JPH0595878A - Control device for vacuum cleaner - Google Patents

Abstract

PURPOSE:To enhance the easiness in handling of an electric vacuum cleaner by optimizing a neuro-fuzzy inferential device according to the study law on the basis of the output of a sensor to sense the internal pressure of the body of vacuum cleaner, and performing fine input control of a motor blower with the aid of this neuro-fuzzy inferential device. CONSTITUTION:Pressure is sensed by a pressure sensor 9 installed in the body of a vacuum cleaner, and the analog output of this pressure sensor 9 is converted into digital signals by an A/D conversion means 12. The digital amount given b the A/D conversion means 12 is averaged within a unit time by a pressure absolute value sensing means 14 to serve calculating the absolute value of pressure, and at the same time, the change amount within unit time is averaged by a pressure change amount sensing means 15 to serve calculating the change amount of pressure. A fuzzy inferential device 16 makes indirect studies from time to time of the dust quantity in the dust chamber and the sort of the floor in compliance with the service environment of the cleaner by reference to the information about the absolute value of pressure and the change amount of pressure stored in a memory means 17 which is capable of storing data even when the power is off, and thereupon the device decides the input to the motor blower 2, and accordingly the input thereto is controlled.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention identifies the floor quality to be cleaned by accumulating the pressure change amount within a unit time, stores the floor quality information, and controls the input control method of the electric blower and the floor suction tool. The present invention relates to an electric vacuum cleaner that learns how to control the rotation speed of a rotating brush.

[0002]

2. Description of the Related Art In recent years, electric vacuum cleaners are required to automatically control the input of an electric blower according to the floor surface to be cleaned, the amount of dust, etc. to control the suction force.

Conventionally, this type of vacuum cleaner generally has a structure as shown in FIG. The configuration will be described below.

As shown in the figure, a cleaner body (hereinafter referred to as a body) 1 has a built-in electric blower 2, and has a suction port 3 having a hose 4, an extension tube 5 and a rotating brush 6 for electric floor suction. The tool 7 is connected. A hand switch 8 is provided at the tip of the hose 4, and the rotation speed of the electric blower 2 arranged in the main body 1 is controlled by operating the hand switch 8.

[0005]

In such a conventional electric vacuum cleaner, the user determines the floor surface to be cleaned, and manually operates the hand switch 7 according to the floor surface (rotation speed).
Was changed to change the suction force. Therefore, there is a problem that the operation is troublesome.

The present invention is to solve the above-mentioned conventional problems, and uses a neuro-fuzzy reasoning device optimized by a learning rule from the output of a sensor for detecting the pressure inside the cleaner body to input the fine electric blower. Control and rotation speed control of the rotating brush of the electric floor suction tool are intended to improve usability.

[0007]

In order to achieve the above object, the present invention provides an electric blower for suction, a pressure sensor for detecting the pressure in the cleaner body, and a digital signal for the analog output of the pressure sensor. A / D conversion means for converting to
A pressure absolute value detection means for averaging digital amounts from the A / D converting means within a unit time to calculate a pressure absolute value, and an average amount of change in digital amount from the A / D converting means within a unit time. A pressure change amount detection means for obtaining a pressure change amount, a storage means capable of storing even when the power is off, and a fuzzy inference device for determining an input of the electric blower, and the fuzzy inference device is stored in the storage device. Based on both the information of the absolute pressure value and the amount of pressure change, the amount of dust in the dust collection chamber and the floor quality are indirectly and sequentially learned according to the usage environment of the vacuum cleaner, and the input of the electric blower is determined. This is the first means for solving the problem.

In addition to the electric blower of the first problem solving means, a rotary brush is provided in the floor suction tool, and the number of rotations of the rotary brush is determined by a fuzzy reasoner. Is used as a means for solving the problem.

Further, the fuzzy reasoner of the first problem solving means optimizes various parameters of fuzzy reasoning by a learning rule such as the steepest descent method of the neuro technique, and uses the antecedent part membership function as various parameters. The third problem solving means is that the shape of the consequent part membership function and the number of inference rules are optimized.

