JPH04309319A - Cleaner - 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]

0001

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vacuum cleaner that automatically controls suction force by detecting the difference between atmospheric pressure and the pressure inside the vacuum cleaner body.

0002

2. Description of the Related Art Conventionally, in this type of vacuum cleaner, the suction power is set in about four levels depending on the amount of dirt, etc. In addition, there are some that set the suction force depending on the floor condition such as the length of the pile of the floor, tatami mat, or carpet.
Again, the condition of the floor surface could only be differentiated into three levels.

0003

[Problems to be Solved by the Invention] In general, with such conventional vacuum cleaners, the amount of garbage and the condition of the floor surface never reach 3 to 4.
It cannot be set in stages, but changes continuously, and the suction force of the fan motor also needs to be set in multiple stages, but since this cannot be accommodated, it is necessary to set the optimal suction power depending on the amount of clogged garbage and the condition of the floor surface. There is a problem in that the speed cannot be set, and at the same time, there is also a problem in that the rotation speed of the floor suction device cannot be controlled.

[0004] The present invention solves the above-mentioned problems, and uses a neuro-fuzzy inference machine optimized by learning rules from the detected values of the pressure sensor to finely adjust the suction force of the fan motor and the rotation speed of the floor suction device. Our aim is to provide you with a vacuum cleaner that you can decide on.

[0005]

[Means for Solving the Problems] In order to achieve the above objects, the present invention provides a fan motor for sucking in dust, a floor suction tool for scraping up dust on the floor, and a front part of the fan motor that connects the front part of the fan motor to the atmosphere. A pressure sensor is installed at a position that can detect the difference in air pressure between The present invention is provided with an optimized neuro-fuzzy inference device, and the neuro-fuzzy inference device determines the rotational speed of the fan motor and the floor suction device based on the output of the pressure sensor.

[0006]

[Operation] The present invention uses the above-mentioned problem-solving means to generate a neurotransmitter that is optimized by a learning rule from the output of a pressure sensor.
Using a fuzzy reasoner, it is possible to set the suction force of the fan motor and the rotation speed of the floor suction device. Therefore, the suction force and the rotation speed of the floor suction device can be determined in detail, and the amount of dust in the dust collection chamber and the floor to be cleaned can be determined in detail. Dust can be removed efficiently regardless of the surface, and the operability can be greatly improved.

[0007]

DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to FIGS. 1 and 2.

As shown in the figure, a pressure sensor 1 is disposed in front of a fan motor 2 and detects the differential pressure value (
(hereinafter referred to as differential pressure), the dust collection chamber 3
It is configured to detect the amount of dust inside and the degree of closeness between the floor suction device 4 and the floor surface as changes in pressure and absolute values, and convert the changes in pressure into electrical signals to obtain an analog output. . The output of the pressure sensor 1 is input to the differential pressure change rate calculation means 5 and the differential pressure absolute value detection means 6 and is calculated. The neuro-fuzzy inference device 7 infers the suction force of the fan motor 2 and the rotation speed of the floor suction tool 4 from the output of the differential pressure change rate calculation means 5 and the output of the differential pressure absolute value detection means 6. The control means 8 drives the fan motor 2 and the floor suction device 4 from the inferred values.

The neuro-fuzzy inference unit 7 is configured as shown in FIG. 2, and the antecedent membership function storage means 9 stores membership functions regarding the differential pressure change rate and the absolute value of the differential pressure. . Differential pressure change rate compatibility calculation means 10
and the differential pressure absolute value compatibility calculating means 11 respectively calculate the differential pressure change rate and the membership function regarding the differential pressure absolute value stored in the antecedent part membership function storage means 9, and the differential pressure change rate and the difference as inputs. Calculate the degree of compatibility with the absolute pressure value. The antecedent minimum calculation means 12 is the output of the differential pressure change rate compatibility calculation means 10 and the differential pressure absolute value compatibility calculation means 11.
The MIN of the goodness of fit is taken as the conclusion of the antecedent part. The suction force inference rule storage means 13 stores inference rules regarding suction force. The suction force membership function storage means 14 stores membership functions related to the suction force of the consequent part. Consequent part minimum calculation means 15
In accordance with the inference rule stored in the suction force inference rule storage means 13, take the MIN of the antecedent part conclusion and the suction force membership function of the consequent part stored in the suction force membership function storage means 14. This is the conclusion of that rule. The center of gravity calculation means 16 calculates the respective conclusions for all the rules, takes the MAX of all the conclusions, and finally calculates the suction force by calculating the center of gravity. Further, a floor suction device rotation speed inference rule storage means 17 storing an inference rule for inferring the rotation speed of the floor suction device 4 and a membership function regarding the rotation speed of the floor suction device 3 are stored. Also included is a floor suction tool rotational speed membership function storage means 18.

