CN114803866B - Staged optimization control method and device for lifting motion state of intelligent tower crane - Google Patents
Staged optimization control method and device for lifting motion state of intelligent tower crane Download PDFInfo
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Abstract
The application relates to a staged optimization control method and device for a lifting motion state of an intelligent tower crane. Wherein the method comprises the following steps: collecting the motion state of a tower crane robot; determining the motion stage of the tower crane robot according to the motion state and the planned path; and according to the motion stage, selecting a track tracking control strategy of a motion track correction model, an energy optimization model and/or a model prediction control model included by a track tracking controller to control the tower crane robot. According to the method and the device, the optimal control of the motion process of the tower crane robot is realized through the track tracking controller, and the track tracking controller can realize robustness, energy conservation and stability while carrying out accurate track tracking.
Description
Technical Field
The invention relates to the technical field of intelligent tower cranes, in particular to a staged optimization control method and device for a lifting motion state of an intelligent tower crane.
Background
The tower crane is the most common hoisting equipment on the construction site. The tower crane is used for hoisting construction raw materials such as reinforcing steel bars, wood ridges, concrete, steel pipes and the like for construction through one-section extension (referred to as 'standard section').
The tower crane is used as indispensable equipment on a site, the production efficiency, the operation are simple and convenient, the maintenance is simple, the operation is reliable and the like are improved, but the operation technology of the tower crane is not fundamentally changed. A large amount of manpower and time cost are consumed for transportation of a tower crane structure and construction of the tower crane, the working position of the tower crane can not be changed almost, and operation of the tower crane has certain limitation due to the problems.
Disclosure of Invention
The embodiment of the application provides a staged optimization control method and device for a lifting motion state of an intelligent tower crane. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a phased optimization control method for a lifting motion state of an intelligent tower crane, where the method includes:
collecting the motion state of a tower crane robot;
determining the motion stage of the tower crane robot according to the motion state and the planned path;
and according to the motion stage, selecting a track tracking control strategy of a motion track correction model, an energy optimization model and/or a model prediction control model included by a track tracking controller to control the tower crane robot.
Optionally, the motion state of gathering tower crane robot includes:
acquiring a real-time pose state of a tower crane robot;
and taking the real-time pose state as the motion state of the tower crane robot.
Optionally, the determining, according to the motion state and the planned path, the motion phase of the tower crane robot includes:
calculating a motion error vector of the tower crane robot according to the motion state and a preset motion state in the planned path;
and determining the motion stage of the tower crane robot according to the motion error vector.
Optionally, the motion trajectory correction model is established by the following process:
forming an ideal motion track of the tower crane robot according to the operation space environment parameters;
and in the motion phase, when the ideal motion track is set to be inconsistent with the actual motion track of the tower crane robot, extracting the operation control parameters of the tower crane robot.
Optionally, the establishing process of the energy optimization model is as follows:
and in the motion stage, according to the ideal motion trail, carrying out shortest path optimization, optimal operation speed optimization and motor stable operation optimization operation on the actual motion trail so as to form the energy optimization model.
Optionally, the process of establishing the model predictive control model is as follows:
summarizing a control rule of the tower crane robot according to the operation data of the motion track correction model and the energy optimization model;
and optimizing the control law by a machine learning method to obtain the fastest correction mode of the tower crane robot.
Optionally, the controlling the tower crane robot by using a trajectory tracking control strategy of a motion trajectory modification model, an energy optimization model and/or a model predictive control model included in a trajectory tracking controller according to the motion phase includes:
when the motion phase is the initial motion stage, the track tracking control strategy of the motion track correction model is selected to control the tower crane robot;
when the motion stage is the middle motion stage, the track tracking control strategy of the energy optimization model is selected to control the tower crane robot;
and when the motion stage is in the later motion stage, the track tracking control strategy of the model prediction control model is selected to control the tower crane robot.
Optionally, the trajectory tracking controller includes: a non-fully constrained trajectory tracking controller.
