Intelligent control method and system for tower crane

Abstract

The application provides a tower crane intelligent control method and system, and relates to the technical field of mechanical control. The method comprises the following steps: establishing a motion equation of a tower crane; determining a state vector and a control vector of the tower crane in a state space in combination with the motion equation, and determining a state space motion equation based on the state vector and the control vector; determining a cost function comprising a load swing term and a load execution duration term based on the state space motion equation; determining adaptive weighting values of the load swing term and the load execution duration term in the cost function in combination with Lyapunov theorem; optimizing the cost function according to the adaptive weighting values; solving the cost function to obtain a target control vector by taking the minimized optimized cost function as a target; and controlling the tower crane according to the target control vector. The method improves work efficiency and reduces safety risks in operation.

Description

Technical Field

[0001] This invention relates to the field of mechanical control technology, and in particular to an intelligent control method and system for tower cranes. Background Technology

[0002] Tower cranes are common large lifting equipment widely used in construction sites, ports, and other engineering fields. They consist of a tower, boom, hoisting mechanism, and slewing mechanism, and can lift materials horizontally and vertically. Due to their high efficiency and long working radius, tower cranes are suitable for use in high-rise buildings and complex engineering environments.

[0003] The importance of tower cranes in the modern construction industry is reflected in several aspects. Firstly, tower cranes possess extremely high lifting capacity, enabling the transport of heavy building materials such as steel, cement, and concrete from the ground to higher levels of the building, making them indispensable equipment for constructing high-rise buildings. Secondly, tower cranes have a long working radius, allowing them to cover the entire construction site without moving equipment, greatly improving construction efficiency. They can precisely control material transport on construction sites with limited space, reducing manpower input and lowering labor costs. Therefore, they play an irreplaceable role in large-scale construction projects.

[0004] However, traditional tower cranes need to perform single-degree-of-freedom actions one by one (such as lifting only, rotating only, or moving only), resulting in redundant operation time. When performing multi-degree-of-freedom operations, it is very easy to cause large load swings, which increases safety risks and severely limits the working efficiency of tower cranes. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide an intelligent control method for tower cranes, which can solve the technical problems of traditional tower cranes needing to perform single-degree-of-freedom actions (such as lifting only, rotating only, or moving only) one after another, resulting in redundant operation time. At the same time, when performing multi-degree-of-freedom operations, it is easy to cause large load swings, which increases safety risks and seriously limits the working efficiency of tower cranes.

[0006] A first aspect of this invention provides an intelligent control method for tower cranes, comprising:

[0007] S1: Establish the motion equations of the tower crane;

[0008] S2: Combine the motion equations to determine the state vector and control vector of the tower crane in the state space, and determine the state space motion equations based on the state vectors and control vectors;

[0009] S3: Based on the state-space motion equation, determine the cost function including the load swing term and the load execution time term, wherein the load execution time is specifically the total time consumed for the load to move from the initial position to the target position;

[0010] S4: Determine the adaptive weighting values ​​of the load swing term and the load execution duration term in the cost function using Lyapunov's theorem;

[0011] S5: Optimize the cost function based on the adaptive weighting value;

[0012] S6: Solve the cost function with the objective of minimizing the function value of the optimized cost function to obtain the target control vector;

[0013] S7: Control the tower crane according to the target control vector.

[0014] A second aspect of the present invention provides an intelligent control system for a tower crane, comprising: a processor and a memory;

[0015] The memory stores programs or instructions that can run on the processor, which, when executed by the processor, implement the steps of the intelligent control method for tower cranes as described in the first aspect.

[0016] A third aspect of the present invention provides a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the intelligent control method for tower cranes as described in the first aspect.

