Hoisting pose adjusting system and shimmy suppression control method based on inertial parameters

Abstract

The invention provides a hoisting pose adjusting system and a shimmy suppression control method based on inertial parameters, and is suitable for the technical field of modular buildings. The method comprises the following steps: collecting state data of a hoisting system in real time through a sensor module; inputting the state data into a self-adaptive parameter identification module, and estimating the mass and rotational inertia of the hoisted object on line by using a real-time parameter estimation algorithm; the estimated mass and rotational inertia are fed back to a dynamic modeling module to be used for updating a nonlinear coupling dynamic equation; based on the updated kinetic model, an optimal control module linearizes the hoisting system in each control period, a state space model is constructed, and an optimal feedback gain matrix is solved by adopting an optimal control algorithm; and finally, according to the state data and the optimal feedback gain matrix, calculating optimal control input data and outputting the optimal control input data to a motor of the horizontal motion platform so as to inhibit swinging of the hoisted object and adjust the posture of the hoisted object.

Description

Technical Field

[0001] This application belongs to the field of modular building technology, and in particular relates to a hoisting posture adjustment system and a sway suppression control method based on inertial parameters. Background Technology

[0002] In modular construction, especially Modular Integrated Construction (MiC), effectively suppressing the swaying of hoisted modules is a key technical challenge. Currently, the common method is to manually pull guide ropes, which, while simple to operate, suffers from high labor intensity, low positioning accuracy, and significant safety hazards. Existing automated sway reduction methods mainly include: (1) Input shaping method: The system vibration is canceled by filtering the control signal, but it is sensitive to modeling errors and is prone to residual vibration; (2) Energy-based control: The vibration is suppressed by adjusting the system energy, but it assumes that the load parameters are fixed and known; (3) Sliding mode control: It is robust to parameter uncertainty and disturbance, but the control input is prone to chattering; (4) Constraint-based control: The swing angle and displacement are limited to a safe range, but it may fail under large disturbances.

[0003] The shortcomings of existing automated sway reduction methods mainly include: (1) poor adaptability: the controller is mostly designed based on fixed load parameters, while the actual MiC modules vary greatly in size, weight and inertia, resulting in performance degradation or even loss of control; (2) disconnect from attitude adjustment: the sway reduction and the precise positioning system are independent of each other, making it difficult to quickly and accurately align and prolonging the installation time; (3) insufficient reliability: the existing methods are unstable under conditions such as model errors, jitter, and sudden disturbances; (4) low efficiency and safety: manual operation is inefficient and risky. Summary of the Invention

[0004] This invention provides a hoisting posture adjustment system and a sway suppression control method based on inertial parameters. It aims to provide a control scheme that can automatically adapt to load changes and integrate sway suppression and position adjustment to solve the safety, efficiency and accuracy problems in modular building installation.

[0005] In a first aspect, this invention application provides a hoisting posture adjustment system, which is installed below the hook 210 of the hoisting equipment, and includes a hoisting system and a control system; The hoisting system includes a horizontal steering device 30, a cable 21, a hoisting platform 11, a horizontal movement platform 13, and a lifting device 12 arranged sequentially in the vertical direction. The horizontal steering device 30 is used to adjust the horizontal orientation of the hoisted object 300. The lifting device 12 is connected to the hoisting platform 11 or the horizontal movement platform 13 and is used to connect the hoisted object 300. The horizontal movement platform 13 is connected to the hoisting platform 11 and is used to adjust the horizontal position of the hoisted object 300. The cable 21 is connected to the hoisting platform 11 and is used to adjust the posture of the hoisted object 300. The control system includes: The sensor module is used to collect the status data of the hoisting system in real time. The status data includes the main swing angle, local swing angle, main swing angular velocity, local swing angular velocity, main swing acceleration, local swing acceleration, displacement of the horizontal motion platform 13, and motion speed of the horizontal motion platform 13. The dynamic modeling module establishes the nonlinear coupled dynamic equations of the hoisting posture adjustment system according to a preset multibody system dynamic modeling method. The dynamic equations are used to describe the motion behavior of the system under at least two rotational degrees of freedom and at least one translational degree of freedom. The adaptive parameter identification module receives the state data collected by the sensor module, uses a real-time parameter estimation algorithm to estimate the mass and moment of inertia of the hoisted object 300 online, and feeds back the estimation results to the dynamic modeling module to dynamically update the inertial parameters in the nonlinear coupled dynamic equations, thereby obtaining the updated dynamic model. The optimal control module, based on the updated dynamic model, linearizes the nonlinear model in each control cycle, constructs a state-space model, and uses the optimal control algorithm to generate control inputs for the motor driving the horizontal motion platform 13, so as to achieve suppression of the swing of the hoisted object 300 and coordinated adjustment of its attitude.

