CN111453616B - Open-loop fuzzy anti-swing control method for bridge crane - Google Patents

Open-loop fuzzy anti-swing control method for bridge crane Download PDF

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CN111453616B
CN111453616B CN202010123285.3A CN202010123285A CN111453616B CN 111453616 B CN111453616 B CN 111453616B CN 202010123285 A CN202010123285 A CN 202010123285A CN 111453616 B CN111453616 B CN 111453616B
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CN111453616A (en
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汪小凯
郑学兵
吴庆祥
夏明辉
华林
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Wuhan University of Technology WUT
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/04—Auxiliary devices for controlling movements of suspended loads, or preventing cable slack
    • B66C13/08—Auxiliary devices for controlling movements of suspended loads, or preventing cable slack for depositing loads in desired attitudes or positions
    • B66C13/085—Auxiliary devices for controlling movements of suspended loads, or preventing cable slack for depositing loads in desired attitudes or positions electrical
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/16—Applications of indicating, registering, or weighing devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/18—Control systems or devices
    • B66C13/22—Control systems or devices for electric drives
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Abstract

The invention provides an open-loop fuzzy anti-swing control method of a bridge crane, which comprises the steps of constructing a two-dimensional dynamic model of a bridge crane system through a system identification experiment; designing an optimal deceleration and anti-shaking curve of the trolley by utilizing a particle swarm optimization algorithm and combining the two-dimensional kinetic model; establishing an optimal deceleration curve look-up table of the trolley; checking an optimal deceleration curve lookup table of the trolley by using a fuzzy control rule; when the system operates, the rope length is measured and the initial angle in the deceleration stage is calculated, the rope length and the initial angle are quantized into different grades respectively, then table lookup is carried out in the optimal deceleration curve lookup table of the crane according to the initial speed in the deceleration stage, and the corresponding optimal deceleration curve is selected, so that the purpose of restraining load swing is achieved. The invention can realize anti-swing control under different working conditions by looking up the table, obviously improves the running safety of the crane, simultaneously reduces the anti-swing distance and the anti-swing time, and improves the working efficiency and the running stability of the bridge crane.

Description

Open-loop fuzzy anti-swing control method for bridge crane
Technical Field
The invention relates to the field of cranes, in particular to an open-loop fuzzy anti-swing control method for a bridge crane.
Background
The bridge crane is an industrial hoisting and hoisting device with wide application. As a special large-scale mechanical device, a series of automatic control technologies for ensuring the safe, efficient and stable operation of the large-scale mechanical device are quite required in the use process of various industrial environments. However, the contradiction between the long-time swing of the bridge crane during transportation and the working efficiency of the bridge crane is increasingly prominent, and the working efficiency of the bridge crane is seriously influenced. Anti-sway control of bridge cranes has therefore begun to receive considerable attention from manufacturers and researchers as a key technique to suppress load sway.
At present, the crane anti-swing technology is mainly divided into open-loop anti-swing technology and closed-loop anti-swing technology: in the aspect of open-loop control, a variational method and a minimum value principle are mainly adopted, along with the technical development, time-lag filtering is taken as a feedforward open-loop control method, and due to the characteristics of simple implementation mode and high reliability, the method is widely applied to crane anti-swing control and high-precision positioning systems; compared with an open-loop control system, the robustness of the closed-loop control system is better, and the wide research is also carried out at present. The main control methods comprise PID control, optimal control, LQG/LQR control, self-adaptive control, sliding mode variable structure control, fuzzy control, neural network control and the like. Regarding the closed-loop anti-swing control method, a large amount of research and attempt are made by domestic and foreign research scholars, but the main work is focused on the optimization design and simulation of different control algorithms, and the problems that the control theory is advanced, the control algorithm is intelligent and the algorithm structure is complicated are brought. The control algorithm depends on an accurate mathematical model of the crane, and is difficult to adapt to the complex environment (which is more and more complicated by external interference) in which the crane works, and to adapt to the changes of various parameters, such as rope length, speed, acceleration and the like, and is difficult to be applied in engineering practice.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the open-loop fuzzy anti-swing control method for the bridge crane is provided, and the anti-swing effect of the crane is improved.
