Tower crane control method, controller, computing equipment and storage medium

Abstract

The embodiment of the invention provides a tower crane control method, a controller, computing equipment and a storage medium, and the method comprises the steps that a dynamic model of the movement of a hanging object is established and serves as a prediction model of an MPC algorithm, and influence factors of the dynamic model comprise disturbance estimators; according to the reference trajectory of the hanging object, the motion constraint condition and the measured value of the motion state in the last MPC period, the predicted value of the motion state in the current MPC period is predicted through an MPC algorithm; and according to the predicted value of the motion state in the current MPC period, the motion control quantity of the tower crane in each PID period in the current MPC period is adjusted through a PID algorithm. According to the technical scheme, the position, the swing angle and the strain are adopted for modeling the motion state of the tower crane, the disturbance estimator is added into the dynamic model of the hanging object, predictive control of MPC and PID real-time closed-loop control are dynamically coupled, the problems of slow dynamic response and poor anti-interference capability of tower crane control during multi-physical domain coupling are solved, and the control accuracy of the tower crane is improved. And the problem of large jitter of traditional PID control is also solved.

Description

Technical Field

[0001] The present application relates to the field of automatic control technology, and in particular to a tower crane control method, controller, computing device and storage medium. Background Art

[0002] The PID control algorithm is currently used for the automatic control of tower crane motion. However, traditional PID control suffers from slow dynamic response and poor anti-interference ability in the tower crane's multivariable coupling system. Even when combined with SLAM-based path planning, the problem of large jitter still exists. Summary of the Invention

[0003] In view of this, the embodiments of the present application provide a tower crane control method, controller, computing device and storage medium. The technical solution of the embodiments of the present application uses position, swing angle and strain to model the motion state of the tower crane, adds a disturbance estimator to the dynamic model of the hoisted object, and dynamically couples the MPC predictive control and PID real-time closed-loop control. It not only solves the problems of slow dynamic response and poor anti-interference ability of tower crane control when multiple physical domains are coupled, but also solves the problem of large jitter in traditional PID control.

[0004] In the first aspect, an embodiment of the present application provides a tower crane control method, including: establishing a dynamic model of the motion of a hoisted object as a prediction model of the motion state of the hoisted object in the MPC algorithm, the motion state including the position, swing angle, and strain of the tower arm set point of the hoisted object, and the influencing factors of the dynamic model including the disturbance estimation; based on the reference trajectory of the hoisted object, the motion constraints and the measured value of the motion state in the previous MPC cycle, the MPC algorithm predicts the predicted value of the motion state in the current MPC cycle; based on the predicted value of the motion state in the current MPC cycle, the PID algorithm adjusts the motion control amount of the tower crane in each PID cycle in the current MPC cycle, and the motion control amount is used to control the motion of the tower crane.

[0005] As described above, the motion state of the tower crane is modeled using position, swing angle and strain, a disturbance estimator is added to the dynamic model of the hoisted object, and the MPC predictive control and PID real-time closed-loop control are dynamically coupled. This not only solves the problems of slow dynamic response and poor anti-interference ability of tower crane control when coupling multiple physical domains, but also solves the large jitter problem of traditional PID control.

[0006] In a possible implementation of the first aspect, the motion constraint condition includes at least one of the following: the maximum value of the swing angle in each coordinate direction, the maximum modulus of the rate of change of the predicted control quantity, and the maximum value of the strain, and the predicted control quantity is the predicted value of the motion control quantity in the MPC algorithm.

[0007] From the above, by adding a constrained integrated energy optimization term for the rate of change of the predicted control quantity to the motion constraint conditions, the tower crane motor control is smoothed, making the motor control smooth and reducing the start and stop frequency; adding a constraint on the maximum value of the swing angle in each coordinate direction to control the swing amplitude reduces the influence of motor acceleration, hoisted object inertia and external disturbances, and the strain constraint of the tower arm set point further reduces the influence of motor acceleration and hoisted object inertia.

[0008] In a possible implementation manner of the first aspect, the motion state further includes a speed for tracking the position of the hanging object and an angular velocity for constraining the swing angle of the hanging object.

[0009] From the above, by taking into account the motion state of the hoisted object, including the velocity based on the position and the angular velocity based on the swing angle, the MPC algorithm can be used to track the various motion speeds of the hoisted object, so as to better predict the actual motion trajectory of the tower crane.

[0010] In a possible implementation manner of the first aspect, the method further includes: obtaining the disturbance estimation value according to the measured value of the wind speed and the measured value of the suspended object vibration.

[0011] From the above, it can be seen that the wind speed and the vibration of the hanging object are highly random and cannot be modeled in the motion state of the hanging object. However, the disturbance estimator is used to compensate for them when predicting the motion state of the hanging object, which further improves the accuracy of the predicted value of the motion state of the hanging object.

[0012] In a possible implementation of the first aspect, the motion control quantity includes a control quantity for tracking the position of the hanging object and a control quantity for constraining the swing angle of the hanging object, and the PID algorithm adjusts the motion control quantity of the tower crane in each PID cycle in the current MPC cycle, including: adjusting the control quantity for tracking the position of the hanging object in each PID cycle in the current MPC cycle by the PID algorithm with the predicted value of the position of the hanging object in the motion state in the current MPC cycle as the target; adjusting the control quantity for constraining the swing angle of the hanging object in each PID cycle in the current MPC cycle by the PID algorithm with 0 swing angle as the target and the predicted swing angle of the current MPC cycle in the motion state as the constraint.

[0013] From the above, the PID algorithm for tracking the position of the hanging object realizes the position change of the hanging object according to the position in the MPC prediction state, and the PID algorithm for constraining the swing angle of the hanging object controls the residual swing amplitude of the hanging object within the safety threshold.

[0014] In a possible implementation of the first aspect, the PID algorithm for constraining the swing angle of the suspended object is a differential-first and acceleration feedforward algorithm.

[0015] From the above, adding the acceleration feedforward algorithm to the PID algorithm for constraining the swing angle of the suspended object is equivalent to the traditional linear, differential and integral algorithms, which improves the control of the swing angle.

[0016] In a possible implementation manner of the first aspect, when obtaining the motion control variable, a maximum modulus value of the motion control variable is constrained according to the strain of the tower arm set point.

[0017] As described above, by constraining the maximum value of the modulus of the motion control variable according to the strain of the tower arm set point, the motion control variable of the tower crane will not cause the strain of the tower arm to exceed the limited range.

[0018] In a possible implementation manner of the first aspect, the method further includes: performing safety braking on the tower crane when an error between a predicted value and a measured value in any dimension of the motion state exceeds a safety threshold.

