Lifting tool heading control method, device, working machine and medium

Abstract

The application discloses a kind of sling course control method, device, working machine and medium, it is related to working machine control technical field.The sling is equipped with the driving component for driving sling movement, and the method comprises: obtaining and inputting the control variable of driving component to the prediction model for predicting the state of sling, to output the state variable for characterizing the state of sling, wherein the state variable includes the course angle of the sling;And according to the output of the prediction model and the preset target course angle, the control variable of driving component is adjusted to change the driving force applied to the sling by the driving component, so that the sling reaches target course angle.The application proposes the sling course control method based on sling state prediction model, so that the sling is adapted to the action of its driving component and automatically reaches target course angle, realizes the accurate control and automatic control for the course of sling.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of work machine control, in particular to a sling heading control method and device, work machine and medium. BACKGROUND

[0002] In traditional work machine hoisting operations, the sling makes a simple pendulum motion during hoisting, and the heading of the sling is random after hoisting is completed, which cannot meet the requirement of accurate control of the sling heading in some scenarios. For example, as one of the important development directions of the construction industry, fabricated buildings require accurate assembly of standard prefabricated slings, and thus require to ensure the accuracy of the sling heading. In addition, in the traditional construction process, the sling can be finally lowered only after the posture is adjusted by the worker through a pull rope, which has the factors of strong work difficulty, high risk coefficient, etc., and affects the hoisting operation efficiency. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide a sling heading control method and device, work machine and medium, to at least partially solve the above technical problems.

[0004] In order to achieve the above purpose, the first aspect of the present application provides a sling heading control method, the sling is provided with a driving component for driving the sling to move, the method comprising: obtaining a control variable of the driving component, and inputting the obtained control variable into a prediction model for predicting the state of the sling established in advance to output a state variable for characterizing the state of the sling, wherein the state variable includes the heading angle of the sling; and adjusting the control variable of the driving component to change the driving force applied to the sling by the driving component according to the state variable output by the prediction model and a preset target heading angle, so that the sling reaches the target heading angle.

[0005] In the embodiments of the present application, the prediction model is pre-established based on any one of a basic Kalman filter algorithm, an extended Kalman filter algorithm, an unscented Kalman filter algorithm, a Gaussian filter algorithm and a particle filter algorithm.

[0006] In the embodiment of the present application, the output of the state variable of the spreader by the prediction model established in advance based on the basic Kalman filtering algorithm comprises: obtaining the state variable posterior estimation value of the spreader in the last period, the posterior state variance matrix, and the state variable observation value in the current period; determining an influence factor for representing the influence degree of different driving forces provided by the driving components due to the change of different control variables of the driving components on the state of the spreader according to the different control variables of the driving components input into the prediction model; processing the state variable posterior estimation value of the spreader in the last period and the determined influence factor by a state prediction equation built in the prediction model to obtain the state variable prior estimation value of the spreader in the current period; processing the posterior state variance matrix of the spreader in the last period by a state variance matrix prediction equation built in the prediction model to obtain the prior state variance matrix of the spreader in the current period; calculating a prediction residual of state prediction according to the state variable observation value and the state variable prior estimation value of the spreader in the current period; calculating a prediction gain of the prior state variance matrix prediction associated with the current period; causing the prediction model to output the state variable posterior estimation value in the current period based on the state variable prior estimation value of the spreader in the current period and the calculated prediction error and prediction gain; and causing the prediction model to output the posterior state variance matrix in the current period based on the prior state variance matrix of the spreader in the current period and the calculated prediction gain.

[0007] In the embodiment of the present application, the heading angle in the state variable observation value and the state variable prior estimation value in the current period is processed by sine or cosine when the prediction residual is calculated.

[0008] In the embodiment of the present application, the prediction value h(x) obtained by processing the heading angle in the state variable prior estimation value in the current period by sine or cosine is further linearized to h(x) = Hx when the prediction gain is calculated, H is a measurement matrix for calculating the prediction gain, and H is obtained by Taylor expansion of h(x) and deletion of high-order terms, and x represents the state variable posterior estimation value of the spreader in the last period.

[0009] In the embodiment of the present application, the prediction model obtains the state variable posterior estimation value x" in the current period by the following formula:

[0010] x" = x' + K * e * con(Ξ)

[0011] wherein x' represents the state variable prior estimation value of the spreader in the current period, K represents the prediction gain, e represents the prediction residual, and con(Ξ) represents a smoothing constraint function, wherein Δe is a constraint width.