[0006] In some embodiments, the sensor module includes an inertial measurement unit mounted on the rope 200 of the hoisting equipment and the hoisting platform 11, and an encoder mounted on the output shaft of the drive motor of the horizontal motion platform 13; The inertial measurement unit is configured to measure the principal swing angle, the local swing angle, and their angular velocity and angular acceleration; The encoder is configured to measure the displacement and velocity of the horizontal motion platform 13.

[0007] In some embodiments, the main swing angle is the lifting point of the hook 210 of the hoisting equipment and the deflection angle of the rope 200 of the hoisting equipment relative to the vertical direction, which is used to reflect the overall swing of the hoisted object 300 caused by the rope 200 of the hoisting equipment. The local swing angle is the deflection angle of the cable 21 between the horizontal steering device 30 and the hoisting platform 11 relative to its equilibrium position, used to reflect the local relative swing between the hoisting platform 11 and the hoisted object 300. The displacement of the horizontal motion platform 13 refers to its relative linear displacement in the horizontal direction with respect to the hoisting platform 11, which is used to adjust the orientation of the hoisted object 300.

[0008] In some embodiments, the dynamic modeling module, the adaptive parameter identification module, and the optimal control module are integrated into the central control unit of the control system. The central control unit is deployed in the hoisting platform 11 or a remote control box to receive the status data of the hoisting system collected by the sensor module.

[0009] Secondly, this application also provides a sway suppression control method based on inertial parameters, executed by the hoisting posture adjustment system described in the first aspect above, the method comprising the following steps: Step S1: Collect the status data of the hoisting system in real time through the sensor module; wherein, the status data includes the main swing angle, local swing angle, corresponding angular velocity and angular acceleration, as well as the displacement of the horizontal motion platform and the motion speed of the motion platform; Step S2: Input the state data into the adaptive parameter identification module, and use the real-time parameter estimation algorithm to estimate the mass and moment of inertia of the hoisted object online; Step S3: Feed back the estimated mass and moment of inertia to the dynamic modeling module to update the nonlinear coupled dynamic equations established according to the preset multibody system dynamic modeling method, and obtain the updated dynamic model; Step S4: Based on the updated dynamic model, the optimal control module linearizes the hoisting system in each control cycle, constructs a state-space model, and uses the optimal control algorithm to solve for the optimal feedback gain matrix; Step S5: Calculate the optimal control input data based on the state data and the optimal feedback gain matrix, and output the optimal control input data to the motor of the horizontal motion platform to suppress the swaying of the hoisted object and adjust the attitude of the hoisted object.

[0010] In some embodiments, prior to executing the oscillation suppression control method based on inertial parameters, the method further includes: Step S01: Model the hoisting system as a three-bar underactuated system including a first rotary joint, a second rotary joint, and a traverse joint; The first rotary joint represents the swing degree of freedom θ1 of the hoisted object in an orthogonal direction, which corresponds to the principal swing angle θ1. The principal swing angle is the lifting point of the hook 210 of the hoisting equipment and the deflection angle of the rope 200 of the hoisting equipment relative to the vertical direction, and is used to reflect the overall swing of the hoisted object 300 caused by the rope 200 of the hoisting equipment. The second rotary joint represents the swing degree of freedom θ2 in another orthogonal direction, which corresponds to the local swing angle θ2. The local swing angle is the deflection angle of the cable 21 between the horizontal steering device 30 and the hoisting platform 11 relative to its equilibrium position, and is used to reflect the local relative swing between the hoisting platform 11 and the hoisted object 300. The movable joint represents the displacement d of the horizontal motion platform 13, and the displacement of the horizontal motion platform 13 refers to the relative linear displacement between it and the hoisting platform 11 in the horizontal direction.

[0011] Step S02: Establish the nonlinear coupled dynamic equations of the three-link underactuated system according to the preset multibody system dynamics modeling method.

[0012] In some embodiments, step S4 further includes: Sub-step S41: Linearize the nonlinear coupled dynamic equations near the current system state operating point to obtain a linear time-varying state space model; Sub-step S42: Determine the quadratic performance index function, which is expressed by the following formula:

[0013] in, Represents the weighted matrix of the adjusted state, and Represents the control input weighting matrix; The state vector in the state space model constructed by the optimal control module includes the main swing angle, local swing angle, main swing angular velocity, local swing angular velocity, main swing angular acceleration, local swing angular acceleration, displacement of the horizontal motion platform, and motion velocity of the horizontal motion platform. T This indicates the operation time of the control system of the hoisting posture adjustment system; This represents the control input variable used to drive the motor of the horizontal motion platform; Sub-step S43: Combining the linear time-varying state-space model and the adjustment state weighting matrix and the control input weighting matrix Calculate the optimal feedback gain matrix K ; Sub-step S44: Based on the state data With the optimal feedback gain matrix K The control law formula is obtained. And use the control law formula to calculate the optimal control input data. and the optimal control input data The motor output to the horizontal motion platform is used to suppress the swaying of the hoisted object and adjust its posture.