[0017] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0018] In this embodiment of the invention, by establishing the motion equations of a tower crane and combining them with state-space analysis methods, the motion state and control inputs of the tower crane can be comprehensively described. The method incorporates the load sway and execution time of the tower crane into the cost function. The load sway term reflects the dangers and instabilities caused by boom swaying during hoisting, while the load execution time term involves the time consumed for the load to move from its initial position to its target position. By simultaneously optimizing load stability and operational efficiency, the tower crane can maintain load stability while reducing ineffective waiting time and unnecessary swaying during multi-degree-of-freedom motion control, thereby improving operational efficiency and reducing safety risks during operation. By introducing Lyapunov's theorem to calculate adaptive weighted values, the weights of these two factors in the cost function can be dynamically adjusted according to the current working conditions, ensuring that load swaying is suppressed and operation time is optimized during control. By dynamically adjusting control parameters, this method can effectively improve response speed and operational accuracy under different working conditions, thereby significantly improving the working performance of the tower crane and further enhancing operational efficiency and safety. Attached Figure Description

[0019] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0020] Figure 1 This is a flowchart illustrating an intelligent control method for a tower crane provided in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the structure of an intelligent control system for a tower crane provided in an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] The intelligent control method for tower cranes provided by the present invention will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0024] Reference manual attached Figure 1 The diagram shows a flowchart of an intelligent control method for a tower crane provided by an embodiment of the present invention.

[0025] This invention provides an intelligent control method for tower cranes, which may include the following steps:

[0026] S1: Establish the motion equations of the tower crane;

[0027] The equations of motion refer to the equations used to describe the motion state and mechanical behavior of the various moving parts of a tower crane. By establishing the equations of motion, the dynamic characteristics of the tower crane can be comprehensively described, ensuring that the system can accurately predict and respond to complex operational demands, and improving control precision and stability.

[0028] In one possible implementation, the tower crane includes a mast, a boom with a moving trolley, and a load, wherein the boom and the load are connected by cables.

[0029] In one possible implementation, S1 specifically includes:

[0030] S101: Establish a first coordinate system with the mobile trolley as the origin, wherein the x-axis of the first coordinate system is collinear with the crane arm, the z-axis of the first coordinate system is parallel to the mast, and the y-axis of the first coordinate system is perpendicular to the x-axis of the first coordinate system.

[0031] S102: Establish a second coordinate system with the intersection of the mast and the ground as the origin, wherein the z-axis of the second coordinate system is collinear with the mast, the x-axis of the second coordinate system is collinear with the initial state of the crane boom, and the y-axis of the second coordinate system is perpendicular to the first coordinate system;

[0032] S103: Obtain the boom rotation angle in the second coordinate system; obtain the interval distance between the moving trolley and the mast, the cable length between the moving trolley and the load, the cable angle between the cable and the cable projection line in the first xz axis plane, and the z-axis angle between the z-axis of the first coordinate system and the projection line in the first coordinate system, wherein the first xz axis plane is specifically the plane containing the x-axis and z-axis of the first coordinate system;

[0033] S104: Establish the motion equations while ignoring the cable angle, the rate of change of the cable angle, the z-axis angle, and the rate of change of the z-axis angle.

[0034] It should be noted that the modeling process precisely defines two coordinate systems for the tower crane, allowing the motion of each component to be described within a unified mathematical framework. First, establishing coordinate systems aligned with the boom and mast clarifies the motion analysis. Then, parameters such as the boom's rotation angle, the distance between the trolley and the mast, and the load are acquired to accurately capture the tower crane's motion state. During this process, the effects of certain high-frequency, small-angle changes are ignored to simplify calculations and ensure the computational efficiency and operability of the motion equations. The advantage of this modeling method is its ability to transform complex multi-degree-of-freedom motion into a clear mathematical description, providing a reliable basis for subsequent control and optimization, reducing redundant calculations, and improving the model's practicality and accuracy.

[0035] The equation of motion is specifically as follows:

[0036]

[0037]

[0038] in, and Let R represent the first and second derivatives of the interval distance R, respectively. and Let the first and second derivatives of the crane boom rotation angle ψ be represented, respectively. and Let these represent the first and second derivatives of the cable angle θ, respectively. and Let represent the first and second derivatives of the z-axis angle φ, respectively. and U1 represents the first and second derivatives of the cable length r, respectively; g represents the gravitational acceleration; u1 represents the horizontal displacement control signal of the trolley; u2 represents the crane boom rotation angle control signal; and u3 represents the cable length control signal.