[0019] From the above, when the error between the predicted value and the measured value in any dimension of the hoisted object's motion state exceeds the safety threshold, the motion of the hoisted object may be unable to be tracked, posing a safety risk. The hoisted object needs to be braked to improve the safety of tower crane control.

[0020] In a possible implementation of the first aspect, each MPC cycle includes several timing cycles, each timing cycle corresponds to a PID cycle and the two have the same duration, and the MPC algorithm also predicts the motion control amount of the tower crane in each timing cycle; the method also includes: in each PID cycle, fusing the motion control amount obtained by the PID algorithm with the motion control amount predicted by the MPC in the corresponding timing cycle to obtain a fused motion control amount of the tower crane, and the fused motion control amount is used to control the movement of the tower crane.

[0021] From the above, by fusing the motion control quantity of each PID cycle obtained by the PID algorithm and the telecontrol quantity of the corresponding timing cycle predicted by the MPC algorithm, not only the motion control quantity of the tower crane can be adjusted based on the feedback of the tower crane encoder, but also the final telecontrol quantity of the tower crane can meet the motion constraint conditions.

[0022] In a possible implementation of the first aspect, in the current PID cycle, the motion control amount obtained by the PID algorithm and the motion control amount predicted by the MPC in the corresponding timing cycle are integrated, specifically including: based on the distance between the actual position of the hanging object in the previous PID cycle and the predicted position of the hanging object in the timing cycle corresponding to the previous PID cycle, the motion control amount obtained by the PID algorithm in the current PID cycle and the motion control amount predicted by the MPC in the corresponding timing cycle are integrated, wherein the smaller the distance, the greater the weight of the motion control amount obtained by the PID algorithm.

[0023] From the above, the fusion coefficient is obtained according to the distance between the actual position of the hoisted object in the last PID cycle and the predicted position of the hoisted object in the motion state in the timing cycle corresponding to the last PID cycle. The smaller the distance, the greater the weight of the motion control amount obtained by the PID algorithm, further realizing not only adjusting the motion control amount of the tower crane based on the feedback of the tower crane encoder, but also further making the final remote control amount of the tower crane meet the motion constraint conditions.

[0024] In the second aspect, an embodiment of the present application provides a controller for a tower crane, comprising: a model module for establishing a dynamic model of the motion of a hoisted object, which serves as a prediction model of the motion state of the hoisted object in the MPC algorithm, wherein the motion state includes the position, swing angle, and strain of the tower arm of the hoisted object, and the influencing factors of the dynamic model include a disturbance estimator; an MPC module for predicting the predicted value of the motion state in the current MPC cycle by the MPC algorithm based on the reference trajectory of the hoisted object, the motion constraints, and the measured value of the motion state in the previous MPC cycle; a PID module for adjusting the motion control amount of the tower crane in each PID cycle in the current MPC cycle by the PID algorithm based on the predicted value of the motion state in the current MPC cycle, and the motion control amount is used to control the motion of the tower crane.

[0025] As described above, the motion state of the tower crane is modeled using position, swing angle and strain, a disturbance estimator is added to the dynamic model of the hoisted object, and the MPC predictive control and PID real-time closed-loop control are dynamically coupled. This not only solves the problems of slow dynamic response and poor anti-interference ability of tower crane control when coupling multiple physical domains, but also solves the large jitter problem of traditional PID control.

[0026] In a possible implementation of the second aspect, the motion constraint condition includes at least one of the following: the maximum value of the swing angle in each coordinate direction, the maximum modulus of the rate of change of the predicted control quantity, and the maximum value of the strain, and the predicted control quantity is the predicted value of the motion control quantity in the MPC algorithm.

[0027] From the above, by adding a constrained integrated energy optimization term for the rate of change of the predicted control quantity to the motion constraint conditions, the tower crane motor control is smoothed, making the motor control smooth and reducing the start and stop frequency; adding a constraint on the maximum value of the swing angle in each coordinate direction to control the swing amplitude reduces the influence of motor acceleration, hoisted object inertia and external disturbances, and the strain constraint of the tower arm set point further reduces the influence of motor acceleration and hoisted object inertia.

[0028] In a possible implementation of the second aspect, the motion state further includes a velocity of the hanging object based on the position and an angular velocity based on the swing angle.

[0029] From the above, by taking into account the motion state of the hoisted object, including the velocity based on the position and the angular velocity based on the swing angle, the MPC algorithm can be used to track the various motion speeds of the hoisted object, so as to better predict the actual motion trajectory of the tower crane.

[0030] In a possible implementation of the second aspect, the model module is further configured to obtain the disturbance estimate based on a measured value of wind speed and a measured value of suspended object vibration.

[0031] From the above, it can be seen that the wind speed and the vibration of the hanging object are highly random and cannot be modeled in the motion state of the hanging object. However, the disturbance estimator is used to compensate for them when predicting the motion state of the hanging object, which further improves the accuracy of the predicted value of the motion state of the hanging object.

[0032] In a possible implementation of the second aspect, the motion control quantity includes a control quantity for tracking the position of the hanging object and a control quantity for constraining the swing angle of the hanging object. The PID module is specifically used to: adjust the control quantity for tracking the position of the hanging object in each PID cycle of the tower crane in the current MPC cycle by using the PID algorithm with the predicted value of the position of the hanging object in the motion state in the current MPC cycle as the target; adjust the control quantity for constraining the swing angle of the hanging object in each PID cycle of the tower crane in the current MPC cycle by using the PID algorithm with 0 swing angle as the target and the predicted swing angle of the current MPC cycle in the motion state as the constraint.

[0033] From the above, the PID algorithm for tracking the position of the hanging object realizes the position change of the hanging object according to the position in the MPC prediction state, and the PID algorithm for constraining the swing angle of the hanging object controls the residual swing amplitude of the hanging object within the safety threshold.

[0034] In a possible implementation of the second aspect, the PID algorithm for constraining the swing angle of the suspended object is a differential advance and acceleration feedforward algorithm.

[0035] From the above, adding the acceleration feedforward algorithm to the PID algorithm for constraining the swing angle of the suspended object is equivalent to the traditional linear, differential and integral algorithms, which improves the control of the swing angle.

[0036] In a possible implementation manner of the second aspect, when obtaining the motion control variable, the PID module further constrains the modulus maximum value of the motion control variable according to the strain of the tower arm set point.

[0037] As described above, by constraining the maximum value of the modulus of the motion control variable according to the strain of the tower arm set point, the motion control variable of the tower crane will not cause the strain of the tower arm to exceed the limited range.