[0010]

According to the first means for solving the above problems, the present invention provides:
By accumulating the outputs of the sensors that detect pressure, you can indirectly obtain information on the amount of dust in the dust collection chamber and the floor quality (wood floor, carpet, etc.). The quality information, the real-time floor quality information, and the amount of dust in the dust collection chamber can be controlled so that the input of the electric blower is appropriate according to the floor surface and the amount of dust.

Further, according to the second means for solving the problem, the number of rotations of the rotary brush of the floor suction tool can be appropriately controlled according to the quality of the floor, and the usability can be improved.

Further, according to the third means for solving the problem, the fuzzy reasoner uses the learning rule such as the steepest descent method of the neuro technique and the shape of the antecedent part membership function and the consequent part membership function, and the inference rule. Since we have the number optimized, qualitative information such as intuition and tips for cleaning is optimally tuned and incorporated into the fuzzy reasoner so that cleaning can be done according to the user. Become.

[0013]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to FIGS. The same components as those in the conventional example are designated by the same reference numerals and the description thereof will be omitted.

As shown in the figure, the sensor 9 is provided between the dust collection chamber 10 of the main body 1 and the electric blower 2 so as to detect the absolute value and change amount of the negative pressure amount. The signal of the pressure sensor 9 is amplified by the amplifier 11, and the A / D conversion means 12
Is converted into a digital signal and input to the microcomputer 13. The microcomputer 13 averages the digital values from the A / D conversion means 12 within a unit time to calculate the pressure absolute value, and the pressure absolute value detection means 14 and the pressure change amount within a unit time to average the pressure change amount. The pressure change amount detecting means 15 and the fuzzy reasoner 16 are provided. Also,
The storage means 17 is connected to the microcomputer 13 and is composed of a non-volatile memory in which information is not lost even when the power supply of the control system is turned off. The storage means 17 stores information such as the absolute value of pressure and the cumulative value / average value of the amount of change for each fixed time. The output of the microcomputer 13 is input to the phase control means 18 to control the input of the electric blower 2. The fuzzy reasoner 16 is a pressure change amount detecting means 15
The input of the electric blower 2 is inferred from the bed quality information obtained by the above, the amount of dust in the dust collection chamber 10 obtained by the absolute pressure value detection means 14, and the output of the storage means 17.

The fuzzy reasoner 16 is constructed as shown in FIG. 3, and the antecedent part membership function storage means 19 is provided.
Stores a membership function relating to the amount of dust in the dust collection chamber 10, floor quality information, and storage floor quality information. The dust amount compatibility degree computing means 20, the floor quality information fitness degree computing means 21, and the storage floor quality information fitness degree computing means 22 are respectively the dust volume in the dust collection chamber stored in the antecedent part membership function storage means 19. , The floor function information, the membership function relating to the memory floor quality information, and the compatibility with the dust collection room dust amount, the floor quality information, and the memory floor quality information which are inputs. Antecedent part minimum calculation means 23
Is the conclusion of the antecedent part by taking the MINs of the three conformance levels which are the outputs of the dust quantity conformity degree calculation means 20, the floor quality information conformity degree calculation means 21, and the storage floor quality information conformity degree calculation means 22. The suction force inference rule storage means 24 stores the inference rule regarding the suction force. The suction force membership function storage means 25 stores the membership function regarding the suction force of the consequent part. The consequent part minimum calculation means 26 follows the inference rule stored in the suction force inference rule storage means 24 and draws the suction force member of the consequent part stored in the antecedent part conclusion and suction force membership function storage means 25. The MIN of the ship function is taken to conclude the rule. The center-of-gravity means 27 finally obtains the suction force by obtaining the respective conclusions for all the rules, then taking the MAX of all the conclusions, and calculating the center of gravity thereof. The phase control unit 18 calculates and controls the phase control amount of the electric blower 2 based on the determined input.