The technical scheme adopted by the invention for solving the technical problems is as follows: an open-loop fuzzy anti-swing control method of a bridge crane is characterized in that: it comprises the following steps:
s1, constructing a two-dimensional dynamic model of the bridge crane system:
through a system identification experiment, a system transfer function of the bridge crane is obtained on a bridge crane experiment bench, and a two-dimensional dynamic model is built by combining a crane dynamic equation;
s2, designing an optimal deceleration anti-rolling curve of the trolley:
dividing the deceleration stage into a plurality of sections, wherein the initial speed of the deceleration stage is a set value, the final speed is 0, the speed between adjacent sections is unknown, and the speed between adjacent sections is optimized by utilizing a particle swarm optimization algorithm and combining the two-dimensional kinetic model, so that the integral criterion IATE index is optimal, and the optimal deceleration curve of the deceleration stage is obtained;
respectively obtaining corresponding optimal deceleration curves at different rope lengths, initial speeds and maximum speeds in a deceleration stage; calculating a corresponding initial angle;
s3, establishing an optimal deceleration curve lookup table of the trolley:
setting a discourse domain of the rope length, the initial angle of the deceleration moment and the speed at the deceleration starting moment, quantizing the rope length into a grades and dividing the initial angle into b grades according to the discourse domain, quantizing the rope length and the initial angle corresponding to the optimal deceleration curve obtained by S2 into each rope length and angle grade respectively, and forming an optimal deceleration curve query table by the corresponding optimal deceleration curve, the rope length and angle grade and the initial speed at the deceleration stage;
s4, checking an optimal deceleration curve lookup table of the trolley:
reasoning by using a fuzzy control rule, and if the change value of the speed is positive after the fuzzy control rule reasoning, increasing the acceleration value during acceleration or reducing the acceleration value during deceleration in a deceleration curve; if the change value of the speed is negative, reducing the acceleration value in the acceleration process in the deceleration curve or increasing the acceleration value in the deceleration process;
the optimal deceleration curve table is verified and adjusted based on experiments, and the speed between corresponding adjacent sections is adjusted to obtain a verified optimal deceleration curve query table;
s5, when the system runs, firstly measuring the rope length and calculating the initial angle of the deceleration stage, quantizing the rope length and the initial angle into different grades, looking up a table in an optimal deceleration curve lookup table of the crane according to the initial speed of the deceleration stage, and selecting a corresponding optimal deceleration curve to achieve the purpose of inhibiting the load from swinging;
the anti-swing control steps of the cart are consistent with those of the trolley.
In the above manner, S2 divides the deceleration phase into (t)0,t1),(t1,t2),(t2,t3) Total 3 segments, t0,t1,t2,t3The velocities corresponding to the moments in time are v0,v1,v2,v3Wherein the initial speed v of the deceleration phase0Is a Vx，v3Optimizing t by utilizing a particle swarm optimization algorithm and combining the two-dimensional kinetic model1、t2Velocity v of time1,v2。
According to the method, the fuzzy control rule is specifically as follows: the running direction of the trolley is taken as positive, if theta is positive and d theta/dt is positive, the change value of the speed of the trolley is negative and large; if theta is positive and d theta/dt is negative, the change value of the speed of the trolley is close to 0; if theta is close to 0 and d theta/dt is positive, the change value of the speed of the trolley is positive; if θ is close to 0 and d θ/dt is also close to 0, the change value of the vehicle speed is close to 0.
In the above method, S3 sets the discourse field of rope length to [0,2.5 ]]Initial angle is set to be [ -3,3 [)]The speed at the deceleration starting moment is divided into 3 gears V1、V2、V3The rope length is quantized into 5 grades according to the domain of discourse, and the initial angle is divided into 6 grades.