[0038] In a possible implementation of the second aspect, the method further includes: a braking module, configured to safely brake the tower crane when the error between the predicted value and the measured value of any dimension in the motion state exceeds a safety threshold.

[0039] From the above, when the error between the predicted value and the measured value in any dimension of the hoisted object's motion state exceeds the safety threshold, the motion of the hoisted object may be unable to be tracked, posing a safety risk. The hoisted object needs to be braked to improve the safety of tower crane control.

[0040] In a possible implementation of the second aspect, each MPC cycle includes several timing cycles, each timing cycle corresponds to a PID cycle and the two have the same duration, and the MPC module is also used to predict the motion control amount of the tower crane in each timing cycle; the controller also includes: a fusion module, which is used to fuse the motion control amount obtained by the PID algorithm with the motion control amount predicted by the MPC in the corresponding timing cycle in each PID cycle, and the fused motion control amount is used to control the movement of the tower crane.

[0041] From the above, by fusing the motion control quantity of each PID cycle obtained by the PID algorithm and the telecontrol quantity of the corresponding timing cycle predicted by the MPC algorithm, not only the motion control quantity of the tower crane can be adjusted based on the feedback of the tower crane encoder, but also the final telecontrol quantity of the tower crane can meet the motion constraint conditions.

[0042] In a possible implementation of the second aspect, the fusion module is specifically used to fuse the motion control amount obtained by the PID algorithm in the current PID cycle with the motion control amount predicted by the MPC in the corresponding timing period based on the distance between the actual position of the hanging object in the previous PID cycle and the predicted position of the hanging object in the timing period corresponding to the previous PID cycle, wherein the smaller the distance, the greater the weight of the motion control amount obtained by the PID algorithm.

[0043] From the above, the fusion coefficient is obtained according to the distance between the actual position of the hanging object in the last PID cycle and the predicted position of the hanging object in the motion state in the timing cycle corresponding to the last PID cycle. The smaller the distance is, the greater the weight of the motion control amount obtained by the PID algorithm. This further realizes not only adjusting the motion control amount of the tower crane based on the feedback of the tower crane encoder, but also further making the final remote control amount of the tower crane meet the motion constraint conditions.

[0044] In a third aspect, an embodiment of the present application provides a computing device, including:

[0045] bus;

[0046] a communication interface connected to the bus;

[0047] at least one processor connected to the bus; and

[0048] At least one memory is connected to the bus and stores program instructions, and when the program instructions are executed by the at least one processor, the at least one processor executes the method described in any embodiment of the first aspect of the present application.

[0049] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having program instructions stored thereon, which, when executed by a computer, causes the computer to execute the method described in any embodiment of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a tower crane control method embodiment 1 of the present application;

[0051] Figure 2 This is a structural diagram of a tower crane model in Example 2 of a tower crane control method of the present application;

[0052] Figure 3 This is a flow chart of a tower crane control method embodiment 2 of the present application;

[0053] Figure 4 A schematic diagram of a dynamic model of a tower crane control method according to a second embodiment of the present application;

[0054] Figure 5 This is a structural diagram of a tower crane controller embodiment 1 of the present application;

[0055] Figure 6 This is a data flow diagram of each module in Example 1 of a tower crane controller of the present application;

[0056] Figure 7 This is a structural diagram of a tower crane controller embodiment 2 of the present application;

[0057] Figure 8 A schematic diagram of the structure of the computing device of this application. DETAILED DESCRIPTION

[0058] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0059] In the following description, the terms "first\second\third, etc." or module A, module B, module C, etc. are not only used to distinguish similar objects, or to distinguish different embodiments, but do not represent a specific ordering of the objects. It can be understood that the specific order or sequence can be interchanged where permitted so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0060] In the following description, the numbers representing the steps, such as S110, S120, etc., do not necessarily mean that the steps must be executed in this manner. If permitted, the order of the steps can be interchanged or they can be executed simultaneously.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0062] An embodiment of the present application provides a tower crane control method, controller, computing device and storage medium, the method comprising: establishing a dynamic model of the motion of a hoisted object as a prediction model of the motion state of the hoisted object in an MPC algorithm, the motion state comprising the position, swing angle and strain of the tower arm of the hoisted object, and the influencing factors of the dynamic model comprising a disturbance estimate; based on the reference trajectory of the hoisted object, the motion constraints and the measured value of the motion state in the previous MPC cycle, the MPC algorithm predicts the predicted value of the motion state in the current MPC cycle; based on the predicted value of the motion state in the current MPC cycle, the PID algorithm adjusts the motion control amount of the tower crane in each PID cycle in the current MPC cycle, and the motion control amount is used to control the motion of the tower crane.

[0063] The technical solution of the embodiment of the present application uses position, swing angle and strain to model the motion state of the tower crane, adds a disturbance estimator to the dynamic model of the hoisted object, and dynamically couples the MPC predictive control and PID real-time closed-loop control. It not only solves the problems of slow dynamic response and poor anti-interference ability of tower crane control when multiple physical domains are coupled, but also solves the problem of large jitter in traditional PID control.

[0064] The following describes various embodiments of the present application in conjunction with the accompanying drawings, first introducing the terms involved in the present application.

[0065] MPC (Model Predictive Control) is a motion control algorithm that predicts the motion state in a finite time domain through a model, iteratively optimizes the predicted trajectory in the finite time domain, and obtains the finite time domain open-loop optimal control.

[0066] PID (proportional-integral-derivative control) controls the object by forming a control deviation based on the predicted trajectory and feedback value, and linearly combining the deviation into a control variable using the proportion, integral, and derivative.

[0067] The hoisted object is the abbreviation of the heavy object lifted by the hook of the tower crane.

[0068] The following combination Figure 1 The following describes a first embodiment of a tower crane control method according to the present application.

[0069] Figure 1 The flowchart of a first embodiment of a tower crane control method is shown, including steps S110 to S130.

[0070] S110: Establish a dynamic model of the movement of the suspended object.

[0071] Among them, the dynamic model is used as the prediction model in the MPC algorithm. The motion state of the hoisted object includes the position of the hoisted object, the swing angle, and the strain (also called deformation) of the tower arm setting point. The setting point is the key point on the tower arm. Therefore, the motion state of the hoisted object not only reflects the position of the hoisted object, but also reflects the nonlinear state such as the flexibility of the tower body and the swing of the hoisted object. Compared with the three degrees of freedom of lifting, rotation and amplitude variation, the description of the motion of the hoisted object is more accurate and the control is also more accurate.

[0072] The dynamic model serves as the prediction model in the MPC algorithm. It predicts the next state of motion of the load based on the current state measurements and motion control variables. In this embodiment, the dynamic model's influencing factors include not only the current state measurements but also the disturbance estimate, thereby improving the accuracy of the predicted state of motion.

