader based on multi-stage model predictive control. The method includes: acquiring control parameters of the gantry crane and determining initial state variables, wherein the control parameters and the initial state variables are used to define the operating boundary and initial state of the gantry crane; establishing a dynamic model of the gantry crane based on the control parameters and the initial state variables, wherein the dynamic model includes trolley acceleration equations, spreader rope length acceleration equations, and spreader swing angle coupling equations; acquiring obstacle distribution information of the spreader in the working area through a spatial sensing device, and calculating the dynamic safety height of the spreader by combining obstacle parameters and the control parameters; determining the control stage of the spreader based on the initial state variables and the dynamic safety height, wherein the control stage includes an obstacle avoidance stage and a target tracking stage; constructing a model predictive controller and controlling the model predictive controller to dynamically adjust the state weight matrix and the control input weight matrix according to the control stage; and generating a control input sequence for the spreader through an optimization algorithm based on the state weight matrix, the control input weight matrix, and the constraints of the spreader.

[0007] Optionally, establishing the dynamic model of the gantry crane based on the control parameters and the initial state variables specifically includes: setting the initial state variables as trolley position information, trolley speed information, spreader rope length information, spreader rope length change rate information, spreader swing angle information, and spreader swing angle change rate information; setting the control input variables as trolley acceleration information and spreader rope length acceleration information; based on the mass parameter, gravity constant, operating boundary conditions, and damping parameter in the control parameters, combined with the initial state variables and the control input variables, constructing the trolley acceleration equation for describing the horizontal motion of the trolley, the spreader rope length acceleration equation for describing the change of the spreader rope length, and the spreader swing angle coupling equation for coupling and describing the dynamic change of the spreader swing angle under the combined action of trolley acceleration, trolley position, and spreader rope length.

[0008] Optionally, the step of acquiring obstacle distribution information of the spreader within the working area through a spatial sensing device, and calculating the dynamic safety height of the spreader by combining obstacle parameters with the control parameters, specifically includes: using the spatial sensing device deployed on the gantry crane to collect three-dimensional environmental data within the corresponding working area of ​​the gantry crane, the three-dimensional environmental data including the horizontal position parameters and vertical height parameters of obstacles; constructing an obstacle height mapping function based on the three-dimensional environmental data, the obstacle height mapping function being used to describe the maximum height of obstacles corresponding to different horizontal positions; constructing a dynamic safety height model by combining the obstacle height mapping function, the sensor error tolerance, the obstacle area expansion factor, and the system equivalent friction factor in the control parameters; and outputting the dynamic safety height according to the dynamic safety height model.

[0009] Optionally, determining the control stage of the spreading device based on the initial state variables and the dynamic safety height specifically includes: generating a horizontal position sequence and a corresponding vertical height sequence of the spreading device within a preset prediction time domain based on the initial state variables using the dynamic model; extracting the corresponding dynamic safety height sequence from the dynamic safety height model based on the horizontal position sequence, and comparing the vertical height sequence with the dynamic safety height sequence one by one to obtain a comparison result; if the comparison result indicates that any height value in the vertical height sequence is lower than the corresponding dynamic safety height value, then the control stage is determined to be the obstacle avoidance stage; otherwise, it is determined to be the target tracking stage.

[0010] Optionally, the construction of the model predictive controller and the control of the model predictive controller to dynamically adjust the state weight matrix and the control input weight matrix according to the control stage specifically includes: setting a state error cost term and a control input cost term in the model predictive controller; the state error cost term is calculated by weighting the deviation cost of the trolley position, the length of the lifting rope, and the swing angle of the lifting rope relative to the target state using the state weight matrix; the control input cost term is calculated by weighting the magnitude cost of the trolley acceleration and the acceleration of the lifting rope length using the control input weight matrix; based on the state error cost term and the control input cost term, if it is determined that... When the control phase is the obstacle avoidance phase, the weight coefficients of the corresponding items for the sling swing angle and sling rope length in the state weight matrix are increased, the weight coefficients of the corresponding items for the trolley position are decreased, and the weight coefficient of the sling rope length acceleration in the control input weight matrix is ​​decreased, while the weight coefficient of the trolley acceleration is increased. If the control phase is determined to be the target tracking phase, the terminal state weight coefficients of the trolley position and sling swing angle in the state weight matrix are increased, the weight coefficients of the corresponding items for the sling rope length are decreased, and the weight coefficient of the trolley acceleration in the control input weight matrix is ​​decreased, while the weight coefficient of the sling rope length acceleration is increased.

[0011] Optionally, the step of generating a control input sequence for the spreader based on the state weight matrix, the control input weight matrix, and the constraints of the spreader using an optimization algorithm specifically includes: recursively generating state trajectories of trolley position, trolley speed, spreader rope length, spreader rope length change rate, spreader swing angle, and spreader swing angle change rate within the preset prediction time domain according to the dynamic model and the initial state variables; adding hard constraints including the spreader vertical height not being lower than the dynamic safety height, trolley speed and acceleration not exceeding the control boundary, and spreader rope length change rate and acceleration being limited, as well as soft constraints that the trolley speed, spreader rope length change rate, and spreader swing angle change rate converge to zero, based on the state weight matrix and the control input weight matrix, to obtain a target cost function; and inputting the target cost function and the constraints into a constrained quadratic programming optimization algorithm to solve for the optimal trolley acceleration sequence and spreader rope length acceleration sequence within the target prediction time domain, thereby obtaining the control input sequence.

[0012] Optionally, a weight term for penalizing the spreader's swing angle and the rate of change of the swing angle is set in the state weight matrix, and the value of the weight term is dynamically increased during the obstacle avoidance phase and the target tracking phase to enhance the control constraint on the spreader's swing angle deviation; a weight term for penalizing the trolley acceleration and the spreader rope length acceleration is set in the control input weight matrix, and the penalty intensity of the trolley acceleration is increased during the path turning interval and the terminal braking phase to smooth input changes and reduce the driving effect of the excitation source on the spreader's self-swing; a constraint condition is set in the constraint conditions to ensure that the rate of change of the spreader's swing angle converges to zero, guiding the spreader's attitude to achieve stable convergence while completing the positioning process.

[0013] A second aspect of this application provides a control device for a gantry crane spreader based on multi-stage model predictive control. The device includes an acquisition module and a processing module. The acquisition module acquires control parameters of the gantry crane and determines initial state variables. The control parameters and the initial state variables define the operating boundaries and initial state of the gantry crane. The processing module establishes a dynamic model of the gantry crane based on the control parameters and the initial state variables. The dynamic model includes equations for trolley acceleration, spreader rope length acceleration, and spreader swing angle coupling. The acquisition module is also used to acquire information about the spreader's operation through a spatial sensing device. The processing module is used to calculate the dynamic safe height of the spreading device by combining obstacle distribution information within the area with obstacle parameters and control parameters; the processing module is also used to determine the control stage of the spreading device based on the initial state variables and the dynamic safe height, the control stage including an obstacle avoidance stage and a target tracking stage; the processing module is also used to construct a model predictive controller and control the model predictive controller to dynamically adjust the state weight matrix and the control input weight matrix according to the control stage; the processing module is also used to generate a control input sequence for the spreading device through an optimization algorithm based on the state weight matrix, the control input weight matrix and the constraints of the spreading device.

[0014] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, and both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described above.

[0015] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described above.

[0016] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] 1. By integrating dynamic modeling, spatial perception, stage recognition, and rolling optimization control, a multi-stage model predictive control system for bridge gantry cranes was constructed. This system achieves dynamic obstacle avoidance while suppressing the spreader's swing angle and ensuring accurate terminal positioning. This method offers advantages such as adjustable control input, flexible state response, strong environmental adaptability, and high path safety. It effectively solves the problem of difficult coordination in multi-objective control in existing technologies, significantly improving the spreader's operational stability and intelligent decision-making capabilities in complex operating scenarios.

