JPS60218291A - Automatic operation system of crane - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed Description of the Invention] [Field of Application of the Invention] The present invention relates to an automatic operation system for cranes used for cargo handling work in ports, etc., and in particular, the present invention relates to an automatic operation system for cranes used for cargo handling work in ports, etc. This relates to an automatic lane driving system that can prevent vibrations.

[Background of the invention]

In this specification, the crane refers to a crane that handles cargo by moving a trolley 11 in a horizontal plane and lowering a rope 12 hanging from the trolley 11, as shown schematically in FIG. .

In addition, the purpose of automatic operation of the crane shown as a model in Fig. 1 is (1) to move the trolley 11 to the target point in the shortest possible time (no movement of the crane so that the load 13 stops swinging at the target point). This steady rest control is, in principle, to control the trolley 1
1 comes close to the target point, the luggage 13 is made to swing in the direction of travel relative to the trolley 11, and the trolley 11 is brought to a stop by catching up with the luggage 13 which has swung almost to the target point and is in a nearly stationary state. Achieve a kill by "let it happen".

Conventional crane automatic operation systems, for example, as shown in FIG. Ta, Kanai; “Optimum Crane Operation Method Focusing on the Maximum Speed of the Trolley,” Proceedings of the Society of Instrument and Control Engineers, vot, 25 + Bian 6. (1979
)).

The speed pattern is calculated so that the angle of view of the rope system becomes zero at the end time.

This crane automatic operation system calculates the speed pattern of the trolley that can achieve the above control in advance using a geometric method or an optimal control method, as shown in Figure 2, and then adjusts the servo system to the speed of the trolley. This system is configured to perform speed pattern tracking. The biggest problem with this method is that the control system is in an open loop with respect to the swing angle of the rope. Therefore, in the automatic operation method described above, various errors such as initial swing, gust of wind, control system delay time, etc. This has a serious problem in that it is not possible to make any correction to the swing angle of the rope when a factor exists.

[Purpose of the invention]

The present invention has been made in view of the above circumstances, and its purpose is to provide a
The purpose is to provide an automatic lane driving system that can stop the swinging of cargo with high precision.

[Summary of the invention]

The key point of the present invention is to evaluate measurement data of trolley position/velocity, rope deflection angle, and angular velocity as fuzzy quantities, and to determine control commands by fuzzy inference, thereby enabling highly accurate crane automatic operation. be.

[Embodiments of the invention]

Hereinafter, first, a first embodiment of the present invention will be described in detail based on the drawings.

FIG. 3 is a block diagram of a crane automatic operation control device showing a first embodiment of the present invention. In the figure, 1 is a device for measuring the angle of the rope system, 2 is a device that calculates the current angle of view θ(1) based on the measured value of the angle of view in the past, and 3 is the angular velocity which is the time differential of the angle of view. This figure shows a device that calculates δ(1) based on past deflection angle measurements.

Further, 4 is a speed generator for measuring the speed of the trolley system, 5 is a device for calculating the trolley writing speed V (t) from the pulses of the speed generator 4, and 6 is a speed generator for measuring the speed of the trolley system in the same way as the calculation device t5. 7 is a load measuring device that measures the weight of the load to be transported, M, and 8 is a control command u (t) based on the above data.
9 indicates a control device that drives and controls the trolley.

FIG. 4 is a diagram for explaining the calculation of fuzzy quantities related to the trolley speed, and FIG. 5 is a diagram for explaining the calculation of Fuzzy Co., Ltd. regarding the deflection of the rope system.

FIG. 6 shows an example of a program processed by the microcomputer 8, in which a certain JUtQ (for example,
100m5ec).

The operation of this embodiment will be described below with reference to FIGS. 3 to 6.

When the program is started, the swing angle θ(t), angular velocity j(t), ) of the rope system calculated by the calculation devices 2 to 6 are calculated.
Input the speed v (t), acceleration α (1), and load amount M measured by the load amount measuring device 7 of the trolley (step 21), and use the following formula to obtain the current control command u until 41 seconds later.
(t) is maintained, and the speeds Vp and Vw are calculated when the control command is increased or decreased by ΔU (Step 2
2).

