JP2009022116A - Motor ripple detector - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a motor ripple detector that makes can accurately determine the operating state of a motor and enhance the accuracy of the detection of ripple pulses outputted from the motor. <P>SOLUTION: Motor ripple detection processing is carried out according to the following procedure. First, a motor to be driven and its driving direction are determined, and the level of a load applied to the motor is estimated using load level data stored in a database (Step S102). Subsequently, based on the estimated load level, a state determination threshold used for the determination of the operating state of the motor is set (Step S103). In parallel with the processing of Steps S102 and S103, the driving of the motor is started (Step S104) and a ripple signal outputted from the motor is estimated (Step S105). Subsequently, the ripple signal outputted from the motor and a preset reference ripple pulse are compared with each other and using the state determination threshold, it is determined whether the operating state of the motor is a startup state or a steady state (Step S106). <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a motor ripple detection device that detects a ripple output from a motor.

  The number of rotations of the motor and the number of ripple pulses output from the motor are closely related, and a system for measuring the number of rotations of the motor by counting the number of ripple pulses has been proposed. This system is also used in a vehicle memory seat system or the like, and calculates a seat movement position from a detected ripple pulse.

As a motor ripple detection device that detects a ripple output from such a motor, for example, a device described in Patent Document 1 is known. The motor ripple detection device described in Patent Document 1 starts the motor in a no-load state, uses the ripple pulse output from the motor at that time as reference data, and uses the ripple pulse output from the motor at the subsequent load as a reference. Compared with the data, when the two do not match sufficiently, the ripple pulse is corrected according to the reference data.
Special table 2003-536355 gazette

  However, the following problems exist in the prior art. That is, the time required for the motor operating state to shift from a noisy startup state to a steady state with little noise varies depending on the load on the motor. For this reason, when estimating the operating state of the motor, it is determined that the actual operation of the motor is in the starting state but the steady state, or the actual operation of the motor is in the steady state. May be judged. In this case, since the detection accuracy of the ripple pulse signal output from the motor is lowered, the rotational speed of the motor cannot be accurately detected.

  An object of the present invention is to provide a motor ripple detection device that can accurately determine the operating state of a motor and improve the detection accuracy of ripple pulses output from the motor.

  The present invention relates to a motor ripple detection device that detects a ripple signal output from a motor, a load estimation unit that estimates a load level applied to the motor based on a location driven by the motor and a rotation direction of the motor, and a load Based on the load level estimated by the estimation means, a threshold setting means for setting a state determination threshold for determining the operation state of the motor, and a motor operation state using the state determination threshold set by the threshold setting means And a state determination means for determining whether the device is in the activated state or the steady state.

  When the ripple signal output from the motor is detected, the load level applied to the motor varies depending on the location driven by the motor and the rotation direction of the motor. Therefore, in the motor ripple detection device of the present invention, the load level applied to the motor is estimated based on the location driven by the motor and the rotation direction of the motor, and the state determination threshold is set based on the load level. Then, using the state determination threshold value, for example, by comparing a ripple signal output from the motor with a ripple pulse for comparison, it is estimated whether the operation state of the motor is a startup state or a steady state. Thus, by using the state determination threshold in consideration of the load level applied to the motor, it is possible to accurately determine the operation state of the motor. Thereby, the detection precision of the ripple pulse output from a motor can be improved.

  Preferably, the apparatus further comprises data storage means for storing a learning ripple pulse to be compared with the ripple signal output from the motor, and the state determination threshold value is stored in the characteristic of the ripple signal output from the motor and the data storage means. The state determination means includes a first threshold value relating to an allowable error between the learning ripple pulse feature and a second threshold value relating to the number of comparisons between the ripple signal feature and the learning ripple pulse feature. Is used to compare the characteristics of the ripple signal output from the motor with the characteristics of the learning ripple pulse stored in the data storage means to determine whether the motor is in the starting state or the steady state. judge.

  Thus, when comparing the characteristics of the ripple signal output from the motor with the characteristics of the learning ripple pulse stored in the data storage means, the allowable error and the number of comparisons of both characteristics are important. By using the first threshold value and the second threshold value related to the above as the state determination threshold value, the operation state of the motor can be determined with higher accuracy.

  The state determination means determines that the operation state of the motor is a steady state when a state where an error between the characteristic of the ripple signal and the characteristic of the learning ripple pulse is within the first threshold range continues for the second threshold or more. Is preferred.

  When the operating state of the motor becomes a steady state, a stable ripple pulse with little distortion is continuously output from the motor. Therefore, by determining whether or not the state where the error between the characteristic of the ripple signal and the characteristic of the learning ripple pulse is within the first threshold range continues for the second threshold or more, the operational state of the motor is determined to be steady. Further, it can be determined with high accuracy.

  The state determination means may include means for updating the ripple signal as a learning ripple pulse and storing it in the data storage means when an error between the characteristic of the ripple signal and the characteristic of the learning ripple pulse is within the first threshold range. preferable.