In a second aspect, an embodiment of the present application provides a control device is optimized in stages towards intelligent tower crane lifting motion state, and the device includes:
the motion state acquisition module is used for acquiring the motion state of the tower crane robot;
the motion phase determination module is used for determining the motion phase of the tower crane robot according to the motion state and the planned path;
and the motion control module is used for controlling the tower crane robot by selecting a track tracking control strategy of a motion track correction model, an energy optimization model and/or a model prediction control model included by the track tracking controller according to the motion stage.
In a third aspect, embodiments of the present application provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, the intelligent tower crane lifting motion state-oriented staged optimization control method comprises the steps of firstly collecting the motion state of a tower crane robot, then determining the motion stage of the tower crane robot according to the motion state and a planned path, and finally controlling the tower crane robot according to the motion stage and a track tracking controller. The tower crane robot control system can realize the optimal control on the tower crane robot through the track tracking controller, and the track tracking controller can realize robustness, energy conservation and stability while carrying out accurate track tracking.
In the embodiment of the application, the phased optimization control method for the lifting motion state of the intelligent tower crane selects the track tracking control strategy of the motion track correction model to control the tower crane robot when the motion phase is the initial motion phase; when the motion stage is the middle motion stage, the track tracking control strategy of the energy optimization model is selected to control the tower crane robot; and when the motion stage is in the motion later stage, selecting the trajectory tracking control strategy of the model predictive control model to control the tower crane robot. This application has adopted many control theories, can adopt different trail tracking control strategies at the different motion stages of tower crane robot, has not only saved the transportation space, has shortened the time that tower crane robot rises and descends, makes the tower crane robot can the even running moreover.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart of a phased optimization control method for a lifting motion state of an intelligent tower crane provided in an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a comparison between an actual motion trajectory and an ideal motion trajectory of a phased optimization control method for a lifting motion state of an intelligent tower crane according to an embodiment of the present application;
fig. 3 is a schematic device diagram of a staged optimization control device for a lifting motion state of an intelligent tower crane according to an embodiment of the present application;
fig. 4 is a schematic diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of systems and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Referring to fig. 1 and fig. 2, a schematic flow chart of a phased optimization control method for a lifting motion state of an intelligent tower crane is provided for the embodiment of the present application. As shown in fig. 1 and 2, the method of the embodiment of the present application may include the steps of:
the practical motion trail control of the tower crane robot is the basis of the research and coordination work of the tower crane robot system.
And S100, acquiring the motion state of the tower crane robot. The tower crane robot is a common hoisting device in construction sites, and can be called as an intelligent tower crane, a wheel type mobile robot and the like.
Wherein, S100 includes: and S110, acquiring the real-time pose state of the tower crane robot. The real-time pose state of the tower crane robot is acquired in a mode that the tower crane robot feeds back the actual pose of the tower crane robot in real time. And S120, taking the real-time pose state as the motion state of the tower crane robot.
And S200, determining the motion stage of the tower crane robot according to the motion state and the planned path. Specifically, S200 includes:
and S210, calculating a motion error vector of the tower crane robot according to the motion state and a preset motion state in the planned path. Planning the operation track of the tower crane robot in advance according to a path planning algorithm to form a planned path; the planned path comprises an ideal motion track, a preset motion state and the like.
In the embodiment of the application, the calculation is performed according to the feedback parameter of the actual motion state and the preset motion state parameter, and the motion error state parameter is obtained by comparing the curve fitting condition of the feedback parameter of the actual motion state and the preset motion state parameter, namely the motion error vector.
And when the motion state is a real-time pose state, the preset motion state is a real-time pose state planned in a planned path, and the motion error vector is a pose error vector.
And S220, determining the motion stage of the tower crane robot according to the motion error vector.
In the embodiment of the application, a mapping relation exists between the motion error vector and the motion phase of the tower crane robot. For example, a first error vector range value, a second error vector range value, a third error vector range value, and the like may be preset, and the preset motion stage includes a motion initial stage, a motion intermediate stage, and a motion late stage. When the motion error vector meets a first error vector range value, the motion stage of the tower crane robot is a motion primary stage; when the motion error vector meets a second error vector range value, the motion stage of the tower crane robot is the middle motion stage; and when the motion error vector meets a third error vector range value, the motion stage of the tower crane robot is the motion later stage.