[0018] 2. By constructing a weighted cost function and combining physical hard constraints and terminal soft constraints, the model prediction problem is standardized into a constrained quadratic programming form. This allows for the real-time solution of the optimal trolley acceleration and spreader rope length acceleration control sequence, while satisfying dynamic safety height, system boundary, and attitude convergence requirements, thus forming a complete predictive control closed-loop path planning mechanism. This achieves a unified control objective of simultaneously satisfying obstacle avoidance, safety control, and precise end-effector sway reduction in complex dynamic environments, significantly improving the scalability of existing MPC methods.

[0019] 3. By clarifying the dynamic adjustment mechanism of the state weight matrix and the control input weight matrix under different control stages, the limitations of the traditional fixed weight configuration mode on system performance are broken through. The model predictive controller can automatically reconstruct the control priority according to whether the lifting device is in the obstacle avoidance stage or the target tracking stage, realize the phased optimization of swing angle stability and path convergence, and effectively alleviate the performance conflict problem of traditional controllers under multi-objective conditions. Attached Figure Description

[0020] Figure 1 A flowchart illustrating the bridge crane spreader control method based on multi-stage model predictive control provided in this application embodiment;

[0021] Figure 2 A schematic diagram of a bridge crane spreader control device based on multi-stage model predictive control provided in an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0023] Explanation of reference numerals in the attached figures: 21. Acquisition module; 22. Processing module; 31. Processor; 32. Communication bus; 33. User interface; 34. Network interface; 35. Memory. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0025] In the description of the embodiments in this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0026] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0027] As port operations continue to accelerate towards unmanned and intelligent operation, the automatic control performance of gantry cranes, as core handling equipment, has become a key foundation for ensuring operational efficiency and system safety. However, in practical applications, the spreader is subject to a combination of factors during operation, including trolley horizontal acceleration, changes in spreader rope length, and environmental interference, which can easily cause significant sway angles that are difficult to suppress effectively within a limited time, directly affecting the terminal positioning accuracy and the overall stability of the lifting operation.

[0028] Existing anti-sway control technologies still have structural shortcomings: On the one hand, traditional methods based on proportional-integral-derivative (PID) rely on additional sway angle sensors, which cannot dynamically adapt to the coupling mechanism between the change in the spreader rope length and the trolley control, especially in obstacle avoidance conditions where attitude overshoot and trajectory instability are likely to occur; on the other hand, the currently commonly used model predictive control methods mostly take a constant rope length as a modeling premise and only control the horizontal movement of the trolley, lacking a joint modeling and constraint mechanism for the dynamic change of rope length and the spatial distribution of obstacles, resulting in conservative path generation and difficulty in coping with highly complex operating environments.

[0029] To address the aforementioned technical problems, this application provides a control method for the spreader of a bridge crane based on multi-stage model predictive control, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the bridge crane spreader control method based on multi-stage model predictive control provided in this application embodiment. The method is applied to a server and includes steps S110 to S160, as follows:

[0030] S110. Obtain the control parameters of the bridge crane and determine the initial state variables. The control parameters and initial state variables are used to define the operating boundary and initial state of the bridge crane.

[0031] Specifically, the server refers to the central processing unit that deploys control algorithms and operational logic, which is an industrial control server or embedded computing platform with real-time data processing capabilities. Its function is to dynamically monitor the operating status of the gantry crane, schedule parameters, perform predictive calculations, and output control decisions. A gantry crane is a heavy-duty mechanical device with a gantry structure used for large-scale horizontal and vertical transport of heavy objects, consisting of components such as a trolley, spreader, wire rope system, and drive mechanism. In this scenario, the gantry crane is the controlled object, and the spreader is its execution end. Control parameters refer to static or semi-static physical boundary conditions that have global constraints or control effects during the operation of the gantry crane. For example, control parameters include the maximum / minimum speed of the trolley's horizontal movement, the upper and lower limits of the trolley's acceleration, the allowable range of variation in the spreader rope length, the speed limit for rope length adjustment, the allowable range of the spreader's swing angle, the system sampling period, and the prediction time domain length. Control parameters are preset by system configuration, mechanical structure, or safety specifications and do not change dynamically with the operating status.

[0032] The initial state variables refer to the state vector variable values ​​of the gantry crane at the beginning of the control cycle. They serve as the initial boundary conditions for trajectory prediction and input solution by the model predictive controller, specifically including six dimensions: trolley position, trolley speed, spreader rope length, spreader rope length change rate, spreader swing angle, and spreader swing angle change rate. The initial state variables are typically obtained by real-time measurement from sensors and filtered by the state estimation unit. The operating boundary refers to the set of boundary constraints defined by the control parameters during the gantry crane's motion. It limits the trolley displacement range, maximum spreader lifting height, minimum safe height, and upper and lower limits of speed and acceleration to prevent the spreader from exceeding these limits or the system from overloading. The operating boundary provides the boundary conditions for the hard constraints in subsequent optimization algorithms. The initial state refers to the set of values ​​corresponding to the initial state variables in the current control cycle, representing the physical operating state of the gantry crane at the current sampling time, serving as the starting point for state prediction recursion in model predictive control.

[0033] In summary, the server obtains the control parameters used to define the system boundary conditions and the initial state variables used to describe the current physical state, establishing the foundation for system controllability and dynamics. This provides accurate boundary criteria and state starting points for subsequent model predictive controller prediction calculations and control input generation.

[0034] S120. Based on the control parameters and initial state variables, a dynamic model of the bridge gantry crane is established. The dynamic model includes the trolley acceleration equation, the spreader rope length acceleration equation, and the spreader swing angle coupling equation.

[0035] Specifically, the dynamic model refers to the set of mathematical expressions describing the time-varying laws of the state variables of a gantry crane system. It characterizes the causal relationship between the controlled input and the state response, and is the core of the model predictive controller's computation for state prediction and input optimization. This model is constructed based on Lagrange's principles of mechanics or the Newton-Euler equations and is updated in real-time using a discrete-time format. The trolley acceleration equation is a dynamic sub-equation describing the horizontal linear acceleration behavior of the trolley under control input. It consists of the trolley mass, driving force, and friction terms, and is used to derive the predicted path of the trolley's position and velocity. It is the main channel for coupling control drive and horizontal displacement in the system. The spreader rope length acceleration equation describes the dynamic response of the spreader rope length under the action of the rope wheel drive system. It expresses the acceleration evolution of the spreader's vertical height under control input, typically considering rope length inertia, tension changes, and damping forces. It is a key module for achieving spreader height adjustment and obstacle avoidance control. The swing angle coupling equation of the spreader refers to the nonlinear coupling equation describing the dynamic evolution of the swing angle of the spreader under the combined action of trolley motion and rope length change. This equation takes into account horizontal driving excitation, vertical length disturbance, gravity and system damping terms. It is the core structure for modeling the attitude instability and self-swing behavior of the spreader and constitutes the basic basis for swing cancellation and attitude adjustment in model predictive control.

[0036] In summary, based on the acquired control parameters and initial state variables, the server constructs a complete dynamic model that includes three types of dynamic behaviors: trolley acceleration behavior, spreader rope length response, and spreader attitude change. This provides a dynamic evolution basis and physical consistency guarantee for subsequent model prediction, path generation, and control input solution.