  In this way, by updating the ripple signal with less error from the learning ripple pulse as the learning ripple pulse, the latest ripple pulse becomes the ripple signal even if the characteristics of the ripple pulse output from the motor fluctuate due to aging etc. The comparison data can be obtained. Thereby, it can be determined with higher accuracy that the operating state of the motor is a steady state.

  Preferably, the location driven by the motor is a drive portion of a mobile instrument on which a user is placed, and the load estimation means includes the location driven by the motor, the direction of rotation of the motor, and the person using the mobile instrument. The load level applied to the motor is estimated based on the above characteristics.

  When the driving part of the mobile device on which a person is placed is driven by a motor, the load level applied to the motor varies depending on the characteristics (weight, posture, etc.) of the person using the mobile device. Accordingly, by estimating the load level applied to the motor in consideration of the characteristics of the person using the mobile device, the load level can be estimated with high accuracy.

  Further preferably, it further comprises means for detecting the ambient temperature of the motor, and the load estimation means estimates the load level applied to the motor based on the location driven by the motor, the rotation direction of the motor, and the ambient temperature of the motor. .

  The starting characteristics of the motor may differ greatly between the low temperature and the normal temperature around the motor. For example, when the temperature is low, the grease for smoothing the operation of the motor becomes hard and the motor is difficult to rotate, so that a large load is applied to the motor. Therefore, by estimating the load level applied to the motor in consideration of the ambient temperature of the motor, the load level can be estimated with high accuracy.

  Further, it is preferable that a means for detecting a driving time interval of the motor is further provided, and the load estimating means further estimates a load level applied to the motor based on the driving time interval of the motor.

  If the motor has been rotating until just before, even if the ambient temperature of the motor is low, the temperature of the motor itself has hardly decreased, and the starting characteristics of the motor are close to normal temperature. There is. Accordingly, by estimating the load level applied to the motor in consideration of the driving time interval of the motor, the load level can be estimated with higher accuracy.

  The load estimation means determines whether the motor driving time interval is longer than the predetermined time and the ambient temperature of the motor is lower than the predetermined temperature, and the motor driving time interval is longer than the predetermined time and the motor ambient temperature is When the temperature is lower than the predetermined temperature, it is preferable to estimate the load level applied to the motor higher than when it is not.

  When the motor driving time interval is longer than the predetermined time and the ambient temperature of the motor is lower than the predetermined temperature, the motor is difficult to rotate, so that a large load is applied to the motor. Therefore, in this case, the operating state of the motor can be determined with higher accuracy by estimating the load level higher than that at normal temperature.

  ADVANTAGE OF THE INVENTION According to this invention, the operating state of a motor can be determined accurately and the detection accuracy of the ripple pulse output from a motor can be improved. This makes it possible to accurately detect the rotation speed of the motor.

  Hereinafter, a preferred embodiment of a motor ripple detection device according to the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a schematic configuration diagram showing a memory sheet system including a first embodiment of a motor ripple detection device according to the present invention. In the figure, a memory seat system 1 includes a slide motor 2, a front vertical motor 3, a lifter motor 4, a reclining motor 5, a motor drive switch 6, motors 2 to 5, and a motor drive switch 6 And a memory sheet ECU (Electronic Control Unit) 7 connected thereto.

  As shown in FIG. 2, the motors 2 to 5 and the memory seat ECU 7 are arranged inside a driver seat (driver seat) 8 of the vehicle. As shown in FIG. 3, the motor drive switch 6 is provided on one side of the seat cushion 9 of the driver seat 8.

  The sliding motor 2 is a motor that moves the entire driver seat 8 in the front-rear direction of the vehicle (the direction of arrow A in FIG. 3). The front vertical motor 3 is a motor that moves the front end of the seat cushion 9 in the vertical direction (the direction of arrow B in FIG. 3). The lifter motor 4 is a motor that moves the entire driver seat 8 in the vertical direction (the direction of arrow C in FIG. 3). The reclining motor 5 is a motor that changes the inclination angle of the seat back 10 with respect to the seat cushion 9.

  As shown in FIG. 4, the motor drive switch 6 includes a slide switch portion 11 for driving the slide motor 2 by manual operation, and a front vertical switch portion for driving the front vertical motor 3 by manual operation. 12, a lifter switch unit 13 for manually driving the lifter motor 4, a reclining switch unit 14 for manually driving the reclining motor 5, and adjustment by operating the switch units 11 to 14. The registered button 15 for storing the position and posture of the driver seat 8 in the sheet movement position memory unit 17 (described later) of the memory seat ECU 7 as sheet movement position data, and the position of the driver seat 8 are automatically moved to the sheet movement position. Start to set the sheet moving position stored in the memory unit 17 And a tongue 16.

  Returning to FIG. 1, the memory sheet ECU 7 is a main part of the motor ripple detection device of the present embodiment. The memory seat ECU 7 includes the seat movement position memory unit 17, the load level database 18, the drive control unit 19, the load estimation unit 20, the ripple detection unit 21, and the motor state determination unit 22. Yes. Each part of the memory sheet ECU 7 may be incorporated as software on the CPU, or may be configured as an ASIC as hardware.