[0014] In some embodiments, step S5 further includes: According to the status data With the optimal feedback gain matrix K Calculate the optimal control input data The optimal control input data is then output to the motor of the horizontal motion platform so that the hoisted object is in a controlled state, which indicates that the swing of the hoisted object is suppressed and its posture is adjusted.

[0015] In some embodiments, after step S5, the control method further includes: Acquire new sensor data collected by the sensor module. The new sensor data is new status data of the hoisting system and is used to reflect the real-time status of the hoisted object under the control action state. The new sensor data is fed back to the adaptive parameter recognition module as input for the next round of state data, so as to realize the closed-loop iterative control of the control system of the hoisting posture adjustment system.

[0016] This invention provides a sway suppression control method based on inertial parameters. This method, through the organic combination of dynamic modeling, real-time parameter identification, and optimal control, aims to effectively overcome the limitations of existing sway reduction technologies. The control scheme possesses excellent adaptability and high control accuracy, and can be seamlessly integrated with the motion planning system of a lifting posture adjustment system (such as the HyPA intelligent lifting robot), thereby significantly improving the safety, efficiency, and final positioning accuracy of modular lifting operations.

[0017] The main technical advantages of this invention are: by identifying the mass and moment of inertia of the hoisted object in real time, it can automatically adapt to different loads without manual preset, demonstrating "high adaptability"; by unifying sway suppression and attitude adjustment in the optimal control framework, it can reduce step-by-step operations and improve accuracy and efficiency through state feedback collaborative control. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the structure of the hoisting posture adjustment system provided in the embodiments of this application; Figure 2 A schematic diagram illustrating the lifting system as a three-bar underactuated system comprising two rotary joints and one locating joint, provided for embodiments of this application; Figure 3 A block diagram of the control system for the hoisting posture adjustment system provided in the embodiments of this application; Figure 4 A schematic diagram of the main process of the oscillation suppression control method based on inertial parameters provided in the embodiments of this application; Figure 5 A data signal flow diagram of the state data (i.e., sensor data) of the hoisting system involved in the sway suppression control method based on inertial parameters provided in the embodiments of this application; Figure 6 Another schematic diagram of the oscillation suppression control method based on inertial parameters provided in the embodiments of this application.

[0020] The following are the labeling elements in the figure: The hoisting equipment includes ropes-200, hoisting platform-11, horizontal movement platform-13, lifting tool-12, hoisted object-300, hoisting equipment hook-210, horizontal steering device-30, cable-21, cable drive-223, movable pulley-13, and spring buckle-23; first horizontal movement platform 13-1 and second horizontal movement platform 13-2. Detailed Implementation

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), unless otherwise expressly and specifically defined.

[0025] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0027] To address the technical deficiencies of the prior art mentioned in the background section, this invention proposes a hoisting posture adjustment system and a sway suppression control method based on inertial parameters, such as... Figure 1 As shown, the hoisting posture adjustment system is applied to hoisting equipment, and the hoisting posture adjustment system includes a hoisting system and a control system; The hoisting posture adjustment system is installed below the hook 210 of the hoisting equipment. The hoisting posture adjustment system includes a horizontal steering device 30, a cable 21, a hoisting platform 11, a horizontal movement platform 13, and a lifting device 12 arranged sequentially along the vertical direction. The horizontal steering device 30 is used to adjust the horizontal orientation of the hoisted object 300. The lifting device 12 is connected to the hoisting platform 11 or the horizontal movement platform 13 and is used to connect the hoisted object 300. The horizontal movement platform 13 is connected to the hoisting platform 11 and is used to adjust the horizontal position of the hoisted object 300. The cable 21 is connected to the hoisting platform 11 and is used to adjust the posture of the hoisted object 300. The horizontal movement platform 13 includes a first horizontal movement platform 13-1 and a second horizontal movement platform 13-2, with the first horizontal direction and the second horizontal direction being perpendicular to each other. In specific applications, the hoisting equipment mentioned in this application can be a crane, and the hoisting posture adjustment system of this application can be a hoisting end effector system with active control capability installed below the crane hook.

[0028] It should be noted that the reference Figure 2 Before implementing the sway suppression control method for the hoisting posture adjustment system based on inertial parameters of this application, the following preprocessing is performed in this embodiment: S01: The hoisting system of the hoisting posture adjustment system is modeled as a three-bar underactuated system containing at least two rotary joints and at least one locating joint; wherein, the two rotary joints respectively represent the swing degrees of freedom of the hoisting module in two orthogonal directions, corresponding to swing angles θ1 and θ2, and the locating joint represents the displacement d of the horizontal motion platform; S02: Establish the nonlinear coupled dynamic equations of the three-bar underactuated system according to the preset multibody system dynamics modeling method.