[0037] In one possible implementation, a dynamic model of the gantry crane is established based on control parameters and initial state variables. Specifically, this includes: setting the initial state variables as trolley position information, trolley speed information, spreader rope length information, spreader rope length change rate information, spreader swing angle information, and spreader swing angle change rate information; setting the control input variables as trolley acceleration information and spreader rope length acceleration information; and based on the mass parameter, gravity constant, operating boundary conditions, and damping parameter in the control parameters, combined with the initial state variables and control input variables, constructing a trolley acceleration equation to describe the horizontal motion of the trolley, a spreader rope length acceleration equation to describe the change in spreader rope length, and a coupled equation to describe the dynamic change in the spreader swing angle under the combined action of trolley acceleration, trolley position, and spreader rope length.

[0038] Specifically, first, the state variable vector of the gantry crane is defined. This state variable vector is defined as a six-dimensional state variable, which includes the trolley position. Car speed Length of lifting rope Speed ​​of change of the lifting rope , lifting gear swing angle With the speed of change of the spreader's swing angle This state vector describes the complete physical state of the system within any control cycle and is the basis for the model predictive controller to perform predictive calculations and recursive deduction of the system response. Next, the control input variable is defined. The control input variable is defined as the vehicle's acceleration. acceleration with the length of the lifting rope These two input variables are used to drive the trolley along the horizontal track and to drive the lifting device's rope pulley for rope length deployment and retraction, respectively. These two input variables will serve as the target output variables for the optimization algorithm, driving the state variables to evolve dynamically in the prediction time domain. Next, the server extracts necessary system constants from the control parameters, including the trolley mass. Lifting gear quality System damping coefficient Gravitational acceleration This includes information on the operating boundaries of the gantry crane, such as maximum rope length and minimum safe height. These control parameters are structural coefficients in the system dynamics equations, ensuring that the model remains consistent with actual physical characteristics.

[0039] Finally, based on the Newton-Euler principles of motion, three sets of dynamic equations for the system are constructed: 1. Equation for the acceleration of the trolley (horizontal direction):

[0040]

[0041] in: : Car acceleration; : Control input variable, representing the control acceleration applied to the vehicle.

[0042] 2. Equation for acceleration of the lifting rope (vertical direction):

[0043]

[0044] in: : Acceleration of the lifting rope length; : Control input variable, representing the rope length acceleration applied to the lifting gear pulley.

[0045] 3. Coupled equations for the swing angle of the lifting device (nonlinear dynamics):

[0046]

[0047] in: : Spreader angular acceleration; : Gravitational acceleration; Current rope length; : The rate of change of rope length; : Rate of change of swing angle; : Car acceleration; : Swing angle damping coefficient; : Spreader mass. This pendulum angle coupling equation realistically describes the nonlinear evolution of the spreader's attitude under the combined effects of trolley horizontal drive, trolley position movement, rope length change, and gravity recovery term.

[0048] In summary, through the above-mentioned state variable setting, input variable specification, system parameter extraction, and construction of three types of dynamic equations, the server can complete the construction of a multivariable coupled dynamic model of a gantry crane, providing an accurate prediction structure and control solution input basis for the model predictive controller, and supporting the unified optimization scheduling of obstacle avoidance, sway elimination, and path tracking of the spreader throughout its entire operation cycle.

[0049] S130. Obtain obstacle distribution information of the spreader in the working area through spatial sensing equipment, and calculate the dynamic safe height of the spreader by combining obstacle parameters and control parameters.

[0050] Specifically, the spreader refers to the end effector unit of a gantry crane used for hooking and transporting loads. In the control system, it is the controlled object for motion control and obstacle avoidance planning. Its movement path must avoid all obstacle boundaries within the working area and meet precise positioning requirements. The working area refers to the combined reachable range of the gantry crane trolley and spreader in the horizontal and vertical directions. It is the complete three-dimensional spatial boundary area that the spreader may travel through. This area is determined by control parameters such as the trolley travel range, maximum rope length, and minimum safe height, and is also the analysis domain for obstacle perception and obstacle avoidance modeling. Obstacle distribution information refers to the three-dimensional obstacle spatial information acquired and processed by spatial sensing equipment. It typically includes the horizontal coordinates (x) and vertical coordinates (z) of the obstacle within the working area, as well as its geometric contour boundary. This information is used to construct the obstacle height mapping function and serves as the input basis for the subsequent dynamic safe height model.

[0051] The obstacle parameters refer to the data structure extracted and formatted from the obstacle distribution information. They typically include the maximum height value, horizontal projection boundary, location label, and obstacle expansion factor for each obstacle, used to expand the obstacle boundary to form a safe avoidance zone. The control parameters mainly provide the system operating boundary constants and error compensation terms required for calculating the obstacle safety height model, including sensor measurement error margin, obstacle space expansion factor, system response hysteresis compensation term, and equivalent friction factor. These parameters enhance the robustness of the safety height model to environmental uncertainties and dynamic errors. The dynamic safety height refers to the minimum vertical height required to ensure the spreader safely crosses obstacles at the current horizontal position of the trolley. It is a height threshold calculated by combining obstacle distribution information and control parameters, used as a hard height constraint in the spreader trajectory planning process to ensure that the operating path is always higher than the reachable boundary of the obstacle at the corresponding position.

[0052] In summary, this section indicates that the server acquires the 3D obstacle distribution through spatial perception devices and, in combination with obstacle parameters and control parameters, generates a set of dynamic safety height threshold sequences corresponding to the feasible paths of the lifting device within the vehicle's position domain. This provides rigid height constraints for the subsequent model prediction controller to generate lifting device trajectories that meet obstacle avoidance requirements.

[0053] In one possible implementation, obstacle distribution information of the spreader within the working area is acquired through a spatial sensing device, and the dynamic safe height of the spreader is calculated by combining obstacle parameters and control parameters. Specifically, this includes: using a spatial sensing device deployed on the gantry crane to collect three-dimensional environmental data within the corresponding working area of ​​the gantry crane, the three-dimensional environmental data including the horizontal position parameters and vertical height parameters of obstacles; constructing an obstacle height mapping function based on the three-dimensional environmental data, the obstacle height mapping function being used to describe the maximum height of obstacles corresponding to different horizontal positions; constructing a dynamic safe height model by combining the obstacle height mapping function, sensor error tolerance, obstacle area expansion factor, and system equivalent friction factor in the control parameters; and outputting the dynamic safe height based on the dynamic safe height model.

[0054] Specifically, firstly, the server uses spatial sensing devices deployed on the gantry crane trolley or spreader to perform real-time scanning of the work area, acquiring 3D environmental data. This 3D environmental data includes the spatial coordinate distribution information of all obstacles within the work area. This data is represented in discrete point cloud or raster format, with each obstacle's position on the horizontal axis (i.e., the direction of the trolley's travel). Height on the vertical axis (i.e., the direction of descent of the spreader) Representation, forming a set of obstacle points .

[0055] Next, the server constructs an obstacle height mapping function based on the collected set of obstacle points. This function is used to describe the horizontal position of any vehicle. The highest boundary height of the corresponding obstacle is determined by searching for the maximum height within the horizontal position interval and constructing a piecewise function, as shown in the following formula:

[0056]

[0057] in: Obstacle height mapping function, representing the height at horizontal position. Maximum height of obstacles at the location; : Horizontal sampling window, used for local region search; Obstacle point set collected and processed by spatial sensing equipment.

[0058] Subsequently, based on the obstacle height mapping function, multiple control parameters are introduced for safety compensation, constructing a dynamic safety height model. This model forms the safety height function by adding a compensation term to the original obstacle height, as shown in the following formula:

[0059]

[0060] in: Dynamic safety height; Obstacle area expansion factor, used to compensate for spreader operating errors and path deviations; Sensor error tolerance, used to account for measurement uncertainties in spatial sensing devices; The equivalent friction factor of the system is used to characterize the insufficient inertial lifting of the spreader due to friction and hysteresis during operation, thereby enhancing the safety margin of the system.