[0012] In the embodiment of the present application, the state variable comprises a sine value of the heading angle, and the state variable further comprises a heading angle angular velocity and a heading angle angular acceleration.

[0013] In the embodiment of the present application, adjusting the control variable of the driving component comprises: adjusting the control variable according to a target heading angle θ t and a current heading angle θ n judging a spreader steering, and adjusting the control variable according to the spreader steering, the target heading angle θ t and a state variable output by the prediction model.

[0014] In the embodiment of the present application, when the driving component is a rotor assembly, the control variable is a rotor speed, and the spreader heading control method further comprises: after the spreader reaches the target heading angle, adjusting the rotor speed based on a rotor speed maximum speed and a difference between the target heading angle θ t and the current heading angle θ n so that the heading angle of the spreader is kept within a preset error range.

[0015] The second aspect of the embodiment of the present application provides a spreader heading control device, comprising: a memory configured to store instructions; and a processor configured to call the instructions from the memory and capable of realizing any of the above-mentioned spreader heading control methods when executing the instructions.

[0016] The third aspect of the embodiment of the present application provides a working machine comprising any of the above-mentioned spreader heading control devices.

[0017] The fourth aspect of the embodiment of the present application provides a machine readable storage medium, on which instructions are stored, the instructions being used to make a machine execute any of the above-mentioned spreader heading control methods.

[0018] Through the above technical solution, the embodiment of the present application proposes a spreader heading control method based on a spreader state prediction model, which is used to replace manual adjustment of a spreader posture, so that the spreader automatically reaches a target heading angle in adaptation to the action of its driving component, thereby realizing accurate and automatic control of the spreader heading, helping to promote the hoisting automation process and greatly improving the hoisting operation efficiency in a hoisting scene such as a fabricated building.

[0019] Other features and advantages of the embodiment of the present application will be described in detail in the following specific implementation part. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings are used to provide further understanding of the embodiments of the present application, and constitute a part of the specification, and are used to explain the embodiments of the present application together with the following specific embodiments, but do not constitute a limitation to the embodiments of the present application. In the drawings:

[0021] Figure 1 A flowchart of a sling heading control method according to an embodiment of the present application is schematically shown;

[0022] Figure 2 A structural block diagram of a rotary drive assembly according to an embodiment of the present application is schematically shown;

[0023] Figure 3 A flowchart of constructing a prediction model by a basic Kalman filtering algorithm according to an embodiment of the present application is schematically shown;

[0024] Figure 4 A flowchart of controlling a control variable of a drive component according to an embodiment of the present application is schematically shown; and

[0025] Figure 5 A structural block diagram of another sling heading control device according to an embodiment of the present application is schematically shown.

[0026] Explanation of reference signs

[0027] 1 sling body 2 rotary wing assembly

[0028] 3 electric control box 4 ear

[0029] 5 support seat DETAILED DESCRIPTION

[0030] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific embodiments described here are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0031] It should be noted that the acquisition, transmission, storage, use, processing and the like of data in the technical scheme of the present application comply with the relevant provisions of national laws and regulations. In the embodiments of the present application, some industry existing schemes such as software, components, models and the like may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility in the implementation of the technical scheme of the present application, but does not mean that the applicant has or will necessarily use the scheme.

[0032] It should be noted that if the application embodiments involve directionality indication (such as up, down, left, right, front, back, etc.), the directionality indication is only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directionality indication also changes accordingly.

[0033] In addition, if the application embodiments involve "first", "second" and the like, the "first", "second" and the like are only for description purposes, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can be explicitly or implicitly included at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the scope of protection claimed in the present application.

[0034] In view of the defects of relying on manual realization of the sling posture adjustment in the traditional construction process, the inventive idea of the application embodiments is to first establish a prediction model of the sling, accurately predict the sling state, and control the target heading angle to be quickly in place on the basis of the prediction model. In addition, the preferred embodiments of the application also consider fine-tuning the sling state after the quick in-place stage, so that the real-time heading angle is kept within the preset heading angle error range.

[0035] It should be noted that the heading angle in the application embodiments refers to the angle between the sling longitudinal axis and the earth's north pole. In addition, the sling of the application embodiments can automatically change the posture according to the instructions of the controller and the like, and belongs to an intelligent sling.