Next, the running speed of the trolley is evaluated using fuzzy quantities. Here, the trolley speed falls within the permissible speed error (
μv1) and (μvz) that match the target speed.

The membership function representing this fuzzy single set is defined as follows, for example. In other words, if X is the speed error and Fl[capacity error is ±0.5 m/m, then the membership function μvt (X) of the fuzzy one-unit set that can track within the permissible error range is: ...(4) The membership function 11 v 2 (X) of the fuzzy one-vehicle set that matches the target speed can be defined as ...
...(5) It can be defined as:

Based on these two membership functions, the fuzzy quantity Cv of the traveling speed is expressed as a pair of two values μvt (X) and μm (X). 7 calculated using formulas (1) and (2)
From the scalar quantities Vz, Vp, and Vw of the running speed after 1 second, if the control command is held, the fuzzy quantity of the running speed when increased or decreased by ΔU CvZ @ CvP and C
Set fN as follows (step 23).

Next, the swing angle θ(t) of the rope system and the angular velocity jl (t)
Similarly to the trolley speed, the deflection angle θ2. Deflection angle θP when the angular velocity θz1 control command is increased or decreased by ΔU,
θN: Calculate the angular velocities δP and δN.

The equation of motion of the rope system is expressed by the following equation.

Here, θ is the angular acceleration of the deflection angle, g is the gravitational acceleration, t is the rope length, and m is the weight of the rope system. When 1 and U are placed in the above equation (7), the following is obtained. Here, if VjT77 is set as the natural frequency W, then θ2. θ2 is θz=RWsin(wΔT×α0)... group...
...can be determined by αQ. Here, .

θp, θN; θ2. θN can also be determined in the same way.

From θ and θ obtained in the above method, the residual runout rz, rp
, rNf, respectively, the current residual runout r is also evaluated using the following equation, and the increase or decrease in the residual runout is evaluated using a fuzzy amount. Here, it is about the same as the current value (μr1).

It is assumed that there are two fuzzy single sets with fewer (μn) than the current number. The membership function representing this fuzzy one-set (10) is defined as follows, for example. In other words, r is the residual runout and the tolerance is 0.00.
In the case of 5 rad, the membership function μr of the same fuzzy one-set set mentioned above can be defined as μr = (r), and the membership function μr of the fuzzy one-set set with a slight fluctuation
=(r) can be defined as follows.

Using these two membership functions, the residual loss fuzzy amount C, is represented by two bare values μrt(r) and μrz(r). The scalar amounts rz, rp of the residual runout after 31 seconds calculated according to the book 2α1) to Oz shown above.

From rN, if the control command is maintained, it will increase by ΔU (11
) The fuzzy amount of residual runout C,,C,P when reduced.

CrN is prepared as follows (step 25).

Cyg = (Cyg11 Cyxg ) = (μyt(
rz), μr2(rz))Cyp = (Crpt
, Crpz)2(μlx(rp), μ72(rp
) )CrN= (CyNl l Cyxg )= (
μys(7”s), , ay2ON) Next, a control law is selected by fuzzy inference according to the following rules.

(1) If the vehicle travels within the allowable error range with the current control command u (t) and the runout is the same, the current control command is maintained.

(2) If the control command is increased by ΔU and the vehicle runs within the allowable error range and the runout is reduced, the control command is increased by Δ11.

(3) Reduce the control command by ΔU, and if the vehicle runs within the allowable error range and the runout is reduced, reduce the control command by 5ΔU.

(4) Increase the control command by ΔU to match the target speed (1
2) If the vehicle runs and the runout is the same, increase the control command by ΔU.

(5) Decrease the control command by ΔU. If the vehicle travels at the target speed and the runout is the same, reduce the control command by ΔU.

The specific operation of the above fuzzy inference is (1) mr (Cvzt, Crzl) (2) ”
(CVPI, C1p2) (3) ” (CvNl
, CrN2) (4) ” (CVP2, Cypt
26
).

