JPH0472280A - Control device for hydraulic elevator - 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] [Purpose of the invention] (Industrial application field) The present invention relates to a control device for a hydraulic elevator.

(Prior Art) Generally, a control device for a hydraulic elevator employs a flow control method using a pressure oil flow control valve.

In this control method, when the elevator is ascending, the electric motor is rotated at a constant speed, and a fixed amount of pressure oil from the hydraulic pump is returned to the tank.When a start command is issued, the amount returned to the tank is adjusted using a flow control valve. The speed of the car is controlled by When the elevator descends, the speed of the car is controlled by controlling the amount of pressure oil in the hydraulic cylinder that is returned to the tank by the weight of the car using a flow control valve.

To explain the operation of such a conventional hydraulic elevator control device, the flow control valve is closed when the car is stopped, and opened in response to an operation command. Therefore, the speed control device raises the car according to a predetermined speed pattern by controlling the amount of pressure oil discharged through the valve.

The speed characteristics at this time follow the speed pattern A shown by the solid line in FIG. 14 by the speed control device. That is, the car starts up in response to a start command, accelerates to the rated speed Vo, and then returns to the rated speed V. The vehicle will rise at 100 degrees and begin to decelerate when it reaches the deceleration switch position.

Then, the robot ascends at a constant landing speed V1, and when the upper limit position is reached, a stop switch is activated to stop the elevation.

Further, during descending operation, the descending speed is controlled by the amount of oil discharged from the hydraulic jack due to the weight of the car.

(Problem to be Solved by the Invention) However, in such a conventional hydraulic control device, when the load pressure (hydraulic pressure) or oil temperature of the hydraulic elevator changes, the viscosity of the pressure oil changes, and the volumetric efficiency of the hydraulic pump decreases. As a result, the running pattern of the car deviates from a predetermined pattern.

For example, when the car is raised, as shown in Figure 15, hydraulic pumps generally decrease the amount of pressure oil discharged as the load pressure (or oil temperature) increases, and decrease the amount of pressure oil discharged as the load pressure (or oil temperature) decreases. Accordingly, when the load pressure (or oil temperature) increases, the car speed pattern becomes pattern B shown by the broken line in FIG. 14, and when the load pressure (or oil temperature) decreases, it becomes the running pattern C shown by the dashed-dotted line. In the case of the travel pattern B indicated by the broken line, the travel time becomes longer, leading to a decrease in service, and in the case of the travel pattern C) indicated by the dotted chain line,
There was a problem in that the ride quality, especially when stopped, deteriorated.

Also, when descending, the opposite phenomenon occurred, and there was a similar problem.

Therefore, a method has been proposed that reduces variations in driving patterns by outputting control command values for a speed pattern that is close to the target speed pattern even when load pressure or oil temperature fluctuates. . An example of this method is a method in which parameters are selected from a preset parameter table for speed pattern generation based on the input values of the oil temperature sensor and load pressure sensor for the current pressure oil.

However, in this case, it is difficult to model the characteristics of the valve to be controlled using a mathematical model using physical laws.

Therefore, the method generally used in the past is modeling based on a statistical model. An example of this is shown in Fig. 16. This Fig. 16 shows an example of a table in which oil temperature and load pressure are registered as sensor input value data, and appropriate levels are set depending on their sizes. . In this method, an address is obtained from the sensor input value table shown in Fig. 16 based on the combination of the current input values of the oil temperature sensor and load pressure sensor, and the acceleration and speed parameters are calculated from the parameter table shown in Fig. 17 using that address. Take the required steps.

However, when modeling a controlled object (here, a valve) statistically as described above, a large amount of homogeneous data is required. As a result, a great deal of effort and time must be spent on collecting this data. Furthermore, since there is no clear standard for the definition of homogeneity, even if a large amount of data is collected, it cannot be said that this data faithfully represents the characteristics of the object to be controlled. Furthermore, the obtained data cannot be applied to all hydraulic elevator j[+ systems. In other words, there are factors other than sensor input values that affect oil characteristics (e.g.
If you ignore these factors and try to apply the data measured under certain conditions to all other systems, it is obvious that the optimum control I can't hope for that.