The fuzzy reasoner 16 is composed of a neuro-fuzzy reasoner that optimizes various parameters of fuzzy reasoning by a learning rule such as the steepest descent method of the neuro technique. This neuro-fuzzy reasoner can be easily realized by the microcomputer 13. The antecedent part membership function storage means 19 and suction force inference rule storage means 24 included in the neuro-fuzzy inference device, and the suction force membership function storage means 24 have membership functions and inference rules stored in the dust chamber dust. From the data of the dust amount, the floor quality information, the memory floor quality information, and the input data of the electric blower 2 which should be set in consideration of the operation feeling when cleaning, the steepest descent method (one of the learning rules used in the neural network is set in advance. , Which is a method of minimizing the error function), and is optimally set by a learning rule such as.

To explain the operation in the above structure, the pressure absolute value detecting means 14 integrates the pressure values detected by the pressure sensor 9 for a predetermined time to obtain an average value, and the dust in the dust collecting chamber 10 is detected from the absolute pressure value. Know the amount of clogging. Further, the quality of the floor surface at that time can be known by the pressure change amount detecting means 15 which obtains the pressure change amount by averaging the change amount of the pressure within the unit time. These two pieces of information are used as inputs for fuzzy inference.

FIG. 4 shows the degree of change in pressure value when cleaning is continuously performed. In FIG. 4, the absolute value of the pressure is low on a floor having a small cleaning surface such as a wooden floor or a cushion floor, and since the floor material is close to a flat surface, the change amount is small. Further, when the floor surface is a carpet, the adhesion between the floor suction tool 7 and the carpet is high, so that the absolute pressure value is high and the pressure change amount is large. By detecting the absolute pressure value and the pressure change amount in this way, it is possible to estimate the amount of dust in the dust collection chamber 10 and the quality of the floor surface currently being cleaned. Here, an indexed value (for example, 0 to 7) is set as the floor information. This index is used as one input for fuzzy reasoning. A small amount of change in pressure indicates that the surface is a flat floor such as a wooden floor, and a large amount of change in pressure indicates that the surface of the rugged carpet has long hair.

The bed quality information obtained from the pressure change amount detecting means 15 is periodically stored in the storing means 17. This storage means 17 uses a non-volatile memory or the like that retains stored contents even when the power is turned off, and a frequency is added to each floor information index every time the main body 1 is used, and all floors using a vacuum cleaner are added. Surface information is obtained. Memory floor information compatibility means 22
As a pre-process for obtaining the goodness of fit, for example, the average floor quality information is obtained from the index of the storage floor surface information and the frequency, and is used as another input for fuzzy reasoning.

The optimum suction force for cleaning is determined by the characteristics of the floor surface, etc., and the fuzzy reasoner 16 uses the outputs of the absolute pressure value detecting means 14, the pressure change amount detecting means 15, and the storing means 17 to perform the cleaning. Reason.

Next, the process of inferring the suction force will be described. The inference rule of the fuzzy inference according to the present embodiment is “the pressure absolute value is low (the amount of dust clogging in the dust collection chamber is small) and the amount of pressure change is small (a flat floor such as a wooden floor or a cushion floor), The floor surface that has been cleaned in the past also has a relatively small change in pressure, and if the floor quality is large, the suction force is set to be very low. " Qualitative concepts such as "absolute pressure" is "low", pressure change is "small", and suction force is "very high". Membership as shown in Figure 5 (a), (b), (c) It is expressed quantitatively by a function. The dust amount compatibility calculating means 20 obtains both of the compatibility from the absolute pressure value detecting means 14 and the membership function relating to the amount of dust stored in the antecedent part membership function storing means 19 as MAX. Ask by. The floor quality information adaptability calculating means 21 similarly obtains the adaptability with respect to the input from the pressure change amount detecting means 15 and the membership function of the waste change rate stored in the antecedent part membership function storing means 19. The memory floor quality information fitness degree calculating means 22 similarly obtains the fitness degree with respect to the input from the memory means 17 and the membership function of the memory floor surface information stored in the antecedent part membership function memory means 19. In the antecedent part minimum computing means 23, the MIN of these three fitness levels is
Take the conclusion of the antecedent section. Consequent part Minimum calculation means 2
In step 6, in accordance with the rule stored in the suction force inference rule storage means 24, 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 25 are obtained. The conclusion of the rule. After obtaining the respective conclusions for all the rules, the center-of-gravity calculation means 27 determines the MAX of all the conclusions.
Then, the suction force is finally obtained by calculating the center of gravity. The phase control unit 18 calculates and controls the phase control amount of the electric blower 2 based on the determined suction force.