  The load level database 18 stores data on load levels applied to the motors 2 to 5. The load level applied to the motors 2 to 5 varies depending on the driving position of the motor 2 to 5 in the driver seat 8 and the rotation direction of the motors 2 to 5. For this reason, the load level database 18 stores data of load levels corresponding to both rotation directions of the motors 2 to 5.

  An example of load level data stored in the load level database 18 is shown in FIG. In FIG. 5, the higher the level number, the higher the load level applied to the motors 2-5. These load levels are obtained by an experiment in which the motors 2 to 5 are driven with a weight of, for example, about 70 kg placed on the driver seat 8.

  The drive control unit 19 controls the driving of the motors 2 to 5 in accordance with the operation of the switch units 11 to 14 of the motor drive switch 6, and when the start button 16 is operated, the position of the driver seat 8 is the sheet moving position. The motors 2 to 5 are driven and controlled so that the sheet moving position registered in the memory unit 17 is reached.

  The load estimation unit 20 estimates the magnitude of the motor load based on the operation signal of the motor drive switch 6. Specifically, the load estimation unit 20 uses the load level data stored in the load level database 18 to estimate the magnitude of the motor load from the motor to be driven and the rotation direction of the motor.

  For example, when load level data as shown in FIG. 5 is used, when the reclining motor 5 is driven to rotate in the clockwise direction, the load level is level 1, so that the load level applied to the motor 5 is low. Presumed. When the reclining motor 5 is rotationally driven in the counterclockwise direction, the load level is level 3, so the load level applied to the motor 5 is estimated to be high.

  The ripple detection unit 21 receives the ripple signal output from the motors 2 to 5, performs filter processing, etc., and estimates and detects the ripple pulse.

  The motor state determination unit 22 is based on the load level estimated by the load estimation unit 20 and the ripple pulse estimated by the ripple detection unit 21, and the operation states of the motors 2 to 5 are in an activated state (a state in which rotation is not stable). Or a steady state (a state in which rotation is stable).

  When the operation state of the motors 2 to 5 is in the activated state, as shown in FIG. 6A, a large amount of noise is generated in the motor output, so that the ripples output from the motors 2 to 5 are distorted. become. When the operation state of the motors 2 to 5 is in a steady state, as shown in FIG. 6B, distortion generated in the ripples output from the motors 2 to 5 is reduced, and a stable ripple pulse can be obtained. Become.

  FIG. 7 is a flowchart showing a procedure of motor ripple detection processing executed by the memory sheet ECU 7.

  In the figure, it is first determined whether or not an operation signal (start signal) for the start button 16 has been input (step S101). When the operation signal of the start button 16 is input, the motor to be driven and the driving direction of the motor are specified from the sheet movement position data stored in the sheet movement position memory unit 17 and stored in the load level database 18. The load level applied to the motor to be driven is estimated using the existing load level data (step S102).

  Subsequently, based on the load level estimated in step S102, a parameter state determination threshold value used for determining the operation state of the motors 2 to 5 is set (step S103). As a parameter used for the determination of the operation state of the motors 2 to 5, for example, the period of ripple pulses output from the motors 2 to 5 can be mentioned. The state determination threshold at this time is a threshold of an allowable error range between a ripple signal output from the motors 2 to 5 and a ripple pulse (reference ripple pulse) serving as a reference for comparison. When the load level estimated in step S102 is high, the threshold of the allowable error range is set stricter than when the load level is low.

  In parallel with the above-described steps S102 and S103, driving of the motors 2 to 5 is started so that the position and posture of the driver seat 8 matches the sheet movement position data stored in the sheet movement position memory unit 17 ( Procedure S104). And it is estimated whether the ripple signal output from the motors 2-5 is a ripple pulse or noise using the parameter suitable at the time of motor starting (procedure S105).

  Subsequently, the ripple signal output from the motors 2 to 5 is compared with a preset reference ripple pulse to determine whether the operation state of the motors 2 to 5 is a start-up state or a steady state (step S106). ). At this time, the operation state of the motors 2 to 5 is determined using the parameter state determination threshold set in step S103. Specifically, if the error between the period of the ripple signal output from the motors 2 to 5 and the period of the reference ripple pulse is within the allowable error range, it is determined that the operation state of the motors 2 to 5 is a steady state. .

  When it is determined that the operation state of the motors 2 to 5 is not the steady state but the activated state, the process of step S105 is executed again. At this time, for example, the ripple signal is estimated by gradually narrowing the passband of the filter processing.

  On the other hand, when it is determined that the operation state of the motors 2 to 5 is a steady state, a ripple pulse output from the motors 2 to 5 is detected (step S107). At this time, since the output waveforms of the motors 2 to 5 are stable, the ripple pulse can be detected with higher accuracy than the ripple estimation processing in step S105. And the rotation speed of the motors 2-5 is detected from the number of ripple pulses output from the motors 2-5.