[0073] In some embodiments, the motion state of the hoisted object also includes the speed of the hoisted object based on the position and the angular velocity based on the swing angle, so as to track the various motion speeds of the hoisted object in the MPC algorithm to better predict the actual motion trajectory of the tower crane.

[0074] S120: The MPC algorithm obtains a predicted value of the motion state of the hanging object.

[0075] Among them, according to the reference trajectory of the hoisted object, the measured value of the motion state and the motion constraints, the MPC algorithm of the dynamic model of the hoisted object is used to obtain the predicted value of the motion state of the hoisted object, that is, the ideal state value of the motion state of the hoisted object during the expected tower crane lifting process.

[0076] The reference trajectory of the hoisted object is the motion trajectory from the starting point to the end point, which is obtained in advance through a trajectory planning algorithm. The trajectory planning algorithm is not limited and can be an S-curve algorithm based on velocity interpolation or an intelligent SLAM algorithm. When the reference trajectory is based on the Cartesian coordinate system, it is converted to coordinate values ​​based on the tower crane coordinate system.

[0077] Among them, the MPC cycle is the working cycle of MPC. Each MPC cycle is divided into several timing cycles. The MPC algorithm includes state prediction and rolling optimization.

[0078] Among them, when predicting the state, in each time period, based on the motion state and motion control amount of the hoisted object in the previous time period, the dynamic model is used to obtain the predicted value of the motion state of the hoisted object in the current time period. The motion state of the hoisted object in the first time period is the measured value of the motion state of the hoisted object in the previous MPC period, and the motion state of other time periods is the predicted value. The motion control amount of the first time period is the actual output value of the motion control amount output by the PID algorithm at the end of the previous MPC period, and the motion control amounts of other time periods are predicted values.

[0079] In rolling optimization, the predicted motion state for each time period is iteratively optimized based on the reference trajectory of the load and the predicted motion state for each time period, within the constraints of the motion. Finally, the predicted motion state for the last time period is output as the predicted motion state for the load in the current MPC cycle. The predicted motion state for each MPC cycle is iteratively optimized to meet the motion constraints and is easier to control than the reference trajectory.

[0080] In some embodiments, the motion constraints of the MPC algorithm include at least one of the following: the maximum value of the swing angle in each coordinate direction, the maximum modulus of the rate of change of the predicted control variable, and the maximum strain of the tower arm set point. The predicted control variable is the predicted value of the tower crane's motion control variable in the MPC algorithm. This constraint is an iterative optimization constraint of the MPC algorithm. The constraint on the rate of change of the predicted control variable integrates an energy optimization term, which smooths the tower crane motor control and reduces the frequency of starts and stops. The constraint on the maximum value of the swing angle in each coordinate direction controls the swing amplitude, reducing the impact of motor acceleration, load inertia, and external disturbances. The strain constraint at the tower arm set point further reduces the impact of motor acceleration and load inertia.

[0081] In the MPC algorithm, not only the motion reference trajectory of the hanging object in the current MPC cycle and the measured value of the motion state at the end of the previous MPC cycle are considered, but also the disturbance estimate of the hanging object's motion is considered, so that the obtained predicted value of the hanging object's motion state is more accurate. In some embodiments, the disturbance estimate of the hanging object's motion is obtained based on the measured value of the wind speed and the measured value of the hanging object's vibration. Among them, a process for obtaining the disturbance estimate of the hanging object's motion includes: obtaining a time function of the disturbance of the hanging object's motion based on the measured value of the wind speed and the measured value of the hanging object's vibration; and performing low-pass filtering on the time function to obtain the disturbance estimate of the hanging object's motion. Because the wind speed and the hanging object's vibration are highly random, they cannot be modeled in the hanging object's motion state. They need to be filtered through low-pass filtering, and the filtering results are used for compensation when predicting the hanging object's motion state, further improving the accuracy of the predicted value of the hanging object's motion state.

[0082] S130: The PID algorithm obtains the motion control variable of the tower crane to control the motion of the hoisted object.

[0083] Based on the predicted motion state of the crane in the next MPC cycle, the PID algorithm calculates the motion control variables for each PID cycle in the next MPC cycle to control the crane's motors. Each MPC cycle consists of several PID cycles. For example, the MPC cycle is 200 milliseconds, and each PID cycle is 10 milliseconds.

[0084] The PID algorithm controls physical quantities in each domain separately, performing closed-loop control based on the load motion predicted by the MPC and feedback from the crane motor encoder. This PID algorithm combines MPC's feedforward path planning with PID's feedback regulation, resolving the conflict between dynamic trajectory tracking and steady-state accuracy maintenance in the PID single-domain control mode.

[0085] Among them, the frequency converter and motor related to the movement of the hoisted object in the tower crane are driven according to the motion control quantity of the tower crane, for example, a frequency conversion instruction is sent to the frequency converter to control the movement of the hoisted object.

[0086] In some embodiments, the motion control variable includes a control variable for tracking the position of the suspended object and a control variable for constraining the swing angle of the suspended object, and the PID algorithm includes one of the following: a PID algorithm for tracking the position of the suspended object and a PID algorithm for constraining the swing angle of the suspended object. The PID algorithm for tracking the position of the suspended object is used to control the motion of the tower crane so that the position of the suspended object changes according to the position change in the motion state predicted by the MPC, thereby obtaining a control variable for tracking the position of the suspended object. The PID algorithm for constraining the swing angle of the suspended object is used to control the motion of the tower crane so that the residual swing amplitude of the suspended object is controlled to zero and within the predicted value, thereby obtaining a control variable for constraining the swing angle of the suspended object.

[0087] In some embodiments, the PID algorithm for constraining the swing angle of the suspended object is a differential-first and acceleration feedforward algorithm, which is equivalent to a traditional linear, differential and integral algorithm, and an acceleration feedforward algorithm is added to improve the control of the swing angle.

[0088] In some embodiments, when obtaining the motion control amount of the tower crane, the modulus maximum value of the motion control amount of the tower crane is constrained according to the strain of the tower arm set point, so that the motion control amount of the tower crane will not cause the strain of the tower arm to exceed the limited range.

[0089] In some embodiments, when the error between the predicted value and the measured value of any dimension in the motion state of the hoisted object exceeds a safety threshold, the tower crane is safely braked. At this time, the motion of the hoisted object may be unable to be tracked, posing a safety risk, and the hoisted object needs to be braked.