The invention has the beneficial effects that: and only considering anti-swing control in the deceleration stage, designing a fuzzy control rule based on manual operation experience, further designing a crane deceleration anti-swing curve, and acquiring an optimal crane deceleration anti-swing curve by using an optimization algorithm. Repeating the experiment under different initial states, constructing an optimal deceleration curve lookup table of the crane, and verifying the optimal deceleration curve based on a fuzzy control rule; the anti-shaking control under different working conditions can be realized by looking up the table, the running safety of the crane is obviously improved, the anti-shaking distance and the anti-shaking time are reduced, and the working efficiency and the running stability of the bridge crane are improved.
Drawings
FIG. 1 is a schematic diagram of an open-loop fuzzy anti-swing control system of a crane.
FIG. 2 is a schematic diagram of a crane drive system identification.
FIG. 3 is a schematic diagram of the empirical speed of the open-loop fuzzy anti-swing control of the crane.
FIG. 4 is a schematic diagram of an expected swing angle of an open-loop fuzzy anti-swing control experiment of a crane.
FIG. 5 is a schematic diagram of the open loop fuzzy anti-roll control speed of the trolley of the crane system.
FIG. 6 is a schematic view of the trolley open loop fuzzy anti-roll control angle of the crane system.
In the figure: the device comprises a controller 1, a lifting frequency converter 2, a lifting motor 3, a lifting encoder 4, a cart frequency converter 5, a trolley frequency converter 6, a cart motor 7, a trolley motor 8, a cart running mechanism 9 and a trolley running mechanism 10.
Detailed Description
The invention is further illustrated by the following specific examples and figures.
As shown in fig. 1, the open-loop fuzzy anti-swing control method for the bridge crane adopts a control system comprising a controller 1, a lifting frequency converter 2, a lifting motor 3, a lifting encoder 4, a cart frequency converter 5, a trolley frequency converter 6, a cart motor 7, a trolley motor 8, a cart running mechanism 9, a trolley running mechanism 10 and the like. The controller measures the rope length in real time according to expected running speeds and rope length sensors set by the cart and the trolley, calculates the running speeds of the cart and the trolley and an initial angle at the beginning of a deceleration stage, and drives the cart running mechanism and the trolley running mechanism to run through the cart frequency converter and the trolley frequency converter respectively to eliminate load swing.
The invention provides an open-loop fuzzy anti-swing control method for a bridge crane, which comprises the following steps:
s1, constructing a two-dimensional dynamic model of the bridge crane system:
through a system identification experiment, a system transfer function of the bridge crane is obtained on a bridge crane experiment bench, and a two-dimensional dynamic model is built by combining a crane dynamic equation.
Firstly, a two-dimensional dynamic model of the crane is established. Taking the trolley displacement x, the load swing angle theta and the hoisting rope length l as generalized coordinates to obtain a system Lagrange equation, and obtaining a transfer function of a crane dynamic model with speed input after linearization and certain simplification:
Figure GDA0002986216050000031
g is the acceleration of gravity.
Considering factors such as friction loss of a transmission system, various resistances and transmission time of a driving system of an actual crane, the control effect of a later controller is influenced. Therefore, a mathematical model of the crane driving system needs to be established through a system identification method. Firstly, system input and output data are obtained, then an identification model set is collected, and model parameters are estimated to obtain a crane driving system mathematical model. According to the existing crane experiment bench, an identification experiment is carried out to obtain the system delay time of t seconds, and the speed transfer function of the whole driving system can be expressed as follows:
Figure GDA0002986216050000041
the system identification schematic shown in FIG. 2 results in an actual maximum velocity v2Will fall below the desired maximum velocity v1And there is a certain delay. And building a system model by combining a crane dynamic model and a driving system speed transfer function.