[0061] Finally, the server determines the vehicle's current predicted trajectory or actual location. ,right Real-time interpolation calculations are performed to obtain the dynamic safety height value corresponding to this location, and this value is used as the lower limit of the lifting rope length in the subsequent hard constraint settings of the model prediction controller to ensure that the predicted path always meets the following conditions throughout the entire time domain:

[0062]

[0063] Therefore, it is necessary to ensure that the vertical projection height of the spreader in the swing state is not lower than the dynamic safety height of the current horizontal position, thereby achieving dynamic avoidance control of the obstacle area.

[0064] In summary, this technical solution, through sensing input, local modeling, parameter compensation, and real-time interpolation, forms a dynamic height threshold model that is dynamically changing, has an adjustable safety margin, and is directly related to the predicted trajectory of the trolley. This model serves as a key height boundary constraint input in the generation of the spreader trajectory.

[0065] S140. Based on the initial state variables and dynamic safety height, determine the control phase of the spreader. The control phase includes the obstacle avoidance phase and the target tracking phase.

[0066] Specifically, the control phase refers to the control logic mode corresponding to the classification of the task state of the lifting device by the model predictive controller based on the current environmental and system states. This is used to dynamically switch cost function weights, control constraint structures, and path planning strategies to ensure that the control objectives can be achieved in a coordinated manner under different working conditions. The obstacle avoidance phase refers to the server determining that the lifting device is about to enter or is already above an obstacle area within the prediction time domain. At this time, the system must prioritize the vertical obstacle avoidance and attitude stability objectives of the lifting device. The controller will increase the penalty weights for the lifting device rope length and swing angle, and limit the trolley speed to suppress lateral disturbances. The target tracking phase refers to the server determining that the lifting device is in a non-obstacle area throughout the prediction time domain, and the system no longer faces height constraints. In this phase, the control objectives are to achieve trolley positioning accuracy and attitude convergence. The controller will increase the control priority of trolley position status and terminal docking accuracy.

[0067] In summary, this passage describes how the server, based on the current system state and obstacle environment information, identifies whether the current path of the spreader is limited by obstacles, and accordingly divides the control process into an obstacle avoidance stage or a target tracking stage, so as to drive the subsequent model predictive controller to select the most suitable control strategy, thereby achieving dynamic coordination of path safety, control accuracy and attitude stability.

[0068] In one possible implementation, the control phase of the spreading device is determined based on the initial state variables and the dynamic safety height. Specifically, this includes: generating a horizontal position sequence and a corresponding vertical height sequence of the spreading device within a preset prediction time domain using a dynamic model based on the initial state variables; extracting the corresponding dynamic safety height sequence from the dynamic safety height model based on the horizontal position sequence, and comparing the vertical height sequence with the dynamic safety height sequence one by one to obtain a comparison result; if the comparison result indicates that any height value in the vertical height sequence is lower than the corresponding dynamic safety height value, then the control phase is determined to be an obstacle avoidance phase; otherwise, it is determined to be a target tracking phase.

[0069] Specifically, the first step is to generate the predicted trajectory of the spreading device. The server bases this on the initial state variables within the current control cycle. Combining the discrete dynamics model of the bridge gantry crane, in the prediction time domain Internal recursion generates the horizontal position sequence of the lifting device. With vertical height sequence The vertical height of each step. It is calculated using the length of the lifting rope and the swing angle, and its expression is:

[0070]

[0071] in: : No. The length of the lifting rope for the step; : No. The swing angle of the lifting device; The lifting gear in the first The vertical projection height of the step.

[0072] The second step is to extract the dynamic safety height sequence. The server will predict the generated horizontal position sequence. Input dynamic safety height model In each prediction step Extract the safety height at the corresponding location to form a dynamic safety height sequence. The calculation expression is:

[0073]

[0074] in: : No. Step position The corresponding maximum height of the obstacle; Obstacle boundary expansion factor; Spatial perception error margin; System friction compensation factor; : No. The dynamic safety height of the step.

[0075] The third step involves performing a stepwise height comparison. The server will predict the vertical height sequence. With dynamic security height sequence A point-by-point comparison is performed to determine whether there are any risk points in the predicted future path where the lifting equipment's height is less than the safety threshold.

[0076]

[0077] If there exists a certain moment If the above conditions are met, it indicates that the spreader will enter the area above the obstacle, and the system will determine the control phase as the "obstacle avoidance phase"; otherwise, if all If so, the control phase is determined to be the "target tracking phase".

[0078] The fourth step is to output the control phase decision result. This decision result serves as the basis for subsequent decisions by the model predictive controller to switch the control weight matrix, path constraint strategy, and swing angle suppression strength, ensuring that the controller performs targeted safety obstacle avoidance or end-point precise positioning control tasks under different operating conditions. In summary, this technical solution, by constructing a comparison system of predicted trajectory, height constraint sequence, and height safety boundary, realizes a segmented decision mechanism for the control logic of gantry crane spreaders in multi-objective control scenarios, providing a structured and adaptive trigger basis for strategy switching for the model predictive controller.

[0079] S150. Construct a model predictive controller and control the model predictive controller to dynamically adjust the state weight matrix and the control input weight matrix according to the control phase.

[0080] Specifically, a model predictive controller (MMC) is a control structure that uses the dynamic model of a gantry crane as the basis for prediction and minimizes the cost function as the optimization objective. It can predict the state evolution process and solve for the optimal control input sequence within each control cycle based on the current system state variables and future reference trajectory. Its core components are: a predictive model, a cost function, constraints, and an optimization solver. Dynamic adjustment refers to the server resetting the parameter values ​​of the state weight matrix and control input weight matrix in the MMC cost function in real time according to the currently determined control stage. This allows the controller to automatically match the current task priorities and safety requirements in different operating stages, achieving coordinated optimization of anti-sway control, obstacle avoidance control, and path convergence control objectives.

[0081] The state weight matrix refers to the weighted coefficient matrix of the state error term in the model predictive controller cost function. It is used to penalize the deviation of state variables such as vehicle position, hoist rope length, and hoist swing angle from the desired target. Different control stages have different weight requirements for different state variables. For example, in the obstacle avoidance stage, the weights of hoist rope length and hoist swing angle should be increased, while in the target tracking stage, the weights of vehicle position and terminal state should be increased. The control input weight matrix refers to the weighted matrix in the model predictive controller cost function used to constrain the changes in vehicle acceleration and hoist rope length acceleration. This matrix is ​​used to limit the aggressiveness of the control input, ensure smooth input, and suppress excessive acceleration leading to swing angle excitation or system response oscillations. Different control stages have different suppression requirements for the control input. For example, at path turning points, the weight of vehicle acceleration needs to be increased to smooth control, while in the initial obstacle avoidance stage, the weight of hoist rope length acceleration needs to be reduced to allow rapid ascent.

[0082] In summary, this passage indicates that the server, based on the control stage of the gantry crane's spreader, dynamically configures the state weight matrix and control input weight matrix in the model predictive controller to adaptively adjust the priority of the control target. This enhances the system's ability to stably control the swing angle and rope length during obstacle avoidance and strengthens its control capabilities for position accuracy and terminal convergence performance during the target alignment stage.