[0029] For example, the preset multibody system dynamics modeling method in this embodiment can use the Lagrange mechanics method to establish the nonlinear coupled dynamic equations of the three-bar underactuated system; the following description uses the Lagrange mechanics method as an example. It should be noted that the use of the Lagrange method is only a preferred example method in the embodiments of this application; in other embodiments, modifications or replacements to the Lagrange method of this example using similar methods (for example, in some other examples, the preset multibody system dynamics modeling method may also be to use Newton-Euler equations to establish the nonlinear coupled dynamic equations of the three-bar underactuated system) do not cause the essence of the corresponding technical solution to deviate from the concept and scope of the technical solution of this application, and should all be included within the protection scope of this application.

[0030] Specifically, as shown in Figure 2, the hoisting posture adjustment system of this embodiment can be simplified into a three-bar linkage model: The lifting point and the rope of the lifting equipment 200 → First rotating joint, indicating the main swing angle The main swing angle is the lifting point of the hook 210 of the hoisting equipment and the deflection angle of the rope 200 of the hoisting equipment relative to the vertical direction, which is used to reflect the overall swing of the hoisted object 300 caused by the rope 200 of the hoisting equipment. The lifting equipment hook 210, horizontal steering device 30, spring buckle 23 and movable pulley 13 → are simplified to point masses; The cable 21 between the horizontal steering device 30 and the hoisting platform 11 → the second rotary joint, indicating the local swing angle. It can be understood that the local swing angle is the deflection angle of the cable 21 between the horizontal steering device 30 and the hoisting platform 11 relative to its equilibrium position, which is used to reflect the local relative swing between the hoisting platform 11 and the hoisted object 300. Lifting platform 11 and horizontal motion platform 13 + hoisted object 300 → movable joint, indicating relative displacement. This can be understood as: relative displacement It refers to the relative linear displacement in the horizontal direction between the hoisting platform 11 and the horizontal motion platform 13, used to adjust the posture alignment of the hoisted object 300.

[0031] Through the above simplification, the complex multi-cable interactive system can be represented as two rotational degrees of freedom ( , Add one translational degree of freedom. This not only retains the main dynamic characteristics of the hoisted object 300, but also facilitates modeling and control design.

[0032] Accordingly, refer to Figure 3 The control system of the hoisting position adjustment system of this application includes: Sensor module 01 is used to collect real-time status data of the hoisting system, including the main swing angle. Local swing angle The main swing angular velocity, local swing angular velocity, main swing angular acceleration, local swing angular acceleration, and displacement of the horizontal motion platform 13. ; In some embodiments, the sensor module includes an inertial measurement unit (IMU) mounted on the rope 200 of the hoisting equipment and the hoisting platform 11, and an encoder mounted on the output shaft of the drive motor of the horizontal motion platform 13; The inertial measurement unit (IMU) is configured to measure the principal swing angle, the local swing angle, and their angular velocity and angular acceleration; The encoder is configured to measure the displacement and velocity of the horizontal motion platform 13.

[0033] The dynamic modeling module 02 establishes the nonlinear coupled dynamic equations of the hoisting posture adjustment system according to the preset multibody system dynamic modeling method. The dynamic equations are used to describe the motion behavior of the system under at least two rotational degrees of freedom and at least one translational degree of freedom. Understandably, the dynamic modeling module in this application implements nonlinear dynamic modeling: it can establish nonlinear coupled dynamic equations for the hoisting system based on Lagrange mechanics methods, fully characterizing the system's translational and rotational kinetic energy, gravitational potential energy, and control force / torque applied by the motor. The derived symbolic equations are then converted into executable code, providing a high-precision mathematical model foundation for subsequent parameter identification and control law design.

[0034] The adaptive parameter identification module 03 receives the state data collected by the sensor module, uses a real-time parameter estimation algorithm to estimate the mass and moment of inertia of the hoisted object 300 online, and feeds back the estimation results to the dynamic modeling module 02 to dynamically update the inertial parameters in the nonlinear coupled dynamic equations, thereby obtaining the updated dynamic model. It is understood that, in order to address the problem of unknown or changing load parameters, the control system in this application embodiment can integrate an adaptive parameter identification module for real-time parameter identification: For example, the adaptive parameter identification module 03 can acquire data from the inertial measurement unit (IMU) and encoder at a high sampling rate (e.g., 200Hz) and perform estimation using a real-time parameter estimation algorithm. The identification process is completed within one control cycle, with a delay typically less than one cycle (a few milliseconds), ensuring that parameter estimation is synchronized with control calculation. The real-time parameter estimation algorithm in this embodiment can employ one of the following state estimation algorithms: Recursive Least Squares (RLS), Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF), to adapt to different noise characteristics. For example, this embodiment uses Recursive Least Squares as an example of the real-time parameter estimation algorithm of the present invention for illustration.