S2, designing an optimal deceleration anti-rolling curve of the trolley:
dividing the deceleration stage into a plurality of sections, wherein the initial speed of the deceleration stage is a set value, the final speed is 0, the speed between adjacent sections is unknown, and the speed between adjacent sections is optimized by utilizing a particle swarm optimization algorithm and combining the two-dimensional kinetic model, so that the integral criterion IATE index is optimal, and the optimal deceleration curve of the deceleration stage is obtained; respectively obtaining corresponding optimal deceleration curves at different rope lengths, initial speeds and maximum speeds in a deceleration stage; and calculates a corresponding initial angle.
Designing an optimal deceleration anti-swing curve of the crane: has rich channels by observationThe operation process of the tested crane operators can find that when the crane operators are about to reach the destination (namely, when the crane operators start to decelerate), the operators can correspondingly control the trolley according to the load swinging rule to restrain the swinging of the crane load, and the operation process can be specifically summarized as the following fuzzy control rule: the running direction of the trolley is taken as positive, if theta is positive and d theta/dt is positive, the change value of the speed of the trolley is negative and large; if theta is positive and d theta/dt is negative, the change value of the speed of the trolley is close to 0; if theta is close to 0 and d theta/dt is positive, the change value of the speed of the trolley is positive; if θ is close to 0 and d θ/dt is also close to 0, the change value of the vehicle speed is close to 0. The whole anti-rolling speed curve can be simulated according to the fuzzy control rule and is shown in figure 3, (0, t)0) In the uniform acceleration stage, the acceleration during acceleration is a, and in this case, the anti-shake is not considered, and in the deceleration stage (t)0,tn) The anti-shaking is carried out, the speed curve during deceleration is f (x), and the functional expression is as follows:
Figure GDA0002986216050000042
will complete the deceleration phase (t)0,tn) Is divided into (t)0,t1),(t1,t2),…,(tn-1,tn) N segments in total, t0,t1,t2…tnThe velocities corresponding to the moments in time are v0,v1,v2…vnWherein v is0To the speed at which deceleration begins, vnIs 0.
Secondly, determining an optimization objective function of the system, wherein an IATE (integration criterion) index is selected, and the IATE index is defined as:
Figure GDA0002986216050000043
where t is the deceleration running time, e (t) is the error value, here the angle value θ (t).
Figure GDA0002986216050000044
Figure GDA0002986216050000045
Setting corresponding constraint conditions according to the deceleration time period of the trolley as t0,tn]Wherein t is0For the deceleration start time, tnIs the stop time. The termination state is that the speed of the trolley is zero, and the final load swing angle is zero. The initial state is defined by the initial speed v at deceleration0And determining the load swing angle at the moment, and deducing and calculating the load swing angle theta at the moment of starting deceleration according to a crane dynamic model0：
Figure GDA0002986216050000051
a represents the acceleration during acceleration operation, t represents the sum of the acceleration operation time and the uniform speed operation time, and l represents the rope length. Determining the length of the rope as l, the speed at the moment of starting deceleration as v and the initial deceleration angle theta0Then, optimizing t by utilizing an optimization algorithm and combining a crane system model1,t2…tn-1And the speed value at the moment enables the IATE index to be optimal, so that the optimal deceleration curve under the working condition is obtained. When the value of n is larger, the generated deceleration speed curve is more complicated, and the optimization time is longer. And repeating the experiment under the conditions of different rope lengths and initial angles and speeds at the deceleration moment to obtain a plurality of groups of optimal deceleration curves.
S3, establishing an optimal deceleration curve lookup table of the trolley:
setting up the discourse domain of the rope length, the initial angle of the deceleration moment and the speed of the deceleration starting moment, quantizing the rope length into a grades and dividing the initial angle into b grades according to the discourse domain, quantizing the rope length and the initial angle corresponding to the optimal deceleration curve obtained by S2 into each rope length and angle grade respectively, and forming an optimal deceleration curve lookup table by the corresponding optimal deceleration curve, the rope length and angle grade and the initial speed of the deceleration stage.