[0083] In one possible implementation, a model predictive controller is constructed, and the model predictive controller is controlled to dynamically adjust the state weight matrix and the control input weight matrix according to the control phase. Specifically, this includes: setting a state error cost term and a control input cost term in the model predictive controller; the state error cost term is calculated by weighting the deviation costs of the trolley position, the length of the lifting rope, and the swing angle of the lifting rope relative to the target state using the state weight matrix; the control input cost term is calculated by weighting the magnitude costs of the trolley acceleration and the acceleration of the lifting rope length using the control input weight matrix; based on the state error cost term and the control input cost term, if... When the control phase is determined to be the obstacle avoidance phase, the weight coefficients of the corresponding items for the sling swing angle and sling rope length in the state weight matrix are increased, the weight coefficients of the corresponding items for the trolley position are decreased, and the weight coefficient of the sling rope length acceleration in the control input weight matrix is ​​decreased, while the weight coefficient of the trolley acceleration is increased. If the control phase is determined to be the target tracking phase, the terminal state weight coefficients of the trolley position and sling swing angle in the state weight matrix are increased, the weight coefficients of the corresponding items for the sling rope length are decreased, and the weight coefficient of the trolley acceleration in the control input weight matrix is ​​decreased, while the weight coefficient of the sling rope length acceleration is increased.

[0084] Specifically, the first step is to construct the cost function structure. The model predictive controller aims to minimize the control objective deviation and control input energy within the prediction time domain, and thus constructs the cost function. It includes the state error cost term and the control input cost term, and the overall formula is as follows:

[0085]

[0086] in: : No. The predicted state variables include the trolley position, the corresponding rope length, and the swing angle; : No. Step reference target state; : No. Step control input variables, including the car's acceleration acceleration with the length of the lifting rope ; State weight matrix; : Control input weight matrix; : Predict the number of time-domain steps.

[0087] Step 2: Initialize the state weight matrix and control input weight matrix. During the controller initialization phase, the state weight matrix is ​​set. and control input weight matrix The basic structure is represented by the weighting strength of each state variable and the control input, in the following form:

[0088]

[0089] in: : Weighting coefficients for the vehicle's position state; Weighting coefficient for the length of the lifting rope; Weighting coefficient for the swing angle state of the spreader; : Control cost weight of the vehicle's acceleration; : Control cost weight of lifting rope length acceleration.

[0090] Step 3: Control Phase Identification. Based on the step-by-step comparison between the spreader height and the dynamic safety height in the predicted path, the server determines whether the current control phase is the "obstacle avoidance phase" or the "target tracking phase." The identification result of the control phase serves as the trigger condition for weight adjustment.

[0091] Step 4: Dynamically adjust the weight matrix parameters. If the server determines that it is currently in the "obstacle avoidance phase," then update the state weight matrix and control input weight matrix as follows:

[0092]

[0093] in, , Increase the penalty for changes in the length of the lifting rope and the swing angle; Reduce the accuracy requirements of the trolley's position status; Enhanced suppression of sudden acceleration changes in the vehicle; Allows for flexible adjustment of the spreader rope length and acceleration for rapid obstacle avoidance. If the current state is determined to be "target tracking phase", then adjust as follows:

[0094]

[0095] in, Enhance the accuracy of the trolley's position control; : Improve the pendulum angle convergence capability in the terminal stage; : Weaken the constraint on rope length stability; Relax the car's response capabilities; This enhances the suppression of vertical disturbances in the spreading device. In summary, this dynamic weight adjustment mechanism ensures that the model predictive controller has policy adaptability at different task stages, prioritizing height and attitude control in obstacle areas, enhancing path accuracy and attitude convergence during target tracking, and improving the system's dynamic coordination performance and path safety for multi-target control tasks.

[0096] S160. Based on the state weight matrix, control input weight matrix, and constraints of the spreader, an optimization algorithm is used to generate a control input sequence for the spreader.

[0097] Specifically, constraints refer to a set of hard and soft constraints imposed on the control and state variables by the model predictive controller during the optimization process. These constraints limit the physical boundaries, safety standards, and terminal stability requirements of the spreader during operation, including but not limited to: upper limits for trolley speed and acceleration, range of spreader rope length variation, swing angle constraints, spreader height not lower than the dynamic safety height, and terminal convergence requirements. The optimization algorithm refers to the mathematical method used to solve for the optimal control input sequence under the objective function and constraints. It employs constrained quadratic programming algorithms, such as the interior-point method and the active set method, to calculate a feasible solution that minimizes the cost function in each control cycle and output the first control variable as the execution command. The control input sequence refers to the set of optimal control inputs output by the optimization algorithm over multiple future time-domain steps. Specifically, it is a set of time-series trolley accelerations and spreader rope length accelerations used to drive the spreader along the predicted trajectory. The controller ultimately applies the first input of this sequence as the control command for the current cycle, and the remaining parts are updated in the next control cycle.

[0098] In summary, the server constructs a complete cost function and physical constraint framework based on the current control strategy in each control cycle, and solves the optimal control input sequence in the prediction time domain through an efficient optimization algorithm, thereby accurately guiding the spreader to achieve multiple task objectives such as path planning, obstacle avoidance control and end-point positioning.

[0099] In one possible implementation, based on the state weight matrix, the control input weight matrix, and the constraints of the spreader, an optimization algorithm generates a control input sequence for the spreader. Specifically, this includes: recursively generating state trajectories for the trolley position, trolley speed, spreader rope length, spreader rope length change rate, spreader swing angle, and spreader swing angle change rate within a preset prediction time domain, based on the dynamic model and initial state variables; adding hard constraints, including that the vertical height of the spreader is not lower than the dynamic safety height, the trolley speed and acceleration do not exceed the control boundary, and the spreader rope length change rate and acceleration are limited, as well as soft constraints, that the trolley speed, spreader rope length change rate, and spreader swing angle change rate converge to zero, to obtain a target cost function; and inputting the target cost function and constraints into a constrained quadratic programming optimization algorithm to solve for the optimal trolley acceleration sequence and spreader rope length acceleration sequence within the target prediction time domain, thus obtaining the control input sequence.

[0100] Specifically, in the first step, the server performs a forward state recursion in the prediction time domain based on the dynamic model and initial state variables of the gantry crane. Specifically, the initial state variables include the trolley position, trolley speed, spreader rope length, rate of change of the spreader rope length, spreader swing angle, and rate of change of the spreader swing angle. Based on the pre-defined initial values ​​of the control input sequence, the server recursively derives the state trajectory through the dynamic equations of the gantry crane, as expressed in the following formula:

[0101]

[0102] in, For the first The state variable vector of the step, For the first The control input variable vector of the step, and These are the state transition matrix and the control input mapping matrix, respectively, which are obtained by linearizing the dynamic model of the bridge gantry crane.

[0103] The second step involves the server incorporating state error cost terms and control input cost terms into the objective cost function based on the state trajectory and control input variables. Objective Cost Function Expressed as:

[0104]

[0105] in, For the target state trajectory, This is the state weight matrix, used to weight the state errors of the trolley position, the length of the spreading rope, and the swing angle of the spreading device. The input weight matrix is ​​used to control the energy used to penalize the acceleration of the trolley and the acceleration of the sling rope.

[0106] Third, the server incorporates safety and operational smoothness as constraints based on the actual operating environment of the spreader. These constraints include the following hard constraints: the vertical height of the spreader is always greater than or equal to the dynamic safe height; the trolley speed and acceleration do not exceed the control boundaries; and the rate of change of the spreader rope length and its acceleration meet the set ranges. Additionally, the server imposes the following soft constraints: the trolley speed, the rate of change of the spreader rope length, and the rate of change of the spreader swing angle converge to zero in the prediction terminal stage, achieving endpoint stability.

[0107] The above constraints are expressed in the following form: Dynamic safety height constraints:

[0108]

[0109] in, For the lifting gear in the Vertical height of the step For the horizontal position The corresponding dynamic safety height function.