[0035] For example, the inertial measurement unit (IMU) in this embodiment can be a six-axis or nine-axis inertial sensor such as MPU-9250, BMI160 or XsensMTi-3, which is installed on the rope 200 and the hoisting platform 11 of the hoisting equipment; the encoder can be a photoelectric encoder or a magnetic encoder (e.g., a 1000 line / coil photoelectric encoder or an AS5048A magnetic encoder), which is installed on the output shaft of the motor of the horizontal motion platform 13 for real-time measurement of the displacement and motion speed of the horizontal motion platform 13.

[0036] In its implementation, the adaptive parameter recognition module 03 reads the real-time system status from sensors such as the inertial measurement unit (IMU) and the motor encoder, including the swing angle (such as the main swing angle). Local swing angle ), angular velocity, angular acceleration, and horizontal platform displacement Then, the adaptive parameter identification module 03 uses a real-time parameter estimation algorithm (taking recursive least squares as an example) to recursively estimate the unknown load parameters by minimizing the error between the sensor measurement value and the model output. In this embodiment, the unknown load parameters mainly include the mass of the hoisted object 300. and moment of inertia Finally, the adaptive parameter identification module 03 continuously feeds back the identified unknown load parameters to update the dynamic model, ensuring that the model is consistent with the real system.

[0037] Through the aforementioned adaptive parameter identification module 03, this embodiment can achieve adaptive identification of different load modules, thereby effectively improving the adaptability and accuracy of the control system.

[0038] The optimal control module 04, based on the updated dynamic model, linearizes the nonlinear model in each control cycle, constructs a state-space model, and uses the optimal control algorithm to generate control inputs for the motor driving the horizontal motion platform 13, so as to achieve the suppression of the swing of the hoisted object 300 and the coordinated adjustment of its attitude.

[0039] In practical applications, the optimal control module 04 in this embodiment can be implemented based on the LQR (linear quadratic regulator) algorithm. The specific implementation process is described in the embodiment of the sway suppression control method based on inertial parameters below. This allows the value of the force applied to the motor of the horizontal motion platform to be optimal, thereby suppressing the sway of the hoisted object while driving it to converge toward the target pose.

[0040] In some embodiments, the dynamic modeling module 02, the adaptive parameter identification module 03, and the optimal control module 04 are integrated into the central control unit of the control system. The central control unit is deployed in the hoisting platform 11 or a remote control box to receive the status data of the hoisting system collected by the sensor module.

[0041] Correspondingly, the control system for the aforementioned hoisting position adjustment system, such as Figure 4 As shown, this application also proposes a sway suppression control method based on inertial parameters. The control method in this embodiment mainly includes the following steps S1 to S5: Step S1: Collect the status data of the hoisting system in real time through the sensor module; wherein, the status data includes the main swing angle, local swing angle, corresponding angular velocity and angular acceleration, as well as the displacement and movement speed of the horizontal motion platform; Step S2: Input the state data into the adaptive parameter identification module, and use the real-time parameter estimation algorithm to estimate the mass and moment of inertia of the hoisted object online; Step S3: Feed back the estimated mass and moment of inertia to the dynamic modeling module to update the nonlinear coupled dynamic equations established according to the preset multibody system dynamic modeling method, and obtain the updated dynamic model; Step S4: Based on the updated dynamic model, the optimal control module linearizes the hoisting system in each control cycle, constructs a state-space model, and uses the optimal control algorithm to solve for the optimal feedback gain matrix; Step S5: Based on the state data and the optimal feedback gain matrix, calculate the optimal control input data and output it to the motor of the horizontal motion platform to suppress the swaying of the hoisted object and adjust the attitude of the hoisted object.

[0042] Understandably, in one embodiment of the present invention, the optimal control module generates control inputs for the motors driving the horizontal motion platform based on the updated dynamic model and using the optimal control algorithm, so as to achieve suppression of the swing of the hoisted object and coordinated adjustment of its attitude. The technical advantages of this embodiment are twofold: Parameter self-adaptation: By identifying the mass and moment of inertia of the hoisted object in real time, it can automatically adapt to different loads without the need for manual preset, demonstrating "high adaptability"; Integrated control: Unifying sway suppression and attitude adjustment within an optimal control framework (such as LQR), and using state feedback for collaborative control, reducing step-by-step operations and improving accuracy and efficiency.

[0043] For example, for steps S4 and S5 above, the optimal control module executes the following sub-steps in each control cycle: Sub-step S41 (Model linearization) The nonlinear coupled dynamic equations are linearized near the current system operating point to obtain a linear time-varying state-space model. For example, the linear time-varying state-space model can be expressed by the following expression: System state vector = Ax + Bu in, x This represents a "state vector" such as "main swing angle, local swing angle, main swing angular velocity, local swing angular velocity, main swing acceleration, local swing acceleration, as well as the displacement and velocity of the horizontal motion platform". u Indicates "control input". A and B The system matrix that varies with time ( A Represents the system state matrix. B (This can be represented by an input matrix), whose values ​​are updated in real time by the adaptive parameter recognition module based on the mass of the hoisted object 300. m and moment of inertia I Decide.