Setting the discourse domain of rope length and initial angle at deceleration moment, e.g. [ A ]1,Am]、[B1,Bt]It is based on the domain of discourseQuantized to several levels, e.g. A1＜l≤A2When the rope length is in the order of L1,A2＜l≤A3The rope length is L2Are sequentially co-divided into L1,L2…Lm-1In total, m-1 levels, the initial angle can be divided into theta1，θ2…θt-1T-1 levels in total, speed v at the start of deceleration0Can be divided into V according to the speed gear of the actual crane1、V2、V3… are provided. At each v0Next, respectively quantifying the rope length and the initial angle corresponding to each optimal deceleration curve to ensure that the optimal deceleration curve and the v0The quantization levels of θ, l are in one-to-one correspondence to form an optimal deceleration curve look-up table, as shown in table 1, for a deceleration curve V11Corresponding to a deceleration start time v0The length of the rope is L1Angle of theta1The lower optimal deceleration sway curve. The more domain levels are divided, the more deceleration curves are required, the better the control effect is, but the workload is greatly increased, so that the proper division level is selected, and the load swing angle is controlled within the required range.
TABLE 1
Figure GDA0002986216050000052
Figure GDA0002986216050000061
S4, checking an optimal deceleration curve lookup table of the trolley:
reasoning by using a fuzzy control rule, and if the change value of the speed is positive after the fuzzy control rule reasoning, increasing the acceleration value during acceleration or reducing the acceleration value during deceleration in a deceleration curve; if the change value of the speed is negative, reducing the acceleration value in the acceleration process in the deceleration curve or increasing the acceleration value in the deceleration process; and verifying and adjusting the optimal deceleration curve table based on experiments, and adjusting the speed between corresponding adjacent sections to obtain a verified optimal deceleration curve query table.
In consideration of the existence of factors such as errors, a large angle margin may exist when the acquired deceleration curve is directly applied to the crane, and the corresponding optimal deceleration curve needs to be properly adjusted. Carrying out fuzzy reasoning on the residual angle and the angular velocity based on a designed fuzzy rule, and if the change value of the velocity is positive after the fuzzy reasoning, properly increasing the acceleration value in the deceleration curve during acceleration or reducing the acceleration value in the deceleration curve; if the change value of the speed is negative, the acceleration value at the time of acceleration in the deceleration curve is appropriately decreased or the acceleration value at the time of deceleration is increased. As shown in fig. 4, the load swing angle is zero, there is a certain negative angular velocity, according to the fuzzy control rule, if θ is close to zero, and d θ/dt is negative and small, the variation value of the velocity is negative and small, the acceleration value during deceleration in the deceleration curve should be increased or decreased appropriately, and the corresponding v value should be adjusted1,v2…vn-1To reduce the swing margin of the final load. And obtaining a final speed curve lookup table through checking the curve.
And S5, when the system runs, firstly measuring the rope length and calculating the initial angle of the deceleration stage, quantizing the rope length and the initial angle into different grades, looking up a table in an optimal deceleration curve lookup table of the crane according to the initial speed of the deceleration stage, and selecting a corresponding optimal deceleration curve to achieve the purpose of inhibiting the load swing.
Actual system runtime, v0The method is known, only the measured rope length and the calculated initial angle value at the deceleration moment are needed to be respectively quantized into each rope length grade and angle grade, and the table is looked up according to the rope length grade and the initial angle grade to obtain the optimal deceleration curve under the working condition so as to achieve the purpose of inhibiting the load swing.
The anti-swing control steps of the cart are consistent with those of the trolley.
The specific implementation steps of this embodiment are as follows:
(1) in the presetting stage, the speed V at the moment of starting deceleration of the trolley is setxSpeed V at the start of deceleration of the cartdThe acceleration in the acceleration process is a, the hoisting load isThe loading length l.