[0039] It should be noted that this equation of motion accurately describes the system's motion state by considering multiple key variables of the tower crane and their dynamic relationships, including trolley displacement, boom rotation, cable length, and angle changes. The second derivatives of each variable reflect the influence of acceleration, and control signals (such as trolley displacement and boom rotation) can directly affect these variables, thereby achieving precise control and optimization. By comprehensively considering all dynamic factors, it provides a precise mathematical basis for subsequent control strategies and optimization, improving the stability, efficiency, and safety of tower crane operation.

[0040] S2: Combine the motion equations to determine the state vector and control vector of the tower crane in the state space, and determine the state space motion equations based on the state vectors and control vectors;

[0041] In this system, the state space is a mathematical model used to describe all possible states of the system. For a tower crane, it includes all the important physical quantities, such as the boom angle, load displacement, velocity, and acceleration, all of which are used to describe the system's state. The state vector contains all the system's state variables, such as the tower crane's degrees of freedom (e.g., boom angle, load position). The state vector describes all the internal states of the system at a given moment. The control vector is a vector containing control inputs, such as applied forces, torques, and driving forces, which influence the tower crane's motion and behavior. The state-space equations of motion are mathematical equations describing the system's dynamic behavior, showing how the system changes over time and is influenced by the control vectors. They calculate the system's evolution at each moment based on the state and control vectors.

[0042] Understandably, transforming the dynamic model of a tower crane into a state-space representation allows for a clearer and more concise representation of the system's behavior, facilitating control analysis and optimization. This approach enables more precise and adaptable controller design, capable of handling complex system dynamics and multi-degree-of-freedom control tasks.

[0043] In one possible implementation, the state vector includes the interval distance at time t, the first derivative of the interval distance, the boom rotation angle, the first derivative of the boom rotation angle, the cable length, the first derivative of the cable length, the cable angle, the first derivative of the cable angle, the z-axis angle, and the first derivative of the z-axis angle.

[0044] The specific formula for the state vector is as follows:

[0045]

[0046] Where x(t) represents the state vector at time t, and the subscript T indicates transpose.

[0047] The control vector includes the horizontal displacement control signal at time t, the boom rotation angle control signal, and the cable length control signal.

[0048] The formula for the control vector is as follows:

[0049] u(t)=(u1(t),u2(t),u3(t)) T

[0050] Where u(t) represents the control vector at time t.

[0051] The state-space motion equations are specifically as follows:

[0052]

[0053] in, Let f(x(t),u(t)) denote the first derivative of x(t), and let f(x(t),u(t)) denote the functional relationship between x(t) and u(t).

[0054] Specifically, the functional relationships, or mapping relationships, of the parameters in the state-space motion equations are as follows:

[0055]

[0056] Where i represents the i-th parameter in the state vector, and t is omitted from the parameter labels. In the specific equation, f i Specifically, it should be f i The function variable in parentheses () is omitted.

[0057] It should be noted that the state-space motion equations, by comprehensively considering all motion states of the tower crane and their derivatives, accurately model the relationship between various dynamic parameters of the system (such as interval distance, boom angle, cable length and angle, etc.) and control signals (such as trolley horizontal displacement, boom rotation, and cable length control signals). This state-space representation clearly describes the dynamic changes of the system and provides a precise mathematical basis for control strategies. The equations can reflect the system's state changes in real time, and through fine-tuning of control inputs, achieve efficient and stable operation of the tower crane. Furthermore, the state-space form gives the model high versatility and adaptability, facilitating subsequent control optimization and stability analysis.

[0058] S3: Based on the state-space motion equation, determine the cost function including the load swing term and the load execution duration term;

[0059] Specifically, the load execution time is the total time consumed for the load to move from its initial position to its target position;

[0060] The load sway term refers to the swaying of the load during tower crane operation due to the movement of the load or jib. This swaying can lead to operational instability and increase safety hazards. Therefore, this term quantifies the degree of load swaying, and controlling its amplitude is part of the optimization control. The load execution time term refers to the time taken from the start of the load moving to the target position. This time affects work efficiency; an excessively long execution time means low work efficiency and increases the overall construction time. The cost function is an objective function used to measure system performance. In this step, the cost function includes the load sway term and the load execution time term. The objective is to minimize this cost function, that is, to reduce the load movement time while ensuring load stability, thereby improving the operating efficiency of the tower crane.

