A bridge crane control method and system

Abstract

The present invention provides a bridge crane control method and system, which relate to the technical field of cranes. The method comprises: constructing a kinematic model of the bridge crane; constructing a state vector and control variables of the bridge crane to determine a state equation of the bridge crane in a nonlinear state space; linearizing the state equation of the nonlinear state space at a zero swing angle to determine a state equation of the bridge crane in a linear state space; constructing a disturbance observer of the bridge crane; constructing a neural network-based PID controller of the bridge crane; obtaining a current crane state; estimating a current disturbance through the disturbance observer according to the current crane state; calculating a current system control force through the neural network-based PID controller according to the current crane state; compensating the current system control force according to the current disturbance to determine a final control force; and controlling the bridge crane according to the final control force.

Description

Technical Field

[0001] The present invention relates to the technical field of cranes, and in particular to a control method and system for a bridge crane. Background Art

[0002] Bridge crane is a kind of lifting equipment widely used in factories, warehouses, ports and other places. It can lift and move heavy objects within a certain area and has the advantages of high efficiency, safety and flexible operation.

[0003] For the control of bridge cranes, control methods such as fuzzy control, sliding mode control, and PID control are often used. However, these current control methods are easily affected by disturbances. In particular, when using PID control, the proportional gain coefficient, integral gain coefficient, and differential gain coefficient often require repeated debugging by skilled technicians and are often fixed. However, the working environment of the crane is changeable, making the current PID control difficult to adapt to the changing working environment. When faced with disturbances, the response is slow or excessive, resulting in reduced control accuracy and unstable control system. Summary of the Invention

[0004] In order to solve the problem that the existing technology is easily affected by disturbances, especially when using PID control, the proportional gain coefficient, integral gain coefficient and differential gain coefficient often need to be repeatedly debugged by skilled technicians and are often fixed. However, the working environment of the crane is changeable, which makes the current PID control difficult to adapt to the changing working environment. When facing disturbances, the response is slow or excessive, resulting in reduced control accuracy and unstable control system. The present invention provides a bridge crane control method and system.

[0005] The technical solutions provided by the embodiments of the present invention are as follows:

[0006] First aspect:

[0007] An embodiment of the present invention provides a bridge crane control method, comprising:

[0008] S1: Construct the kinematic model of the bridge crane;

[0009] S2: Constructing the state vector and control variables of the bridge crane, and determining the state equation of the bridge crane in the nonlinear state space according to the kinematic model;

[0010] S3: At zero swing angle, linearize the state equation of the nonlinear state space to determine the state equation of the bridge crane in the linear state space;

[0011] S4: Construct a disturbance observer for the bridge crane based on the state equation in the linear state space;

[0012] S5: Construct a neural network-based PID controller for a bridge crane;

[0013] S6: Get the current crane status;

[0014] S7: estimating a current disturbance through the disturbance observer according to the current crane state;

[0015] S8: Calculating the current system control force through the neural network-based PID controller according to the current crane state;

[0016] S9: Compensating the current system control force according to the current disturbance to determine a final control force;

[0017] S10: Control the bridge crane according to the final control force, return to S6, and continue controlling until a stop condition is reached.

[0018] Second aspect:

[0019] An embodiment of the present invention provides a bridge crane control system, comprising:

[0020] processor;

[0021] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the bridge crane control method as described in the first aspect is implemented.

[0022] The third aspect:

[0023] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the bridge crane control method as described in the first aspect is implemented.

[0024] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0025] In the present invention, a disturbance observer and a neural network-based PID controller are constructed, which can effectively determine the disturbance in the control process of the bridge crane and compensate for the system control force determined by the neural network-based PID controller, thereby obtaining an accurate final control force. At the same time, the neural network-based PID controller can adjust the proportional gain coefficient, integral gain coefficient and differential gain coefficient in real time according to actual conditions, improve the adaptability to changing working environments, optimize control performance, improve control accuracy, improve the stability of the control system, and realize efficient and stable crane operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A schematic flow chart of a bridge crane control method provided by an embodiment of the present invention;

[0028] Figure 2 A schematic structural diagram of a bridge crane control system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0030] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0031] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0032] In the embodiment of the present invention, sometimes a subscript such as W1 may be written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0033] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0034] Reference Manual Figure 1 , which shows a flow chart of a bridge crane control method provided by an embodiment of the present invention.