  Subsequently, it is determined whether or not the number of rotations of the motors 2 to 5 has reached a value corresponding to the sheet movement position stored in the sheet movement position memory unit 17 (step S108). When the value corresponding to the movement position has not been reached, the process of step S107 is executed again. On the other hand, when the number of rotations of the motors 2 to 5 reaches a value corresponding to the sheet moving position, the motors 2 to 5 are stopped (step S109).

  Here, since the motors 2 to 5 are not driven at the same time, the processes of the above steps S104 to S109 are executed for each of the motors 2 to 5. Further, the processes of steps S104, S108, and S109 are executed by the drive control unit 19, the processes of steps S102 and S103 are executed by the load estimating unit 20, and the processes of steps S105 and S107 are executed by the ripple detecting unit 21. The process of S106 is executed by the motor state determination unit 22.

  In the above, the load level database 18 and the procedure S102 of the load estimation unit 20 are loads for estimating the load level applied to the motors 2 to 5 based on the location driven by the motors 2 to 5 and the rotation direction of the motors 2 to 5. The estimation means is configured. Step S103 of the load estimation unit 20 constitutes a threshold setting unit that sets a state determination threshold for determining the operation state of the motors 2 to 5 based on the load level estimated by the load estimation unit. The ripple detection unit 21 and the motor state determination unit 22 use the state determination threshold set by the threshold setting unit to determine whether the operation state of the motors 2 to 5 is the start state or the steady state. Configure.

  By the way, in the detection of the ripple pulse output from the motor, the most detection error is that when the motor operating state is estimated, it is determined that the motor is in a steady state even though the actual operation of the motor is in the starting state. It becomes a factor of. For this reason, as the state determination threshold value of the parameter used for determining the operation state of the motor, it takes the longest time to shift from the start state to the steady state because the noise at the start of the motor is large (the SN ratio is small). Conventionally, the threshold value is often set in accordance with the motor at the drive location and the rotation direction thereof.

  However, in this case, if the state of the motor is estimated with respect to the motor at the drive location where the noise at the start of the motor is small (large SN ratio), that is, the load is small, and its rotation direction, the actual operation of the motor is already Although it is in a steady state, the period during which it is estimated to be in an activated state becomes longer. When the motor operating state is estimated to be in the starting state, the number of ripple pulses can be estimated by various methods. The detection accuracy is lower than. As a result, the detection accuracy of the ripple pulse is lowered. In addition, since it takes a long time to shift from the starting state to the steady state, it takes a long time to detect the ripple pulse.

  On the other hand, in this embodiment, paying attention to the fact that the time until the operation state of the motors 2 to 5 shifts from the starting state to the steady state varies depending on the load applied to the motors 2 to 5, Based on the load level data registered in the load level database 18, the load level is estimated, a state determination threshold corresponding to the load level is determined, and the motors 2 to 5 are started using the state determination threshold. Since it is determined whether it is a state or a steady state, the operation states of the motors 2 to 5 can be accurately determined. For this reason, when the noise generated in the ripple signals output from the motors 2 to 5 is small, a normal ripple pulse is detected early, and when the noise generated in the ripple signals output from the motors 2 to 5 is large, Ripple detection can be performed by a corresponding ripple estimation method.

  Thereby, although the actual operation of the motors 2 to 5 has transitioned from the activated state to the steady state, the period during which the activated state is determined can be sufficiently shortened. Therefore, the detection accuracy of the ripple pulses output from the motors 2 to 5 can be improved, and consequently the detection accuracy of the rotational speeds of the motors 2 to 5 can be improved.

  In addition, when an experiment for actually operating the driver seat was performed, when the operation state of the motor was determined using a different state determination threshold depending on the load level, the operation state of the motor was all determined using the same state determination threshold. Compared to the conventional method for determining the ripple pulse, an improvement of the ripple pulse detection accuracy of up to about 20% per operation was observed.

  FIG. 8 is a schematic configuration diagram showing a memory sheet system provided with a second embodiment of the motor ripple detection device according to the present invention. In the figure, the same or equivalent elements as those in the first embodiment are denoted by the same reference numerals, and the description thereof is omitted.

  In FIG. 8, the memory sheet system 30 includes a memory sheet ECU 31 that is a main part of the motor ripple detection device of the present embodiment. The memory seat ECU 31 includes a learning database 23 in addition to the above-described seat movement position memory unit 17, load level database 18, drive control unit 19, load estimation unit 20, ripple detection unit 21, and motor state determination unit 22. ing. In the learning database 23, the latest ripple pulse data (see FIG. 11A) output from the motors 2 to 5 is registered as learning data.

  The outline of the procedure of the motor ripple detection process executed by the memory sheet ECU 31 is basically the same as the flowchart shown in FIG.

  FIG. 9 is a functional block diagram showing details of the load estimation unit 20 that executes the state determination threshold value setting process of step S103 shown in FIG. 7 in the present embodiment. In the figure, the load estimation unit 20 includes a feature allowable error calculation unit 32 and a feature comparison number calculation unit 33.