According to this embodiment, it is possible to maintain the running speed of the trolley system constant and to reduce the residual swing of the rope system, thereby realizing lane control.

Next, a second embodiment of the present invention will be described. In this example, the position and speed of the trolley, the deflection angle of the lobe, and
Control results based on control commands determined based on state quantities such as angular velocity are evaluated as fuzzy quantities, and the best control command is determined from among them.

FIG. 7 is a block diagram of a crane automatic operation control device showing a second embodiment of the present invention. In the figure, 31 is the target speed vT based on the measured value X of the crane position and the elapsed time.
, target acceleration α! , a device that calculates a target deflection angle θT, and a target angular velocity θT (hereinafter referred to as a "pattern generator").

Reference numeral 32 denotes an arithmetic device having a function combining the functions of component 5.6 shown in FIG. 3, which calculates estimated values V and α of the speed and acceleration of the trolley from the measured value X of the crane position;
is the current swing angle from the measured rope swing angle θ.

Calculate estimated values θ and θ of angular velocity. This figure shows an arithmetic device having a combined function of the component 2.3 shown in FIG.

Also, 41, 42.43 are trolley speed V and target value Vy
From the difference ΔV between
(VZ)J where the speed deviation is O.

(14) ``51, 52, 5: ( is the difference Δα between the trolley acceleration α and the target acceleration 6). Positive (αP) J, V Acceleration deviation is 0 (aZ) J, r
61.62.63 is a device that calculates each fuzzy amount of (αN)J where the acceleration deviation is negative, and 61.62.63 calculates from the difference Δθ between the rope deflection angle θ and the target deflection angle 0, “J deflection angle deviation is positive. (aP)J, r deflection angle deviation is 0 (aZ).

71, 72.73 is a device that calculates each fuzzy quantity of "(θN) J when the deflection angle deviation is negative," and 71, 72. )J, (aZ)J in which the r angular velocity deviation is O, and (θN)J in which the r angular velocity deviation is negative are respectively shown.

In addition, 34 is a fuzzy inference device that evaluates the fuzzy amount of the deviation from the target value determined by each of the fuzzy amount calculation devices #41 to 73 using a fuzzy control law, and infers the change ΔU of the control command U. (15) A control command integration device that adds the change in control command ΔU to the previous control command U; 36 is a drive controller that adjusts the speed of the trolley;
Braking device 37 indicates a crane device.

Figure 8 shows the fuzzy quantities related to the speed deviation mentioned above, VP, V
It is a diagram for explaining the operation of devices 41, 42 and 43 that calculate Z and VN.

Hereinafter, using FIG. 8, the fuzzy amount VP of the speed deviation ΔV
, VZ, and VN will be explained.

First, in the vP calculation device 41, as shown in FIG. 8, when the input speed deviation ΔV is 0
．． The evaluation value of 0 is divided by ΔV (0.0 to 4 between 0 and 4)
The linearly changing evaluation value of 1.0, and the linearly changing evaluation value of 0.4≦Δ
In the case of V, an evaluation value of 1 or 0 is output.

In the vZ arithmetic unit 42, when ΔV≦−0,5 and 0.5≦ΔV, the input speed deviation ΔV is 0.
．． An evaluation value of 0, and an evaluation value that changes linearly from 0.0 to 1.0 in the case of -0,5 (ΔV<-0o1 and 0.1<ΔV<O,S, -〇 , 1≦(16) If ΔV≦0.1, an evaluation value of 1.0 is output.

■N arithmetic units (in W2B, the aforementioned VP arithmetic unit 4
1 and outputs the d・1 value of the symmetric form.

A calculation device for the fuzzy amount of the speed deviation 41゜42.43
VP, VZ when the speed deviation ΔV is given by
, VN's mutter is heard. If you go, ΔV U T −0,
2m/l! [lO) When VN=0.5. VZ=0
．． 7 seconds, the evaluation value is VP=0.0.

Similarly, the fuzzy amount αP of the acceleration deviation Δα.

α2. αN: Fuzzy amount θP of deflection angle deviation Δθ.