[0036] The application embodiments will be specifically introduced below with reference to the drawings.

[0037] Figure 1 The flowchart of the sling heading control method according to the application embodiments is schematically shown. As shown in Figure 1 The application embodiments provide a sling heading control method, the sling is provided with a driving component for driving the movement of the sling, and the method can include the following steps S100-S200.

[0038] Step S100, obtaining the control variable of the driving component, and inputting the obtained control variable into the prediction model for predicting the sling state, to output the state variable for characterizing the sling state.

[0039] That is, the prediction model takes control variables of driving components for driving the spreader as input, and takes state variables representing a state of the spreader as output, wherein the state variables at least include a heading angle of the spreader.

[0040] In examples, the driving component is a rotary driving assembly or an airflow driving assembly, and when the driving component is the rotary driving assembly, the control variable is a rotating speed of the rotary driving assembly, and when the driving component is the airflow driving assembly, the control variable is a gas flow rate of the airflow driving assembly.

[0041] For ease of description, the following takes a rotary driving assembly as shown in Figure 2 as an example. In Figure 2 , the spreader includes a spreader body 1 and a rotor assembly 2 as the rotary driving assembly, the rotor assembly 2 is arranged on the spreader body 1, and the rotor of the rotor assembly 2 can generate a thrust to drive the spreader body 1 to rotate around a vertical axis. The spreader further includes an electric control box 3 arranged on the spreader body 1 and formed with a receiving cavity to accommodate a controller and the like for controlling the spreader. The top and bottom of the spreader body 1 are both provided with a plurality of spreader lugs 4 arranged in a matrix. In this way, the spreader body 1 can be connected to a hoisting device and a hoisted object through the spreader lugs 4 on the top and the spreader lugs 4 on the bottom via hooks and ropes, respectively, and is suitable for various hoisting devices and hoisted objects. In addition, the bottom of the spreader body 1 is provided with a plurality of support seats 5 extending downward. In this way, when the spreader is in a non-use state, the plurality of support seats 5 can support the spreader on the ground or other base, facilitating storage.

[0042] Step S200: adjusting the control variable of the driving component according to the state variable output by the prediction model and a preset target heading angle, so as to change the driving force applied to the spreader by the driving component, so that the spreader reaches the target heading angle.

[0043] That is, through the above steps S100-S200, the embodiment of the present application proposes a spreader heading control method based on a spreader state prediction model, which is used to replace manual adjustment of the spreader attitude, so that the spreader automatically reaches the target heading angle in adaptation to the action of its driving component, and realizes accurate and automatic control of the spreader heading, which helps to promote the process of hoisting automation and greatly improves the hoisting efficiency in hoisting scenes such as prefabricated buildings.

[0044] For step S100, in a preferred embodiment, the prediction model can be previously established based on any one of a basic Kalman filtering algorithm, an extended Kalman filtering algorithm, an unscented Kalman filtering algorithm, a Gaussian filtering algorithm, and a particle filtering algorithm. Taking the basic Kalman filtering algorithm as an example, as shown in Figure 3As shown, outputting the state variables of the spreader through the prediction model previously established based on the basic Kalman filtering algorithm can include the following steps S110-S180.

[0045] In step S110, the state variable posterior estimation value, posterior state variance matrix of the spreader in the last period and the state variable observation value in the current period are obtained.

[0046] In step S120, according to different control variables of the driving component input into the prediction model, an influence factor is determined to represent the influence degree of different driving forces provided by the driving component due to the change of the control variable on the state of the spreader.

[0047] In step S130, the state prediction equation built in the prediction model is used to process the state variable posterior estimation value of the spreader in the last period and the determined influence factor to obtain the state variable prior estimation value of the spreader in the current period.

[0048] For steps S110-S130, the state prediction equation is described as follows:

[0049] x′=Fx+u (1)

[0050] Wherein, x′ represents the state variable prior estimation value of the spreader in the current period, which is not the final output result of the prediction model for the current period, so it is defined as “prior estimation value”, and the use of “prior” below is similar. x represents the state variable posterior estimation value of the last period, which is the final output result of the prediction model for the last period, so it is defined as “posterior estimation value”, and the use of “posterior” below is similar. And, a x The heading angle, the heading angle angular velocity and the heading angle angular acceleration of the spreader, i.e. the state variables can include the heading angle, the heading angle angular velocity and the heading angle angular acceleration of the spreader; F represents a preset state transition matrix; and u is the influence factor.