[0061] It should be noted that by incorporating load sway and execution time into the cost function, the stability and efficiency of the tower crane can be optimized simultaneously. This avoids system imbalance caused by simply optimizing one objective, ensuring efficient operation while improving operational safety and accuracy. This comprehensive optimization method significantly improves the overall performance of the tower crane.

[0062] In one possible implementation, S3 specifically includes:

[0063] S301: Establish an initial cost function including a load sway term and a load execution duration term, wherein the load sway term is related to the cable length, the first derivative of the cable length, the cable angle, the first derivative of the cable angle, the z-axis angle, and the first derivative of the z-axis angle;

[0064] S302: Determine the load swing term based on the state-space motion equation;

[0065] The load swing term specifically refers to:

[0066]

[0067] S303: Substitute the load swing term into the initial cost function to obtain the cost function.

[0068] The cost function is specifically:

[0069]

[0070] a+b=1

[0071] Where J represents the cost function value, t0 represents the time corresponding to the load being at its initial position, and t fThis indicates the time when the load is at the target position, 'a' represents the weighted value of the load execution duration, 'b' represents the weighted value of the load oscillation term, and x3(t), x4(t), x5(t), x9(t), and x 10 (t) represents the time t values ​​of the 3rd, 4th, 9th, and 10th parameters in the state vector, respectively.

[0072] It's important to note that by establishing a cost function that includes load sway and execution time terms, two key factors in tower crane operation can be comprehensively considered: load stability and operational efficiency. The load sway term, by introducing variables related to cable length, angle changes, and the z-axis angle, accurately describes the degree of load sway, effectively limiting instability and safety hazards during crane operation. The execution time term reflects the time it takes for the load to travel from its initial position to its target position; optimizing this term helps improve operational efficiency. By dynamically adjusting the weighted values ​​of the load sway and execution time terms, the control strategy can be optimized based on real-time operating conditions, thereby minimizing operation time while ensuring stability. This cost function design allows the control system to flexibly balance safety and efficiency, improving the overall performance of the tower crane.

[0073] S4: Determine the adaptive weighting values ​​of the load swing term and the load execution duration term in the cost function using Lyapunov's theorem;

[0074] Lyapunov's theorem is a mathematical tool used to prove the stability of dynamic systems. Adaptive weighting refers to dynamically adjusting the weights of various objectives (load fluctuations and execution duration) in the cost function based on the real-time state of the system during the control process.

[0075] It should be noted that by combining Lyapunov's theorem with the calculation of adaptive weighting, the priority of different objectives can be dynamically adjusted according to the system state, thereby ensuring system stability and optimizing operational efficiency.

[0076] In one possible implementation, S4 specifically includes:

[0077] S401: Construct a Lyapunov function for the coupled load swing energy and time penalty;

[0078] The Lyapunov function is specifically:

[0079]

[0080] Among them, x3(t), x4(t), x5(t), x9(t) and x 10(t) represent the values ​​of the 3rd, 4th, 9th and 10th parameters in the state vector at time t, respectively; ρ represents the coupling coefficient; α represents the nonlinear decay exponent with a value greater than 2; and V(t) represents the Lyapunov function value at time t.

[0081] Optionally, the coupling coefficient can be 0.3 or 0.7. A value of 0.3 results in low load inertia (naturally low sway energy) under light loads, necessitating time reduction for long-distance operations. A value of 0.7 results in high load inertia (naturally high sway energy) under heavy loads, requiring priority for sway mitigation and collision prevention for short-distance operations. Additionally, the power-law exponent can be 3 or 5.

[0082] S402: Determine the derivative conditions of Lyapunov functions based on the finite-time stability theory;

[0083] The derivative condition is specifically as follows:

[0084]

[0085] γ>0

[0086] 0<β<1

[0087] in, Let V(t) denote the derivative of V(t), γ denote the Lyapunov decay exponent, and β denote the nonlinear power-law exponent that controls the convergence of the Lyapunov function over a finite time period.