Next, another embodiment of the present invention will be described with reference to FIGS. 6 and 7. The same components as those in the above-described embodiment are designated by the same reference numerals and the description thereof will be omitted.

As shown in the figure, the microcomputer 2
Reference numeral 8 is a pressure absolute value detection means 1 for averaging the digital amounts from the A / D conversion means 12 within a unit time to calculate a pressure absolute value.
4 and a fuzzy reasoner 29, which are pressure change amount detecting means 15 for averaging the change amounts of pressure in a unit time to obtain a pressure change amount. The output of the microcomputer 28 is input to the phase control means 18 to control the input of the electric blower 2, and further input to the phase control means 30 to control the rotation speed of the rotary brush 6 of the floor suction tool. The fuzzy reasoner 29 uses the bed quality information obtained by the pressure change amount detection means 15, the dust amount in the dust collection chamber 10 obtained by the pressure absolute value detection means 14, and the output of the storage means 17 to output the electric blower 2. The input and the rotation speed of the rotary brush 6 are inferred and determined.

In the fuzzy reasoner 29, the rotating brush rotation speed inference rule storage means 31 stores inference rules regarding the rotation speed of the rotating brush 6. The rotating brush rotation speed membership function storage means 32 stores a membership function relating to the rotation speed of the rotating brush 6. The other structure is the same as that of the above-mentioned embodiment.

The fuzzy reasoner 29 is a neuro-fuzzy reasoner that optimizes various parameters of fuzzy reasoning by a learning rule such as the steepest descent method of neuro technology. The membership functions and inference rules stored in the antecedent part membership function storage means 19, the rotating brush rotation speed inference rule storage means 31, and the rotating brush rotation speed membership function storage means 32 included in the neuro-fuzzy reasoner are From the data of the dust collection chamber dust amount, floor quality information, storage floor quality information, and the rotation speed data of the rotary brush 6 to be set in consideration of the operation feeling at the time of cleaning, the steepest descent method (used for neural network in advance One of the learning rules, which is a method of minimizing the error function) and is optimally set by the learning rule.

The rotation speed of the rotary brush 6 is determined by calculating the conclusion of the antecedent part in the same manner as the suction force determination process, and the rotary brush rotation speed inference rule storage means 31 and the rotary brush rotation speed membership function storage means are shown. 32 and rotating brush 6
Determines the rotation speed of. The phase control means 18 and 30 calculate and control the phase control amounts of the electric blower 2 and the rotary brush 6 based on the determined input and the rotational speed of the rotary brush.

In this embodiment, MAX is included in the inference methods.
-Although the MIN composition method and the center of gravity method are used, other methods are also possible, and the suction force, which is the consequent part, was expressed by a membership function, but it can be expressed by a real value or linear form. There is no end.