  The feature allowable error calculation unit 32 uses the load level data registered in the load level database 18 to determine the characteristics of the ripple pulse registered in the learning database 23 (hereinafter, “learning ripple pulse”) and the motors 2 to 5. An allowable error threshold value (first threshold value) used when comparing the characteristics of the output ripple signal (hereinafter, ripple pulse candidate) is calculated. This allowable error threshold varies depending on the load level. Here, the period of the pulse is used as a feature of the learning ripple pulse and the ripple pulse candidate. Therefore, the allowable error threshold is a threshold of an allowable error range between the learning ripple pulse period and the ripple pulse candidate period.

  The feature comparison number calculation unit 33 uses the load level data registered in the load level database 18 to compare the learning ripple pulse period and the ripple pulse candidate period with a comparison number threshold value (first number). 2 threshold). This comparison number threshold also varies depending on the load level.

  For example, when the load level estimated in step S102 shown in FIG. 7 is low (level 1), the allowable error threshold is 30% and the comparison frequency threshold is 2 times, whereas the load level is high (level 3). ), The allowable error threshold is 10%, and the comparison frequency threshold is 4 times. In this way, by using a stricter threshold value as the load level becomes higher, it is possible to ensure that the operation state of the motors 2 to 5 is in a steady state when the subsequent motor state determination is completed.

  FIG. 10 is a flowchart showing details of the motor state determination processing in step S106 shown in FIG. 7 in the present embodiment. This process is executed in the motor state determination unit 22.

  In the figure, first, a comparison number counter for storing the number of comparisons between the learning ripple pulse and the ripple pulse candidate is set to 0 (step S111). Subsequently, the allowable error threshold obtained by the feature allowable error calculation unit 32 and the comparison frequency threshold obtained by the feature comparison frequency calculation unit 33 are read (step S112).

  Subsequently, ripple pulse candidates output from the motors 2 to 5 are input (step S113). Then, the learning ripple pulse period and the ripple pulse candidate period and error registered in the learning database 23 are obtained, and it is determined whether or not the error is within the allowable error threshold value read in step S112 (step S114). ).

  When the error between the period of the learning ripple pulse and the period of the ripple pulse candidate is within the allowable error threshold, the value of the comparison number counter is incremented, that is, 1 is added (step S115). On the other hand, if the error between the period of the learning ripple pulse and the period of the ripple pulse candidate is not within the allowable error threshold range, the value of the comparison counter is reset to 0 (step S116), and the process returns to step S113.

  After the process of step S115 is executed, it is determined whether or not the value of the comparison number counter is greater than or equal to the comparison number threshold value read in step S112 (step S117). When the value of the comparison number counter is equal to or greater than the comparison number threshold, it is determined that the operation state of the motors 2 to 5 is a steady state (step S118).

  On the other hand, when the value of the comparison number counter is not equal to or greater than the comparison number threshold, the ripple pulse candidate at that time is formally recognized as a ripple pulse, and this ripple pulse is used as new learning ripple pulse data (learning data). (Step S119), and the process returns to step S113.

  An example of determining the operation state of the motors 2 to 5 is shown in FIG. In the example shown in FIG. 11, the allowable error threshold when comparing the learning ripple pulse and the ripple pulse candidate at a certain load level is 20%, and the comparison frequency threshold is four.

  In the case of the pattern as shown in FIG. 11A, the period of the ripple pulse candidate output from the motor is within 20% of the error for four consecutive times with respect to the period of the learning ripple pulse. It is determined that the state is a steady state. On the other hand, in the case of the pattern shown in FIG. 11B, there is a state in which the period of the ripple pulse candidate output from the motor exceeds the error 20% with respect to the period of the learning ripple pulse. Is not in a state where the error is within 20% for four consecutive times, it is determined that the operating state of the motor is still in the activated state.

  As described above, in the present embodiment, the allowable error threshold value and the comparison frequency threshold value are changed according to the load level applied to the motors 2 to 5, and the learning ripple pulse and the ripple pulse candidate are compared using these threshold values. Since the operation states of the motors 2 to 5 are determined, the operation states of the motors 2 to 5 can be determined correctly at an early stage.

  Further, when the error between the learning ripple pulse and the ripple pulse candidate is within the allowable error threshold range, the ripple pulse candidate is updated and registered in the learning database 23 as new learning data. Even if the characteristics of the motor fluctuate and the characteristics of the ripple pulses output from the motors 2 to 5 and the noise output change, the operating state of the motors 2 to 5 is accurately determined and output from the motors 2 to 5 The ripple pulse detection accuracy can be improved.

  FIG. 12 is a schematic configuration diagram showing a memory sheet system including a third embodiment of the motor ripple detection device according to the present invention. In the figure, the same or equivalent elements as those in the first embodiment are denoted by the same reference numerals, and the description thereof is omitted.

  In FIG. 12, the memory seat system 40 includes a memory seat ECU 41 that is a main part of the motor ripple detection device of the present embodiment, and a user number switch 42 for specifying a user who is seated on the driver seat 8. The user number switch 42 is provided, for example, at a position adjacent to the motor drive switch.