[0088] Optionally, the Lyapunov decay exponent can be 0.5 or 1.2. The nonlinear power law exponent can be 0.2 or 0.8.

[0089] S403: Combine the derivative conditions of the cost function and the Lyapunov function to determine the nonlinear mapping relationship between the adaptive weighted value of the load swing term and the adaptive weighted value of the load execution duration term;

[0090] The nonlinear mapping relationship is specifically as follows:

[0091]

[0092] Where, x swing denoted as intermediate variables, a` represents the adaptive weighted value of the load execution duration term, and b` represents the adaptive weighted value of the load swing term;

[0093] S404: Combining the cost function and the nonlinear mapping relationship, determine the adaptive weighting value of the load swing term and the adaptive weighting value of the load execution duration term.

[0094]

[0095] Here, K(t) represents an intermediate variable.

[0096] Specifically, by combining the Lyapunov method with an adaptive weighting strategy, the weights of load sway and execution time terms are effectively and dynamically adjusted, thereby optimizing system efficiency while ensuring the operational stability of the tower crane. The Lyapunov function ensures system stability by constructing an energy function related to load sway energy and execution time penalty, guaranteeing system convergence within a finite time. When the system is in a state of large sway, the weight of the load sway term (i.e., the value of b) is automatically increased to control sway and prevent system instability; while when the system is close to stability, the weight of the execution time term (i.e., the value of a) is increased to optimize operational efficiency and reduce unnecessary time consumption. By designing a time-varying weighting strategy, the system can adaptively balance stability and efficiency, significantly improving the operational performance and safety of the tower crane while reducing energy waste and operation time.

[0097] S5: Optimize the cost function based on the adaptive weighting value;

[0098] In one possible implementation, S5 specifically refers to:

[0099] The adaptive weighted value is substituted into the cost function to complete the optimization of the cost function.

[0100] S6: Solve the cost function with the objective of minimizing the function value of the optimized cost function to obtain the target control vector;

[0101] It should be noted that by minimizing the optimized cost function, the optimal control vector can be accurately solved, thereby minimizing load sway and execution time while ensuring system stability. This method ensures the high efficiency and safety of tower crane operation, enabling it to make optimal responses in dynamic environments and improving overall work efficiency and accuracy.

[0102] S7: Control the tower crane according to the target control vector.

[0103] In one possible implementation, after S7, the following is further included:

[0104] Real-time calculation of load swing value and load execution duration value;

[0105] If the load swing term value is greater than the preset load swing term value or the load execution duration term value is greater than the preset load execution duration term value, the preset load swing term value and the preset load execution duration term value are used as constraint terms of the optimized cost function, and the target control vector is solved again.

[0106] Understandably, by monitoring the values ​​of load sway and load execution duration in real time and comparing them with preset thresholds, the optimization strategy is adjusted and optimized in real time during the control process. When the system's sway or execution duration exceeds the preset range, the control vector is re-optimized using constraint terms, thereby ensuring the system's stability and efficiency. This allows for dynamic adaptation to different working conditions, avoiding safety risks or inefficient operations caused by deviations from ideal states, and improving the operational accuracy and reliability of tower cranes.

[0107] It should be noted that those skilled in the art can set the value of the preset load swing term and the value of the preset load execution duration term according to actual needs, and the present invention does not limit this.

[0108] In practical applications, this intelligent control method for tower cranes comprehensively improves their operational efficiency and safety through a series of precise mathematical modeling and optimization strategies. First, by establishing the motion equations and state-space model of the tower crane, the dynamic behavior of the system is fully described. Then, a cost function including load sway terms and load execution time terms is constructed, and the control process is optimized using Lyapunov's theorem and an adaptive weighted strategy. This allows the system to dynamically adjust weights based on real-time status, balancing stability and operational efficiency. During optimization, finite-time stability theory is incorporated to ensure stable convergence of the system. Furthermore, by monitoring load sway and execution time in real time, the control strategy is further adjusted to avoid excessive swaying or excessively long execution times. This method can dynamically adjust under different working conditions, enabling tower cranes to exhibit higher operational accuracy and adaptability in complex environments, effectively improving operational efficiency and safety while reducing energy consumption and operating time.