[0028]

As is apparent from the above embodiments, according to the present invention, an electric blower for suction, a pressure sensor for detecting the pressure inside the cleaner body, and an analog output of the pressure sensor as a digital signal. A / D conversion means for converting to A / D conversion means, pressure absolute value detection means for averaging digital amounts from the A / D conversion means within a unit time to calculate a pressure absolute value, and A / D conversion means
Pressure change amount detection means for averaging the change amount of the digital amount from the D conversion means within a unit time to obtain the pressure change amount, storage means capable of storing even when the power is off, and fuzzy for determining the input of the electric blower. The fuzzy inference unit indirectly determines the amount of dust in the dust collection chamber and the floor quality in accordance with the environment in which the vacuum cleaner is used, based on both the information of the absolute pressure value and the amount of pressure change stored in the storage means. Since the input of the electric blower is determined sequentially, the absolute value of pressure (amount of dust in the dust collection chamber) and the amount of change (floor surface information) and the stored floor quality information are used for fuzzy inference. Since you can finely determine the optimum suction force, you can efficiently remove dust regardless of the floor surface you are cleaning, and the operation feel is very good, improving usability.

In addition to the electric blower, a rotary brush is provided in the floor suction tool, and the number of rotations of the rotary brush is determined by a fuzzy reasoner, so that the number of rotations of the rotary brush can be finely determined by fuzzy inference. I can decide.

Further, the fuzzy reasoner optimizes various parameters of fuzzy inference by a learning rule such as the steepest descent method of the neuro technique, and uses the antecedent part membership function and the consequent part membership function as various parameters. shape,
Since the number of inference rules is optimized, it becomes difficult for human beings to optimize the inference rules and their composition when the number of inputs and outputs in fuzzy inference increases, but as a result, fuzzy inference makes fine and detailed adjustments possible. The optimum suction force and rotation speed of the rotating brush can be determined, and the suction function and rotation speed of the rotating brush are optimized more and more by the learning function depending on the environment in which the vacuum cleaner is used (uniqueness of the floor quality). The usability can be improved.

[Brief description of drawings]

FIG. 1 is a block diagram of a controller for an electric vacuum cleaner according to an embodiment of the present invention.

FIG. 2 is a cross-sectional view of an electric vacuum cleaner including the control device.

FIG. 3 is a block diagram of a fuzzy reasoner of the control device of the electric vacuum cleaner.

FIG. 4 is a view showing a pressure change of a pressure sensor of the control device of the electric vacuum cleaner.

5A to 5C are diagrams showing membership functions of a fuzzy reasoner of the control device of the electric vacuum cleaner.

FIG. 6 is a block diagram of a control device for a vacuum cleaner according to another embodiment of the present invention.

FIG. 7 is a block diagram of a fuzzy reasoner of the controller of the electric vacuum cleaner.

FIG. 8 is a perspective view of a conventional vacuum cleaner.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Vacuum cleaner main body 2 Electric blower 9 Pressure sensor 10 Dust collection chamber 12 A / D conversion means 14 Absolute pressure value detection means 15 Pressure change amount detection means 16 Fuzzy reasoner 17 Storage means

Claims (3)

[Claims] 1. An electric blower for suction, a pressure sensor for detecting the pressure inside the cleaner body, A / D conversion means for converting an analog output of the pressure sensor into a digital signal, and the A / D conversion. A pressure absolute value detecting means for averaging the digital amount from the means within a unit time to calculate the pressure absolute value,
A pressure change amount detecting means for averaging a change amount of the digital amount from the A / D converting means in a unit time to obtain a pressure change amount, a storage means capable of storing even when the power is off, and an input of the electric blower And a fuzzy inference device for determining the amount of dust in the dust collection chamber and the floor quality from the information of both the absolute pressure value and the pressure change amount stored in the storage means to the environment in which the vacuum cleaner is used. A control device for an electric vacuum cleaner that is configured to indirectly learn sequentially and determine an input of the electric blower. 2. The control device for the electric vacuum cleaner according to claim 1, wherein a rotary brush is provided in the floor suction tool in addition to the electric blower, and the number of rotations of the rotary brush is determined by a fuzzy reasoner. 3. A fuzzy reasoner optimizes various parameters of fuzzy inference by a learning rule such as the steepest descent method of neuro technology, and uses the membership functions of the antecedent part and the consequent part as various parameters. The control device for the electric vacuum cleaner according to claim 1, wherein the shape and the number of inference rules are optimized.