  The sheet movement position memory unit 17 of the memory sheet ECU 41 stores sheet movement position data for each user. When the sheet movement position data is stored in the sheet movement position memory unit 17, the sheet movement position data is designated by the registration button 15 (see FIG. 4) of the motor drive switch 6 with the user number designated by the user number switch 42. Register. Further, load level data for each user is stored in the load level database 18 of the memory seat ECU 41.

  Incidentally, the load applied to the motors 2 to 5 may change depending on the characteristics (weight, posture, etc.) of the person using the driver seat 8 in addition to the weight of the driver seat 8. Therefore, in the present embodiment, the operation state of the motors 2 to 5 is determined in consideration of the case where a plurality of people use the driver seat 8.

  FIG. 13 is a flowchart showing a procedure of motor ripple detection processing executed by the memory sheet ECU 41.

  In the figure, steps S101 to S109 are substantially the same as the flowchart shown in FIG. In the initial state of the load level database 18, the same load level data is registered for all users.

  When the operation signal of the start button 16 of the motor drive switch 6 is input in step S101, the load level applied to the motor to be driven is estimated using the load level data stored in the load level database 18 (step S102). ). At this time, the load level estimation process is executed using the sheet movement position data and the load level data corresponding to the user number designated by the user number switch 42. Then, step S103 is executed.

  In parallel with the steps S102 to S105, counting of the time (starting time) from when the motors 2 to 5 are started is started (step S121). Thereafter, when it is determined in step S106 that the operation state of the motors 2 to 5 has shifted from the start state to the steady state, the start time counting of the motors 2 to 5 is ended (step S122).

  Subsequently, the load level corresponding to the person currently in use is obtained from the time from when the motors 2 to 5 are started until the motors 2 to 5 shift to a steady state, and the load level is stored in the load level database 18. It is determined whether it is registered (procedure S123). If the load level is not registered, the load level is updated and registered in the load level database 18 (step S124).

  The processes of the above steps S121 to S124 are also executed for each motor 2 to 5. Note that the processing of steps S121 to S124 is executed by the load estimation unit 20.

  Here, as shown in FIG. 14A, only the load level data corresponding to the user A (user number 1) is registered in the load level database 18, for example, the user B who is heavier than the user A The operation when using is described.

  The user B first operates the switch portions 11 to 14 of the motor drive switch 6 to determine an appropriate position and posture of the driver seat 8, and then designates the user number 2 by the user number switch 42 and presses the registration button 15. By pressing, the sheet position is stored in the sheet movement position memory unit 17 as sheet movement position data.

  Thereafter, when the user number 2 is designated by the user number switch 42 and the start button 16 is pressed, the motors 2 to 5 are driven so that the position and posture of the driver seat 8 become the sheet movement position data corresponding to the user B. . Then, when the operation state of the motors 2 to 5 shifts from the startup state to the steady state, the load level corresponding to the user B is obtained from the startup time of the motors 2 to 5.

  At this time, for example, when the front vertical motor 3 is driven and it takes longer than necessary to determine that the motor 3 is in a steady state, the load level applied to the motor 3 is set to the user A. It is determined that the load level is higher than the standard load level. In this case, as shown in FIG. 14B, the load level (level 2 and level 4) applied to the front vertical motor 3 is updated and registered in the load level database 18 as load level data for the user B. .

  Therefore, when the user B uses the driver seat 8 next time, the load level data for the user B is stored in the load level database 18. Therefore, the load level applied to the motors 2 to 5 is estimated using the load level data for the user B, and the subsequent processing is performed.

  As described above, in this embodiment, the load level applied to the motors 2 to 5 is set for each user, and the operation state of the motors 2 to 5 is determined to be a steady state using the load level data suitable for each user. Since it is determined whether it is in a state, the operation states of the motors 2 to 5 can be determined with higher accuracy. Therefore, the detection accuracy of ripple pulses output from the motors 2 to 5 can be further improved.

  In addition, when an experiment for actually operating the driver seat was performed, when the operation state of the motor was determined using a state determination threshold considering the characteristics of each user, the operation was performed once compared to the conventional method. Further improvement of the ripple pulse detection accuracy of about 10% at maximum was observed.

  FIG. 15 is a schematic configuration diagram showing a memory sheet system provided with a modification of the third embodiment of the motor ripple detection device according to the present invention.

  In the figure, the memory sheet system 40 includes a user specifying sensor 43 for specifying a user (or an additional load) such as a weight sensor or a fingerprint sensor, instead of the user number switch 42 described above. Even in this case, the same effect as the third embodiment can be obtained.

  FIG. 16 is a schematic configuration diagram showing a memory sheet system including a fourth embodiment of the motor ripple detection device according to the present invention. In the figure, the same or equivalent elements as those in the first embodiment are denoted by the same reference numerals, and the description thereof is omitted.

  In FIG. 16, the memory sheet system 50 includes a memory sheet ECU 51 that forms a main part of the motor ripple detection device of the present embodiment, a temperature sensor 52 that measures the temperature around the motors 2 to 5, and activation of the motors 2 to 5. And a timer 53 for monitoring the time at the time of stop and stop.