Therefore, attempts are being made to create a fuzzy model of the controlled object. That is, when correcting a control command value based on a predetermined speed pattern based on input values from an oil temperature sensor and a load pressure sensor, the antecedent part determines the magnitude of the input value from the sensor, and the consequent part determines the magnitude of the input value from the sensor. Rules representing each control command value are set in advance, the degree of attribution of input values from these sensors to the antecedent part of the rule is determined, and based on this degree, the control command value is determined from the consequent part of the rule. is calculated, and the control command value is corrected based on this calculated value. Furthermore, by providing a learning function that compares the actual elevator speed and the target speed and automatically corrects the control command value of the consequent of the rule according to this difference, it is possible to improve the usage environment of hydraulic elevators. This makes it possible to achieve optimal control according to the customer's needs.

However, in such an automatic rule correction function using a learning function, if all rules are corrected by the learning function, if an abnormality occurs in the microcomputer, etc., there is a possibility that the values will not converge to a predetermined value. Therefore, if such a state continues for a long time, the correction value of the control command value obtained by fuzzy inference becomes abnormal, and as a result, there is a problem that the speed control of the hydraulic elevator may be seriously damaged.

This invention was made in view of such conventional problems, and when automatically correcting rules using a learning function,
To provide a control device for a hydraulic elevator that can always provide stable running characteristics even when an abnormality occurs in a microcomputer or the like and rules are not corrected normally.

[Structure of the Invention] (Means for Solving the Problems) A control device for a hydraulic elevator according to the present invention includes a hydraulic pump that is connected to a hydraulic jack via a valve and that allows pressure oil to pass through the hydraulic jack, and this hydraulic pump. an electric motor rotationally connected to the hydraulic jack; an oil temperature sensor for pressure oil flowing through the hydraulic jack; a load pressure sensor for the load pressure applied to the hydraulic jack; a speed sensor for detecting the speed of the elevator; A control command value is generated for the valve based on a predetermined speed pattern so that the hydraulic jack can move up and down at a predetermined speed by inputting signals from a valve that controls the amount, the oil temperature sensor, and the load pressure sensor. and speed control means for determining a control command value as a fuzzy quantity by fuzzy inference from the input values from both of the sensors, correcting and outputting the control command value based on the calculated value, and rules used for the fuzzy inference. The invention comprises: a rule correction means for correcting the fuzzy inference rules by learning; and a rule correction restriction means for limiting the correction of the fuzzy inference rules by the rule correction means.

(Function) In the hydraulic elevator control device of the present invention, pressure oil is supplied to the hydraulic jack by a hydraulic pump driven by the rotation of an electric motor, and the temperature of the pressure oil supplied to the hydraulic jack is measured by an oil temperature sensor. , and the load pressure is measured by a load pressure sensor.

The signals from the oil temperature sensor and load pressure sensor are then sent to the speed control means, which sets a predetermined speed pattern to the valve that controls the amount of oil flowing so that the hydraulic jack can move up and down at a predetermined speed. At the same time, the control command value is determined as a fuzzy quantity by fuzzy inference with respect to the input values from both sensors, and the control command value is corrected and output based on this calculated value, and the hydraulic Controls the lifting and lowering speed of the

The rule correction means compares the actual elevator speed measured by the speed sensor and the target speed determined by the speed control means during the speed control, and adjusts the consequent part of the fuzzy inference rule according to the difference. Although the control command value is automatically corrected, some of the rules are corrected by the rule correction restriction means when the rules are corrected, but the correction of other rules is restricted, so as to avoid problems such as microcomputer failure. To always obtain stable running characteristics even when an abnormality occurs and rules cannot be corrected normally.

(Example) Hereinafter, embodiments of the present invention will be explained in detail based on the drawings.

FIG. 1 is a diagram of one embodiment of the present invention.

Reference numeral 1 designates a car, and the car 1 is suspended from a rope 5 wrapped around a pulley 4 that is moved up and down by a plunger 3 of a hydraulic jack 2.