[0090] In some embodiments, each MPC cycle includes several timing cycles, each timing cycle corresponds to a PID cycle and the two have the same duration, and the MPC algorithm also predicts the motion control amount of the tower crane in each timing cycle; the method further includes: in each PID cycle, fusing the motion control amount obtained by the PID algorithm with the motion control amount predicted by the MPC in the corresponding timing cycle to obtain a fused motion control amount of the tower crane, and the fused motion control amount ultimately controls the motion of the hoisted object. By fusing the motion control amount of each PID cycle obtained by the PID algorithm with the motion control amount of the corresponding timing cycle predicted by the MPC algorithm, not only is the motion control amount of the tower crane adjusted based on feedback from the tower crane encoder, but the final motion control amount of the tower crane also satisfies the motion constraint conditions.

[0091] In some embodiments, in the current PID cycle, the motion control variable obtained by the PID algorithm in the current PID cycle is fused with the motion control variable predicted by the MPC algorithm in the corresponding sequential cycle based on the distance between the actual position of the hanging object in the previous PID cycle and the predicted position of the hanging object in the sequential cycle corresponding to the previous PID cycle. The smaller the distance, the greater the weight of the motion control variable obtained by the PID algorithm. The predicted position of the hanging object in the sequential cycle corresponding to the previous PID cycle is obtained by the MPC algorithm or is obtained by interpolating the predicted position of the hanging object output in the MPC cycle corresponding to the previous PID cycle, with the number of interpolation points being the same as the number of PID cycles included in each MPC cycle.

[0092] In summary, a tower crane control method embodiment 1 uses position, swing angle and strain to model the motion state of the tower crane, adds a disturbance estimator to the dynamic model of the hoisted object, and dynamically couples the MPC predictive control and PID real-time closed-loop control. It not only solves the problems of slow dynamic response and poor anti-interference ability of tower crane control when multiple physical domains are coupled, but also solves the contradiction between dynamic trajectory tracking and steady-state accuracy maintenance in the single control mode of the tower crane and the large jitter problem of traditional PID control, reducing the positioning error to within ±2cm.

[0093] The following combination Figures 2 to 4 A second embodiment of a tower crane control method of the present application is introduced.

[0094] A tower crane control method embodiment 2 is a specific implementation of a tower crane control method embodiment 1, and has all the advantages thereof.

[0095] Figure 2 The structure of a tower crane model in Example 2 of a tower crane control method is shown. In the figure, the movement of the hanging object is a combination of the lifting, rotation and translation of the tower arm controlled by three motors. The control amount of the tower crane on these three motors is the motion control amount of the tower crane.

[0096] Figure 3 The flowchart of a second embodiment of a tower crane control method is shown, including steps S210 to S270.

[0097] S210: Establish a dynamic model of the movement of the suspended object.

[0098] Figure 4 An example of a dynamic model of a second embodiment of a tower crane control method is shown.

[0099] In the figure, the motion of the load is affected by inertia or wind resistance, resulting in swing. The angle θ in the figure is the swing angle, i.e., the angle of the swing. Simultaneously, the tower arm inevitably experiences strain under the influence of F, f, and F1 in the figure. Therefore, the tower crane can be considered a combined mechanism: a three-degree-of-freedom mechanism + a mobile pendulum formed by the boom trolley and the load. The motion state of the load is described by six degrees of freedom, including the three position coordinates of the lifting, rotation, and boom direction in the tower crane coordinate system, the swing angle of the load in two directions, and the strain at the tower arm's set position. Furthermore, to represent the motion velocity, the motion state of the load also includes the spatial position velocity and the angular velocity of the swing angle. Equation (1) represents the motion state x(k) of the load at point k, where k is a point in the time domain.

[0100]

[0101] Among them, q(k) is the spatial position of the load at point k, which has three directions: lifting, rotation and amplitude variation. is the spatial velocity of the hanging object at point k, which also has the same three directions; θ(k) is the swing angle of the hanging object at point k, which has two directions. is the angular velocity of the swing angle of the hanging object at point k, which has two directions, and ε(k) is the strain of the tower arm set point at point k.

[0102] The tower crane is equipped with the following sensors to obtain the position coordinates and position velocity, swing angle and angular velocity, strain and disturbance of the hoisted object, and establish a dynamic model of the hoisted object movement based on this.

[0103] The posture sensor uses a high-precision laser radar and a MEMS inertial measurement unit (IMU) to obtain the three directions of the load in Cartesian space in real time and convert them into the three-dimensional coordinates of lifting, rotation, and luffing in the crane coordinate system (q x ,q y ,q z ) and the swing angle θ(θ x ,θ y ); The posture sensor also obtains the spatial velocity of the hanging object according to the change of q According to the change of θ, the angular velocity of the pendulum angle is also obtained. For example, the accuracy of the laser radar is 2 mm;

[0104] Tower arm deformation sensor, based on distributed fiber grating (FBG) sensors, measures the strain ε at key tower arm nodes;

[0105] Environmental disturbance sensor, integrating a three-dimensional ultrasonic anemometer (0.1m / s resolution) and a vibration accelerometer; for example, the resolution of the anemometer is 0.1m / s, and the sampling period of the vibration accelerometer is 50Hz. The disturbance observer (DOB) is used to calculate the motion disturbance of the unmodeled hanging object using formula (2). Make an estimate.

[0106]

[0107] Where Gf(s) is a low-pass filter with a cutoff frequency set. For example, it is set to 10 Hz. s is the frequency, q y is the coordinate in the lifting direction in the position coordinate, P(s) is the observed vibration of the hanging object, and w is the three-dimensional wind speed.

[0108] Among them, the dynamic model of the hanging object movement is established according to formula (3) to predict the movement state of the hanging object.

[0109]

[0110] Among them, k is the time domain point, u mpc (k) is the predicted value of the motion control quantity at the kth point in the current working cycle of the MPC algorithm, and x(k) is the predicted value of the motion state at the kth point in the current working cycle of the MPC algorithm.

[0111] S220: Determine whether the error between the predicted value and the measured value of any dimension in the motion state of the hanging object exceeds a safety threshold.

[0112] Among them, when the error between the predicted value and the measured value of any dimension in the motion state of the hoisted object does not exceed the corresponding safety threshold, steps S230 to S260 are executed; when the error between the predicted value and the measured value of any dimension in the motion state of the hoisted object exceeds the corresponding safety threshold, step S270 is executed.

[0113] S230: The MPC algorithm obtains a predicted value of the motion state of the hoisted object in the current MPC cycle based on the reference trajectory of the hoisted object in the current MPC cycle, the measured value of the motion state when the last MPC cycle was received, the motion disturbance amount and the motion constraint condition.