S300, according to the motion stage, selecting a track tracking control strategy of a motion track correction model, an energy optimization model and/or a model prediction control model included in a track tracking controller to control the tower crane robot; the trajectory tracking controller may be a non-fully constrained trajectory tracking controller.
The motion trail correction model, the energy optimization model and the model prediction control model can be constructed through a convolutional neural network.
The process of establishing the motion trajectory correction model can be as follows:
acquiring the space passing capacity of the robot according to the running space environment parameters of the tower crane robot by adopting a path planning algorithm, and planning the running track of the tower crane robot in stages according to the space passing capacity to form an ideal motion track of the tower crane robot; setting a motion error vector and a judgment condition of the initial motion stage in the ideal motion trail; and when the judgment condition is not met, correct operation control parameters are extracted from the motion error vector.
The judgment condition is to judge whether the ideal motion track is consistent with the actual motion track, and if the judgment condition is not satisfied, the ideal motion track is inconsistent with the actual motion track.
In the embodiment of the application, the historical actual motion track and the historical ideal motion track of the tower crane robot can be controlled according to the track tracking controller, and the actual motion track and the ideal motion track are trained and updated on the motion track correction model.
The energy optimization model can be established by the following steps:
and according to the ideal motion track in the motion track correction model, carrying out shortest path optimization, optimal operation speed optimization and motor stable operation optimization operation on the actual motion track of the tower crane robot in the middle motion period to form an energy optimization model.
In the embodiment of the application, the historical actual motion track and the historical ideal motion track of the tower crane robot can be controlled according to the track tracking controller, and the actual motion track and the ideal motion track are used for training and updating the energy optimization model.
The process of establishing the model predictive control model can be as follows:
and summarizing a control rule through the running data of the motion trail correction model and the energy optimization model, and further optimizing the control rule through a machine learning method to obtain a fastest correction mode. The control law can be the force of up-and-down movement, the force of speed and acceleration, and the force of revolution when the tower crane robot runs; the control rule can be optimized to be the downward movement of the tower crane robot; the fastest correction mode is that the force of the tower crane robot moving up and down, the force of the speed and the acceleration and the force of the revolution are decomposed through the force of the tower crane robot moving down; the tower crane robot motion control method has the advantages that the tower crane robot motion can be reasonably controlled through the model prediction control model, so that the tower crane robot can stably stop in the motion later stage.
In the embodiment of the present application, the model predictive control model may also be trained and updated according to the updated operation data of the motion trajectory modification model and the energy optimization model.
In the embodiment of the present application, S300 includes: respectively determining the trajectory tracking control strategies of the motion trajectory correction model, the energy optimization model and/or the model predictive control model, and controlling the tower crane robot according to the trajectory tracking control strategies of the motion trajectory correction model, the energy optimization model and/or the model predictive control model.
Specifically, when the actual motion track is judged to be inconsistent with the ideal motion track through the motion track correction model in the initial motion stage of the tower crane robot, correct operation control parameters are extracted from the motion error state parameters, and the actual motion track of the tower crane robot in operation is corrected, so that the actual motion track of the tower crane robot can approximately accord with the ideal motion track in a planned path.
When the motion phase is the initial motion phase, the trajectory tracking control strategy of the motion trajectory correction model is selected to control the tower crane robot, and the specific implementation process is as follows:
based on the inertia of static materials, the track tracking controller can control the vertical motion of the tower crane robot when the materials are just lifted: the lifting is slowly carried out, and then the lifting is accelerated.