Reference numeral 6 denotes a hydraulic pipe, in which oil from a hydraulic pump 8 rotatably driven by an electric motor 7 is supplied to a hydraulic jack 2 via a valve 9.
It is provided between the hydraulic pump 8 and the hydraulic jack 2 in order to supply oil from the hydraulic jack 2 to the tank 10 via a valve 9.

] 1 is a power source for the electric motor 7.

Reference numeral 12 is an elevator control device that controls all operations of this hydraulic elevator, and reference numeral 13 is a speed control device that controls the traveling speed of the car 1.

In order to provide the necessary signals to this speed control device 13, a deceleration switch 1. is installed near each floor of the hoistway. Furthermore, the hydraulic pipe 6 is provided with a load pressure sensor 16, the tank 10 is provided with an oil temperature sensor 17, and the car 1 is provided with a speed detector 18. There is.

The detailed structure of the speed control device 13 is shown in FIG. Deceleration switch 14, stop switch 15 and speed detector 1
External signal input circuit 13 for inputting digital signals from 8
0 and an operation command from the elevator control device 12, the valve control controller 13 generates a speed pattern.
1, a valve control unit 132 that outputs control signals to the ascending valve 91, descending valve 92, and check valve 93 related to the valve 9 based on the speed pattern, and a pump control unit 133 that drives the electric motor 7. ing.

Note that the location where the oil temperature sensor 17 is provided is not particularly limited to inside the tank 10, and may be provided on the hydraulic jack 2 or in the middle of the piping between the hydraulic jack 2 and the hydraulic pump 8. Furthermore, although oil temperature and load pressure were used as control factors in the above embodiments, the control factors are not limited to these factors, and for example, valve temperature, tank temperature, oil flow rate, etc. may also be used.

Next, the operation of the hydraulic elevator control device having the above configuration will be explained.

When the car 1 is stopped, the electromagnetic switching valve of the valve 9 is closed. Then, when a driving command is given from the elevator control device 12, the electric motor 7 starts rotating. Therefore, based on the speed pattern generated by the speed control device 13, the opening and closing of the valve 9 is controlled, thereby controlling the discharge amount of the hydraulic pump 8, and the lifting speed of the plunger 3 of the hydraulic jack 2 and the car 1. is controlled.

Next, the detailed internal operation of the valve 9 that controls the speed of the hydraulic elevator will be explained based on FIG.

First, to explain the operation when ascending, the pump 8 is started by the ascending command, and the ascending flow rate control valve 91 is fully open, so the pump flow rate is at its full level or is bled off to the tank 10, and the car 1 is stopped. There is.

Here, the control current from the valve control unit 132 operates the ascending electromagnetic proportional pilot control valve 93, and the ascending flow rate control valve 91 operates in the closing direction. As a result, the bleed-off flow rate to the tank 10 decreases, and this flow rate flows into the cylinder of the hydraulic jack 2 via the stroke sensor 95 and check valve 96, so that the car 1 rises.

Next, to explain the operation when descending, the electromagnetic pilot switching valve 97 is energized by the descending command, and the check valve 9
6 operates in the opening direction. Furthermore, the descending electromagnetic proportional pilot control valve 94 operates, and the descending flow rate control valve 92 operates in the opening direction. As a result, the oil in the cylinder of the hydraulic jack 2 flows into the tank 10, so that the car 1 descends.

In Fig. 3, 98 is an emergency manual lowering valve, 99 is a maximum pressure limiting relief valve, 910.911 is a strainer,
912 and 913 are filters, and 914 to 918 are diaphragms.

Here, if you try to control the speed always according to a constant speed pattern regardless of changes in oil temperature and load pressure, the first
As shown in Fig. 5, the characteristics of the ura change due to changes in load pressure and oil temperature, so the actual running waveform changes significantly as shown in Fig. 14.

Therefore, in order to ensure that the actual speed always accelerates, travels at the rated speed, and decelerates in a predetermined pattern even when the load pressure and oil temperature change, it is necessary to use the dashed line shown in Figure 5. Pattern A1 and pattern A2 shown by broken lines must be generated.