[0109] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0110] In this embodiment of the invention, by establishing the motion equations of a tower crane and combining them with state-space analysis methods, the motion state and control inputs of the tower crane can be comprehensively described. The method incorporates the load sway and execution time of the tower crane into the cost function. The load sway term reflects the dangers and instabilities caused by boom swaying during hoisting, while the load execution time term involves the time consumed for the load to move from its initial position to its target position. By simultaneously optimizing load stability and operational efficiency, the tower crane can maintain load stability while reducing ineffective waiting time and unnecessary swaying during multi-degree-of-freedom motion control, thereby improving operational efficiency and reducing safety risks during operation. By introducing Lyapunov's theorem to calculate adaptive weighted values, the weights of these two factors in the cost function can be dynamically adjusted according to the current working conditions, ensuring that load swaying is suppressed and operation time is optimized during control. By dynamically adjusting control parameters, this method can effectively improve response speed and operational accuracy under different working conditions, thereby significantly improving the working performance of the tower crane and further enhancing operational efficiency and safety.

[0111] Reference manual attached Figure 2 The diagram shows a structural schematic of an intelligent control system for a tower crane provided in an embodiment of the present invention.

[0112] This invention provides an intelligent control system 20 for a tower crane, comprising: a processor 201 and a memory 202;

[0113] The memory 202 stores programs or instructions that can run on the processor 201. When the program or instructions are executed by the processor 201, they implement the steps of the above-described intelligent control method for tower cranes and achieve the same technical effect. To avoid repetition, the present invention will not elaborate further.

[0114] It should be understood that the processor 201 in this embodiment of the invention may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0115] It should also be understood that the memory 202 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM).

[0116] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0117] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0119] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0120] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods 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 device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0121] 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.

[0122] In addition, the functional units in the various embodiments of the present invention 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.

[0123] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium 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 described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] This invention provides a readable storage medium comprising: storing a program or instructions on the readable storage medium, wherein when the program or instructions are executed by a processor, the program or instructions implement the steps of the above-described intelligent control method for tower cranes and achieve the same technical effect. To avoid repetition, this invention will not elaborate further.

[0125] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method of intelligent control of a tower crane, characterized in that, The method comprises the following steps: S1: establishing a motion equation of the tower crane; S2: determining a state vector and a control vector of the tower crane in a state space in combination with the motion equation, and determining a state space motion equation based on the state vector and the control vector; S3: determining a cost function comprising a load swing term and a load execution time length term based on the state space motion equation, wherein the load execution time length is specifically a total consumption time length of the load moving from an initial position to a target position; S4: determining adaptive weighting values of the load swing term and the load execution time length term in the cost function respectively in combination with Lyapunov theorem; S5: optimizing the cost function according to the adaptive weighting values; S6: solving the cost function to obtain a target control vector, with the function value of the optimized cost function being minimized as the target; S7: controlling the tower crane according to the target control vector; The motion equation is specifically: ； ； ； ； ； wherein, and respectively represent a first derivative and a second derivative of the interval distance , and respectively represent a first derivative and a second derivative of the boom rotation angle , and respectively represent a first derivative and a second derivative of the cable angle , and respectively represent a first derivative and a second derivative of the z-axis angle , and respectively represent a first derivative and a second derivative of the cable length , represents the gravitational acceleration, represents the horizontal displacement control signal of the trolley, represents the boom rotation angle control signal, represents the cable length control signal; The state vector comprises the interval distance at time t, the first derivative of the interval distance, the crane boom rotation angle, the first derivative of the crane boom rotation angle, the cable length, the first derivative of the cable length, the cable angle, the first derivative of the cable angle, the z-axis angle, and the first derivative of the z-axis angle. The formula form of the state vector is specifically: ； wherein denotes the state vector at time t, the index T denotes the transpose; The control vector comprises the horizontal displacement control signal at time t, the crane boom rotation angle control signal, and the cable length control signal. The formula form of the control vector is specifically: ； wherein, denotes the control vector at time t; The state space motion equation is specifically: ； wherein represents a first derivative of represents a functional relationship with and and Specifically, the function relationship, i.e., the mapping relationship, of each parameter of the state space motion equation is specifically: ； ； ； Wherein, i represents the i-th parameter in the state vector, and each parameter omits t; The S3 specifically comprises: S301: establishing an initial cost function comprising a load swing term and a load execution time length term, wherein the load swing term is related to the cable length, the first derivative of the cable length, the cable angle, the first derivative of the cable angle, the z-axis angle, and the first derivative of the z-axis angle; S302: determining the load swing term based on the state space motion equation; The load swing term is specifically: ； S303: bringing the load swing term into the initial cost function to obtain the cost function; The cost function is specifically: ； ； wherein, represents a cost function value, represents a time when the load is located at the initial position, represents a time when the load is located at the target position, represents a weighting value of the load execution duration term, represents a weighting value of the load swing term, , , , and respectively represent the t-time value of the 3rd parameter, the 4th parameter, the 9th parameter and the 10th parameter in the state vector. The S4 specifically comprises: S401: constructing a Lyapunov function coupling load swing energy and time penalty; The Lyapunov function is specifically: ； wherein, represents a coupling coefficient, represents a non-linear decay exponent with a value greater than 2, represents the value of the Lyapunov function at time t. S402: determining a Lyapunov function derivative condition based on finite time stability theory; The derivative condition is specifically: ； ； ； wherein denotes the derivative of denotes the Lyapunov decay exponent, denotes the nonlinear power law exponent for which the control Lyapunov function converges in finite time. S403: determining a nonlinear mapping relationship between the adaptive weighting value of the load swing term and the adaptive weighting value of the load execution time length term in combination with the cost function and the Lyapunov function derivative condition; The nonlinear mapping relationship is specifically: ； ； wherein, denotes an intermediate variable, denotes an adaptive weighting value for the load execution duration term, denotes an adaptive weighting value for the load swing term; S404: determine the adaptive weighting value of the load swing term and the adaptive weighting value of the load execution duration term in combination with the cost function and the nonlinear mapping relationship; ； ； ； wherein represents an intermediate variable.