[0035] An embodiment of the present invention provides a bridge crane control method, which can be implemented by a bridge crane control device, which can be a terminal or a server. The processing flow of the bridge crane control method can include the following steps:

[0036] S1: Construct a kinematic model of a bridge crane.

[0037] In a possible implementation, the kinematic model of the bridge crane is specifically as follows:

[0038]

[0039]

[0040] Among them, M represents the mass of the trolley, m represents the load mass, and x represents the displacement of the trolley. represents the acceleration of the trolley, L represents the cable length, θ represents the load swing angle, represents the load angular velocity, represents the load angular acceleration, and F represents the system control force.

[0041] It should be noted that building a kinematic model for a bridge crane is the basis for precise control. A specific kinematic model can effectively describe the system's dynamic behavior, allowing the control system to be adjusted and optimized based on actual conditions.

[0042] In this paper, a specific kinematic model of a bridge crane is constructed, providing a mathematical foundation for accurately describing the system's dynamic behavior. This provides essential support for controller design, system optimization, disturbance compensation, and safety analysis. By utilizing this model, efficient, stable, and reliable bridge crane control can be achieved, improving the system's overall performance and operational efficiency.

[0043] S2: Construct the state vector and control variables of the bridge crane, and determine the state equation of the bridge crane in the nonlinear state space based on the kinematic model.

[0044] In a possible implementation, the state vector of the bridge crane is specifically:

[0045] X = [x1, x2, x3, x4]

[0046] x1=x, x3=θ,

[0047] Where X represents the state vector, x1, x2, x3 and x4 are the elements in the state vector, x1 corresponds to the trolley displacement x, and x2 corresponds to the trolley speed x3 corresponds to the load swing angle θ, and x4 corresponds to the load angular velocity

[0048] The specific control variable is: system control force F.

[0049] The state equation of the bridge crane in the nonlinear state space is specifically:

[0050]

[0051]

[0052]

[0053]

[0054] Wherein, a represents the comprehensive quality, a=M+m.

[0055] It should be noted that in nonlinear state space, the system behavior is closer to reality. By constructing nonlinear state space equations, we can provide a basis for designing nonlinear controllers (such as fuzzy control and sliding mode control), enabling the controller to better cope with the nonlinear characteristics and complex dynamics of the system.

[0056] This method constructs the state vector and control variables of a bridge crane and determines its state equation in a nonlinear state space, providing a powerful tool and foundation for system control, simulation, analysis, and optimization. This not only improves the system's control accuracy and response speed, but also enhances its robustness and stability, adapting to various complex operating conditions and ultimately achieving efficient and safe operation of the bridge crane.

[0057] S3: At zero swing angle, linearize the state equation of the nonlinear state space and determine the state equation of the bridge crane in the linear state space.

[0058] In a possible implementation, at zero swing angle x3=0, then:

[0059] sin(x3)=x3,cos(x3)=1.

[0060] The state equation of the bridge crane in the linear state space is specifically:

[0061]

[0062]

[0063]

[0064] It's important to note that linearizing the system at zero swing angle allows for higher control accuracy when the system state approaches this point, which is particularly important during the initial startup of a crane and during light-load operation. The linearized state equations are more easily adaptable to classic linear control methods, such as PID control and LQR (Linear Quadratic Regulator). These methods are highly effective in linear systems and are relatively mature and simple to design and implement.

[0065] In this invention, by linearizing the nonlinear state-space equations of a bridge crane at zero swing angle, the design and implementation of the controller can be simplified, facilitating system analysis, improving control accuracy, simplifying calculations and simulations, and ultimately enhancing the overall performance and reliability of the system. This method is particularly suitable for precise control when the system state is close to the linearization point (small swing angles).

[0066] S4: Construct a disturbance observer for the bridge crane based on the state equation in the linear state space.

[0067] In a possible implementation, the construction method of the disturbance observer specifically includes:

[0068] Add the disturbance term of the system control force to the state equation in the linear state space:

[0069]

[0070]

[0071]

[0072] Where d represents the disturbance.