[0110] Control boundary constraints: ;

[0111] Soft constraints (weighted penalties) for state convergence: ;

[0112] The fourth step involves inputting the constructed objective cost function and the aforementioned constraints into a constrained quadratic programming optimization algorithm. This algorithm aims to minimize the quadratic objective function while strictly satisfying linear constraints, thereby solving for the optimal control input sequence.

[0113]

[0114] in This is the concatenated control input vector. and These are the cost function coefficient matrices derived from the weight matrix. and These are the constraint coefficient matrix and boundary vectors obtained by linearizing the constraint conditions.

[0115] Finally, the server extracts the trolley acceleration and spreader rope length acceleration corresponding to the first control step in the optimization sequence as the control input for the current cycle, and applies them to the actual bridge gantry crane system to achieve refined spreader trajectory control and obstacle avoidance path guidance. The state is acquired again in the next cycle and iteratively updated, forming a complete rolling optimization closed-loop control mechanism.

[0116] The formulas involved in the aforementioned optimized control embody the multi-objective coordinated optimization logic of the multi-stage model predictive control strategy for the gantry crane's spreader regarding path safety, attitude stability, and control input energy consumption. Its core calculation process can be described as follows: First, based on the known trolley position, trolley speed, spreader rope length, rate of change of the spreader rope length, spreader swing angle, and rate of change of the spreader swing angle within the current period, the server uses the gantry crane's dynamic model to perform step-by-step recursive calculations within a future prediction time domain, obtaining a state prediction sequence for several future moments. This state prediction sequence reflects the possible state trajectory of the system under different combinations of control inputs and forms the basis for subsequent optimization.

[0117] Secondly, the server measures the deviation between the predicted state value and the corresponding target state at each moment, and calculates the total cost of the overall state error during the prediction process by combining the state weight matrix. Simultaneously, it penalizes the amplitude of the trolley acceleration and the spreader rope acceleration during the prediction process, weighted by the control input weight matrix, to calculate the energy consumption cost generated by the control signals. These two factors together form the target cost function, which essentially evaluates the "cost" incurred by different control input sequences in achieving the system objective. Thirdly, the server introduces mandatory constraints, or "hard constraints," during the construction of the optimization problem. For example, the vertical height of the spreader must always be higher than the dynamic safety height, and the trolley motion parameters must be limited by physical boundaries, ensuring that the spreader will not cause actual collisions or loss of control due to misoperation or model errors during operation. Furthermore, "soft constraints" are introduced, such as guiding the trolley speed, the rate of change of the spreader rope length, and the rate of change of the spreader swing angle to gradually converge to zero at the prediction endpoint. This strategy can achieve stable attitude convergence while maintaining trajectory accuracy, improving the endpoint positioning quality.

[0118] Finally, the server constructs the aforementioned cost function and all constraints into a standard constrained quadratic programming optimization problem. By solving the problem, the optimal control input sequence for multiple future control cycles is obtained. The first control input command of the sequence is then applied to the current control cycle, and the next round of prediction-optimization-execution rolling control process begins.

[0119] In summary, the above technical solution can simultaneously address path accuracy, attitude suppression, and energy consumption control, thereby improving the overall system performance. Embedding a dynamic safety height model in the constraints guides the spreader's swing angle convergence while achieving obstacle avoidance, enhancing path safety. The model predictive controller dynamically generates the optimal control scheme based on state updates, exhibiting strong robustness and environmental adaptability. Weighted penalties on acceleration inputs effectively suppress excessive system responses, reducing structural impact and energy loss. The introduction of soft constraints guides final-state convergence, improving the attitude convergence and positioning accuracy of the end-effector, which is beneficial for subsequent operational follow-up. Therefore, this computational logic not only ensures the physical feasibility and safety of the spreader path planning process but also enhances the intelligence and stability of the overall operation of the gantry crane through multi-dimensional control strategies.

[0120] In one possible implementation, a weight term is set in the state weight matrix to penalize the spreader's swing angle and the rate of change of the swing angle, and the value of the weight term is dynamically increased during the obstacle avoidance and target tracking phases to enhance the control constraint on the spreader's swing angle deviation; a weight term is set in the control input weight matrix to penalize the trolley acceleration and the spreader rope length acceleration, and the penalty intensity of the trolley acceleration is increased during the path turning interval and the terminal braking phase to smooth input changes and reduce the driving effect of the excitation source on the spreader's self-swing; a constraint condition is set in the constraint condition for the spreader's swing angle change rate to converge to zero, guiding the spreader's attitude to achieve stable convergence while completing the positioning process.

[0121] Specifically, firstly, in the cost function construction phase of the model predictive controller, the server explicitly sets the penalty for the hanger swing angle in the state weight matrix of the state error term. With the speed of change of the spreader's swing angle The weights are denoted as . Let the state weight matrix be . Then, higher weight values ​​are assigned to the diagonal positions. and These are used to measure the severity of the spreader's attitude deviating from the target attitude. The server increases these two weight values ​​in real time when entering the obstacle avoidance or target tracking phase, thereby enhancing the convergence capability of controlling the spreader's attitude change trend. In terms of expression, if the original weights are... The improved value is:

[0122]

[0123] in The control phase weighting coefficient is used to adjust the urgency of attitude control.

[0124] Secondly, in the control input weight matrix for the control input error term, the server sets the vehicle acceleration. acceleration with the length of the lifting rope The penalty term. Let the control input weight matrix be... In the prediction time domain, when a turning point occurs in the predicted path or when the target docking phase is about to begin, the server identifies that the current location is in a region of intensified dynamic change or a braking period, and accordingly improves... This term value allows the system to adjust the trolley acceleration more conservatively, thereby suppressing the self-swinging effect of the spreader caused by excessive drive. Expressed as:

[0125]

[0126] in, The adjusted control input weights, Based on weights, This is an amplification factor for local changes in the path or terminal adjustments.

[0127] Furthermore, the server incorporates a soft constraint term into the constraint model, ensuring that the rate of change of the spreader's swing angle converges to zero. Specifically, this means that at the predicted endpoint time step... Place, expectation ,Right now:

[0128]

[0129] in This is the set attitude stability tolerance threshold. This constraint can be introduced into the cost function as a penalty term, or it can be transformed into a soft constraint by slack variables, thereby guiding the dynamic convergence characteristics of the system's terminal phase.

[0130] In summary, the server identifies the operational stage and path characteristics of the gantry crane spreader in real time, dynamically adjusts key coefficients in the state weight matrix and control input weight matrix based on the system's current state, and supplements this with endpoint attitude convergence constraints. This synergistically enhances the control capability of the spreader's swing angle, preventing continuous swaying caused by inertial disturbances, thereby improving the control stability, safety, and end-point positioning accuracy in spreader path planning. This dynamic adjustment and multi-constraint fusion approach gives the model predictive controller stronger environmental adaptability and operational robustness.

[0131] This application also provides a bridge crane spreader control device based on multi-stage model predictive control, with reference to... Figure 2 , Figure 2 This is a schematic diagram of a bridge crane spreader control device based on multi-stage model predictive control provided in an embodiment of this application. The device is a server, comprising an acquisition module 21 and a processing module 22. The acquisition module 21 acquires the control parameters of the gantry crane and determines the initial state variables. The control parameters and initial state variables define the operating boundaries and initial state of the gantry crane. The processing module 22 establishes a dynamic model of the gantry crane based on the control parameters and initial state variables. The dynamic model includes the trolley acceleration equation, the spreader rope length acceleration equation, and the spreader swing angle coupling equation. The processing module 22 acquires obstacle distribution information of the spreader within the working area through a spatial sensing device and calculates the dynamic safe height of the spreader by combining obstacle parameters and control parameters. Based on the initial state variables and dynamic safe height, the processing module 22 determines the control stage of the spreader, which includes an obstacle avoidance stage and a target tracking stage. The processing module 22 constructs a model predictive controller and controls the model predictive controller to dynamically adjust the state weight matrix and control input weight matrix according to the control stage. Based on the state weight matrix, control input weight matrix, and constraints of the spreader, the processing module 22 generates a control input sequence for the spreader through an optimization algorithm.