[0051] For example, the state transition matrix Then Wherein, Δt is the prediction period. And, for the rotor assembly of Figure 2 For the spreader, it can be mathematically described as a rotation model rotating around the Z axis, and the force required for rotation is provided by the reaction force of the two side rotors, so the acceleration of the spreader due to the two side rotors is:

[0052]

[0053] wherein k1, k2 are the thrust coefficients of the two side rotors, ω1, ω2 are the rotating speeds of the two side rotors (clockwise rotor, counterclockwise rotor), R is the radius of the spreader, I z is the moment of inertia of the spreader rotating around the Z axis.

[0054] Thus, for formula (1), the influence factor u represents the influence of the reaction force exerted by the rotor on the intelligent spreader, and the following is obtained:

[0055]

[0056] wherein a f is determined by formula (2).

[0057] In step S140, the posterior state variance matrix of the spreader in the last period is processed by a state variance matrix prediction equation built in the prediction model to obtain the prior state variance matrix of the spreader in the current period.

[0058] For example, the state variance matrix prediction equation is described as:

[0059] P' = FPF T + Q

[0060] wherein P' represents the prior state variance matrix in the current period, P represents the posterior state variance matrix in the last period, F represents the state transition matrix as above, and Q represents the process noise matrix. Wherein P can be used to indicate the uncertainty degree of the spreader, and its initial value can be set as the unit matrix I; the value of Q can be obtained by measurement.

[0061] In step S150, the prediction residual of state prediction is calculated according to the state variable observation value and the state variable prior estimation value of the spreader in the current period.

[0062] In the example, the observation value z is measured by the inertial element sensor installed on the spreader. However, considering that the heading angle will jump between 360° and 0° (i.e. when the heading angle is 360° and continues to rotate, the heading angle will become 0°), the observation of the heading angle is processed by taking its sine value, and the following is obtained:

[0063]

[0064] wherein θ, v, a are the observed heading angle, heading angle angular velocity, and heading angle angular acceleration of the spreader, respectively. It should be noted that the cosine processing of the heading angle of the spreader can also be performed.

[0065] Referring to the sine processing of formula (4), the heading angle is processed by taking its sine value to obtain the prediction value h(x) according to the state variable prior estimation value of the spreader in the current period as follows:

[0066]

[0067] Wherein, the meaning of each parameter can refer to the above, not described here.

[0068] According to formula (4) and formula (5), the prediction residual is calculated by the following formula:

[0069] e=z-h(x) (6)

[0070] Wherein, e represents the prediction residual.

[0071] Step S160, the prediction gain associated with the prediction of the prior state variance matrix of the current period is calculated.

[0072] In examples, the prediction gain is calculated by the following formula:

[0073] K=P'H T (HP'H T +R) -1 (7)

[0074] In the formula, K represents the prediction gain, P' represents the prior state variance matrix of the current period, R represents the noise matrix, and H represents the measurement matrix.

[0075] Wherein, the noise matrix R is obtained by actual measurement, and for the measurement matrix H, the determination process of the example includes:

[0076] According to formula (5), the prediction value h(x) is described as the following formula:

[0077]

[0078] Due to the nonlinear characteristics of the trigonometric function, there is no constant matrix H that makes the equation true, so the example of the embodiment of the application uses an approximate linearization method for processing, which is a Taylor expansion of h(x) and ignores high-order terms to complete the approximate linearization process, that is, to obtain:

[0079]

[0080] Then based on formula (8) and formula (9) for the measurement matrix H, we can get:

[0081]

[0082] In this way, the prediction gain K can be determined.

[0083] Step S170, based on the state variable prior estimate value of the spreader in the current cycle and the calculated prediction error and prediction gain, the prediction model outputs the state variable posterior estimate value of the current cycle.

[0084] Here, step S170 is equivalent to updating the state variable prior estimate value of the spreader in the current cycle, and the updating process can be described as:

[0085] x'' = x' + K * e (11)

[0086] Where x'' represents the state variable posterior estimate value of the spreader in the current cycle, that is, the final output of the prediction model. However, considering the smoothness of the prediction value, based on the above formulas, in the example of the embodiment of the present application, the state variable posterior estimate value of the current cycle is obtained by:

[0087] x'' = x' + K * e * con (Ξ) (12)

[0088] Where e represents the prediction residual, and con (Ξ) represents a smoothing constraint function, which is intended to correct the prediction residual e to ensure the smoothness of the prediction value. Wherein:

[0089]

[0090] Where Δe is the constraint width.