  The load estimation unit 20 of the memory sheet ECU 51 estimates the load applied to the motors 2 to 5 based on the operation signal of the motor drive switch 6, the ambient temperature measured by the temperature sensor 53, and the time measured by the timer 53. To do. FIG. 17 is a flowchart showing details of the procedure of the load estimation process executed by the load estimation unit 20.

  In the figure, first, it is determined whether or not the motors 2 to 5 are activated based on the ripple signals output from the motors 2 to 5 and the detection values of the rotation sensors provided in the motors 2 to 5 (step S131). . When the motors 2 to 5 are activated, the time at the time of activation is read from the timer 53 and the ambient temperature at the time of activation is read from the temperature sensor 52. Further, load level data registered in the load level database 18 is read (step S132).

  Here, before the motors 2 to 5 are started, the load estimation unit 20 determines the time when the motors 2 to 5 were stopped when the motors 2 to 5 were last driven as shown in FIG. It is read and stored by the timer 53. Therefore, immediately after the motors 2 to 5 are started, the load estimation unit 20, as shown in FIG. 18 (b), the stop time of the motors 2 to 5 during the previous drive, the current start time of the motors 2 to 5, Data such as the ambient temperature and the load level when the motors 2 to 5 are started up are included.

  Subsequently, the motor drive time interval from the stop time of the motors 2 to 5 at the previous drive to the start time of the current motors 2 to 5 is calculated (step S133). Subsequently, it is determined whether or not the motor drive time interval is shorter than a predetermined time (step S134). When the motor drive time interval is shorter than the predetermined time, the load level data read in step S132 is sent to the motor state determination unit 22 (step S135).

  On the other hand, when the motor drive time interval is not shorter than the predetermined time, it is further determined whether or not the ambient temperature measured by the temperature sensor 52 is lower than the predetermined temperature (step S136). When the ambient temperature is not lower than the predetermined temperature, the load level data read in step S132 is sent to the motor state determination unit 22 as it is (step S135).

  When the ambient temperature is lower than the predetermined temperature, the load level read in step S132 is multiplied by a temperature coefficient to correct the load level (step S137). Then, the corrected load level data is sent to the motor state determination unit 22 (step S135).

  By the way, when the temperature is low, for example, below freezing temperature, since the grease for smoothing the operation of the motor becomes hard, the motor is difficult to rotate. For this reason, the starting characteristics (rotational characteristics) of the motor are greatly different between a low temperature and a normal temperature (for example, 25 ° C.). Specifically, at a low temperature, a greater load is applied to the motor than at a normal temperature.

  In addition, when the motor driving time interval is short, the motor has been rotating until just before, so even if the ambient temperature is low, the temperature of the motor itself remains high. . For this reason, the motor in this case has characteristics at substantially room temperature. However, when the motor driving time interval is long, the temperature of the motor itself is substantially the same as the ambient temperature.

  In contrast, in this embodiment, the motor driving time interval and the ambient temperature are detected, and when the driving time interval of the motors 2 to 5 is shorter than the predetermined time, the driving time interval of the motors 2 to 5 is longer than the predetermined time. However, when the ambient temperature is higher than the predetermined temperature, the load level registered in the load level database 18 is used as it is. On the other hand, when the driving time interval of the motors 2 to 5 is longer than the predetermined time and the ambient temperature is lower than the predetermined temperature, the load level registered in the load level database 18 is corrected according to the ambient temperature, and the correction is made. The operating state of the motor is determined according to the load level. Thereby, for example, even in a cold region, the operation state of the motors 2 to 5 can be accurately determined, and the detection accuracy of the ripple pulses output from the motors 2 to 5 can be improved.

  In addition, when an experiment for actually operating the driver seat was performed, when the operation state of the motor was determined using the state determination threshold considering the motor driving time interval and the ambient temperature, compared to the conventional method, The ripple pulse detection accuracy was improved by about 12% per operation.

  The present invention is not limited to the above embodiment. For example, in the above embodiment, when a ripple signal (ripple pulse candidate) output from the motors 2 to 5 is compared with a reference ripple pulse (learning ripple pulse or the like), an error between the period of the ripple signal and the period of the reference ripple pulse. However, the characteristics of the ripple pulse to be determined are not limited to the period, and pulse attributes such as ripple pulse amplitude, rise time, fall time, and waveform shape may be used. But the same effect can be obtained.

  In addition, the motor ripple detection device of the present embodiment is applied to a memory sheet system, but the present invention is not limited to a memory sheet, for example, a mobile instrument on which a person such as an electric mobile bed is placed, It can also be applied to vehicle power windows.