[0114] The MPC cycle is the working cycle of the MPC. For example, the MPC cycle is 200 ms. Every 200 ms, 20 time domain points are predicted through the dynamic model of the hanging object movement, and each time domain point is a time series cycle.

[0115] Among them, the MPC algorithm predicts the predicted value of the motion state of the hanging object according to the dynamic model of formula (3).

[0116] It should be noted that when predicting the load motion state x(1) at the first time point in the current MPC cycle, x(0) is the measured value of the load motion state at the end of the previous MPC cycle, u mpc (0) is the actual motion control amount used by the tower crane at the end of the last MPC cycle.

[0117] Among them, the MPC algorithm obtains the predicted value of the motion state of the hanging object through optimization iteration according to formula (4).

[0118]

[0119] Where J is the cost function of iterative optimization, and its value must reach the minimum or no longer decrease after multiple iterations; q(k) is the coordinate q of the hanging object at point k in the tower crane coordinate system ref (k) is the median of the reference trajectory at point k in the time domain, Δu mpc (k) is u mpc (k) and u mpc The difference of (k-1), represents the secondary norm weighted by the Q matrix, It represents the secondary norm weighted by the R matrix after the MPC algorithm. The Q matrix, R matrix and ρ are obtained in advance. The Q matrix and R matrix are the matrices in the QR planning of the MPC algorithm. N is the number of timing cycles included in an MPC cycle.

[0120] Among them, during iterative optimization, the swing angle constraints of the hanging object are |θx| and |θy|≤5°; the predicted control variable change rate constraint is |Δu mpc (k)|≤umax; the strain constraint at the key points of the tower arm is ε≤εyield. umax and εyield are set values.

[0121] In each timing cycle, the MPC algorithm also outputs the iteratively optimized tower crane motion control variable u mpc (k), k is a timing point, corresponding to a timing cycle.

[0122] S240: Based on the predicted value of the motion state of the hoisted object in the current MPC cycle, the PID algorithm obtains the motion control quantity of the tower crane in each PID cycle in the current MPC cycle.

[0123] Each PID cycle is 10ms, which is synchronized with and identical to a timing cycle in the MPC algorithm. Within each PID cycle, the PID algorithm includes a PID algorithm for tracking the load position and a PID algorithm for constraining the load's swing angle. The PID algorithm for tracking the load position is expressed using Equation (5), and the PID algorithm for constraining the load's swing angle is expressed using Equation (6).

[0124]

[0125] Among them, u pos is the control quantity of the PID algorithm based on the position of the hanging object, K pq , K iq and K dq are the linear coefficient, integral coefficient and differential coefficient in the PID algorithm based on the position of the hanging object, q mpc The coordinates of the spatial position in the motion state predicted by MPC are interpolated to the values ​​in each PID cycle, including the predicted values ​​in the three directions of lifting, rotation and luffing in the tower crane coordinate system. actual It is the actual value fed back by the encoder in the lifting, rotation and luffing directions in the tower crane coordinate system.

[0126]

[0127] Among them, u θ is the control quantity of the PID algorithm based on the swing angle of the hanging object, K pθ , K dθ and K aθ are the linear coefficient, differential coefficient, and acceleration coefficient in the PID algorithm based on the swing angle of the hanging object, respectively. θ is the swing angle measured in each PID cycle. The ideal value of the swing angle is 0, and θ is less than or equal to the predicted value of the swing angle in the motion state predicted by MPC. Less than or equal to the predicted value of the pendulum angular velocity in the motion state predicted by MPC.

[0128] Among them, according to u pos and u θ Get the motion control value u of the tower crane pid , u is obtained by equation (7) pid The maximum value of is limited, ε is the maximum strain at the tower arm set point in the motion state predicted by MPC, u pidmax for u pid The maximum value of .

[0129]

[0130] S250: The motion control quantities obtained by the PID algorithm and the MPC algorithm are integrated to obtain the integrated motion control quantity of the tower crane.

[0131] Among them, in each PID cycle, the motion control quantity obtained by the fusion PID algorithm is combined with the motion control quantity predicted in the timing cycle of the MPC calculation corresponding to the PID cycle, and the tower crane fusion motion control quantity is obtained according to formula (8).

[0132]

[0133] Among them, u com (k) represents the fusion motion control quantity of the tower crane obtained in the PID cycle (k), u pid (k) represents the motion control quantity of the tower crane obtained by the PID algorithm in the PID cycle (k), u mpc (k) represents the motion control quantity of the tower crane obtained by the MPC algorithm in the MPC timing cycle corresponding to the PID cycle (k), α is the fusion coefficient, q mpc (k) is the value of the coordinate of the spatial position in the motion state predicted by MPC interpolated to the PID period (k), q actual (k) is the actual value of the encoder feedback in the three directions of lifting, rotation and luffing in the tower crane coordinate system during the PID cycle (k), that is, the actual position of the load. ‖qep(k-1)‖ is q mpc (k) and q actual (k) distance.

[0134] S260: Control the movement of the hoisted object according to the fusion motion control amount of the tower crane.

[0135] Based on the crane's motion control variables, control instructions for the crane's motor inverters for the three degrees of freedom (HOF), namely, lift, slew, and luffing, are generated to drive the load. These motion control variables can include control voltages, currents, or torques for the crane's motors for hoisting, slewing, and luffing.

[0136] S270: Perform safety brake on the tower crane.

[0137] Among them, when the error between the predicted value and the measured value of any dimension in the motion state of the suspended object exceeds the corresponding safety threshold, it means that there may be a risk in the motion of the suspended object at this time, and the suspended object needs to be braked.

[0138] In summary, the technical solution of the second embodiment of a tower crane control method has at least the following effects:

[0139] (1) An MPC+PID dual closed-loop collaborative control architecture is proposed, which can solve the contradiction between dynamic trajectory tracking and steady-state accuracy maintenance in a single control mode, reducing the positioning error to within ±2cm;

[0140] (2) Establish an enhanced load motion state model that includes the tower arm and load structure, as well as the rigidity and flexibility characteristics. Add wind disturbance torque to the load motion dynamics model, thereby achieving disturbance pre-compensation through the rolling optimization mechanism of MPC, enhancing anti-interference capabilities, and shortening the system recovery time under sudden load conditions by more than 40%;

[0141] (3) Add trajectory optimization based on the object swing angle constraint to the MPC algorithm, and add control adjustment based on the object swing angle to the PID algorithm to control the residual swing amplitude of the object to less than 30% of the safety threshold;

[0142] (4) Integrate energy optimization terms into the cost function of the MPC algorithm and reduce the frequency of motor start and stop by smoothing the control quantity, achieving a 15%-25% energy saving effect;

[0143] (5) When the deviation between the sensor data and the model prediction value exceeds the threshold, it automatically switches to the safety braking mode to ensure that the system failure protection response time is ≤200ms.