[0073] Construct a disturbance observer for the overhead crane:

[0074]

[0075] in, represents the disturbance observation value, ε1 and ε2 represent auxiliary variables, λ represents the observer gain, Represents the state estimates of x2, x3, and x4.

[0076] Among them, those skilled in the art can set the size of the observer gain λ according to actual conditions, and the present invention does not limit this.

[0077] Specifically, the specific size of the observer gain can be determined by pole placement method, LQR design method, etc.

[0078] Pole placement is a common method that determines the observer gain by placing the observer's poles at desired locations. Specifically, the desired pole locations are selected based on the system's response requirements (usually in the left half plane to ensure system stability). The state-space equations of the system are used to construct the observer's characteristic equation. The observer gain is determined by matching the coefficients in the characteristic equation.

[0079] In this paper, by adding a disturbance term to the system's control force into the state equation and constructing a disturbance observer, real-time estimation and compensation of disturbances can be achieved. This not only enhances the system's robustness and control accuracy, but also simplifies the controller design and improves the system's response speed and adaptability.

[0080] S5: Construct a neural network based PID controller for a bridge crane.

[0081] In one possible implementation, the neural network-based PID controller includes an input layer, a hidden layer, and an output layer.

[0082] The input layer is used to input real-time displacement deviation.

[0083] Optionally, the displacement deviation is specifically:

[0084] e t =x t -x g

[0085] Among them, e represents the displacement deviation, e t represents the displacement deviation at time t, x represents the displacement of the crane trolley, xt represents the displacement at time t, x g Indicates the target displacement.

[0086] The hidden layer includes proportional neurons, integral neurons and differential neurons, which correspond to proportional gain coefficients, integral gain coefficients and differential gain coefficients respectively.

[0087] The hidden layer is used to determine the real-time proportional gain coefficient, integral gain coefficient and differential gain coefficient during the crane control process.

[0088] Traditional PID controllers have fixed proportional, integral, and differential gain coefficients, which typically require adjustment through experimentation during the design and commissioning phases. However, neural network-based PID controllers can automatically adjust these gain coefficients based on the system's real-time state, adapting to varying operating conditions and environmental changes, and improving the control system's response speed and stability.

[0089] Optionally, the output of the proportional neuron is specifically:

[0090] f1(t)=e(t)K p(t)

[0091] Among them, f1(t) represents the output of the proportional neuron at time t, e(t) represents the displacement deviation at time t, K p (t) represents the proportional gain coefficient at time t.

[0092] Optionally, the output of the integrating neuron is specifically:

[0093] f2(t)=e(t)K i (t)+f2(t-1)

[0094] Among them, f2(t) represents the output of the integrating neuron at time t, K i (t) represents the integral gain coefficient at time t, and a2(t-1) represents the output of the integrating neuron at time t-1.

[0095] Optionally, the output of the differentiable neuron is specifically:

[0096] f3(t)=e(t)K d (t)+K d (t-1)e(t-1)

[0097] Among them, f3(t) represents the output of the differential neuron at time t, K d (t) represents the differential gain coefficient at time t, K d (t-1) represents the differential gain coefficient at time t-1, and e(t-1) represents the displacement deviation at time t-1.

[0098] Optionally, the system control force output by the output layer is specifically:

[0099] F(t)=w1f1(t)+w2f2(t)+w3f3(t)

[0100] Among them, F(t) represents the system control force output at time t, w1 represents the weight coefficient between the proportional neuron and the output layer, w2 represents the weight coefficient between the integral neuron and the output layer, and w3 represents the weight coefficient between the differential neuron and the output layer.

[0101] Among them, those skilled in the art can set the size of the weight coefficient w1 between the proportional neuron and the output layer, the weight coefficient w2 between the integral neuron and the output layer, and the weight coefficient w3 between the differential neuron and the output layer according to actual conditions, and the present invention does not limit this.

[0102] Specifically, the optimal weight coefficient can be determined by using optimization algorithms such as particle swarm optimization algorithm and genetic algorithm.