[0010] This neuro-fuzzy inference device 7 can be easily realized by a microcomputer. Neuro-fuzzy inference device 7 includes antecedent membership function storage means 9, suction force inference rule storage means 13, suction force membership function storage means 14, floor suction device rotation speed inference rule storage means 17, and floor suction tool rotation speed inference rule storage means 17. The membership functions and inference rules stored in the suction device rotation speed membership function storage means 18 are based on the fan motor 2 that should be set in consideration of the differential pressure change rate, the differential pressure absolute value data, and the operational feel when cleaning. Suction power and floor suction tool 4
is optimally set in advance using a learning rule such as the steepest descent method (one of the learning rules used in neural networks, which is a method of minimizing the error function) based on the rotation speed data. The control means 7 controls the fan motor 2 based on the determined suction force and the rotation speed of the floor suction device 4.
And the phase control amount of the floor suction tool 4 is calculated and controlled.

Next, to explain the operation of the above configuration, as dust is sucked in during normal cleaning and the amount of dust in the dust collection chamber 3 increases, the degree of vacuum in the dust collection chamber 3 increases due to the dust clogging, and the dust collection The pressure difference between the inside of the dust chamber 3 and the atmospheric pressure tends to increase. Therefore, the amount of dust in the dust collection chamber 3 can be electrically detected by detecting the change in this differential pressure using the pressure sensor 1 disposed in the front part of the fan motor 2. The output of the pressure sensor 1 also changes depending on the floor surface to be cleaned. That is, if the object to be cleaned is a carpet, the floor suction tool 4 will stick to the carpet, and if the object to be cleaned is a wooden floor, the floor suction tool 4 will hardly stick to the wooden floor. Even in these two cases, there is a difference in the degree of vacuum within the dust collection chamber 3, and if this is detected as the absolute value of the differential pressure by the pressure sensor 1, it is possible to detect the difference in the floor surface to be cleaned.

Next, the process of inferring the suction force will be explained. The inference rule of the fuzzy inference in this example is ``If the amount of dust in the dust collection chamber is large (the amount of change in differential pressure is large) and the object to be cleaned is a carpet (the absolute value of differential pressure is large), the suction force should be very large. It is formed based on general judgments such as "to do so." Qualitative concepts such as the rate of change of differential pressure being "large", the absolute value of differential pressure being "small", and the suction force being "very large" are shown in Figures 3 (A), (B) and Figure 4 (A). , (B). Differential pressure change rate compatibility calculation means 10
Now, the degree of compatibility between the input from the differential pressure change rate calculating means 5 and the membership function regarding the rate of change of the differential pressure stored in the antecedent membership function storage means 9 is expressed as M
Determine by taking AX. The differential pressure absolute value compatibility calculating means 11 similarly calculates the compatibility regarding the input from the differential pressure absolute value calculating means 6 and the membership function of the absolute value of the differential pressure stored in the antecedent membership function storage means 9. seek. The antecedent minimum calculation means 12 takes the MIN of the two fitness degrees and uses it as the conclusion of the antecedent. In the consequent minimum calculation means 15, the suction force inference rule storage means 1
3, the conclusion of the antecedent part and the MIN of the suction force membership function of the consequent part stored in the suction force membership function storage means 14 are taken as the conclusion of the rule.