In other words, if the normal load pressure and oil temperature pattern is A, when the load pressure and oil temperature are high, the volumetric efficiency of the hydraulic pump decreases, resulting in a small discharge volume, which reduces the elevator speed. Therefore, it is necessary to provide a speed pattern in the direction of increasing the speed, and pattern A1 is generated. In addition, when the load pressure and oil temperature are low, the discharge amount of the hydraulic pump is large, and the elevator speed must be increased. Therefore, it is necessary to provide a speed pattern that decreases the speed, so pattern A2 is generated. That's what I do.

The method for generating this speed pattern will be explained next.

FIG. 4 shows a detailed configuration of the speed pattern parameter automatic correction device 20 provided in the valve control controller 131 in the speed control device 13 of FIG. 2. This speed pattern parameter automatic correction device 20 provides a target value to the speed pattern control process in order to obtain target running characteristics, and outputs a speed control current value to the valve 9 based on this. At this time, since oil characteristics change depending on oil temperature and load pressure, the speed control current value is set to 4.
The correction is performed by an automatic correction device 20 shown in the figure.

This automatic correction device 20 includes a correction rule group storage unit 21 that stores a correction rule group, a fuzzy inference unit 22, a correction data calculation unit 23 that outputs correction data based on the fuzzy inference result of the fuzzy inference unit 22, and a learning The fuzzy inference unit 22 performs fuzzy inference based on the correction rule group in the correction rule group storage unit 21 and the input values from the load pressure sensor 16 and the oil temperature sensor 17.
The correction data calculation unit 23 calculates a correction current value for the speed control current value from the fuzzy inference result, and outputs it to the speed pattern control unit 25.

To explain this operation in detail, the correction rule group storage section 21
The table includes a table of control command values according to the magnitude of sensor input values such as load pressure and oil temperature, and a set of rules regarding how to use these control command values, such as the one shown in Figure 6. , a group of rules regarding the use of speed, acceleration, and deceleration pattern control command values based on oil temperature are stored.

Here, the data in the control command value table based on the input values of the sensors 16 and 17 such as load pressure and oil temperature are expressed as functions or values, and these are determined from many experiments, but this In the example, data in the control command value table is set as a correction current for the control current corresponding to the rated speed, acceleration, and deceleration in a predetermined speed pattern. In addition, in Fig. 6, the load pressure sensor]
6 and oil temperature sensor 17, respectively.
It is set in three stages: "low", "medium", and "high", and by combining these, correction current values can be obtained for each of speed, acceleration, and deceleration.
The upper limit or lower limit of the corrected current value is set in the "high" data to limit the range of values that the corrected current value obtained by fuzzy inference can take. The correspondence between these sensor input values and speed pattern control command values is determined by the accumulation of empirical knowledge and know-how.

The fuzzy inference unit 22 uses continuous fuzzy variables, uses a bell-shaped membership function, and performs calculations based on the composition rule of fuzzy relationships. PB, ZO, and NB in Fig. 9 are fuzzy variables, and are set as shown in Fig. 11 in accordance with the ranks of the above-mentioned sensor input values of "low,""medium," and "high." has been done.

Therefore, the fuzzy inference unit 22 performs fuzzy inference as follows from the data in the control command value table based on sensor input values such as load pressure and oil temperature, and a set of rules for ordering the use of control command values. Determine speed, acceleration, and deceleration pattern control command values.

1) Obtain sensor input value data, oil temperature t, and load pressure p.

2) Attribution degree θ2 of the input value to the antecedent part of the rule. θ,
seek. That is, the sensor input value data t is calculated using the membership degree function graph shown in FIG.

The degree of belonging of p is determined. Furthermore, degree of membership θ
1. The smaller degree of membership θ, - of θ1 is determined.

m’n (θ roh 1n (θ, 2゜ 1n (6,3 m1n (θ, 4゜ ff1in (θ2..

θ ++) θ 12) θ , 3) θ 14) θ 1. ) sin (θ, 6°win (θ, 7°tin (θ, 8°1) (θ, 9°θ 16) θ 17) θ 18) θ 1.) 3) Correction current for each of the above degrees of belonging and rules , weighted and synthesized by averaging.