It is a schematic block diagram which shows the memory sheet system provided with 1st Embodiment of the motor ripple detection apparatus concerning this invention. It is a perspective view which shows the drive mechanism of the driver seat containing each motor and memory seat ECU which were shown in FIG. FIG. 3 is a side view showing a drive direction of a drive mechanism of the driver seat shown in FIG. 2. It is a schematic block diagram of the motor drive switch shown in FIG. 3 is a table showing an example of load level data registered in a load level database shown in FIG. 1. It is a wave form diagram which shows an example of the ripple pulse output from a motor in the starting state and steady state of a motor. It is a flowchart which shows the procedure of the motor ripple detection process performed by memory sheet ECU shown in FIG. It is a schematic block diagram which shows the memory sheet system provided with 2nd Embodiment of the motor ripple detection apparatus concerning this invention. It is a functional block diagram which shows the detail of the load estimation part shown in FIG. It is a flowchart which shows the detail of the procedure of the motor state determination process performed by the motor state determination part shown in FIG. It is a wave form diagram which shows an example which determines the operation state of a motor by the motor state determination process shown in FIG. It is a schematic block diagram which shows the memory sheet system provided with 3rd Embodiment of the motor ripple detection apparatus concerning this invention. It is a flowchart which shows the procedure of the motor ripple detection process performed by memory sheet ECU shown in FIG. 13 is a table showing an example of load level data registered in the load level database shown in FIG. 12. It is a schematic block diagram which shows the memory sheet system provided with the modification of 3rd Embodiment of the motor ripple detection apparatus concerning this invention. It is a schematic block diagram which shows the memory sheet system provided with 4th Embodiment of the motor ripple detection apparatus concerning this invention. It is a flowchart which shows the detail of the procedure of the load estimation process performed by the load estimation part shown in FIG. It is a schematic block diagram which shows the data which a load estimation part acquires before and after starting of the motor shown in FIG.

Explanation of symbols

  2 ... Slide motor, 3 ... Front vertical motor, 4 ... Lifter motor, 5 ... Reclining motor, 7 ... Memory seat ECU (motor ripple detector), 8 ... Driver seat (mobile instrument), 18 ... Load Level database (load estimation means), 20 ... load estimation section (load estimation means, threshold setting means), 21 ... ripple detection section (state determination means), 22 ... motor state determination section (state determination means), 23 ... learning Database (data storage means), 31 ... memory sheet ECU (motor ripple detection device), 32 ... feature allowable error calculation unit, 33 ... feature comparison frequency calculation unit, 41 ... memory sheet ECU (motor ripple detection device), 42 ... User number switch (motor ripple detection device), 43... User specific sensor (motor ripple detection device), 5 ... memory seat ECU (motor ripple detecting device), 52 ... temperature sensor (motor ripple detecting device), 53 ... timer (motor ripple detecting device).

Claims (8)

A motor ripple detection device for detecting a ripple signal output from a motor,
Load estimation means for estimating a load level applied to the motor based on a location driven by the motor and a rotation direction of the motor;
Threshold setting means for setting a state determination threshold for determining the operating state of the motor based on the load level estimated by the load estimating means;
A motor ripple detection device comprising: a state determination unit that determines whether the operation state of the motor is a startup state or a steady state using a state determination threshold set by the threshold setting unit. Data storage means for storing a learning ripple pulse to be compared with a ripple signal output from the motor;
The state determination threshold includes a first threshold relating to an allowable error between a characteristic of the ripple signal output from the motor and a characteristic of the learning ripple pulse stored in the data storage unit, and the characteristic of the ripple signal and the learning ripple pulse. A second threshold for the number of times to compare with the features of
The state determination unit compares the characteristics of the ripple signal output from the motor with the characteristics of the learning ripple pulse stored in the data storage unit by using the first threshold value and the second threshold value. The motor ripple detection device according to claim 1, wherein the motor ripple detection device determines whether the operation state of the motor is a startup state or a steady state.   The state determination means determines that the motor operating state is the steady state when a state where an error between the characteristic of the ripple signal and the characteristic of the learning ripple pulse is within the first threshold range continues for the second threshold value or more. The motor ripple detection device according to claim 2, wherein the motor ripple detection device is determined to be in a state.   The state determination unit updates the ripple signal as the learning ripple pulse and stores it in the data storage unit when an error between the characteristic of the ripple signal and the characteristic of the learning ripple pulse is within the first threshold range. 4. The motor ripple detection device according to claim 3, further comprising means for causing the motor ripple. The location to be driven by the motor is the drive part of the mobile instrument on which the user is placed,
The load estimation means estimates a load level applied to the motor based on a location driven by the motor, a rotation direction of the motor, and characteristics of a person using the mobile instrument. The motor ripple detection apparatus as described in any one of 1-4. Means for detecting the ambient temperature of the motor;
6. The load estimating unit estimates a load level applied to the motor based on a location driven by the motor, a rotation direction of the motor, and an ambient temperature of the motor. The motor ripple detection device according to claim 1. Means for detecting a driving time interval of the motor;
The motor ripple detection device according to claim 6, wherein the load estimation unit further estimates a load level applied to the motor based on a driving time interval of the motor.   The load estimation means determines whether the driving time interval of the motor is longer than a predetermined time and the ambient temperature of the motor is lower than a predetermined temperature, and the driving time interval of the motor is longer than a predetermined time and the 8. The motor ripple detection device according to claim 7, wherein when the ambient temperature of the motor is lower than a predetermined temperature, the load level applied to the motor is estimated to be higher than when the ambient temperature is not.

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