[0132] In one possible implementation, a dynamic model of the gantry crane is established based on control parameters and initial state variables. Specifically, the processing module 22 sets the initial state variables as trolley position information, trolley speed information, spreader rope length information, spreader rope length change rate information, spreader swing angle information, and spreader swing angle change rate information, and sets the control input variables as trolley acceleration information and spreader rope length acceleration information. Based on the mass parameter, gravity constant, operating boundary conditions, and damping parameter in the control parameters, and combined with the initial state variables and control input variables, the processing module 22 constructs a trolley acceleration equation to describe the horizontal motion of the trolley, a spreader rope length acceleration equation to describe the change of the spreader rope length, and a coupled equation to describe the dynamic change of the spreader swing angle under the combined action of trolley acceleration, trolley position, and spreader rope length.

[0133] In one possible implementation, the processing module 22 acquires obstacle distribution information of the spreader within the working area through a spatial sensing device, and calculates the dynamic safe height of the spreader by combining obstacle parameters and control parameters. Specifically, the processing module 22 uses a spatial sensing device deployed on the gantry crane to collect three-dimensional environmental data within the corresponding working area of ​​the gantry crane. The three-dimensional environmental data includes the horizontal position parameters and vertical height parameters of the obstacles. The processing module 22 constructs an obstacle height mapping function based on the three-dimensional environmental data. The obstacle height mapping function describes the maximum height of obstacles corresponding to different horizontal positions. The processing module 22 combines the obstacle height mapping function, sensor error tolerance, obstacle area expansion factor, and system equivalent friction factor in the control parameters to construct a dynamic safe height model. The processing module 22 outputs the dynamic safe height based on the dynamic safe height model.

[0134] In one possible implementation, the control stage of the spreading device is determined based on the initial state variables and the dynamic safety height. Specifically, the processing module 22 generates a horizontal position sequence and a corresponding vertical height sequence of the spreading device within a preset prediction time domain based on the initial state variables and through a dynamic model. The processing module 22 extracts the corresponding dynamic safety height sequence from the dynamic safety height model based on the horizontal position sequence and compares the vertical height sequence with the dynamic safety height sequence one by one to obtain a comparison result. If the comparison result indicates that any height value in the vertical height sequence is lower than the corresponding dynamic safety height value, the processing module 22 determines that the control stage is the obstacle avoidance stage; otherwise, it determines that the control stage is the target tracking stage.

[0135] In one possible implementation, the processing module 22 constructs a model predictive controller and controls the model predictive controller to dynamically adjust the state weight matrix and the control input weight matrix according to the control phase. Specifically, the processing module 22 sets state error cost terms and control input cost terms in the model predictive controller. The state error cost terms are calculated by weighting the deviation costs of the trolley position, the length of the lifting rope, and the swing angle of the lifting rope relative to the target state using the state weight matrix. The control input cost terms are calculated by weighting the magnitude costs of the trolley acceleration and the acceleration of the lifting rope using the control input weight matrix. The processing module 22 adjusts the state error cost terms and control input cost terms according to the control input weight matrix. If the control phase is determined to be an obstacle avoidance phase, the weight coefficients of the corresponding items for the sling swing angle and sling rope length in the state weight matrix are increased, the weight coefficients of the corresponding items for the trolley position are decreased, the weight coefficients of the sling rope length acceleration in the control input weight matrix are decreased, and the weight coefficients of the trolley acceleration are increased. If the control phase is determined to be a target tracking phase, the processing module 22 increases the terminal state weight coefficients of the trolley position and sling swing angle in the state weight matrix, decreases the weight coefficients of the corresponding items for the sling rope length, decreases the weight coefficients of the trolley acceleration in the control input weight matrix, and increases the weight coefficients of the sling rope length acceleration.

[0136] In one possible implementation, processing module 22 generates a control input sequence for the spreader based on the state weight matrix, control input weight matrix, and constraints of the spreader using an optimization algorithm. Specifically, processing module 22 recursively generates state trajectories of trolley position, trolley speed, spreader rope length, spreader rope length change rate, spreader swing angle, and spreader swing angle change rate within a preset prediction time domain based on the dynamic model and initial state variables. Processing module 22 adds hard constraints, including the spreader vertical height not being lower than the dynamic safety height, trolley speed and acceleration not exceeding control boundaries, and spreader rope length change rate and acceleration being limited, as well as soft constraints, to ensure that the trolley speed, spreader rope length change rate, and spreader swing angle change rate converge to zero, based on the state weight matrix and control input weight matrix, to obtain a target cost function. Processing module 22 inputs the target cost function and constraints into a constrained quadratic programming optimization algorithm to solve for the optimal trolley acceleration sequence and spreader rope length acceleration sequence within the target prediction time domain, thereby obtaining the control input sequence.

[0137] In one possible implementation, the processing module 22 sets weights in the state weight matrix for penalizing the spreader swing angle and the rate of change of the spreader swing angle, and dynamically increases the value of the weights during the obstacle avoidance and target tracking phases to enhance the control constraints on the spreader swing angle deviation; the processing module 22 sets weights in the control input weight matrix for penalizing the trolley acceleration and the spreader rope length acceleration, and increases the penalty intensity of the trolley acceleration during the path turning interval and the terminal braking phase to smooth input changes and reduce the driving effect of the excitation source on the spreader's self-swing; the processing module 22 sets a constraint condition in the constraint conditions for the spreader swing angle change rate to converge to zero, guiding the spreader attitude to achieve stable convergence while completing the positioning process.

[0138] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0139] This application also provides an electronic device, with reference to... Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 31, at least one network interface 34, a user interface 33, a memory 35, and at least one communication bus 32.

[0140] The communication bus 32 is used to enable communication between these components.

[0141] The user interface 33 may include a display screen, and optionally, the user interface 33 may also include a standard wired interface or a wireless interface.

[0142] The network interface 34 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0143] The processor 31 may include one or more processing cores. The processor 31 connects to various parts of the server via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in the memory 35, and calling data stored in the memory 35 to perform various server functions and process data. Optionally, the processor 31 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 31 and may be implemented as a separate chip.

[0144] The memory 35 may include random access memory (RAM) or read-only memory. Optionally, the memory 35 may include a non-transitory computer-readable storage medium. The memory 35 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 35 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 35 may also be at least one storage device located remotely from the aforementioned processor 31. Figure 3 As shown, the memory 35, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a bridge crane spreader control method based on multi-stage model predictive control.