[0091] Step S180, based on the prior state variance matrix of the spreader in the current cycle and the calculated prediction gain, the prediction model outputs the posterior state variance matrix of the current cycle.

[0092] For example, based on the above formulas, the posterior state variance matrix P'' of the current cycle is obtained by:

[0093] P'' = (I - KH) P' (14)

[0094] Where I represents the identity matrix, and the meanings of other parameters can be understood with reference to the above formulas, which will not be described here.

[0095] Repeat the above steps S110-S180 to optimize the prediction model, and output the state variable of the spreader through the optimized prediction model, thereby realizing continuous prediction and optimization of the heading angle, heading angle angular velocity and heading angle angular acceleration of the intelligent spreader.

[0096] Thus, through the above steps S110-S180, a prediction model for the spreading gear is established. It should be noted that the basic Kalman filter algorithm is used as an example here, but other filtering algorithms listed above, based on their algorithmic architecture, can also complete the construction of a prediction model by combining the sine processing of the observed heading angle and the application of smoothing constraint functions in the example of this application.

[0097] Based on the completion of the prediction model construction, return to Figure 1 Continue with the rapid arrival phase of step S200.

[0098] For step S200, with Figure 2 Taking the rotor assembly as an example, the heading angle is controlled by controlling the rotation speed of the two rotors. In order to achieve the rapid positioning of the intelligent spreader to the target heading angle, the embodiments of this application consider two aspects of optimization: first, judging the direction of the spreader; second, considering the step error caused by the jump between 360° and 0°.

[0099] Thus, in the embodiments of this application, as Figure 4 As shown, adjusting the control variable of the drive component may include the following steps S210-S220.

[0100] Step S210: Determine the direction of rotation of the spreader.

[0101] Specifically, based on the target heading angle θ t With the current heading angle θ n Determine the direction of the spreader. For example, first calculate the target heading angle θ. t With the current heading angle θ in the current state n The difference θ between t -θ n If θ t -θ n If θ > 180°, then the direction of rotation of the lifting device is determined to be counterclockwise, and if θ n <180°, then θ n =θ n +360°; if 0°≤θ t -θ n If -180° ≤ θ, then the direction of rotation of the lifting device is determined to be clockwise; if -180° ≤ θ t -θ n If θ < 0°, then the direction of rotation of the lifting device is determined to be clockwise; if θ t -θ n If θ < -180°, then the direction of rotation of the lifting device is determined to be clockwise, and if θ t <180°, θ t =θ t +360°.

[0102] Thus, the judgment of the sling turning is realized, and the following step S220 is continuously executed.

[0103] In step S220, the control variable is adjusted according to the sling turning, the target heading angle, and the state variable output by the prediction model.

[0104] For example, after the judgment of the sling turning direction is completed, the current sling state (heading angle, heading angle angular velocity, heading angle angular acceleration) [θ n v n a n ] is known, the target state is [θ t 0 0], and the planning of the sling heading angle movement can be completed by means of polynomial interpolation; and the prediction model has the input of the rotor speed and the output of the sling state, and the control of the sling heading angle can be completed by means of feedback control, so as to quickly reach the target heading angle.

[0105] Thus, through the judgment of the sling turning direction, the problem of the control effect divergence caused by the large error of the 360° to 0° heading angle jump in the control process can be solved.

[0106] Returning to Figure 1 , considering the error caused by the approximate linearization in the prediction model and the hysteresis characteristic of the prediction model, an error range [-Δθ, Δθ] of the heading angle prediction is allowed, and within the range, accurate hoisting can be ensured. Thus, after the step S200 realizes the quick positioning of the target heading angle, the controlled sling heading angle enters the [θ n -Δθ, θ n +Δθ] region, and then the corresponding control method enters the maintaining and fine-tuning stage.