(2) Through a system identification experiment, a transfer function of a crane driving system is obtained on an existing crane experiment bench, and a model is built by combining a crane dynamic equation.
(3) Acquiring an optimal deceleration curve of the trolley: the deceleration stage is divided into (t)0,t1),(t1,t2),(t2,t3) In total 3 segments, i.e. taking n as 3, t0,t1,t2,t3The velocities corresponding to the moments in time are v0,v1,v2,v3Wherein v is0Is a Vx，v3For zero, optimizing t by utilizing a particle swarm optimization algorithm and combining a crane system model1、t2And the speed value at the moment enables the IATE index to be optimal, so that the optimal deceleration curve under the working condition is obtained. And repeating the steps under different rope lengths, initial angles at the deceleration moment and maximum speeds to obtain a plurality of groups of optimal deceleration curves, as shown in fig. 5, 3 groups of speed curves with the deceleration starting moment speed of 0.176m/s and the rope length of 1m, and the initial swing angles of the curves 1, 2 and 3 are respectively 2.045 degrees, -2.055 degrees and 1.755 degrees according to an initial angle calculation formula, and the corresponding angle response curves are only considered to prevent shaking at the deceleration stage as shown in fig. 6.
(4) Constructing a trolley optimal deceleration curve look-up table: setting the domains of the rope length, the initial angle at the deceleration moment and the speed at the deceleration starting moment, wherein the domain of the rope length is set to be 0,2.5 in consideration of the parameters of the conventional crane experiment bench]Initial angle is set to [ -3,3 [)]The speed at the deceleration starting moment is divided into 3 gears V1、V2、V3Quantifying the rope length to 5 grades L according to discourse domain1,L2,L3,L4,L5The initial angle is divided into 6 levels theta1…θ6. And respectively quantizing the rope length and the initial angle value corresponding to the obtained optimal deceleration curve into each rope length and angle grade, wherein the corresponding optimal deceleration curve, the rope length and angle grade and the speed at the deceleration starting moment form an optimal deceleration curve look-up table.
(5) Reference fuzzy control rule to optimal deceleration curveAnd (5) checking the line lookup table: and if the change value of the speed is positive after fuzzy rule reasoning, properly increasing the acceleration value in the acceleration curve or reducing the acceleration value in the deceleration curve. If the change value of the speed is negative, the acceleration value at the time of acceleration in the deceleration curve is appropriately decreased or the acceleration value at the time of deceleration is increased. The optimal deceleration curve table is verified and adjusted based on experiments, and v of the corresponding optimal deceleration curve is adjusted1,v2For reducing the workload, only the adjustment v may be considered2And obtaining the checked optimal deceleration curve look-up table.
(6) When the system operates, the rope length is measured, the initial angle of the deceleration moment is calculated, the rope length and the initial angle are quantized into different grades, table lookup is carried out according to the speed of the deceleration starting moment, the optimal deceleration curve under the working condition is selected, and the purpose of restraining load swing is achieved.
(7) The anti-swing control steps in the cart direction are the same as those of the trolley.
The above test cases are only for better illustrating the intrinsic nature of the present patent, and do not limit the scope of application of the present invention. The open-loop fuzzy anti-swing control method of the bridge crane only considers anti-swing control in a deceleration stage, obtains a transfer function of a crane driving system by a system identification method, and constructs a mathematical model of the crane system considering driving friction and system response delay. And designing a fuzzy control rule based on manual operation experience, further designing a crane deceleration anti-swing curve, and acquiring an optimal crane deceleration anti-swing curve by using an optimization algorithm. Under different working conditions, multiple groups of deceleration curves are obtained through repeated experiments, an optimal deceleration anti-shaking curve query table is constructed, the optimal deceleration curves are verified based on fuzzy control rules, and finally table lookup is carried out, so that anti-shaking control under different rope lengths and different working conditions is realized, the running safety of the crane is obviously improved, the anti-shaking distance and the anti-shaking time are reduced, and the working efficiency and the running stability of the bridge crane are improved. The method is simple and clear, has low cost and is easy for engineering application.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.