When the tower crane robot is lifted to a certain height, the dynamic motion process is started. The forces in other directions are increased in the dynamic movement process, so that the movement fluctuation range of the tower crane robot is large. Therefore, in order to reduce the fluctuation range of the motion of the tower crane robot as much as possible in the embodiment of the application, the motor and the rotation can be utilized to carry out motion stability-increasing precontrol on the tower crane robot; the three motors are controlled to decompose the ascending force to adjust the actual motion track of the tower crane robot, so that the actual motion track curve of the tower crane robot is close to the ideal motion track curve, the stability and the continuity can be kept when the tower crane robot accelerates, and the early-stage preset target of stable starting and high-speed operation is achieved.
And when the motion stage is the middle motion stage, the track tracking control strategy of the energy optimization model is selected to control the tower crane robot. The specific implementation process comprises the following steps:
in the middle movement period of the tower crane robot, except for an emergency, under the condition of normal and stable operation of the tower crane robot, the track tracking controller controls the tower crane robot to operate according to the optimized optimal operation speed, so that the condition of instantaneous acceleration or deceleration is avoided, and the tower crane robot keeps stable operation. The track tracking controller controls the tower crane robot to stably run according to the optimized motor speed, so that the motor is in a power-saving state; the track tracking controller controls the tower crane robot to move according to the optimized shortest path; the purpose of energy saving is achieved.
In this application embodiment, tower crane robot's motion later stage can produce inertial force, inertial force is the size, direction and the acceleration of the power that produces through the three power pack of model predictive control model self to the power that combines the weight of object to produce forms. The tower crane robot comprises three power units, wherein the three power units are used for generating the force of the vertical movement of the tower crane robot, the force of the speed and the acceleration of the tower crane robot generated by the second power unit and the force of the rotation of the tower crane robot generated by the third power unit, namely the force of the left rotation, the right rotation and the rotation angle of the tower crane robot.
And the model prediction control model predicts the downward movement force of the tower crane robot according to the inertia force, compares the downward movement force with the inertia force at a high frequency, and gradually corrects the downward movement force.
And when the motion stage is in the later motion stage, the track tracking control strategy of the model prediction control model is selected to control the tower crane robot. The specific implementation process can be as follows:
in the embodiment of the application, a specified position point when the tower crane robot finishes moving is preset. When the tower crane robot is about to reach a specified position point, the track tracking controller controls the tower crane robot to continuously perform downward movement auxiliary action, and stops the auxiliary action when the tower crane robot reaches a position near the specified position point, so that the downward movement auxiliary action decomposes inertia force generated by the movement of the tower crane robot. The tower crane robot can be stably stopped when running to a specified position point, and the motion curve is kept smooth.
Fig. 2 is a comparison diagram of an actual motion trajectory and an ideal motion trajectory of the tower crane robot controlled by the trajectory tracking controller to operate.
In the embodiment of the application, the intelligent tower crane lifting motion state-oriented staged optimization control method comprises the steps of firstly collecting the motion state of a tower crane robot, then determining the motion stage of the tower crane robot according to the motion state and a planned path, and finally controlling the tower crane robot according to the motion stage and a track tracking controller. The tower crane robot control system can realize the optimal control on the tower crane robot through the track tracking controller, and the track tracking controller can realize robustness, energy conservation and stability while carrying out accurate track tracking.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
Fig. 3 is a schematic structural diagram illustrating a staged optimization control device for a lifting motion state of an intelligent tower crane according to an exemplary embodiment of the present invention. The device includes: a motion state acquisition module 10, a motion phase determination module 20, and a motion control module 30.
The motion state acquisition module 10 is used for acquiring the motion state of the tower crane robot;
the motion phase determination module 20 is configured to determine a motion phase where the tower crane robot is located according to the motion state and the planned path;
and the motion control module 30 is configured to select a trajectory tracking control strategy of a motion trajectory correction model, an energy optimization model and/or a model prediction control model included in the trajectory tracking controller to control the tower crane robot according to the motion phase.
It should be noted that, when the phased optimization control device for the lifting motion state of the intelligent tower crane provided in the above embodiment executes the phased optimization control method for the lifting motion state of the intelligent tower crane, only the division of the functional modules is used for illustration, and in practical application, the function allocation may be completed by different functional modules according to needs, that is, the internal structure of the equipment is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the phased optimization control device for the lifting motion state of the intelligent tower crane provided by the embodiment and the phased optimization control method for the lifting motion state of the intelligent tower crane belong to the same concept, and the implementation process is detailed in the method embodiment, which is not described herein again.