[0107] Taking the rotor assembly of Figure 2 as an example, first, the direction in which the sling needs to be adjusted is judged. If v n > 0, the counterclockwise adjustment is controlled, and only the counterclockwise rotor motor is controlled to rotate; if v n < 0, the clockwise adjustment is controlled, and only the clockwise rotor motor is controlled to rotate; and if v n = 0, the rotors on both sides are not rotated. Secondly, after the sling reaches the target heading angle, the rotor speed is adjusted based on the maximum rotor speed and the difference between the target heading angle θ t and the current heading angle θ n , so that the heading angle of the sling is kept within a preset error range. That is, based on the momentum theorem, the thrust generated by the rotor at the moment is used to offset the rotation of the sling, so that the heading angle is kept within the error range. In the example, the adjustment strategy of the rotor speed can be described as:

[0108]

[0109] wherein the duration is T << Δt, k3 is a proportional coefficient, ω max is the maximum rotational speed of the clockwise (counterclockwise) rotor, and ω is the rotational speed of the clockwise (counterclockwise) rotor in this stage.

[0110] In summary, the autonomous adjustment of the sling posture is one of the key technologies restricting the automation of hoisting operations. In order to realize this technology, the embodiment of the present application proposes a sling heading control method based on a sling state prediction model, so that the sling automatically reaches the target heading angle in adaptation to the action of its driving component, realizing accurate and automatic control of the sling heading, which helps to promote the hoisting automation process and greatly improves the hoisting operation efficiency in hoisting scenes such as prefabricated buildings. In addition, in the example of the sling heading control method of the present application, the following advantages are also provided:

[0111] 1) In the prediction model, the observation of the sine value of the heading angle is used to solve the problem of invalid prediction state caused by large error due to 360° to 0° heading angle jump.

[0112] 2) By judging the turning direction of the sling and correcting the target heading angle and the current heading angle, the problem of control effect divergence caused by large error due to 360° to 0° heading angle jump in the control process is solved, especially the divergence of the basic PID controller or the fuzzy PID controller which causes the algorithm to fail to converge.

[0113] 3) A smoothing constraint function is added to the prediction value update to correct the prediction residual value.

[0114] 4) By controlling the heading angle in stages, the sling heading angle is kept near the target heading angle.

[0115] The embodiment of the present application also provides a sling heading control device, which is based on the same inventive idea as the sling heading control method of the above-mentioned embodiment.

[0116] The sling heading control device comprises: a state determination module, configured to obtain a control variable of the driving component and input the obtained control variable into a prediction model for predicting the state of the sling, so as to output a state variable for representing the state of the sling, wherein the state variable comprises a heading angle of the sling; and a control module, configured to adjust the control variable of the driving component according to the state variable output by the prediction model and a preset target heading angle, so as to change the driving force applied to the sling by the driving component, so that the sling reaches the target heading angle.

[0117] The sling heading control device is, for example, integrated in Figure 2The controller in the electric control box 3, and the state determining module can be a memory in which the established prediction model is stored or a processor that executes instructions to build the prediction model, and the control module can be a processor that executes feedback control, for example.

[0118] For other implementation details and effects of the sling heading control device, reference can be made to the above-mentioned embodiments of the sling heading control method, which will not be repeated here.

[0119] Figure 5 An illustrative structural block diagram of a sling heading control device according to an embodiment of the present application is shown. As shown in the figure, Figure 5 The sling heading control device includes a memory configured to store instructions, and a processor configured to call the instructions from the memory and implement any of the above-mentioned sling heading control methods when executing the instructions.

[0120] For other implementation details and effects of the sling heading control device, reference can be made to the above-mentioned embodiments of the sling heading control method, which will not be repeated here.

[0121] The present application also provides a working machine including any of the above-mentioned sling heading control devices. The working machine is, for example, a hoisting device such as various types of cranes, elevators, etc.

[0122] The present application also provides a machine-readable storage medium having instructions stored thereon for causing a machine to execute any of the above-mentioned sling heading control methods.

[0123] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) having computer-usable program code embodied therein.

[0124] The present application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions, which are executed via the processor of the computer or other programmable data processing apparatus, generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 an apparatus with the functionality to achieve the specified processes or functions in the block or blocks.

[0125] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a Figure 1 one or more processes and / or blocks Figure 1 an apparatus with the functionality to achieve the specified processes or functions in the block or blocks.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the processes specified in the flowchart Figure 1 one or more processes and / or blocks Figure 1 an apparatus with the functionality to achieve the specified processes or functions in the block or blocks.