[0044] Sub-step S42 (Constructing the cost function) Determine the quadratic performance index function:

[0045] in: This is a state weighting matrix (to suppress swaying), used to adjust the priority of each state variable (main sway angle, local sway angle, corresponding angular velocity and angular acceleration, and displacement of the horizontal motion platform) in the control objective; The control input weighting matrix (limiting motor force) is used to limit the amplitude of control force or torque to prevent actuator saturation or excessive energy consumption. The state vector in the state space model constructed by the optimal control module includes the main swing angle, local swing angle, main swing angular velocity, local swing angular velocity, main swing angular acceleration, local swing angular acceleration, displacement of the horizontal motion platform, and motion velocity of the horizontal motion platform. T This indicates the operation time of the control system of the hoisting posture adjustment system; This represents the control input variable used to drive the motor of the horizontal motion platform (which can be understood as the force applied to the motor of the horizontal motion platform). It is a differential element; It is understood that in the linear quadratic regulator (LQR) control design of this application embodiment, the above quadratic performance index function can be used to measure the performance of the control system and thereby optimize the controller design. Specifically, the quadratic performance index function... This represents an integral cost function used to evaluate the system's performance throughout the control process.

[0046] This is a measure of performance or a cost function throughout the control process. Its purpose is to minimize this cost by selecting appropriate control inputs. In the design of a linear quadratic regulator (LQR), it mainly consists of two parts: the first part is related to the state variables (hoping the system state is as close to the target state as possible), and the second part is related to the control input (hoping the control energy is as low as possible). These two parts are connected by a state weighting matrix. and control input weighting matrix To achieve balance; by adjusting the state weighting matrix. and control input weighting matrix It can affect the speed and stability of the system response, as well as control input variables. The energy level, the goal is to minimize this performance metric. In order to find the optimal control strategy.

[0047] Sub-step S43 (Solve for the optimal feedback gain matrix) Combining the aforementioned linear time-varying state-space model, the expression "system state vector = A x+ Bu State weighting matrix and control input weighting matrix In this embodiment, the optimal feedback gain matrix can be calculated by solving the algebraic Riccati equation. K ; Then perform step S5 (oscillation suppression control) as described above. According to the state data With the optimal feedback gain matrix K Through the control law formula Calculate the optimal control input data and the optimal control input data The motor outputs power to the horizontal motion platform so that the hoisted object is in a controlled state, which indicates that the swing of the hoisted object is suppressed and its posture is adjusted.

[0048] Understandably, reference Figure 5 Control law formula The performance index of the above quadratic performance index function Minimize, that is, optimize the control input data This allows the force applied to the motor on the horizontal motion platform to be optimized, thereby suppressing the swaying of the hoisted object while driving it to converge toward the target pose.

[0049] In some embodiments, for the adjustment state weighting matrix It can be used for the main swing angle Local swing angle The principal angular velocity, local angular velocity, principal angular acceleration, and local angular acceleration are given relatively large weight values ​​(e.g., on the order of magnitude of 1). To prioritize suppressing swaying; to assign moderate weights (e.g., on the order of magnitude) to the displacement d and velocity of the horizontal motion platform. This is to achieve a smooth posture adjustment.

[0050] Furthermore, the control input weighting matrix The range of values ​​can be It is used to balance the control response speed and motor energy consumption, and to avoid motor saturation or excessive energy consumption.

[0051] Furthermore, after step S5, the control method further includes: Acquire new sensor data collected by the sensor module. The new sensor data is new status data of the hoisting system, which is used to reflect the real-time status of the hoisted object under the control action state. The new sensor data is fed back to the adaptive parameter recognition module as input for the next round of state data, so as to realize the closed-loop iterative control of the control system of the hoisting posture adjustment system.

[0052] Understandably, reference Figure 6 The optimal control module repeatedly executes the above sub-steps (from) a predetermined control period (e.g., 5ms, corresponding to 200Hz). Sub-steps S41 to S43 and step S5 It generates control commands in real time and outputs them to the motor of the horizontal motion platform to achieve continuous and adaptive control of the hoisted object.

[0053] The technical advantage of this embodiment lies in the control based on a continuously updated dynamic model. The model can reflect the actual system state, enhance the compensation capability for external disturbances (such as wind disturbance), and improve the system robustness.