(Synthesis of speed) (Synthesis of acceleration) (Synthesis of deceleration) Here, the data in the control command value table based on the sensor input values of the load pressure and oil temperature sensor is defined as discrete values, but this can be expressed as a function. It can also be defined as

The learning unit 24 performs self-learning of rules as follows. First, the reference oil temperature t. , control current value at reference load pressure po, reference oil temperature, and reference load pressure ■. This reference oil temperature t is determined. , reference load pressure p. The speed control current value is automatically adjusted so that the corrected current value becomes zero. Further, a correction current value is calculated based on the speed control current value actually output at this time, the actual speed, and the deviation speed from the target speed, and this value is stored as learning data.

Also, among the data in the corrected current value table shown in Fig. 8,
Corrected current value when oil temperature is “medium” and load pressure is “medium”■,
This can only be changed using the learning function. This means that if all the data in the corrected current value table in Figure 8 is changed by the learning function, the possible values of the corrected current value obtained by fuzzy inference will diverge, or an abnormality in the microcomputer will occur. The purpose of this method is to limit the possible values of the correction current value, since there is a possibility that the correction of the rule will not be performed normally.

This situation is shown in Figure 9, where in the initial state PB is Δ
l+, ZO is 0, and NB is ΔI-. As a result of learning, when the corrected current value when the oil temperature is "medium" and the load pressure is "medium" is not 0, but decreases by Δi-,
As shown in the figure, move by the ZO position.

However, even in this case, the positions of PB and NB are assumed to be fixed regardless of learning, thereby limiting the possible range of the corrected speed current value.

This situation is shown in FIG. This FIG. 10 shows the state of correction of the speed current value depending on the oil temperature.

Therefore, it is now assumed that the controllable oil temperature range of the hydraulic elevator is 0°C to 70°C. Also, if the standard oil temperature is 35°C and the standard load pressure is 20 kgr/cm', the rated speed v
The control current value for running at 1 is assumed to be adjusted at the time of on-site installation.

Here, when the oil temperature is 50°C, the hydraulic elevator is
From the figure, the value obtained by correcting the control current value I and the current correction value ΔI■
2, and as a result, the actual running speed of the hydraulic elevator is V2 (V,>V2).
This means that the control current value I2 was not an appropriate value in the control device for this hydraulic elevator. Therefore, this 50℃
Based on the data, the standard oil temperature is 35℃, and the standard load pressure is 20℃.
The control current value at kgf/Cl112 is determined.

This control current value is determined by linear approximation as follows.

VI: (VI V2)
-1+ 2 Im As a result, the standard oil temperature is 35℃,
Let the control current value at a reference load pressure of 20 kgf/cm2 be (II-M3).

As a result, the position of ZO moves to the right in FIG. By the way, the positions of PB and NB are fixed regardless of learning. Therefore, the standard oil temperature is 35°C, and the standard load pressure is 20°C.
The possible range of the control current value in kgf'/cm2 is 11n to l1llax in FIG. 10. As a result, the range of speed current values generated based on the correction values obtained by fuzzy inference becomes the region R surrounded by the dashed-dotted line in FIG.

Next, the above operating procedure will be explained in detail.

1) Collect data on elevator speed V1 acceleration A and deceleration at fixed time intervals.

2) Find the average value of the collected data as follows.

3) Compare each average value with the theoretical value and find the difference.

Δv−1v,,,−v.

ΔA-A,,,-A.

ΔD-l D,,,-D.

4) Speed ■, acceleration A at standard oil temperature and standard load pressure
, control current value 1 vol 1 at deceleration D1 reference oil temperature and reference load pressure. o+ Id. and said ΔV.

From ΔA and ΔD, corrected current value ΔIll Δ161 Δ1,
seek. In this embodiment, these corrected current values ΔI1
．． Δ1. ．． Δ■6 is determined by proportional calculation.

In other words, Δv: V−ΔI, 2■.

ΔAHA−Δr, :r.