[0103] In the present invention, through the neural network-based PID controller, precise control of the bridge crane can be achieved, control parameters can be dynamically adjusted, the control accuracy and robustness of the system can be improved, the workload of manual parameter adjustment can be reduced, and it can adapt to complex nonlinear systems and have good compatibility and scalability.

[0104] S6: Get the current crane status.

[0105] S7: Estimate the current disturbance through the disturbance observer according to the current crane state.

[0106] S8: According to the current crane status, the current system control force is calculated through the neural network-based PID controller.

[0107] S9: Compensate the current system control force based on the current disturbance and determine the final control force.

[0108] In a possible implementation, the final control force is calculated as follows:

[0109] F * (t)=F(t)+d(t)

[0110] Where F* represents the final control force, F*(t) represents the final control force at time t, d represents the disturbance, and d(t) represents the disturbance at time t.

[0111] In the present invention, the current system control force is compensated according to the current disturbance, which can accurately compensate for the error caused by the disturbance, so that the control accuracy of the system is significantly improved.

[0112] S10: Control the bridge crane according to the final control force, return to S6, and continue controlling until the stop condition is reached.

[0113] Optionally, the stopping condition is specifically that the lifting trolley reaches the target displacement.

[0114] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0115] In the present invention, a disturbance observer and a neural network-based PID controller are constructed, which can effectively determine the disturbance in the control process of the bridge crane and compensate for the system control force determined by the neural network-based PID controller, thereby obtaining an accurate final control force. At the same time, the neural network-based PID controller can adjust the proportional gain coefficient, integral gain coefficient and differential gain coefficient in real time according to actual conditions, improve the adaptability to changing working environments, optimize control performance, improve control accuracy, improve the stability of the control system, and realize efficient and stable crane operation.

[0116] Reference Manual Figure 2, which shows a structural schematic diagram of a bridge crane control system provided by the present invention.

[0117] The present invention further provides a bridge crane control system 20, which is applied to the above-mentioned bridge crane control method, comprising:

[0118] Processor 201;

[0119] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the bridge crane control method as described in the method embodiment is implemented.

[0120] The bridge crane control system 20 provided by the present invention can execute the above-mentioned bridge crane control method and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate on it.

[0121] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0122] In the present invention, a disturbance observer and a neural network-based PID controller are constructed, which can effectively determine the disturbance in the control process of the bridge crane and compensate for the system control force determined by the neural network-based PID controller, thereby obtaining an accurate final control force. At the same time, the neural network-based PID controller can adjust the proportional gain coefficient, integral gain coefficient and differential gain coefficient in real time according to actual conditions, improve the adaptability to changing working environments, optimize control performance, improve control accuracy, improve the stability of the control system, and realize efficient and stable crane operation.

[0123] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) 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, etc.

[0124] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM).

[0125] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. 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 a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0126] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0127] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0128] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean 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.

[0129] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0130] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0133] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0134] If the functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0135] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the bridge crane control method as described in the method embodiment.

[0136] The computer-readable storage medium provided by the present invention can implement the steps and effects of the bridge crane control method of the above method embodiment. To avoid repetition, the present invention will not elaborate on them.

[0137] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0138] In the present invention, a disturbance observer and a neural network-based PID controller are constructed, which can effectively determine the disturbance in the control process of the bridge crane and compensate for the system control force determined by the neural network-based PID controller, thereby obtaining an accurate final control force. At the same time, the neural network-based PID controller can adjust the proportional gain coefficient, integral gain coefficient and differential gain coefficient in real time according to actual conditions, improve the adaptability to changing working environments, optimize control performance, improve control accuracy, improve the stability of the control system, and realize efficient and stable crane operation.

[0139] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0140] There are a few points to note:

[0141] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0142] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or intervening elements may be present.