2. The tower crane intelligent control method of claim 1, wherein, The tower crane comprises a mast, a hoisting arm with a moving trolley, and a load, wherein the hoisting arm and the load are connected by a cable.

3. The tower crane intelligent control method of claim 2, wherein, The S1 specifically comprises: S101: establish a first coordinate system with the moving trolley as the origin, wherein the first coordinate system x-axis is collinear with the hoisting arm, the first coordinate system z-axis is parallel to the mast, and the first coordinate system y-axis is perpendicular to the first coordinate system x-axis; S102: establish a second coordinate system with the intersection of the mast and the ground as the origin, wherein the second coordinate system z-axis is collinear with the mast, the second coordinate system x-axis is collinear with the initial state hoisting arm, and the second coordinate system y-axis is perpendicular to the first coordinate system; S103: obtain the hoisting arm rotation angle in the second coordinate system, and obtain the interval distance between the moving trolley and the mast, the cable length between the moving trolley and the load, the cable angle between the cable and the projection line of the cable in the first xz-axis plane, and the z-axis angle between the first coordinate system z-axis and the projection line in the first coordinate system; S104: establish the motion equation by ignoring the cable angle, the cable angle change rate, the z-axis angle, and the z-axis angle change rate.

4. The tower crane intelligent control method of claim 1, wherein, The S5 specifically comprises: Substitute the adaptive weighting value into the cost function to complete the optimization of the cost function.

5. The tower crane intelligent control method of claim 1, wherein, After the S7, further comprising: Real-time calculation of the load swing term value and the load execution duration term value; In the case that the load swing term value is greater than the preset load swing term value or the load execution duration term value is greater than the preset load execution duration term value, the preset load swing term value and the preset load execution duration term value are taken as the constraint term of the optimized cost function, and the target control vector is re-solved.

6. A tower crane intelligent control system, characterized in that, Comprise: A processor and a memory; The memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the tower crane intelligent control method according to any one of claims 1 to 5.

7. A readable storage medium characterized by, The readable storage medium stores programs or instructions, and the programs or instructions are executed by the processor to implement the steps of the tower crane intelligent control method according to any one of claims 1 to 5.

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