The values of δZ and θN can be calculated. Using these fuzzy quantities, the fuzzy inference device 34 infers the amount of change ΔU in the control command. Examples of inference rules are as follows.

(1) Speed deviation is 0 (VZ) and acceleration deviation is 0 (α2)
If so, hold the control command.

Δu=Q (2) Velocity deviation is positive (VP) and acceleration deviation is positive (αP)
If so, increase the control command a little.

(17) For example, Δu=-1-0, 1 m/5ea (3) If the velocity deviation is negative (VN) and the acceleration deviation is negative (αN), reduce the control command a little.

For example, Δu': Q, l m/(8) (4) If the angle of view deviation is 0 (aZ) and the angular velocity deviation is 0<jz>,
Holds control commands.

Δu=Q (5) If the deflection angle deviation is positive (aP) and the angular velocity deviation is positive (aP)
), increase the control command slightly.

For example, Δu=+0.1m/, +ec (6) The deflection angle deviation is negative (θN) and the angular velocity deviation is negative (θN
), reduce the control command a little.

For example, Δu = -Q, l m /nadaThe concrete calculation of the above inference is to calculate the evaluation value of each rule as (1) R1=m (VZ, aZ) (2) R2=m (VP, aP) (3 ) fLs
= m (VN, αN) (4) R4 = lock (θZ, aZ) (5) Rs = m (θP, aP) (18) (6) Ra = m (θN, δN) Among these rules, The rule with the highest evaluation value is selected, and the control command change instruction specified by that rule is output to the control command integration device 35 as the inference result ΔU.

According to this fruit flow example, the same as that according to the above-mentioned example,
It is possible to reduce the residual swing of the rope system, and lane control can be realized.

In the above embodiment, each arithmetic unit d is assumed to be an independent unit i (explained), but a part or all of it is realized by a microcomputer, and a program is started at a constant cycle to determine control commands. I can't say anything good about it.

〔Effect of the invention〕

As described above, according to the present invention, the measurement data of trolley position/velocity, rope deflection angle, and angular velocity are evaluated as fuzzy quantities [7], and control commands are determined by fuzzy inference. This has the remarkable effect of being able to stop the swing of the load with high precision even in the presence of external disturbances such as waves and gusts of wind, making it possible to realize an automatic lane driving system (19).

[Brief explanation of drawings]

Fig. 1 is a diagram showing a model crane, Fig. 2 is a diagram showing speed patterns in a conventional automatic crane operation system, Fig. 3 is a block diagram showing the first embodiment of the present invention, Figs. Fig. 5 is a diagram for explaining the operation of fuzzy quantities, Fig. 6 is a flowchart showing the processing procedure, Fig. 7 is a block diagram showing the second embodiment of the present invention, and Fig. 8 is a diagram showing fuzzy quantities. FIG. DESCRIPTION OF SYMBOLS 1... Rope deflection angle measuring device, 2... Deflection angle calculation device, 3... Angular velocity calculation device, 4... Speed generator, 5
...Trolley speed calculation device, 6.. Acceleration calculation device, 7.. Load amount measurement device, 8.. Microcomputer, 9.. Trolley control device, 31.. Pattern generation device, 32.・Trolley speed/acceleration calculation device,
33...Rope deflection angle/angular velocity calculation device, 34...
Fuzzy guess [Sμ]

Claims (1)

[Claims] 1. A device for measuring crane state quantities such as the position and speed of the trolley, the swing angle and swing angular speed of a rope hanging from the trolley, an actuator for adjusting the speed of the trolley, and control given to the actuator. An automatic crane comprising a control mechanism for calculating and determining commands, characterized in that the output of the measuring device is evaluated as a fuzzy set, and the control command to be given to the speed adjusting actuator of the trolley is determined by fuzzy reasoning. Driving method. 2. The fuzzy inference format is characterized in that control results based on control commands determined based on the output of the measuring device are evaluated from time to time as fuzzy quantities, and the best control command is determined from among them. Claim No. 1
Crane automatic operation method described in section.