Claims (4)

1. An open-loop fuzzy anti-swing control method of a bridge crane is characterized in that: it comprises the following steps:
s1, constructing a two-dimensional dynamic model of the bridge crane system:
through a system identification experiment, a system transfer function of the bridge crane is obtained on a bridge crane experiment bench, and a two-dimensional dynamic model is built by combining a crane dynamic equation;
s2, designing an optimal deceleration anti-rolling curve of the trolley:
dividing the deceleration stage into a plurality of sections, wherein the initial speed of the deceleration stage is a set value, the final speed is 0, the speed between adjacent sections is unknown, and the speed between adjacent sections is optimized by utilizing a particle swarm optimization algorithm and combining the two-dimensional kinetic model, so that the integral criterion IATE index is optimal, and the optimal deceleration curve of the deceleration stage is obtained;
respectively obtaining corresponding optimal deceleration curves at different rope lengths, initial speeds and maximum speeds in a deceleration stage; calculating a corresponding initial angle;
s3, establishing an optimal deceleration curve lookup table of the trolley:
setting a discourse domain of the rope length, the initial angle of the deceleration moment and the speed at the deceleration starting moment, quantizing the rope length into a grades and dividing the initial angle into b grades according to the discourse domain, quantizing the rope length and the initial angle corresponding to the optimal deceleration curve obtained by S2 into each rope length and angle grade respectively, and forming an optimal deceleration curve query table by the corresponding optimal deceleration curve, the rope length and angle grade and the initial speed at the deceleration stage;
s4, checking an optimal deceleration curve lookup table of the trolley:
reasoning by using a fuzzy control rule, and if the change value of the speed is positive after the fuzzy control rule reasoning, increasing the acceleration value during acceleration or reducing the acceleration value during deceleration in a deceleration curve; if the change value of the speed is negative, reducing the acceleration value in the acceleration process in the deceleration curve or increasing the acceleration value in the deceleration process;
the optimal deceleration curve table is verified and adjusted based on experiments, and the speed between corresponding adjacent sections is adjusted to obtain a verified optimal deceleration curve query table;
s5, when the system runs, firstly measuring the rope length and calculating the initial angle of the deceleration stage, quantizing the rope length and the initial angle into different grades, looking up a table in an optimal deceleration curve lookup table of the crane according to the initial speed of the deceleration stage, and selecting a corresponding optimal deceleration curve to achieve the purpose of inhibiting the load from swinging;
the anti-swing control steps of the cart are consistent with those of the trolley.
2. The method of claim 1, wherein: s2 dividing the deceleration stage into (t)0,t1),(t1,t2),(t2,t3) Total 3 segments, t0,t1,t2,t3The velocities corresponding to the moments in time are v0,v1,v2,v3Wherein the initial speed v of the deceleration phase0Is a Vx，v3Optimizing t by utilizing a particle swarm optimization algorithm and combining the two-dimensional kinetic model1、t2Velocity v of time1,v2。
3. The method of claim 1, wherein: the fuzzy control rule is specifically as follows: the running direction of the trolley is taken as positive, if theta is positive and d theta/dt is positive, the change value of the speed of the trolley is negative and large; if theta is positive and d theta/dt is negative, the change value of the speed of the trolley is close to 0; if theta is close to 0 and d theta/dt is positive, the change value of the speed of the trolley is positive; if θ is close to 0 and d θ/dt is also close to 0, the change value of the vehicle speed is close to 0.
4. The method of claim 2The method of (2), characterized by: s3, setting the discourse domain of the rope length to 0,2.5]Initial angle is set to be [ -3,3 [)]The speed at the deceleration starting moment is divided into 3 gears V1、V2、V3The rope length is quantized into 5 grades according to the domain of discourse, and the initial angle is divided into 6 grades.
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