[0054] In other embodiments, to ensure the reliability of the optimal control module in complex construction environments, the embodiments of this application add safety and fault-tolerant designs to the above-mentioned linear quadratic regulator (LQR) control design: To enhance safety and fault tolerance mechanisms, the control system of the hoisting posture adjustment system in this embodiment may further include a safety and fault tolerance control module to ensure safe system operation when sensors malfunction, actuators fail, or the state exceeds limits. The security and fault tolerance control module includes: The sensor redundancy unit is used to perform multi-source measurements of the main swing angle, the local swing angle and the displacement of the horizontal motion platform, and output the fused state value through a data fusion algorithm; Specifically, critical states are measured in parallel by multiple types of sensors (such as IMUs and encoders), and robustness is improved through data fusion.

[0055] An emergency stop logic unit is used to cut off the power supply of the control system when the main swing angle, the local swing angle, the control input (e.g., control input torque) of the motor driving the horizontal motion platform, or the displacement of the horizontal motion platform exceeds a preset safety threshold. Understandably, the drive should be immediately cut off when the swing angle, motor force, or displacement exceeds the safety threshold.

[0056] The boundary protection unit dynamically adjusts the control law formula when the system state approaches the physical boundary. The gain may switch to a low-gain safe mode; The fault-tolerant control unit switches to a backup control channel or enables a simplified control law in the event of sensor failure or actuator malfunction, thereby maintaining basic system stability.

[0057] The technical advantage of this embodiment lies in integrating mechanisms such as sensor redundancy, emergency stop logic, and fault-tolerant control to achieve fully automated operation, reduce the risk of human intervention, and ensure construction safety.

[0058] The beneficial effects of this invention compared to the prior art can be seen in the table below:

[0059] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional systems, devices, and controllers is used as an example. In practical applications, the above functions can be assigned to different functional units or modules as needed, that is, the internal structure can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0060] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0061] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such 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 this application, and should all be included within the protection scope of this application.

Claims

1. A hoisting position adjustment system, characterized in that, The hoisting posture adjustment system is installed below the hook (210) of the hoisting equipment. The hoisting posture adjustment system includes a hoisting system and a control system. The hoisting system includes a horizontal steering device (30), a cable (21), a hoisting platform (11), a horizontal movement platform (13), and a lifting device (12) arranged sequentially in the vertical direction; the horizontal steering device (30) is used to adjust the horizontal orientation of the hoisted object (300); the lifting device (12) is connected to the hoisting platform (11) or the horizontal movement platform (13) and is used to connect the hoisted object (300); the horizontal movement platform (13) is connected to the hoisting platform (11) and is used to adjust the horizontal position of the hoisted object (300); the cable (21) is connected to the hoisting platform (11) and is used to adjust the posture of the hoisted object (300); The control system includes: The sensor module is used to collect the status data of the hoisting system in real time. The status data includes the main swing angle, local swing angle, main swing angular velocity, local swing angular velocity, main swing acceleration, local swing acceleration, displacement of the horizontal motion platform (13), and motion speed of the horizontal motion platform (13). The dynamic modeling module establishes the nonlinear coupled dynamic equations of the hoisting posture adjustment system according to a preset multibody system dynamic modeling method. The dynamic equations are used to describe the motion behavior of the system under at least two rotational degrees of freedom and at least one translational degree of freedom. The adaptive parameter identification module receives the state data collected by the sensor module, uses a real-time parameter estimation algorithm to estimate the mass and moment of inertia of the hoisted object (300) online, and feeds back the estimation results to the dynamic modeling module to dynamically update the inertial parameters in the nonlinear coupled dynamic equations, thereby obtaining the updated dynamic model. The optimal control module, based on the updated dynamic model, linearizes the nonlinear model in each control cycle, constructs a state space model, and uses the optimal control algorithm to generate control inputs for the motor driving the horizontal motion platform (13), so as to suppress the swing of the hoisted object (300) and coordinate the attitude adjustment.

2. The hoisting position adjustment system as described in claim 1, characterized in that, The sensor module includes an inertial measurement unit installed on the rope (200) and the hoisting platform (11) of the hoisting equipment, and an encoder installed on the output shaft of the drive motor of the horizontal motion platform (13); The inertial measurement unit is configured to measure the principal swing angle, the local swing angle, and their angular velocity and angular acceleration; The encoder is configured to measure the displacement and velocity of the horizontal motion platform (13).

3. The hoisting position adjustment system as described in claim 1 or 2, characterized in that: The main swing angle is the lifting point of the hook (210) of the hoisting equipment and the deflection angle of the rope (200) of the hoisting equipment relative to the vertical direction, which is used to reflect the overall swing of the hoisted object (300) caused by the rope (200) of the hoisting equipment. The local swing angle is the deflection angle of the cable (21) between the horizontal steering device (30) and the hoisting platform (11) relative to its equilibrium position, used to reflect the local relative swing between the hoisting platform (11) and the hoisted object (300); The displacement of the horizontal motion platform (13) represents the relative linear displacement between it and the hoisting platform (11) in the horizontal direction, and is used to adjust the orientation alignment of the hoisted object (300).