The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the advantages and disadvantages of the embodiments.
In the embodiment of the application, the phased optimization control device for the intelligent tower crane lifting motion state firstly collects the motion state of the tower crane robot, then determines the motion stage of the tower crane robot according to the motion state and the planned path, and finally controls the tower crane robot according to the motion stage and the track tracking controller. The tower crane robot control system can realize the optimal control on the tower crane robot through the track tracking controller, and the track tracking controller can realize robustness, energy conservation and stability while carrying out accurate track tracking.
The invention further provides a computer readable medium, on which program instructions are stored, and when the program instructions are executed by a processor, the method for performing the phased optimization control on the lifting motion state of the intelligent tower crane provided by the above method embodiments is implemented.
The invention also provides a computer program product containing instructions, which when run on a computer causes the computer to execute the phased optimization control method facing the lifting motion state of the intelligent tower crane in the above method embodiments.
Please refer to fig. 4, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 4, terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
The communication bus 1002 is used to implement connection communication among these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may alternatively be at least one memory device located remotely from the processor 1001. As shown in fig. 4, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a usability analysis application program of vehicle running trajectory data.
In the terminal 1000 shown in fig. 4, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; the processor 1001 may be configured to invoke a staged optimization control application program oriented to a lifting motion state of the smart tower crane stored in the memory 1005, and specifically execute the following operations:
collecting the motion state of a tower crane robot;
determining the motion stage of the tower crane robot according to the motion state and the planned path;
according to the motion stage, a track tracking control strategy of a motion track correction model, an energy optimization model and/or a model prediction control model which is/are included in a track tracking controller is selected to control the tower crane robot; wherein the trajectory tracking controller comprises: a non-fully constrained trajectory tracking controller.
In an embodiment, when the processor 1001 executes the acquiring of the motion state of the tower crane robot, the following operations are specifically executed:
acquiring a real-time pose state of a tower crane robot;
and taking the real-time pose state as the motion state of the tower crane robot.
In an embodiment, when the processor 1001 determines the motion phase of the tower crane robot according to the motion state and the planned path, the following operations are specifically performed:
calculating a motion error vector of the tower crane robot according to the motion state and a preset motion state in the planned path;
and determining the motion stage of the tower crane robot according to the motion error vector.
In an embodiment, when executing the process of establishing the motion trajectory modification model, the processor 1001 specifically performs the following operations:
forming an ideal motion track of the tower crane robot according to the operation space environment parameters;
and in the motion phase, when the ideal motion track is set to be inconsistent with the actual motion track of the tower crane robot, extracting the operation control parameters of the tower crane robot.
In an embodiment, when executing the establishing process of the energy optimization model, the processor 1001 specifically performs the following operations:
and in the motion stage, according to the ideal motion trail, carrying out shortest path optimization, optimal operation speed optimization and motor stable operation optimization operation on the actual motion trail so as to form the energy optimization model.
In one embodiment, when executing the process of establishing the model predictive control model, the processor 1001 specifically performs the following operations:
summarizing a control rule of the tower crane robot according to the operation data of the motion track correction model and the energy optimization model;
and optimizing the control law by a machine learning method to obtain the fastest correction mode of the tower crane robot.
In an embodiment, when the processor 1001 executes the trajectory tracking control strategy of the motion trajectory correction model, the energy optimization model and/or the model predictive control model included in the trajectory tracking controller to control the tower crane robot according to the motion phase, the following operations are specifically executed:
when the motion phase is the initial motion stage, the track tracking control strategy of the motion track correction model is selected to control the tower crane robot;
when the motion stage is the middle motion stage, the track tracking control strategy of the energy optimization model is selected to control the tower crane robot;
and when the motion stage is in the later motion stage, the track tracking control strategy of the model prediction control model is selected to control the tower crane robot.