[0143] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0144] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A bridge crane control method, characterized in that: include: S1: Construct the kinematic model of the bridge crane; S2: Constructing the state vector and control variables of the bridge crane, and determining the state equation of the bridge crane in the nonlinear state space according to the kinematic model; S3: At zero swing angle, linearize the state equation of the nonlinear state space to determine the state equation of the bridge crane in the linear state space; S4: Construct a disturbance observer for the bridge crane based on the state equation in the linear state space; S5: Construct a neural network-based PID controller for a bridge crane; S6: Get the current crane status; S7: estimating a current disturbance through the disturbance observer according to the current crane state; S8: Calculating the current system control force through the neural network-based PID controller according to the current crane state; S9: Compensating the current system control force according to the current disturbance to determine a final control force; S10: Control the bridge crane according to the final control force, return to S6, and continue controlling until a stop condition is reached; The kinematic model of the bridge crane is specifically as follows: Among them, M represents the mass of the trolley, m represents the load mass, and x represents the displacement of the trolley. represents the acceleration of the trolley, L represents the cable length, θ represents the load swing angle, represents the load angular velocity, represents the load angular acceleration, and F represents the system control force; The construction method of the disturbance observer specifically includes: Add the disturbance term of the system control force to the state equation in the linear state space: Among them, x1, x2, x3 and x4 are the elements in the state vector, x1 corresponds to the trolley displacement x, and x2 corresponds to the trolley speed x3 corresponds to the load swing angle θ, and x4 corresponds to the load angular velocity m represents the load mass, M represents the mass of the trolley, F represents the system control force, d represents the disturbance, and L represents the cable length; Construct a disturbance observer for the overhead crane: in, represents the disturbance observation value, ε1 and ε2 represent auxiliary variables, λ represents the observer gain, represents the state estimation value of x2, x3, and x4; The neural network-based PID controller includes an input layer, a hidden layer, and an output layer; The input layer is used to input real-time displacement deviation; The hidden layer includes proportional neurons, integral neurons and differential neurons, which are used to control the proportional gain coefficient, the integral gain coefficient and the differential gain coefficient respectively; The hidden layer is used to determine the real-time proportional gain coefficient, integral gain coefficient and differential gain coefficient during the crane control process; The output layer is used to output system control force; The displacement deviation is specifically: e t =x t -x g Among them, e represents the displacement deviation, e t represents the displacement deviation at time t, x represents the displacement of the crane trolley, and x t represents the displacement at time t, x g represents the target displacement; The output of the proportional neuron is specifically: f1(t)=e(t)K p (t) Among them, f1(t) represents the output of the proportional neuron at time t, e(t) represents the displacement deviation at time t, K p (t) represents the proportional gain coefficient at time t; The output of the integrating neuron is specifically: f2(t)=e(t)K i (t)+f2(t-1) Among them, f2(t) represents the output of the integrating neuron at time t, K i (t) represents the integral gain coefficient at time t, and f2(t-1) represents the output of the integrating neuron at time t-1; The output of the differential neuron is specifically: f3(t)=e(t)K d (t)+K d (t-1)e(t-1) Among them, f3(t) represents the output of the differential neuron at time t, K d (t) represents the differential gain coefficient at time t, K d (t-1) represents the differential gain coefficient at time t-1, and e(t-1) represents the displacement deviation at time t-1; The system control force output by the output layer is specifically: F(t)=w1f1(t)+w2f2(t)+w3f3(t) Where F(t) represents the system control force output at time t, w1 represents the weight coefficient between the proportional neuron and the output layer, w2 represents the weight coefficient between the integral neuron and the output layer, and w3 represents the weight coefficient between the differential neuron and the output layer. The final control force is calculated as follows: F * (t)=F(t)+d(t) Among them, F * Indicates the ultimate control force, F * (t) represents the final control force at time t, d represents the disturbance, and d(t) represents the disturbance at time t.

2. The bridge crane control method according to claim 1, characterized in that: The state vector of the bridge crane is specifically: X=[x1,x2,x3,x4] Where X represents the state vector, x1, x2, x3 and x4 are the elements in the state vector, x1 corresponds to the trolley displacement x, and x2 corresponds to the trolley speed x3 corresponds to the load swing angle θ, and x4 corresponds to the load angular velocity The control variables are specifically: system control force F; The state equation of the bridge crane in the nonlinear state space is specifically: Wherein, a represents the comprehensive quality, a=M+m.

3. The bridge crane control method according to claim 2, characterized in that: At zero swing angle x3=0, then: sin(x3)=x3,cos(x3)=1; The state equation of the bridge crane in the linear state space is specifically:

4. A bridge crane control system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the bridge crane control method according to any one of claims 1 to 3 is implemented.

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