4. The hoisting position adjustment system as described in claim 1, characterized in that: The dynamic modeling module, the adaptive parameter identification module, and the optimal control module are integrated in the central control unit of the control system. The central control unit is deployed in the hoisting platform (11) or remote control box to receive the status data of the hoisting system collected by the sensor module.

5. A method for suppressing and controlling oscillations based on inertial parameters, characterized in that, Performed by the hoisting position adjustment system as described in any one of claims 1 to 4, the method includes the following steps: Step S1: Collect the status data of the hoisting system in real time through the sensor module; wherein, the status data includes the main swing angle, local swing angle, corresponding angular velocity and angular acceleration, as well as the displacement of the horizontal motion platform and the motion speed of the motion platform; Step S2: Input the state data into the adaptive parameter identification module, and use the real-time parameter estimation algorithm to estimate the mass and moment of inertia of the hoisted object online; Step S3: Feed back the estimated mass and moment of inertia to the dynamic modeling module to update the nonlinear coupled dynamic equations established according to the preset multibody system dynamic modeling method, and obtain the updated dynamic model; Step S4: Based on the updated dynamic model, the optimal control module linearizes the hoisting system in each control cycle, constructs a state-space model, and uses the optimal control algorithm to solve for the optimal feedback gain matrix; Step S5: Calculate the optimal control input data based on the state data and the optimal feedback gain matrix, and output the optimal control input data to the motor of the horizontal motion platform to suppress the swaying of the hoisted object and adjust the attitude of the hoisted object.

6. The oscillation suppression control method based on inertial parameters as described in claim 5, characterized in that, Before implementing the oscillation suppression control method based on inertial parameters, the method further includes: Step S01: Model the hoisting system as a three-bar underactuated system including a first rotary joint, a second rotary joint, and a traverse joint; The first rotary joint represents the swing degree of freedom of the hoisted object in an orthogonal direction, and corresponds to the principal swing angle. The principal swing angle is the lifting point of the hook of the hoisting equipment and the deflection angle of the rope of the hoisting equipment relative to the vertical direction, which is used to reflect the overall swing of the hoisted object caused by the rope of the hoisting equipment. The second rotary joint represents the swing degree of freedom in another orthogonal direction, corresponding to the local swing angle, which is the deflection angle of the cable between the horizontal steering device and the hoisting platform relative to its equilibrium position, used to reflect the local relative swing between the hoisting platform and the hoisted object. Wherein, the movable joint represents the displacement of the horizontal motion platform, and the displacement of the horizontal motion platform is its relative linear displacement with respect to the hoisting platform in the horizontal direction; Step S02: Establish the nonlinear coupled dynamic equations of the three-link underactuated system according to the preset multibody system dynamics modeling method.

7. The oscillation suppression control method based on inertial parameters as described in claim 5, characterized in that, Step S4 further includes: Sub-step S41: Linearize the nonlinear coupled dynamic equations near the current system state operating point to obtain a linear time-varying state space model; Sub-step S42: Determine the quadratic performance index function, which is expressed by the following formula: in, Represents the weighted matrix of the adjusted state, and Represents the control input weighting matrix; The state vector in the state space model constructed by the optimal control module includes the main swing angle, local swing angle, main swing angular velocity, local swing angular velocity, main swing angular acceleration, local swing angular acceleration, displacement of the horizontal motion platform, and motion velocity of the horizontal motion platform. T This indicates the operation time of the control system of the hoisting posture adjustment system; This represents the control input variable used to drive the motor of the horizontal motion platform; Sub-step S43: Combining the linear time-varying state-space model and the adjustment state weighting matrix Q and the control input weighting matrix R Calculate the optimal feedback gain matrix K ; Sub-step S44: Based on the state data With the optimal feedback gain matrix K The control law formula is obtained. And use the control law formula to calculate the optimal control input data. and the optimal control input data The motor output to the horizontal motion platform is used to suppress the swaying of the hoisted object and adjust its posture.

8. The oscillation suppression control method based on inertial parameters as described in claim 7, characterized in that, Step S5 further includes: According to the status data With the optimal feedback gain matrix K Calculate the optimal control input data The optimal control input data is then output to the motor of the horizontal motion platform so that the hoisted object is in a controlled state, which indicates that the swing of the hoisted object is suppressed and its posture is adjusted.

9. The oscillation suppression control method based on inertial parameters as described in claim 8, characterized in that, After step S5, the control method further includes: Acquire new sensor data collected by the sensor module. The new sensor data is new status data of the hoisting system and is used to reflect the real-time status of the hoisted object under the control action state. The new sensor data is fed back to the adaptive parameter recognition module as input for the next round of state data, so as to realize the closed-loop iterative control of the control system of the hoisting posture adjustment system.

10. The oscillation suppression control method based on inertial parameters as described in claim 5, characterized in that, The real-time parameter estimation algorithm is one of recursive least squares, extended Kalman filtering, or unscented Kalman filtering.

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