In the embodiment of the application, the intelligent tower crane lifting motion state-oriented staged optimization control method comprises the steps of firstly collecting the motion state of a tower crane robot, then determining the motion stage of the tower crane robot according to the motion state and a planned path, and finally controlling the tower crane robot according to the motion stage and a track tracking controller. According to the tower crane robot control system and method, the optimal control on the tower crane robot can be achieved through the track tracking controller, and the track tracking controller can achieve robustness, energy conservation and stability when accurate track tracking is conducted.
It can be understood by those skilled in the art that all or part of the processes in the methods of the embodiments described above can be implemented by a computer program that can be stored in a computer readable storage medium and that can be executed by a computer program that instructs related hardware to implement the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and should not be taken as limiting the scope of the present application, so that the present application will be covered by the appended claims.
Claims (9)
1. A staged optimization control method for a lifting motion state of an intelligent tower crane is characterized by comprising the following steps:
collecting the motion state of a tower crane robot;
determining the motion stage of the tower crane robot according to the motion state and the planned path;
according to the motion stage, selecting a motion track correction model, an energy optimization model and/or a track tracking control strategy of a model predictive control model which are/is included by a track tracking controller to control the tower crane robot;
the track tracking control strategy of a motion track correction model, an energy optimization model and/or a model prediction control model selected by a track tracking controller according to the motion stage to control the tower crane robot comprises the following steps:
when the motion phase is the initial motion stage, the track tracking control strategy of the motion track correction model is selected to control the tower crane robot;
when the motion stage is the middle motion stage, the track tracking control strategy of the energy optimization model is selected to control the tower crane robot;
and when the motion stage is in the later motion stage, the track tracking control strategy of the model prediction control model is selected to control the tower crane robot.
2. The phased optimization control method according to claim 1, wherein the acquiring of the motion state of the tower crane robot comprises:
acquiring a real-time pose state of a tower crane robot;
and taking the real-time pose state as the motion state of the tower crane robot.
3. The staged optimization control method according to claim 1, wherein the determining the motion stage of the tower crane robot according to the motion state and the planned path comprises:
calculating a motion error vector of the tower crane robot according to the motion state and a preset motion state in the planned path;
and determining the motion stage of the tower crane robot according to the motion error vector.
4. The phased optimization control method according to claim 1, wherein the motion trajectory modification model is established by:
forming an ideal motion track of the tower crane robot according to the operation space environment parameters;
and in the motion phase, when the ideal motion track is set to be inconsistent with the actual motion track of the tower crane robot, extracting the operation control parameters of the tower crane robot.
5. The phased optimization control method according to claim 4, wherein the energy optimization model is established by:
and in the motion stage, according to the ideal motion track, carrying out shortest path optimization, optimal operation speed optimization and motor stable operation optimization operation on the actual motion track so as to form the energy optimization model.
6. The phased optimization control method according to claim 5, wherein the model predictive control model is established by:
summarizing a control rule of the tower crane robot according to the operation data of the motion track correction model and the energy optimization model;
and optimizing the control law by a machine learning method to obtain the fastest correction mode of the tower crane robot.
7. The utility model provides a play to rise to move staged optimization control device of state towards intelligent tower crane which characterized in that includes:
the motion state acquisition module is used for acquiring the motion state of the tower crane robot;
the motion phase determination module is used for determining the motion phase of the tower crane robot according to the motion state and the planned path;
the motion control module is used for selecting a track tracking control strategy of a motion track correction model, an energy optimization model and/or a model prediction control model included by a track tracking controller to control the tower crane robot according to the motion stage, and comprises the following steps:
when the motion phase is in the initial motion stage, the track tracking control strategy of the motion track correction model is selected to control the tower crane robot;
when the motion stage is the middle motion stage, the track tracking control strategy of the energy optimization model is selected to control the tower crane robot;
and when the motion stage is in the later motion stage, the track tracking control strategy of the model prediction control model is selected to control the tower crane robot.
8. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to perform the method steps according to any of claims 1-6.
9. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1-6.
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