[0144] The following combination Figure 5 and Figure 6 A first embodiment of a tower crane controller according to the present application is introduced.

[0145] A tower crane controller embodiment 1 executes a tower crane control method embodiment 1, with all its advantages.

[0146] Figure 5 The structure of a first embodiment of a tower crane controller is shown, including: a model module 510 , an MPC module 520 and a PID module 530 .

[0147] The model module 510 is used to establish a dynamic model of the movement of the suspended object. For its working principle and advantages, please refer to step S110 of the first embodiment of a tower crane control method.

[0148] The MPC module 520 is used to obtain a predicted value of the motion state of the hanging object based on the reference trajectory of the hanging object, the measured value of the motion state, and the motion constraints. For its working principle and advantages, please refer to step S120 of the first embodiment of a tower crane control method.

[0149] The PID module 530 is used to obtain the motion control variable of the tower crane based on the predicted value of the motion state of the hanging object by the PID algorithm to control the motion of the hanging object. Its working principle and advantages can be seen in step S130 of the first embodiment of a tower crane control method.

[0150] Figure 6 A data flow diagram of a second embodiment of a tower crane controller is shown.

[0151] The model module 510 collects wind speed from the three-dimensional ultrasonic anemometer and collects vibration of the hanging object from the vibration accelerometer, estimates the disturbance of the hanging object movement, and establishes a dynamic model of the hanging object movement.

[0152] The MPC module 520 collects the position coordinates and swing angle of the hoisted object from the posture sensor, detects the strain of the key points of the tower arm from the tower arm deformation detector, and combines the disturbance of the hoisted object's motion, the reference trajectory of the hoisted object, and the motion constraints. The MPC algorithm obtains the predicted value of the motion state of the hoisted object; every 200 milliseconds, it combines the reference trajectory of the hoisted object's motion and the motion constraints to predict 20 motion states of the hoisted object in the time domain.

[0153] The PID module 520 obtains the value of the corresponding actual motion control quantity based on the predicted value of the motion state of the hoisted object and the encoders of the three motors of lifting, rotating and luffing of the tower crane. The motion control quantity of the motion state of the hoisted object is obtained every 10 milliseconds, which is used to control the frequency converters of the three motors of lifting, rotating and luffing of the tower crane to drive the movement of the hoisted object.

[0154] The following combination Figure 7 A second embodiment of a tower crane controller according to the present application is introduced.

[0155] A tower crane controller embodiment 2 executes a tower crane control method embodiment 2, with all its advantages.

[0156] Figure 7 The structure of a first embodiment of a tower crane controller is shown, including: a model module 610 , a judgment module 620 , an MPC module 630 , a PID module 640 , a fusion module 650 and a braking module 660 .

[0157] The model module 610 is used to establish a dynamic model of the movement of the suspended object. For its working principle and advantages, please refer to step S210 of the second embodiment of a tower crane control method.

[0158] The judgment module 620 is used to judge whether the error between the predicted value and the measured value of any dimension of the hanging object motion state exceeds the safety threshold. For its working principle and advantages, please refer to step S220 of the second embodiment of a tower crane control method.

[0159] MPC module 630 is used to use an MPC algorithm to obtain a predicted value of the load's motion state for the current MPC cycle based on the load's reference trajectory for the current MPC cycle, the measured value of the load's motion state during the previous MPC cycle, the motion disturbance, and the motion constraints. For details on its operating principles and advantages, please refer to step S230 of Example 2 of a tower crane control method.

[0160] PID module 640 is used to control the movement of the load using a PID algorithm based on the predicted value of the load's motion state in the current MPC cycle. The PID algorithm calculates the crane's motion control variable for each PID cycle within the current MPC cycle, thereby controlling the load's motion. For its operating principles and advantages, please refer to steps S240 and S250 of Example 2 of a tower crane control method.

[0161] The fusion module 650 is used to fuse the motion control variables obtained by the PID algorithm and the MPC algorithm to obtain the fused motion control variable of the tower crane. For its working principle and advantages, please refer to step S260 of the second embodiment of a tower crane control method.

[0162] The braking module 660 performs a safe braking on the tower crane. For its working principle and advantages, please refer to step S270 of the second embodiment of a tower crane control method.

[0163] The following introduces the hardware architecture of embodiments 1 and 2 of a tower crane controller of the present application.

[0164] In terms of control hardware configuration, the MPC+PID fusion controller unit adopts Qualcomm QCS8550 SOC embedded hardware platform (large and small core heterogeneous CPU: 1 3.2GHz main frequency super core + 4 2.8GHz large cores + 3 2.0GHz medium cores, GPU is Adreno TM 740, NPU computing power 48TOPS INT8).

[0165] The QCS8550 SoC embedded hardware platform is installed with the domestically produced Intewell Hongdao industrial operating system. Ubuntu 20.04+RT is virtualized as a weak real-time environment to run MPC-related modules. The Intewell RTOS real-time virtual machine is used as a strong real-time environment. The interrupt response and timer jitter of the Intewell RTOS real-time virtual machine can reach the microsecond level, allowing the tower crane PID control-related modules to run.

[0166] At the sensor level, the posture sensors include: SICK DT50-HiVision laser rangefinder (±1mm accuracy, 10-30Hz adjustable), ADI ADIS16470 MEMS inertial unit (three-axis gyroscope ±2000° / s, 100Hz sampling), and visual assistance: Basler ace2 camera (5 million pixels, 30fps, used for hanging object posture verification).

[0167] In the execution layer equipment, the variable frequency drive is a Siemens Senlan SB70 series inverter (supporting S-curve acceleration and deceleration algorithm), and the braking mechanism is an electromagnetic power-off brake.

[0168] At the communication architecture level, a hybrid heterogeneous communication network is adopted, including:

[0169] Ethernet, TSN-based Gigabit Industrial Ethernet (realizing mixed transmission of periodic and aperiodic data), connecting rangefinders, inertial units, cameras, etc.

[0170] AUTBUS bus (real-time transmission of encoder and inverter signals) establishes a unified synchronous clock from the upper-layer TSN Ethernet to the lower-layer AUTBUS.

[0171] At the control parameter instance level, MPC online optimization parameters include:

[0172] horizon=20#prediction time domain step,

[0173] dt=0.01#discretized time interval,

[0174] Each iteration time of the MPC solver is ≤15ms, and the MPC period of the solver is 200ms.