[0127] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0128] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the processor can execute instructions. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), erasable programmable read only memory (EPROM), flash memory, or a combination of non-volatile memories in different types. The memory can also include a storage device, such as a hard disk drive, a solid state drive, or a combination of storage devices in different types. The memory can be configured to store data and / or instructions that can be executed by the processor. The memory is an example of computer readable media.

[0129] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to computing devices. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0130] It should also be noted that the terms "comprising", "comprises" or other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0131] The above embodiments are only used to illustrate the present application, but not to limit it. Instead of the above, various modifications and changes can be made to the application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall into the scope of the claims of the application.

Claims

1. A method for controlling the heading of a spreader, characterized in that, The lifting device is equipped with a drive component for driving the movement of the lifting device, and the method includes: The control variables of the drive component are acquired, and the acquired control variables are input into a pre-established prediction model for predicting the spreader state, to output state variables characterizing the spreader state, wherein the state variables include the spreader's heading angle, and wherein the prediction model is pre-established based on a basic Kalman filter algorithm; and Based on the state variables output by the prediction model and the preset target heading angle, the control variables of the drive component are adjusted to change the driving force applied by the drive component to the spreader, so that the spreader reaches the target heading angle; The state variables of the lifting device output by the prediction model include: Obtain the posterior estimate of the state variables of the spreader in the previous cycle, the posterior state variance matrix, and the observed state variables in the current cycle; Based on the different control variables of the drive component input into the prediction model, an influence factor is determined to characterize the degree of influence of the different driving forces provided by the drive component due to the changes in the control variables on the state of the spreader. By processing the posterior estimates of the state variables of the lifting device in the previous period and the determined influencing factors through the state prediction equation built into the prediction model, the prior estimates of the state variables of the lifting device in the current period are obtained. The posterior state variance matrix of the lifting device in the previous period is processed by the state variance matrix prediction equation built into the prediction model to obtain the prior state variance matrix of the lifting device in the current period. The prediction residuals of the state prediction are calculated based on the observed values ​​of the state variables of the spreader in the current period and the prior estimates of the state variables. Calculate the prediction gain of the prior state variance matrix prediction associated with the current period; Based on the prior estimates of the state variables of the lifting device in the current period, and the calculated prediction error and prediction gain, the prediction model outputs the posterior estimates of the state variables for the current period; and Based on the prior state variance matrix of the lifting device in the current period and the calculated prediction gain, the prediction model outputs the posterior state variance matrix of the current period.

2. The spreading gear heading control method according to claim 1, characterized in that, When calculating the predicted residual, the heading angle in the observed state variable value and the prior estimate of the state variable value in the current period is processed by sine or cosine.

3. The spreading gear heading control method according to claim 2, characterized in that, When calculating the prediction gain, the heading angle in the prior estimate of the state variable for the current period is obtained by sine or cosine processing. h ( x ) was further linearized to , H The measurement matrix is ​​used to calculate the predicted gain, and H To be We obtain the result by performing a Taylor expansion and removing higher-order terms. x This represents the posterior estimate of the state variable of the lifting device in the previous period.

4. The spreading gear heading control method according to claim 1, characterized in that, The prediction model obtains the posterior estimate of the state variable for the current period using the following formula. : in, This represents the prior estimate of the state variable of the lifting device in the current cycle. K Indicates the predicted gain. e This represents the predicted residual. Let represent the smoothing constraint function, where , To constrain the width.

5. The spreading gear heading control method according to claim 1, characterized in that, The state variables include the sine value of the heading angle, and the state variables also include the heading angle angular velocity and the heading angle angular acceleration.

6. The spreading gear heading control method according to claim 1, characterized in that, Adjusting the control variables of the drive component includes: Based on the target heading angle With current heading angle Determine the direction of the spreader rotation; and Based on the direction of the lifting device and the target heading angle The control variables are adjusted based on the state variables output by the prediction model.

7. The spreader heading control method according to any one of claims 1 to 6, characterized in that, When the driving component is a rotor assembly, the control variable is the rotor speed; Furthermore, the spreader heading control method also includes: After the spreader reaches the target heading angle, based on the maximum rotor speed and the target heading angle... and current heading angle The difference is used to adjust the rotor speed so that the heading angle of the spreader remains within a preset error range.

8. A spreading gear heading control device, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the spreader heading control method according to any one of claims 1 to 7.

9. A type of operating machinery, characterized in that, Includes the spreader heading control device of claim 8.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the spreader heading control method according to any one of claims 1 to 7.

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