After determining the respective conclusions for all the rules, the center of gravity calculation means 16 takes the MAX of all the conclusions and calculates the center of gravity to finally determine the suction force. The control means 8 calculates and controls the phase control amount of the fan motor 2 based on the determined suction force. The rotation speed of the floor suction device 4 is determined by calculating the conclusion of the antecedent in the same manner as in the process of determining the suction force described above, and using the floor suction device rotation speed inference rule storage means 17 and the floor suction device rotation speed membership. The rotation speed of the floor suction tool 4 is determined from the function storage means 18.

[0014] In this embodiment, MAX is included in the inference method.
-The MIN synthesis method and center of gravity method are used, but other methods are also possible, and although the suction force, which is the consequent, is expressed using a membership function, it is also possible to express it using real values or linear form. Not even.

[0015]

As is clear from the above embodiments, according to the present invention, there is provided a fan motor for sucking in dust, a floor suction tool for scraping up dust from the floor, and a front part of the fan motor that connects the front part of the fan motor with the atmosphere. A pressure sensor is installed at a position that can detect the difference in air pressure between The neuro-fuzzy inference device is equipped with an optimized neuro-fuzzy inference device, and the neuro-fuzzy inference device determines the rotational speed of the fan motor and the floor suction device based on the output of the pressure sensor. Using fuzzy inference from the rate of change and absolute value of the difference in atmospheric pressure, the optimal suction force and rotation speed of the floor suction tool can be determined in detail, and efficiently regardless of the amount of dust in the dust collection chamber or the floor surface to be cleaned. Remove garbage,
Furthermore, it is possible to provide a vacuum cleaner that is extremely easy to operate. Furthermore, as the number of inputs and outputs in fuzzy inference increases, it becomes difficult for humans to optimize the inference rules and their configurations between them, but the present invention uses learning rules such as steepest descent to・Since the configuration of the fuzzy inference machine is optimized, the above-mentioned effects can be obtained.

[0016] Furthermore, the neuro-fuzzy reasoner recognizes the amount of dust in the dust collection chamber and the degree of contact between the floor suction device and the floor from the output of the pressure sensor, and determines the rotation speed of the fan motor and the floor suction device. This makes it possible to precisely determine the optimal suction force and rotational speed of the floor water-filling device depending on the condition of the floor surface, improving the operating feel.

[Brief explanation of drawings]

FIG. 1 is a system configuration diagram of a vacuum cleaner according to an embodiment of the present invention.

[
Figure 2: Block diagram of the vacuum cleaner's neuro-fuzzy inference device

[Figure 3] (A), (B) Diagram showing the membership function of the same vacuum cleaner

[Figure 4] (A), (B) Diagram showing the membership function of the same vacuum cleaner

[Explanation of symbols]

1 Pressure sensor 2 Fan motor 4. Floor suction device 7 Neuro-fuzzy inference machine

Claims (3)

[Claims] [Claim 1] A fan motor for sucking in dust, a floor suction tool for scraping up dust from the floor, and a floor suction device disposed at a position where the difference in air pressure between the front part of the fan motor and the atmosphere can be detected. The neuro-fuzzy inference device is equipped with a pressure sensor and a neuro-fuzzy inference machine that optimizes various parameters of fuzzy inference using learning rules such as steepest descent method in order to determine the rotational speed of the fan motor and the floor suction device. In the vacuum cleaner, the inference device determines the rotational speed of the fan motor and the floor suction device based on the output of the pressure sensor. [Claim 2] The neuro-fuzzy reasoner recognizes the amount of dust in the dust collection chamber and the degree of closeness between the floor suction device and the floor surface from the output of the pressure sensor, and determines the rotation speed of the fan motor and the floor suction device. The vacuum cleaner according to claim 1, wherein the vacuum cleaner is configured to: 3. The vacuum cleaner according to claim 1, wherein the neuro-fuzzy inference device optimizes the shapes of the antecedent membership function and the consequent membership function and the number of inference rules as various parameters.