[0175] The present application embodiment also provides a computing device, Figure 8 Detailed introduction.

[0176] The computing device 800 includes a processor 810 , a memory 820 , a communication interface 830 , and a bus 840 .

[0177] It should be understood that the communication interface 830 in the computing device 800 shown in this figure can be used to communicate with other devices.

[0178] The processor 810 may be connected to a memory 820. The memory 820 may be used to store the program code and data. Therefore, the memory 820 may be a storage unit within the processor 810, an external storage unit independent of the processor 810, or a component including both a storage unit within the processor 810 and an external storage unit independent of the processor 810.

[0179] Optionally, computing device 800 may further include a bus 840. Memory 820 and communication interface 830 may be connected to processor 810 via bus 840. Bus 840 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, for example. Bus 840 may be classified as an address bus, a data bus, a control bus, and the like. For ease of illustration, the figure uses only one line, but this does not imply that there is only one bus or only one type of bus.

[0180] It should be understood that in the embodiment of the present application, the processor 810 can adopt a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. Alternatively, the processor 810 adopts one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiment of the present application.

[0181] The memory 820 may include a read-only memory and a random access memory, and provides instructions and data to the processor 810. A portion of the processor 810 may also include a non-volatile random access memory. For example, the processor 810 may also store information about the device type.

[0182] When the computing device 800 is running, the processor 810 executes the computer-executable instructions in the memory 820 to perform the operating steps of each method embodiment.

[0183] It should be understood that the computing device 800 according to the embodiment of the present application can correspond to the corresponding subject in executing the method according to each embodiment of the present application, and the above-mentioned and other operations and / or functions of each module in the computing device 800 are respectively for implementing the corresponding processes of each method of the present embodiment. For the sake of brevity, they will not be repeated here.

[0184] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0185] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0186] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0187] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0188] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0189] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0190] An embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it is used to perform the operating steps of each method embodiment.

[0191] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media.Computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium.Computer-readable storage medium can be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof.More specific examples (non-exhaustive list) of computer-readable storage medium include, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0192] A computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, which carries computer-readable program code. Such a transmitted data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, transfer, or convey a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0193] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0194] The computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0195] Note that the above are only preferred embodiments of the present application and the technical principles employed. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of protection of the present application, all of which fall within the scope of protection of the present application.

Claims

1. A tower crane control method, characterized in that: include: Establishing a dynamic model of the hanging object movement as a prediction model of the movement state of the hanging object movement in the MPC algorithm, wherein the movement state includes the position, swing angle, and strain of the tower arm of the hanging object, and the influencing factors of the dynamic model include a disturbance estimator; The MPC algorithm predicts a predicted value of the motion state in the current MPC cycle based on the reference trajectory of the hoisted object, the motion constraints, and the measured value of the motion state in the previous MPC cycle; According to the predicted value of the motion state in the current MPC cycle, the PID algorithm adjusts the motion control amount of the tower crane in each PID cycle in the current MPC cycle, and the motion control amount is used to control the motion of the tower crane.

2. The method according to claim 1, characterized in that The motion constraint condition includes at least one of the following: the maximum value of the swing angle in each coordinate direction, the maximum modulus of the rate of change of the predicted control quantity and the maximum value of the strain, and the predicted control quantity is the predicted value of the motion control quantity in the MPC algorithm.

3. The method according to claim 1, characterized in that The motion state also includes the speed of the hanging object based on the position and the angular velocity based on the swing angle.

4. The method according to claim 1, characterized in that The motion control quantity includes the control quantity for tracking the position of the hanging object and the control quantity for constraining the swing angle of the hanging object. The PID algorithm adjusts the motion control quantity of each PID cycle of the tower crane in the current MPC cycle, including: The PID algorithm takes the predicted value of the position of the suspended object in the motion state in the current MPC cycle as the target and adjusts the control amount of the tower crane tracking the position of the suspended object in each PID cycle in the current MPC cycle; With a swing angle of 0 as a target and a predicted swing angle of the current MPC cycle in the motion state as a constraint, the PID algorithm adjusts the control amount of the constrained swing angle of the tower crane in each PID cycle in the current MPC cycle.

5. The method according to claim 1, characterized in that: Also includes: When obtaining the motion control variable, a maximum value of the modulus of the motion control variable is constrained according to the strain of the tower arm set point.

6. The method according to claim 1, characterized in that Each MPC cycle includes several timing cycles, each timing cycle corresponds to a PID cycle and the two have the same duration. The MPC algorithm also predicts the motion control amount of the tower crane in each timing cycle; The method also includes: in each PID cycle, fusing the motion control amount of the tower crane obtained by the PID algorithm with the motion control amount of the tower crane predicted by the MPC in the corresponding timing cycle to obtain a fused motion control amount of the tower crane, and the fused motion control amount is used to control the motion of the tower crane.

7. The method according to claim 6, characterized in that In the current PID cycle, the fusion of the motion control amount of the tower crane obtained by the PID algorithm and the motion control amount of the tower crane predicted by the MPC in the corresponding time series cycle specifically includes: According to the distance between the actual position of the hanging object in the previous PID cycle and the predicted position of the hanging object in the timing cycle corresponding to the previous PID cycle, the motion control amount of the tower crane obtained by the PID algorithm in the current PID cycle and the motion control amount of the tower crane predicted by the MPC in the corresponding timing cycle are integrated, wherein the smaller the distance, the greater the weight of the motion control amount obtained by the PID algorithm.

8. A tower crane controller, characterized in that: include: A model module is used to establish a dynamic model of the hanging object movement, the dynamic model is used as a prediction model of the motion state of the hanging object movement in the MPC algorithm, the motion state of the hanging object movement includes the position of the hanging object, the swing angle, and the strain of the tower arm, and the influencing factors of the dynamic model include the disturbance estimator; An MPC module is configured to predict a predicted value of the motion state in a current MPC cycle using an MPC algorithm based on a reference trajectory of the hoisted object, motion constraints, and a measured value of the motion state in a previous MPC cycle; The PID module is used to adjust the motion control amount of the tower crane in each PID cycle in the current MPC cycle according to the predicted value of the motion state in the current MPC cycle using a PID algorithm, and the motion control amount is used to control the motion of the tower crane.

9. A computing device, characterized in that include, bus; a communication interface connected to the bus; at least one processor connected to the bus; as well as At least one memory is connected to the bus and stores program instructions, and when the program instructions are executed by the at least one processor, the at least one processor executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Program instructions are stored thereon, and when the program instructions are executed by a computer, the computer is caused to perform the method according to any one of claims 1 to 7.

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