ΔDAD−ΔI, :I.

from, ΔI, -(1,o*ΔV) /V Δ1. − (1,.*ΔA) /A ΔId-<111o*ΔD)/D We seek.

5) Calculated corrected current value Δ■1. Δ11. Save ΔI6 as learning data. This is done in this example by exponential smoothing.

I Im, -(1-K) * I ldv ten *Δ
I.

1 14a -(1-K) * I la,
＋＊Δ■.

I Iaa − (I K) * I +
* Δ I to ad +.

Note that here, the value of K is usually set so that (1-K)>K. However, since the number of times of learning is small at the time of delivery, the value of K is set large.

Further, the value of K may be automatically changed as shown below depending on the number of times of learning.

I) If the number of learning times is 10 times, K-0, 8 ii) If 10 times ≦ Number of learning times < 20 times, K-0, 6 iii) If 2Q times ≦ Number of learning times, K-0, 4 Next The speed current pattern output process in this embodiment will be explained based on the flowchart of FIG. 11. First, when an operation command is input from the elevator control device 12 while the elevator is stopped, step 5101 becomes true. Next, protective operations such as confirmation of the closed state of the door are confirmed, and if confirmed, step 5102 becomes true. Since the elevator is currently stopped, step 3103 becomes false, and the process branches to step 5112.

In step 5112, data is input from the oil temperature sensor 17, and in step 8113, data is input from the load pressure sensor 16.

In step 5114, the degree of attribution of oil temperature to the antecedent part of the rule group shown in FIG. 6 is determined, and in step 5115, the degree of attribution of load pressure is determined. Using these attribution degrees, in step 5116 the velocity and in step 5
In step 117, correction currents for acceleration, and further for deceleration in step 5118, are calculated by weighting and combining them by averaging.

Further, when an operation command is input from the elevator control device 12 while the elevator is stopped, step 5IO1, 5102 becomes true and step 8103 becomes false, so step 510
Branch to 4.

In step 5104, a speed current pattern is generated depending on the running state of the elevator. Further, in step 5105, an acceleration start jerk pattern at the start is generated,
When the predetermined acceleration is reached, the process moves to the acceleration process of step 5106. Here, the predetermined acceleration is obtained by adding the acceleration current value stored in advance in the valve controller 131 as fixed data, the previously obtained acceleration correction current, and this value.

Next, when the predetermined speed is reached, step 3. An acceleration end jerk pattern is generated in step 5107, and when the rated speed is reached, the process moves to constant speed processing in step 5108. Here, the rated speed is determined by adding the speed current value stored in the valve controller 131 as predetermined fixed data, the previously determined correction current, and this value.

When the deceleration start point is reached during rated running, step 510
9 is generated, and when a predetermined deceleration is reached, the process moves to deceleration processing in step 5110. Here, the predetermined deceleration is determined by adding the deceleration current value stored in advance in the valve controller 131 as fixed data, the previously determined deceleration correction current, and this value.

Furthermore, when the elevator car 1 reaches a predetermined distance before the target floor, the process moves to step 5111, a deceleration end jerk process, and the car 1 lands on the floor.

In step 5119, the speed current pattern generated by these processes and the correction currents for the speed, acceleration, and deceleration obtained earlier are added, and a new previously obtained speed to be used for the next speed pattern correction is obtained. , output as acceleration and deceleration.

Next, the flow of the learning processing operation will be explained.

The learning processing operation, which is a feature of this embodiment, is executed according to the flowcharts shown in FIGS. 13 and 14, and the learning processing can be broadly divided into processing while the vehicle is running and processing while the vehicle is stopped. First, we will explain the processing while the vehicle is running.

While driving, step 5201 is true, and step 52
Branch to 02. And during rated driving, step 520
2 becomes true, and in step 8203, the actual speed of the elevator is inputted at regular intervals by the speed detector 18 and stored.

Also, if the vehicle is accelerating, step 5204 becomes true.
In step 5205, the actual acceleration of the elevator is input to the speed detector 18 at regular intervals and stored. Furthermore, if the vehicle is decelerating, step 5206 becomes true, and step 5
At step 207, the actual deceleration of the elevator is manually measured at regular intervals by the speed detector 18 and stored.