[0145] exist Figure 3In the electronic device shown, the user interface 33 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 31 can be used to call the application program stored in the memory 35 that is based on the control method of the bridge crane spreader based on multi-stage model predictive control. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0146] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0147] This application also provides a computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0148] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0153] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A control method for the spreader of a bridge crane based on multi-stage model predictive control, characterized in that, The method includes: The control parameters of the bridge gantry crane are obtained, and the initial state variables are determined. The control parameters and the initial state variables are used to define the operating boundary and initial state of the bridge gantry crane. Based on the control parameters and the initial state variables, a dynamic model of the bridge gantry crane is established. The dynamic model includes the trolley acceleration equation, the spreader rope length acceleration equation, and the spreader swing angle coupling equation. The obstacle distribution information of the lifting device in the working area is obtained by spatial sensing device, and the dynamic safe height of the lifting device is calculated by combining the obstacle parameters and the control parameters. Based on the initial state variables and the dynamic safety height, the control phase of the spreading device is determined, and the control phase includes an obstacle avoidance phase and a target tracking phase. Construct a model predictive controller and control the model predictive controller to dynamically adjust the state weight matrix and the control input weight matrix according to the control stage; Based on the state weight matrix, the control input weight matrix, and the constraints of the spreader, an optimization algorithm is used to generate a control input sequence for the spreader. The establishment of the dynamic model of the bridge gantry crane based on the control parameters and the initial state variables specifically includes: The initial state variables are set as trolley position information, trolley speed information, spreader rope length information, spreader rope length change rate information, spreader swing angle information, and spreader swing angle change rate information. The control input variables are set as trolley acceleration information and spreader rope length acceleration information. Based on the mass parameter, gravity constant, operating boundary conditions, and damping parameter in the control parameters, and combined with the initial state variable and the control input variable, the following equations are constructed: the trolley acceleration equation for describing the horizontal motion of the trolley, the spreader rope length acceleration equation for describing the change in the spreader rope length, and the spreader swing angle coupling equation for coupling and describing the dynamic change in the spreader swing angle under the combined action of the trolley acceleration, trolley position, and spreader rope length. The step of acquiring obstacle distribution information of the lifting device within the working area through spatial sensing equipment, and calculating the dynamic safe height of the lifting device by combining obstacle parameters with the control parameters, specifically includes: The spatial sensing device deployed on the gantry crane collects three-dimensional environmental data within the corresponding working area of ​​the gantry crane. The three-dimensional environmental data includes the horizontal position parameters and vertical height parameters of obstacles. An obstacle height mapping function is constructed based on the three-dimensional environmental data. The obstacle height mapping function is used to describe the maximum height of obstacles corresponding to different horizontal positions. By combining the obstacle height mapping function, the sensor error tolerance, the obstacle area expansion factor, and the system equivalent friction factor in the control parameters, a dynamic safety height model is constructed. Based on the dynamic safety height model, the dynamic safety height is output.

2. The bridge crane spreader control method based on multi-stage model predictive control according to claim 1, characterized in that, The step of determining the control stage of the spreading device based on the initial state variables and the dynamic safety height specifically includes: Based on the initial state variables, the dynamic model generates the horizontal position sequence and the corresponding vertical height sequence of the lifting device within a preset prediction time domain. Based on the horizontal position sequence, the corresponding dynamic safety height sequence is extracted from the dynamic safety height model, and the vertical height sequence is compared with the dynamic safety height sequence one by one to obtain the comparison result. If the comparison result indicates that any height value in the vertical height sequence is lower than the corresponding dynamic safe height value, then the control phase is determined to be the obstacle avoidance phase; otherwise, it is determined to be the target tracking phase.

3. The bridge crane spreader control method based on multi-stage model predictive control according to claim 1, characterized in that, The construction of the model predictive controller, and the control of the model predictive controller to dynamically adjust the state weight matrix and the control input weight matrix according to the control phase, specifically includes: In the model predictive controller, a state error cost term and a control input cost term are set. The state error cost term is calculated by weighting the deviation cost of the trolley position, the length of the sling rope, and the swing angle of the sling relative to the target state through the state weight matrix. The control input cost term is calculated by weighting the magnitude cost of the trolley acceleration and the acceleration of the sling rope through the control input weight matrix. Based on the state error cost term and the control input cost term, if the control phase is determined to be the obstacle avoidance phase, then the weight coefficients of the corresponding terms of the sling swing angle and sling rope length in the state weight matrix are increased, the weight coefficients of the corresponding terms of the trolley position are decreased, and the weight coefficients of the sling rope length acceleration in the control input weight matrix are decreased, while the weight coefficients of the trolley acceleration are increased. If the control phase is determined to be the target tracking phase, then the terminal state weight coefficients of the trolley position and the spreader swing angle in the state weight matrix are increased, the weight coefficient of the corresponding item of the spreader rope length is decreased, the weight coefficient of the trolley acceleration in the control input weight matrix is ​​decreased, and the weight coefficient of the spreader rope length acceleration is increased.

4. The bridge crane spreader control method based on multi-stage model predictive control according to claim 2, characterized in that, The step of generating a control input sequence for the spreader based on the state weight matrix, the control input weight matrix, and the constraints of the spreader using an optimization algorithm specifically includes: Based on the dynamic model and the initial state variables, the state trajectories of the trolley position, trolley speed, lifting rope length, lifting rope length change rate, lifting swing angle and lifting swing angle change rate are recursively generated within the preset prediction time domain. Based on the state weight matrix and the control input weight matrix, hard constraints are added, including the vertical height of the spreader not being lower than the dynamic safety height, the speed and acceleration of the trolley not exceeding the control boundary, and the speed and acceleration of the spreader rope length change being limited, as well as soft constraints, the speed of the trolley speed, the speed of the spreader rope length change, and the speed of the spreader swing angle change converging to zero, to obtain the target cost function. The objective cost function and the constraints are input into a constrained quadratic programming optimization algorithm to solve for the optimal trolley acceleration sequence and the sling rope length acceleration sequence in the target prediction time domain, thereby obtaining the control input sequence.

5. The bridge crane spreader control method based on multi-stage model predictive control according to claim 1, characterized in that, The method further includes: In the state weight matrix, a weight term is set to penalize the spreader swing angle and the rate of change of the spreader swing angle, and the value of the weight term is dynamically increased in the obstacle avoidance stage and the target tracking stage to enhance the control constraint on the spreader swing angle deviation. In the control input weight matrix, weight terms are set for penalizing the trolley acceleration and the spreader rope length acceleration, and the penalty intensity of the trolley acceleration is increased in the path turning section and the terminal braking stage to smooth the input changes and reduce the driving effect of the excitation source on the spreader's self-swing. The constraint condition is set in the constraint condition that the speed of change of the spreader's swing angle converges to zero, so as to guide the spreader's attitude to achieve stable convergence while completing the positioning process.

6. A bridge crane spreader control device based on multi-stage model predictive control, the device being used to execute the bridge crane spreader control method based on multi-stage model predictive control as described in any one of claims 1 to 5, characterized in that, The device includes an acquisition module (21) and a processing module (22), wherein, The acquisition module (21) is used to acquire the control parameters of the bridge gantry crane and determine the initial state variables. The control parameters and the initial state variables are used to define the operating boundary and initial state of the bridge gantry crane. The processing module (22) is used to establish a dynamic model of the bridge gantry crane based on the control parameters and the initial state variables. The dynamic model includes the trolley acceleration equation, the spreader rope length acceleration equation, and the spreader swing angle coupling equation. The acquisition module (21) is also used to acquire obstacle distribution information of the lifting device in the working area through the spatial sensing device, and calculate the dynamic safety height of the lifting device by combining the obstacle parameters and the control parameters; The processing module (22) is also used to determine the control stage of the spreader based on the initial state variables and the dynamic safety height, wherein the control stage includes an obstacle avoidance stage and a target tracking stage; The processing module (22) is also used to construct a model prediction controller and control the model prediction controller to dynamically adjust the state weight matrix and the control input weight matrix according to the control stage; The processing module (22) is further configured to generate a control input sequence for the spreader based on the state weight matrix, the control input weight matrix and the constraints of the spreader, using an optimization algorithm.

7. An electronic device, characterized in that, The electronic device includes a processor (31), a memory (35), a user interface (33), and a network interface (34). The memory (35) is used to store instructions. The user interface (33) and the network interface (34) are both used to communicate with other devices. The processor (31) is used to execute the instructions stored in the memory (35) to cause the electronic device to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 5.

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