If step 5201 is stopped, step 5208
The average value of the actual speed, actual acceleration, and actual deceleration stored during driving is calculated (steps 8208 to 210).
), step 5211 performs learning processing.

In this learning process, as shown in the flowchart of FIG. 14, first, in step 5301, the difference between the average speed obtained in step 8208 and the ideal speed (rated speed) is determined. Next, in step 5302, a speed correction current value is determined according to the method described above.

Next, in step 8303, the step 520
Find the difference between the average acceleration found in step 9 and the ideal acceleration. Subsequently, in step 5304, an acceleration correction current value is determined, and in step 5305, the difference between the average deceleration determined in step 5210 and the ideal deceleration is determined. Then, step 530
Step 6 determines the deceleration correction current value.

Then, in step 5307, each of the corrected current values is stored as learning data.

"Effect of the Invention J As described above, according to the present invention, the actual speed of the hydraulic elevator is compared with the target speed, and the control command value of the consequent part of the rule is automatically corrected according to this difference. When modifying the rules, only some of the rules are modified and other rules are not modified, so even if a malfunction occurs in the microcomputer and the rules are not modified normally, the Stable running characteristics can be achieved.

[Brief explanation of drawings]

Fig. 1 is a system diagram of an embodiment of the present invention, Fig. 2 is a block diagram of a speed control device in the above embodiment, Fig. 3 is a configuration circuit diagram of a valve in the above embodiment, and Fig. 4 is a diagram of the above embodiment. 5 is a running pattern graph according to the above embodiment, and FIG. 6 is a rule group table for ordering the use of control command values in the above embodiment. Figure 7 is the membership degree function graph of the antecedent part of the rule in the above example.
The figure shows the corrected current value table in the above embodiment, FIG. 9 is a graph showing the relationship between the corrected current value and the degree of belonging in the above embodiment, and FIG. 10 shows the relationship between the speed current value and the oil temperature in the above embodiment. The graph shown in FIG. 11 is a flowchart of speed current pattern output processing in the above embodiment, and FIG.
The figure is a schematic flowchart of the learning process in the above embodiment, Figure 13 is a detailed flowchart of the learning process in the above embodiment, Figure 14 is a graph showing the running pattern of a conventional QF, and Figure 15 is a characteristic of a general hydraulic pump. Figure, No. 16
1ffl is a sensor input value table of the conventional example, and FIG. 17 is a parameter table of the conventional example. 1... Ride 3... Plunger 7... Electric motor 9... Valve 1]... Oil temperature sensor 2... Hydraulic jack 6... Hydraulic piping 8... Hydraulic pump 10... Tank 12 ...Elevator control device 13...Speed control device 16...Load pressure sensor 1
7.Oil temperature sensor 18...Speed detector 20...Speed parameter automatic correction device 2]...Correction rule storage section 22...Fuzzy inference section 23.Correction data calculation section 24..Learning section 25... Speed pattern control section 130...External signal input circuit 131...Valve control controller 132...Valve control unit 133...Pump control unit

Claims (1)

[Scope of Claims] A hydraulic pump connected to a hydraulic jack via a valve to allow pressure oil to pass through the hydraulic jack, an electric motor rotatably connected to the hydraulic pump, and an oil pump connected to the hydraulic jack for the pressure oil flowing through the hydraulic jack. a temperature sensor; a load pressure sensor for the load pressure applied to the hydraulic jack; a speed sensor that detects the speed of the elevator; a valve that controls the amount of oil in the hydraulic jack; and signals from the oil temperature sensor and the load pressure sensor. is input, and a control command value is generated for the valve based on a predetermined speed pattern so that the hydraulic jack can move up and down at a predetermined speed, and a control command value is generated by fuzzy inference from the input values from both the sensors. a speed control means for correcting and outputting the control command value based on the calculated value as a fuzzy quantity; a rule correction means for correcting the rule used for the fuzzy inference by learning; and a fuzzy inference rule of the rule correction means. A control device for a hydraulic elevator, comprising: rule correction restriction means for restricting correction of the hydraulic elevator.